Journal Description
World Electric Vehicle Journal
World Electric Vehicle Journal
is the first peer-reviewed, international, scientific journal that comprehensively covers all studies related to battery, hybrid, and fuel cell electric vehicles. The journal is owned by the World Electric Vehicle Association (WEVA) and its members, the European Association for e-Mobility (AVERE), Electric Drive Transportation Association (EDTA), and Electric Vehicle Association of Asia Pacific (EVAAP). It has been published monthly online by MDPI since Volume 9, Issue 1 (2018).
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, and other databases.
- Journal Rank: JCR - Q2 (Transportation Science and Technology) / CiteScore - Q2 (Automotive Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.7 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.6 (2023)
Latest Articles
Driving Under Cognitive Control: The Impact of Executive Functions in Driving
World Electr. Veh. J. 2024, 15(10), 474; https://doi.org/10.3390/wevj15100474 - 16 Oct 2024
Abstract
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This review will explore the role of executive functions and the impact they have in facilitating the skills of vehicle operation. Executive functions are critical for the decision-making process, problem-solving, and multitasking. They are considered the primary factors in driving cases that demand
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This review will explore the role of executive functions and the impact they have in facilitating the skills of vehicle operation. Executive functions are critical for the decision-making process, problem-solving, and multitasking. They are considered the primary factors in driving cases that demand drivers to react quickly and adapt to certain situations. Based on the PRISMA 2020 guidelines, this study aims to investigate, analyze, and categorize higher mental skills and their qualities directly related to driving. The literature review was performed in the following databases: PubMed, Web of Science, Scopus, and Google Scholar, using the article collections’ snowball search technique. The results suggest that key executive functions like working memory and inhibitory control are closely related to risky behavior and driving errors that lead to accidents. This review adds valuable insight by highlighting the significance of their contribution to future research, driver educational programs, and technology for improving driver safety. Consequently, collecting recent data will contribute to understanding new parameters that influence driving behavior, creating the possibility for appropriate proposals for future research.
Full article
Open AccessArticle
Event-Triggered Two-Part Separation Control of Multiple Autonomous Underwater Vehicles Based on Extended Observer
by
Yunyang Gu, Yueru Xu, Mingzuo Jiang and Zhigang Zhou
World Electr. Veh. J. 2024, 15(10), 473; https://doi.org/10.3390/wevj15100473 - 16 Oct 2024
Abstract
In this paper, we investigate the formation isolation regulation issue regarding multiple Autonomous Underwater Vehicles (AUVs) characterized by a “leader–follower” framework. Considering the cooperative–competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer is designed based
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In this paper, we investigate the formation isolation regulation issue regarding multiple Autonomous Underwater Vehicles (AUVs) characterized by a “leader–follower” framework. Considering the cooperative–competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer is designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme is designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from Zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source, and the structure convergence is achieved, with the absence of Zeno behavior also demonstrated.
Full article
(This article belongs to the Special Issue Cooperative Perception, Communication and Computing for Autonomous Vehicles)
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<p>Inertial and body-stationary coordinate frames, reprinted from Ref. [<a href="#B13-wevj-15-00473" class="html-bibr">13</a>].</p> Full article ">Figure 2
<p>Communication topology of multi-AUV systems under directed graphs.</p> Full article ">Figure 3
<p>Detailed trajectories during the formation separation of multi-AUV systems.</p> Full article ">Figure 4
<p>Control input trigger frequency per AUV.</p> Full article ">Figure 5
<p>The leader leads two groups of followers in two stable rectangular formations.</p> Full article ">Figure 5 Cont.
<p>The leader leads two groups of followers in two stable rectangular formations.</p> Full article ">Figure 6
<p>Estimation error of unknown perturbations.</p> Full article ">Figure 6 Cont.
<p>Estimation error of unknown perturbations.</p> Full article ">Figure 7
<p>Velocity error convergence for each AUV (from a 3D perspective).</p> Full article ">Figure 7 Cont.
<p>Velocity error convergence for each AUV (from a 3D perspective).</p> Full article ">Figure 8
<p>Convergence of position errors for each AUV (from a 3D viewpoint).</p> Full article ">Figure 8 Cont.
<p>Convergence of position errors for each AUV (from a 3D viewpoint).</p> Full article ">Figure 9
<p>System formation position tracking error (from a three-dimensional perspective).</p> Full article ">Figure 9 Cont.
<p>System formation position tracking error (from a three-dimensional perspective).</p> Full article ">Figure 10
<p>System formation speed tracking error (from a three-dimensional perspective).</p> Full article ">Figure 10 Cont.
<p>System formation speed tracking error (from a three-dimensional perspective).</p> Full article ">
<p>Inertial and body-stationary coordinate frames, reprinted from Ref. [<a href="#B13-wevj-15-00473" class="html-bibr">13</a>].</p> Full article ">Figure 2
<p>Communication topology of multi-AUV systems under directed graphs.</p> Full article ">Figure 3
<p>Detailed trajectories during the formation separation of multi-AUV systems.</p> Full article ">Figure 4
<p>Control input trigger frequency per AUV.</p> Full article ">Figure 5
<p>The leader leads two groups of followers in two stable rectangular formations.</p> Full article ">Figure 5 Cont.
<p>The leader leads two groups of followers in two stable rectangular formations.</p> Full article ">Figure 6
<p>Estimation error of unknown perturbations.</p> Full article ">Figure 6 Cont.
<p>Estimation error of unknown perturbations.</p> Full article ">Figure 7
<p>Velocity error convergence for each AUV (from a 3D perspective).</p> Full article ">Figure 7 Cont.
<p>Velocity error convergence for each AUV (from a 3D perspective).</p> Full article ">Figure 8
<p>Convergence of position errors for each AUV (from a 3D viewpoint).</p> Full article ">Figure 8 Cont.
<p>Convergence of position errors for each AUV (from a 3D viewpoint).</p> Full article ">Figure 9
<p>System formation position tracking error (from a three-dimensional perspective).</p> Full article ">Figure 9 Cont.
<p>System formation position tracking error (from a three-dimensional perspective).</p> Full article ">Figure 10
<p>System formation speed tracking error (from a three-dimensional perspective).</p> Full article ">Figure 10 Cont.
<p>System formation speed tracking error (from a three-dimensional perspective).</p> Full article ">
Open AccessArticle
Prediction of the Resource of the Power Plant Hybrid Vehicle
by
Juraj Gerlici, Oleksiy Bazhinov, Oleksandr Kravchenko, Tatiana Bazhynova and Kateryna Kravchenko
World Electr. Veh. J. 2024, 15(10), 472; https://doi.org/10.3390/wevj15100472 - 16 Oct 2024
Abstract
This article substantiates the possibility of solving a difficult task to offer a reliable estimate of the residual resource of the power plant of a hybrid car, which is exposed during operation to the influence of a complex set of external factors. This
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This article substantiates the possibility of solving a difficult task to offer a reliable estimate of the residual resource of the power plant of a hybrid car, which is exposed during operation to the influence of a complex set of external factors. This task is solved by monitoring the time change of diagnostic parameters and controlling the load–speed mode of the hybrid power plant, as well as using physical and chemical processes that cause the degradation of the traction battery. Methodological principles for assessing the resource of a hybrid power plant of a car have been developed, which are based on the disclosure of cause-and-effect relationships between realized and nominal quality indicators, operating conditions, and indicators of technical condition. The choice of rational solutions for the operation of a hybrid car is given, and the external conditions are determined, under which the residual life of the hybrid power plant will change. The scientific result of the study is a theoretical generalization of the scientific provisions of forecasting the resource of the hybrid power plant of a car. This ensures the efficient use of hybrid vehicles and reduces maintenance costs.
Full article
(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
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Figure 1
<p>Experimental equipment for the research of the cylinder–piston group and the crank–rod mechanism wear: 1—data collection and primary processing unit, 2—personal computer, 3—power supply unit, and 4—tester.</p> Full article ">Figure 2
<p>Changing the parameters characterizing the technical condition of the cylinder–piston group during the starter start-up of the internal combustion engine: (<b>a</b>)—the signal of the phase sensor of the internal combustion engine operating cycle; (<b>b</b>)—starter current; (<b>c</b>)—battery voltage; and (<b>d</b>)—crankshaft rotation frequency min<sup>−1</sup>.</p> Full article ">Figure 3
<p>Dependence of the starter current (<b>a</b>) and the active power consumed by the starter (<b>b</b>) on the increase in the area of the cylinder–piston group leakage and the crankshaft rotation frequency; ▽—cold engine <span class="html-italic">T<sub>m</sub></span> = +(10 ÷ 30) °C; □—after heating to the temperature <span class="html-italic">T<sub>m</sub></span> = +(40 ÷ 60) °C; ○—after heating to the temperature <span class="html-italic">T<sub>m</sub></span> = +(60 ÷ 80) °C; and ΔS—area of non-density CPG, mm<sup>2</sup>.</p> Full article ">Figure 4
<p>Change in oil pressure (<b>a</b>) and crankshaft rotation frequency (<b>b</b>) at oil temperature <span class="html-italic">T<sub>m</sub></span> = 54 °C and the relative density of the crank-connecting mechanism with the equivalent cross-sectional area <span class="html-italic">S<sub>hole</sub></span>.</p> Full article ">Figure 5
<p>Connection diagram of diagnostic sensors for simplified evaluation: (S2.4—voltage sensor in the traction battery circuit (<span class="html-italic">U<sub>TB</sub></span>); S2.5—current sensor in the traction battery circuit (<span class="html-italic">I<sub>TB</sub></span>); MG—motor generator).</p> Full article ">
<p>Experimental equipment for the research of the cylinder–piston group and the crank–rod mechanism wear: 1—data collection and primary processing unit, 2—personal computer, 3—power supply unit, and 4—tester.</p> Full article ">Figure 2
<p>Changing the parameters characterizing the technical condition of the cylinder–piston group during the starter start-up of the internal combustion engine: (<b>a</b>)—the signal of the phase sensor of the internal combustion engine operating cycle; (<b>b</b>)—starter current; (<b>c</b>)—battery voltage; and (<b>d</b>)—crankshaft rotation frequency min<sup>−1</sup>.</p> Full article ">Figure 3
<p>Dependence of the starter current (<b>a</b>) and the active power consumed by the starter (<b>b</b>) on the increase in the area of the cylinder–piston group leakage and the crankshaft rotation frequency; ▽—cold engine <span class="html-italic">T<sub>m</sub></span> = +(10 ÷ 30) °C; □—after heating to the temperature <span class="html-italic">T<sub>m</sub></span> = +(40 ÷ 60) °C; ○—after heating to the temperature <span class="html-italic">T<sub>m</sub></span> = +(60 ÷ 80) °C; and ΔS—area of non-density CPG, mm<sup>2</sup>.</p> Full article ">Figure 4
<p>Change in oil pressure (<b>a</b>) and crankshaft rotation frequency (<b>b</b>) at oil temperature <span class="html-italic">T<sub>m</sub></span> = 54 °C and the relative density of the crank-connecting mechanism with the equivalent cross-sectional area <span class="html-italic">S<sub>hole</sub></span>.</p> Full article ">Figure 5
<p>Connection diagram of diagnostic sensors for simplified evaluation: (S2.4—voltage sensor in the traction battery circuit (<span class="html-italic">U<sub>TB</sub></span>); S2.5—current sensor in the traction battery circuit (<span class="html-italic">I<sub>TB</sub></span>); MG—motor generator).</p> Full article ">
Open AccessArticle
Electric Vehicle Adoption: Implications for Employment in South Africa’s Automotive Component Industry
by
Nalini Sooknanan Pillay and Alaize Dall-Orsoletta
World Electr. Veh. J. 2024, 15(10), 471; https://doi.org/10.3390/wevj15100471 (registering DOI) - 15 Oct 2024
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The transition to electric vehicles (EVs) will require significant changes in the automotive industry, particularly concerning its labour force. This study evaluates the impact of EVs on employment within South Africa’s automotive component manufacturing sector. A system dynamics model was developed to assess
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The transition to electric vehicles (EVs) will require significant changes in the automotive industry, particularly concerning its labour force. This study evaluates the impact of EVs on employment within South Africa’s automotive component manufacturing sector. A system dynamics model was developed to assess the effect of EV market penetration on component manufacturing employment over time. Key drivers of employment in the conventional and the EV component industries were identified and incorporated into the model. The results indicate a negative impact of EV penetration on employment of 18.3% when considering 20.0% EV sales (EV20) in 2040. Scenario analyses highlighted the influence of individual components, battery localisation, and load shedding on labour. Tyre and wheel manufacturing was found to be the most labour impactful component in the conventional industry against electrical engines in the EV counterpart. Localising 25.0% of battery production could increase employment by 6.9% and 2.7% in the EV40 and EV20 Scenarios. Load shedding has a detrimental effect on the country’s economy, assumed to reduce employment by 30.0%. However, strategic industry and policy interventions can mitigate the adverse effects of this transition.
Full article
Figure 1
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<p>Employment trend in the automotive manufacturing sector in South Africa, the data comes from Ref. [<a href="#B9-wevj-15-00471" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>Annual sales of passenger vehicles in South Africa, the data comes from Ref. [<a href="#B48-wevj-15-00471" class="html-bibr">48</a>].</p> Full article ">Figure 3
<p>Scenario results for employment in automotive component manufacturing in South Africa. Source: Own elaboration (2024).</p> Full article ">Figure 4
<p>Scenario results for employment impacts due to drivers: Percentage change from the Baseline Scenario in 2040. Source: Own elaboration (2024).</p> Full article ">Figure 5
<p>Scenario results for employment impacts due to load shedding: Total number of workers in 2040. Source: Own elaboration (2024).</p> Full article ">Figure A1
<p>Simulator interface.</p> Full article ">
<p>Employment trend in the automotive manufacturing sector in South Africa, the data comes from Ref. [<a href="#B9-wevj-15-00471" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>Annual sales of passenger vehicles in South Africa, the data comes from Ref. [<a href="#B48-wevj-15-00471" class="html-bibr">48</a>].</p> Full article ">Figure 3
<p>Scenario results for employment in automotive component manufacturing in South Africa. Source: Own elaboration (2024).</p> Full article ">Figure 4
<p>Scenario results for employment impacts due to drivers: Percentage change from the Baseline Scenario in 2040. Source: Own elaboration (2024).</p> Full article ">Figure 5
<p>Scenario results for employment impacts due to load shedding: Total number of workers in 2040. Source: Own elaboration (2024).</p> Full article ">Figure A1
<p>Simulator interface.</p> Full article ">
Open AccessArticle
Research on Performance of Interior Permanent Magnet Synchronous Motor with Fractional Slot Concentrated Winding for Electric Vehicles Applications
by
Zhiqiang Xi, Lianbo Niu, Xianghai Yan and Liyou Xu
World Electr. Veh. J. 2024, 15(10), 470; https://doi.org/10.3390/wevj15100470 - 14 Oct 2024
Abstract
The fractional-slot, concentrated-winding, interior permanent magnet synchronous motor (FSCW IPMSM) has advantages, such as reducing motor copper consumption, improving flux-weakening capability, and motor fault tolerance, and has certain development potential in application fields such as electric vehicles. However, fractional-slot concentrated-winding motors often contain
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The fractional-slot, concentrated-winding, interior permanent magnet synchronous motor (FSCW IPMSM) has advantages, such as reducing motor copper consumption, improving flux-weakening capability, and motor fault tolerance, and has certain development potential in application fields such as electric vehicles. However, fractional-slot concentrated-winding motors often contain rich harmonic components due to their winding characteristics, leading to increased motor losses and back electromotive force harmonics, thereby affecting the efficiency and constant power speed regulation range of the motor. Based on this, this article first uses the winding function method to explore the inductance and saliency ratio of the interior permanent magnet synchronous motor with different slot pole combinations in the fractional-slot concentrated- winding of electric vehicles. Secondly, this article will establish a 2D finite element parameterized model to analyze and compare the performance of fractional-slot concentrated-winding motors with different slot pole combinations, including air gap magnetic density, back electromotive force distortion rate, overload multiple, and torque. The structural parameters of the motor were optimized with the objective of minimizing the torque ripple under the constraint of minimizing the average torque reduction. The motor slot width, permanent magnet angle, and permanent magnet pole arc angle were analyzed and optimized. The simulation results showed that 12 slots and 8 poles were the optimal design schemes, providing a theoretical basis for the selection of slot pole coordination in the fractional-slot concentrated-winding interior permanent magnet synchronous motor for electric vehicles.
Full article
(This article belongs to the Special Issue Design, Analysis and Optimization of Electrical Machines and Drives for Electric Vehicles, 2nd Edition)
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Figure 1
<p>Relationship between the main inductance coefficient of the motor and the number of slots per pole and phase of <span class="html-italic">q</span>.</p> Full article ">Figure 2
<p>Relationship diagram between second-order harmonic self-inductance, mutual inductance, inductance coefficient and the number of slots per pole and phase in <span class="html-italic">q</span> of the motor.</p> Full article ">Figure 3
<p>Relationship between the ratio of <span class="html-italic">b</span><sub>2</sub> to <span class="html-italic">b</span><sub>0</sub> and the polar arc coefficient <span class="html-italic">α</span><sub>pm</sub>.</p> Full article ">Figure 4
<p>Relationship between d axis air gap inductance coefficient <span class="html-italic">α</span><sub>d</sub> and <span class="html-italic">q</span>.</p> Full article ">Figure 5
<p>Waveform of motor cogging torque under different pole slot combinations.</p> Full article ">Figure 6
<p>Waveform of motor output torque under different pole slot combinations.</p> Full article ">Figure 7
<p>Trend and harmonic distribution of no-load back electromotive force under different pole slot combinations: (<b>a</b>) back electromotive force waveform trends; (<b>b</b>) harmonic distribution of the phase no-load back EMF.</p> Full article ">Figure 8
<p>Distribution of motor magnetic field under different pole slot combinations: (<b>a</b>) 8-pole-12-slot magnetic density cloud diagram; (<b>b</b>) 10-pole-12-slot magnetic density cloud diagram; (<b>c</b>) 14-pole-12-slot magnetic density cloud diagram.</p> Full article ">Figure 9
<p>Waveform of cogging torque under different pole slot combinations.</p> Full article ">Figure 10
<p>Waveform of output torque under different pole slot combinations.</p> Full article ">Figure 11
<p>Waveform of no-load back electromotive force of motor under different pole slot combinations.</p> Full article ">Figure 12
<p>Trend of the average output torque of the motor.</p> Full article ">Figure 13
<p>Trend of motor output torque ripple changes.</p> Full article ">Figure 14
<p>Trend of motor output cogging torque variation.</p> Full article ">Figure 15
<p>Diagram of the effective value change of back electromotive force.</p> Full article ">Figure 16
<p>Trend of THD change of motor’s back electromotive force.</p> Full article ">Figure 17
<p>Trend of output torque and torque ripple changes in the motor of electric vehicles.</p> Full article ">Figure 18
<p>Motor operation map under three pole slot combinations. (<b>a</b>) 8-pole-12-slot; (<b>b</b>) 10-poles-12-slots; (<b>c</b>) 14-poles-12-slots.</p> Full article ">Figure 18 Cont.
<p>Motor operation map under three pole slot combinations. (<b>a</b>) 8-pole-12-slot; (<b>b</b>) 10-poles-12-slots; (<b>c</b>) 14-poles-12-slots.</p> Full article ">Figure 19
<p>Distribution of motor external characteristic curves under three pole slot combinations. (<b>a</b>) External characteristic curve of motor torque under three pole slot combinations; (<b>b</b>) Electric motor power external characteristic curve under three pole slot combinations.</p> Full article ">
<p>Relationship between the main inductance coefficient of the motor and the number of slots per pole and phase of <span class="html-italic">q</span>.</p> Full article ">Figure 2
<p>Relationship diagram between second-order harmonic self-inductance, mutual inductance, inductance coefficient and the number of slots per pole and phase in <span class="html-italic">q</span> of the motor.</p> Full article ">Figure 3
<p>Relationship between the ratio of <span class="html-italic">b</span><sub>2</sub> to <span class="html-italic">b</span><sub>0</sub> and the polar arc coefficient <span class="html-italic">α</span><sub>pm</sub>.</p> Full article ">Figure 4
<p>Relationship between d axis air gap inductance coefficient <span class="html-italic">α</span><sub>d</sub> and <span class="html-italic">q</span>.</p> Full article ">Figure 5
<p>Waveform of motor cogging torque under different pole slot combinations.</p> Full article ">Figure 6
<p>Waveform of motor output torque under different pole slot combinations.</p> Full article ">Figure 7
<p>Trend and harmonic distribution of no-load back electromotive force under different pole slot combinations: (<b>a</b>) back electromotive force waveform trends; (<b>b</b>) harmonic distribution of the phase no-load back EMF.</p> Full article ">Figure 8
<p>Distribution of motor magnetic field under different pole slot combinations: (<b>a</b>) 8-pole-12-slot magnetic density cloud diagram; (<b>b</b>) 10-pole-12-slot magnetic density cloud diagram; (<b>c</b>) 14-pole-12-slot magnetic density cloud diagram.</p> Full article ">Figure 9
<p>Waveform of cogging torque under different pole slot combinations.</p> Full article ">Figure 10
<p>Waveform of output torque under different pole slot combinations.</p> Full article ">Figure 11
<p>Waveform of no-load back electromotive force of motor under different pole slot combinations.</p> Full article ">Figure 12
<p>Trend of the average output torque of the motor.</p> Full article ">Figure 13
<p>Trend of motor output torque ripple changes.</p> Full article ">Figure 14
<p>Trend of motor output cogging torque variation.</p> Full article ">Figure 15
<p>Diagram of the effective value change of back electromotive force.</p> Full article ">Figure 16
<p>Trend of THD change of motor’s back electromotive force.</p> Full article ">Figure 17
<p>Trend of output torque and torque ripple changes in the motor of electric vehicles.</p> Full article ">Figure 18
<p>Motor operation map under three pole slot combinations. (<b>a</b>) 8-pole-12-slot; (<b>b</b>) 10-poles-12-slots; (<b>c</b>) 14-poles-12-slots.</p> Full article ">Figure 18 Cont.
<p>Motor operation map under three pole slot combinations. (<b>a</b>) 8-pole-12-slot; (<b>b</b>) 10-poles-12-slots; (<b>c</b>) 14-poles-12-slots.</p> Full article ">Figure 19
<p>Distribution of motor external characteristic curves under three pole slot combinations. (<b>a</b>) External characteristic curve of motor torque under three pole slot combinations; (<b>b</b>) Electric motor power external characteristic curve under three pole slot combinations.</p> Full article ">
Open AccessArticle
Optimization Control Strategy for Light Load Efficiency of Four-Switch Buck-Boost Converter
by
Siyuan Gao, Fanghua Zhang and Hongxin Mei
World Electr. Veh. J. 2024, 15(10), 469; https://doi.org/10.3390/wevj15100469 - 14 Oct 2024
Abstract
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The four-switch buck-boost (FSBB) converter usually adopts a pseudo-continuous conduction mode (PCCM) soft switching (ZVS) control strategy, but there is a problem with the low efficiency of FSBB converters under light loads. Firstly, the constraints that the control variables of the FSBB converter
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The four-switch buck-boost (FSBB) converter usually adopts a pseudo-continuous conduction mode (PCCM) soft switching (ZVS) control strategy, but there is a problem with the low efficiency of FSBB converters under light loads. Firstly, the constraints that the control variables of the FSBB converter need to satisfy are analyzed, and it is pointed out that the fixed frequency constraint is not necessary. Then, the switching frequency is used to control the degree of freedom, and the quantitative relationship between the FSBB converter loss and the switching frequency is obtained. Finally, for different input voltages and loads, the switching frequency corresponding to the minimum power loss is calculated offline. By optimizing the switching frequency, the light-load efficiency of the FSBB converter is improved. A prototype with an input voltage range of 210 V–330 V, an output voltage of 270 V, and an output power of 3 kW was developed. The loss was reduced by 15% at 20% load, and the peak efficiency of the converter reached 99.23%. The experimental results verified the effectiveness of the proposed control strategy.
Full article
Figure 1
Figure 1
<p>FSBB converter circuit and key waveforms. (<b>a</b>) circuit. (<b>b</b>) key waveforms.</p> Full article ">Figure 2
<p>The waveform of <span class="html-italic">i<sub>L</sub></span> when <span class="html-italic">I<sub>Lrms</sub></span> and <span class="html-italic">I<sub>Lp</sub></span> are at their minimum. (<b>a</b>) PDCM. (<b>b</b>) PCRM.</p> Full article ">Figure 3
<p>The <span class="html-italic">i<sub>L</sub></span> waveform at different <span class="html-italic">f<sub>s</sub></span> under the same <span class="html-italic">V<sub>in</sub></span>, <span class="html-italic">V<sub>o</sub></span>, <span class="html-italic">P<sub>o</sub></span>.</p> Full article ">Figure 4
<p>Inductor current waveform.</p> Full article ">Figure 5
<p>Loss and distribution of FSBB converter under different operating conditions with <span class="html-italic">L</span> = 7.0 μH and <span class="html-italic">f<sub>s</sub></span> = 200 kHz. (<b>a</b>) Loss. (<b>b</b>) distribution.</p> Full article ">Figure 6
<p>The variation of peak current with input voltage and switching frequency.</p> Full article ">Figure 7
<p>Inductor current waveforms have the same output current at different <span class="html-italic">f<sub>s</sub></span>.</p> Full article ">Figure 8
<p>Relationship between <span class="html-italic">P<sub>omax</sub></span> and <span class="html-italic">f<sub>s</sub></span>.</p> Full article ">Figure 9
<p>Frequency 3-D diagram and Reduced losses based on multi-frequency PCCM-ZVS control strategy. (<b>a</b>) Frequency 3-D diagram. (<b>b</b>) Reduced losses.</p> Full article ">Figure 10
<p>FSBB converter closed-loop control system.</p> Full article ">Figure 11
<p>FSBB converter closed-loop control flowchart.</p> Full article ">Figure 12
<p>Experimental Platform.</p> Full article ">Figure 13
<p>Waveform diagram achieving ZVS under different input voltages and 10%full-load. (<b>a</b>) <span class="html-italic">V<sub>in</sub></span> = 210 V. (<b>b</b>) <span class="html-italic">V<sub>in</sub></span> = 270 V. (<b>c</b>) <span class="html-italic">V<sub>in</sub></span> = 330 V.</p> Full article ">Figure 14
<p>Waveform diagram achieving ZVS under different input voltages and 90% full-load. (<b>a</b>) <span class="html-italic">V<sub>in</sub></span> = 210 V. (<b>b</b>) <span class="html-italic">V<sub>in</sub></span> = 270 V. (<b>c</b>) <span class="html-italic">V<sub>in</sub></span> = 330 V.</p> Full article ">Figure 15
<p>Efficiency comparison curve. (<b>a</b>) Efficiency comparison curve at 20% load. (<b>b</b>) Efficiency comparison curve at rated input voltage.</p> Full article ">Figure 16
<p>The dynamic waveform of FSBB converter during sudden load changes. (<b>a</b>) Load increases from 10% to 90%. (<b>b</b>) Load reduction from 90% to 10%.</p> Full article ">
<p>FSBB converter circuit and key waveforms. (<b>a</b>) circuit. (<b>b</b>) key waveforms.</p> Full article ">Figure 2
<p>The waveform of <span class="html-italic">i<sub>L</sub></span> when <span class="html-italic">I<sub>Lrms</sub></span> and <span class="html-italic">I<sub>Lp</sub></span> are at their minimum. (<b>a</b>) PDCM. (<b>b</b>) PCRM.</p> Full article ">Figure 3
<p>The <span class="html-italic">i<sub>L</sub></span> waveform at different <span class="html-italic">f<sub>s</sub></span> under the same <span class="html-italic">V<sub>in</sub></span>, <span class="html-italic">V<sub>o</sub></span>, <span class="html-italic">P<sub>o</sub></span>.</p> Full article ">Figure 4
<p>Inductor current waveform.</p> Full article ">Figure 5
<p>Loss and distribution of FSBB converter under different operating conditions with <span class="html-italic">L</span> = 7.0 μH and <span class="html-italic">f<sub>s</sub></span> = 200 kHz. (<b>a</b>) Loss. (<b>b</b>) distribution.</p> Full article ">Figure 6
<p>The variation of peak current with input voltage and switching frequency.</p> Full article ">Figure 7
<p>Inductor current waveforms have the same output current at different <span class="html-italic">f<sub>s</sub></span>.</p> Full article ">Figure 8
<p>Relationship between <span class="html-italic">P<sub>omax</sub></span> and <span class="html-italic">f<sub>s</sub></span>.</p> Full article ">Figure 9
<p>Frequency 3-D diagram and Reduced losses based on multi-frequency PCCM-ZVS control strategy. (<b>a</b>) Frequency 3-D diagram. (<b>b</b>) Reduced losses.</p> Full article ">Figure 10
<p>FSBB converter closed-loop control system.</p> Full article ">Figure 11
<p>FSBB converter closed-loop control flowchart.</p> Full article ">Figure 12
<p>Experimental Platform.</p> Full article ">Figure 13
<p>Waveform diagram achieving ZVS under different input voltages and 10%full-load. (<b>a</b>) <span class="html-italic">V<sub>in</sub></span> = 210 V. (<b>b</b>) <span class="html-italic">V<sub>in</sub></span> = 270 V. (<b>c</b>) <span class="html-italic">V<sub>in</sub></span> = 330 V.</p> Full article ">Figure 14
<p>Waveform diagram achieving ZVS under different input voltages and 90% full-load. (<b>a</b>) <span class="html-italic">V<sub>in</sub></span> = 210 V. (<b>b</b>) <span class="html-italic">V<sub>in</sub></span> = 270 V. (<b>c</b>) <span class="html-italic">V<sub>in</sub></span> = 330 V.</p> Full article ">Figure 15
<p>Efficiency comparison curve. (<b>a</b>) Efficiency comparison curve at 20% load. (<b>b</b>) Efficiency comparison curve at rated input voltage.</p> Full article ">Figure 16
<p>The dynamic waveform of FSBB converter during sudden load changes. (<b>a</b>) Load increases from 10% to 90%. (<b>b</b>) Load reduction from 90% to 10%.</p> Full article ">
Open AccessArticle
The Control Strategies for Charging and Discharging of Electric Vehicles in the Vehicle–Grid Interaction Modes
by
Tao Wang, Jihui Zhang, Xin Li, Shenhui Chen, Jinhao Ma and Honglin Han
World Electr. Veh. J. 2024, 15(10), 468; https://doi.org/10.3390/wevj15100468 - 14 Oct 2024
Abstract
In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy
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In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy exchanges between them. The key component for implementing V2G technology is the bidirectional AC/DC converter. This study concentrates on the non-isolated bidirectional AC/DC converter, providing a detailed analysis of its two-stage operation and creating a mathematical model. A dual closed-loop control structure for voltage and current is designed based on nonlinear control theory, along with a constant current charge–discharge control strategy. Furthermore, midpoint potential balance is achieved through zero-sequence voltage injection control, and power signals for the switching devices are generated using Space Vector Pulse Width Modulation (SVPWM) technology. A simulation model of the V2G system is then constructed in MATLAB/Simulink for analysis and validation. The findings demonstrate that the control strategy proposed in this paper improves the system’s robustness, dynamic performance, and resistance to interference, thus reducing the effects of large-scale, uncoordinated electric vehicle charging on the power grid.
Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
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Figure 1
<p>Two-stage bidirectional three-level AC/DC topology configuration.</p> Full article ">Figure 2
<p>Simplified model of T-type three-level converter.</p> Full article ">Figure 3
<p>Voltage outer loop sliding mode variable structure control.</p> Full article ">Figure 4
<p>Current inner loop system control block diagram.</p> Full article ">Figure 5
<p>Block diagram for constant current charge and discharge control system.</p> Full article ">Figure 6
<p>Equivalent circuit representation of the direct current side.</p> Full article ">Figure 7
<p>Schematic representation of V2G simulation model: (<b>a</b>) Simulation model of a two-stage bidirectional three-level AC/DC topology configuration; (<b>b</b>) simulation model of AC/DC and DC/DC control strategies.</p> Full article ">Figure 8
<p>Current voltage during charging phase A.</p> Full article ">Figure 9
<p>Bus voltage Udc during charging process.</p> Full article ">Figure 10
<p>Waveform of charging current for battery.</p> Full article ">Figure 11
<p>Condition of battery while undergoing charging process.</p> Full article ">Figure 12
<p>THD of grid-side current during charging process.</p> Full article ">Figure 13
<p>Current voltage during discharge phase A.</p> Full article ">Figure 14
<p>During discharge process, bus voltage, denoted as U<sub>dc</sub>, is observed.</p> Full article ">Figure 15
<p>Waveform of discharge current associated with battery.</p> Full article ">Figure 16
<p>Condition of battery while undergoing discharging process.</p> Full article ">Figure 17
<p>THD of grid-side current during discharge process.</p> Full article ">Figure 18
<p>DC voltage-divider capacitor: (<b>a</b>) DC voltage dividing capacitor without introduction of zero-sequence voltage injection method; (<b>b</b>) implementation of the zero-sequence voltage injection technique for purpose of voltage division among capacitors on DC side.</p> Full article ">
<p>Two-stage bidirectional three-level AC/DC topology configuration.</p> Full article ">Figure 2
<p>Simplified model of T-type three-level converter.</p> Full article ">Figure 3
<p>Voltage outer loop sliding mode variable structure control.</p> Full article ">Figure 4
<p>Current inner loop system control block diagram.</p> Full article ">Figure 5
<p>Block diagram for constant current charge and discharge control system.</p> Full article ">Figure 6
<p>Equivalent circuit representation of the direct current side.</p> Full article ">Figure 7
<p>Schematic representation of V2G simulation model: (<b>a</b>) Simulation model of a two-stage bidirectional three-level AC/DC topology configuration; (<b>b</b>) simulation model of AC/DC and DC/DC control strategies.</p> Full article ">Figure 8
<p>Current voltage during charging phase A.</p> Full article ">Figure 9
<p>Bus voltage Udc during charging process.</p> Full article ">Figure 10
<p>Waveform of charging current for battery.</p> Full article ">Figure 11
<p>Condition of battery while undergoing charging process.</p> Full article ">Figure 12
<p>THD of grid-side current during charging process.</p> Full article ">Figure 13
<p>Current voltage during discharge phase A.</p> Full article ">Figure 14
<p>During discharge process, bus voltage, denoted as U<sub>dc</sub>, is observed.</p> Full article ">Figure 15
<p>Waveform of discharge current associated with battery.</p> Full article ">Figure 16
<p>Condition of battery while undergoing discharging process.</p> Full article ">Figure 17
<p>THD of grid-side current during discharge process.</p> Full article ">Figure 18
<p>DC voltage-divider capacitor: (<b>a</b>) DC voltage dividing capacitor without introduction of zero-sequence voltage injection method; (<b>b</b>) implementation of the zero-sequence voltage injection technique for purpose of voltage division among capacitors on DC side.</p> Full article ">
Open AccessArticle
Enhanced Vehicle Logo Detection Method Based on Self-Attention Mechanism for Electric Vehicle Application
by
Shuo Yang, Yisu Liu, Ziyue Liu, Changhua Xu and Xueting Du
World Electr. Veh. J. 2024, 15(10), 467; https://doi.org/10.3390/wevj15100467 - 14 Oct 2024
Abstract
Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature
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Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the multi-head attention for multi-scale feature fusion module to capture multi-scale contextual information effectively. Moreover, we incorporate the bidirectional aggregation mechanism to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Our experimental evaluation using the VLD-45 dataset achieves a state-of-the-art result of 90.3% mAP. Our method has also improved AP by 10% for difficult samples, such as HAVAL and LAND ROVER. Our method provides a new detection framework for small-size objects, with potential applications in various fields.
Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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<p>Example of proportion of vehicle logo. The proportion of small objects in this paper is ±0.2%.</p> Full article ">Figure 2
<p>The pipeline of our method. It includes the down-sampling feature extraction network with attention and a multi-scale feature fusion detection head.</p> Full article ">Figure 3
<p>Example of structure for attention residual block. It helps the feature extraction network to establish the local texture feature monitoring mechanism.</p> Full article ">Figure 4
<p>Example of structure for feature fusion convolution block.</p> Full article ">Figure 5
<p>Example of structure for our detection head. We propose the multi-scale prediction method based on anchor box generation. It includes an anchor box from the three scales for solving the scale change problem of small-size objects.</p> Full article ">Figure 6
<p>The example of VLD-45 dataset for 45 categories.</p> Full article ">Figure 7
<p>The samples of detailed VLD-45 dataset.</p> Full article ">Figure 8
<p>The examples of qualitative results for our method on the VLD-45 dataset.</p> Full article ">
<p>Example of proportion of vehicle logo. The proportion of small objects in this paper is ±0.2%.</p> Full article ">Figure 2
<p>The pipeline of our method. It includes the down-sampling feature extraction network with attention and a multi-scale feature fusion detection head.</p> Full article ">Figure 3
<p>Example of structure for attention residual block. It helps the feature extraction network to establish the local texture feature monitoring mechanism.</p> Full article ">Figure 4
<p>Example of structure for feature fusion convolution block.</p> Full article ">Figure 5
<p>Example of structure for our detection head. We propose the multi-scale prediction method based on anchor box generation. It includes an anchor box from the three scales for solving the scale change problem of small-size objects.</p> Full article ">Figure 6
<p>The example of VLD-45 dataset for 45 categories.</p> Full article ">Figure 7
<p>The samples of detailed VLD-45 dataset.</p> Full article ">Figure 8
<p>The examples of qualitative results for our method on the VLD-45 dataset.</p> Full article ">
Open AccessArticle
Two-Stage Multiple-Vector Model Predictive Control for Multiple-Phase Electric-Drive-Reconstructed Power Management for Solar-Powered Vehicles
by
Qingyun Zhu, Zhen Zhang and Zhihao Zhu
World Electr. Veh. J. 2024, 15(10), 466; https://doi.org/10.3390/wevj15100466 - 14 Oct 2024
Abstract
Electric-drive-reconstructed onboard chargers (EDROCs), also known as electric-drive-reconstructed power management systems, are a promising alternative to conventional onboard chargers due to their characteristics of low cost and high power density. The model predictive control offers a fast dynamic response, simple implementation, and the
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Electric-drive-reconstructed onboard chargers (EDROCs), also known as electric-drive-reconstructed power management systems, are a promising alternative to conventional onboard chargers due to their characteristics of low cost and high power density. The model predictive control offers a fast dynamic response, simple implementation, and the ability to control multiple targets simultaneously. In this paper, a two-stage multi-vector model predictive current control (MPCC) of a six-phase EDROC for solar-powered electric vehicles (EVs) is proposed. Firstly, the topology for the EDROC incorporating a six-phase symmetrical permanent magnet synchronous machine (PMSM) is introduced, and the operation principles of the DC charge mode, the drive mode, and, especially, the in-motion charge mode are analyzed in detail. After that, a two-stage multi-vector MPCC method is proposed by using the multi-vector MPC technique and designing a two-stage MPC structure to eliminate the regulation of the weighting factor of the MPC. Finally, the effectiveness of the proposed method is verified on a self-designed 2 kW EDROC platform.
Full article
(This article belongs to the Special Issue Emerging Topologies and Control of Electric-Drive-Reconstructed Onboard Charger for Electric Vehicles)
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Figure 1
<p>Topology of the EDROC.</p> Full article ">Figure 2
<p>Operation states of the EDROC. (<b>a</b>) State 1; (<b>b</b>) state 2; (<b>c</b>) state 3; (<b>d</b>) state 4.</p> Full article ">Figure 3
<p>Voltage vectors in (<b>a</b>) αβ subspace and (<b>b</b>) xy subspace.</p> Full article ">Figure 4
<p><span class="html-italic">V</span><sub>1</sub>–<span class="html-italic">V</span><sub>6</sub> in αβ subspace.</p> Full article ">Figure 5
<p>Control diagram of the proposed MPCC.</p> Full article ">Figure 6
<p>Experimental platform.</p> Full article ">Figure 7
<p>Experimental results in normal drive mode.</p> Full article ">Figure 8
<p>Experimental results in DC charge model. (<b>a</b>) Charging by the VVs. (<b>b</b>) Charging by DC power supply.</p> Full article ">Figure 9
<p>Experimental result in in-motion charging mode.</p> Full article ">Figure 10
<p>Experimental results when mode switching from normal driving to in-motion charging modes.</p> Full article ">Figure 11
<p>Efficiency results in DC charging mode and in-motion charging mode.</p> Full article ">
<p>Topology of the EDROC.</p> Full article ">Figure 2
<p>Operation states of the EDROC. (<b>a</b>) State 1; (<b>b</b>) state 2; (<b>c</b>) state 3; (<b>d</b>) state 4.</p> Full article ">Figure 3
<p>Voltage vectors in (<b>a</b>) αβ subspace and (<b>b</b>) xy subspace.</p> Full article ">Figure 4
<p><span class="html-italic">V</span><sub>1</sub>–<span class="html-italic">V</span><sub>6</sub> in αβ subspace.</p> Full article ">Figure 5
<p>Control diagram of the proposed MPCC.</p> Full article ">Figure 6
<p>Experimental platform.</p> Full article ">Figure 7
<p>Experimental results in normal drive mode.</p> Full article ">Figure 8
<p>Experimental results in DC charge model. (<b>a</b>) Charging by the VVs. (<b>b</b>) Charging by DC power supply.</p> Full article ">Figure 9
<p>Experimental result in in-motion charging mode.</p> Full article ">Figure 10
<p>Experimental results when mode switching from normal driving to in-motion charging modes.</p> Full article ">Figure 11
<p>Efficiency results in DC charging mode and in-motion charging mode.</p> Full article ">
Open AccessArticle
Lateral-Stability-Oriented Path-Tracking Control Design for Four-Wheel Independent Drive Autonomous Vehicles with Tire Dynamic Characteristics under Extreme Conditions
by
Zhencheng Yu, Rongchen Zhao and Tengfei Yuan
World Electr. Veh. J. 2024, 15(10), 465; https://doi.org/10.3390/wevj15100465 - 13 Oct 2024
Abstract
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This paper proposes a lateral-stability-oriented path-tracking controller for four-wheel independent drive (4WID) autonomous vehicles. The proposed controller aims to maintain vehicle stability under extreme conditions while minimizing lateral deviation. Firstly, a tiered control framework comprising upper-level and lower-level controllers is introduced. The upper-level
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This paper proposes a lateral-stability-oriented path-tracking controller for four-wheel independent drive (4WID) autonomous vehicles. The proposed controller aims to maintain vehicle stability under extreme conditions while minimizing lateral deviation. Firstly, a tiered control framework comprising upper-level and lower-level controllers is introduced. The upper-level controller is a lateral stability path-tracking controller that incorporates tire dynamic characteristics, developed using model predictive control (MPC) theory. This controller dynamically updates the tire lateral force constraints in real time to account for variations in tire dynamics under extreme conditions. Additionally, it enhances lateral stability and reduces path-tracking errors by applying additional yaw torque based on minimum tire utilization. The lower-level controllers execute the required steering angles and yaw moments through the appropriate component equipment and torque distribution. The joint simulation results from CarSim and MATLAB/Simulink show that, compared to the traditional MPC controller with unstable sideslip, this controller can maintain vehicle lateral stability under extreme conditions. Compared to the MPC controller, which only considers lateral force constraints, this controller can significantly reduce lateral tracking errors, with an average yaw rate reduction of 31.62% and an average sideslip angle reduction of 40.21%.
Full article
Figure 1
Figure 1
<p>The proposed path-tracking control system utilizes a hierarchical control architecture.</p> Full article ">Figure 2
<p>Seven-degree vehicle dynamics model.</p> Full article ">Figure 3
<p>A simplified vehicle model with two degrees of freedom.</p> Full article ">Figure 4
<p>Co-simulation block diagram.</p> Full article ">Figure 5
<p>Comparison results: (<b>a</b>) global path; (<b>b</b>) front wheel angle.</p> Full article ">Figure 6
<p>Comparison results: (<b>a</b>) lateral error; (<b>b</b>) yaw rate; (<b>c</b>) sideslip angle.</p> Full article ">Figure 7
<p>Control outputs: (<b>a</b>) additional yaw moment; (<b>b</b>) wheel torque of controller C.</p> Full article ">Figure 8
<p>Comparison results: (<b>a</b>) Global path; (<b>b</b>) Front wheel angle.</p> Full article ">Figure 9
<p>Comparison results: (<b>a</b>) lateral error; (<b>b</b>) yaw rate; (<b>c</b>) sideslip angle.</p> Full article ">Figure 10
<p>Control outputs: (<b>a</b>) additional yaw moment; (<b>b</b>) wheel torque of controller C.</p> Full article ">Figure 11
<p>Comparison results of simulation scenario 3: (<b>a</b>) global path; (<b>b</b>) lateral error; (<b>c</b>) front wheel angle; (<b>d</b>) sideslip angle.</p> Full article ">Figure 12
<p>Comparison results of simulation scenario 4: (<b>a</b>) global path; (<b>b</b>) lateral error; (<b>c</b>) front wheel angle; (<b>d</b>) sideslip angle.</p> Full article ">Figure 13
<p>Controller calculation time: (<b>a</b>) scenario 1; (<b>b</b>) scenario 2.</p> Full article ">
<p>The proposed path-tracking control system utilizes a hierarchical control architecture.</p> Full article ">Figure 2
<p>Seven-degree vehicle dynamics model.</p> Full article ">Figure 3
<p>A simplified vehicle model with two degrees of freedom.</p> Full article ">Figure 4
<p>Co-simulation block diagram.</p> Full article ">Figure 5
<p>Comparison results: (<b>a</b>) global path; (<b>b</b>) front wheel angle.</p> Full article ">Figure 6
<p>Comparison results: (<b>a</b>) lateral error; (<b>b</b>) yaw rate; (<b>c</b>) sideslip angle.</p> Full article ">Figure 7
<p>Control outputs: (<b>a</b>) additional yaw moment; (<b>b</b>) wheel torque of controller C.</p> Full article ">Figure 8
<p>Comparison results: (<b>a</b>) Global path; (<b>b</b>) Front wheel angle.</p> Full article ">Figure 9
<p>Comparison results: (<b>a</b>) lateral error; (<b>b</b>) yaw rate; (<b>c</b>) sideslip angle.</p> Full article ">Figure 10
<p>Control outputs: (<b>a</b>) additional yaw moment; (<b>b</b>) wheel torque of controller C.</p> Full article ">Figure 11
<p>Comparison results of simulation scenario 3: (<b>a</b>) global path; (<b>b</b>) lateral error; (<b>c</b>) front wheel angle; (<b>d</b>) sideslip angle.</p> Full article ">Figure 12
<p>Comparison results of simulation scenario 4: (<b>a</b>) global path; (<b>b</b>) lateral error; (<b>c</b>) front wheel angle; (<b>d</b>) sideslip angle.</p> Full article ">Figure 13
<p>Controller calculation time: (<b>a</b>) scenario 1; (<b>b</b>) scenario 2.</p> Full article ">
Open AccessArticle
Simulation and Experimental Study on Heat Transfer Performance of Bionic Structure-Based Battery Liquid Cooling Plate
by
Zhizhong Wang, Dinghong Liu, Zhaoyang Li, Xin Qi and Chaoyi Wan
World Electr. Veh. J. 2024, 15(10), 464; https://doi.org/10.3390/wevj15100464 - 12 Oct 2024
Abstract
This study presents a bionic structure-based liquid cooling plate designed to address the heat generation characteristics of prismatic lithium-ion batteries. The size of the lithium-ion battery is 148 mm × 26 mm × 97 mm, the positive pole size is 20 mm ×
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This study presents a bionic structure-based liquid cooling plate designed to address the heat generation characteristics of prismatic lithium-ion batteries. The size of the lithium-ion battery is 148 mm × 26 mm × 97 mm, the positive pole size is 20 mm × 20 mm × 3 mm, and the negative pole size is 22 mm × 20 mm × 3 mm. Experimental testing of the Li-ion battery’s heat generation model parameters, in conjunction with bionic structure and micro-channel features, has led to the development of this innovative cooling system. The traditional bionic liquid cooling plate’s structure is often singular; however, the flow path of the liquid cooling plate designed in this paper is based on the combination of the distribution of human blood vessel branches and the structure of insect wing veins. The external dimension of the liquid cooling plate is 152 mm × 100 mm × 6 mm (length × width × height). Utilizing numerical simulation and thermodynamic principles, we analyzed the heat transfer efficacy of the bionic liquid cooling module for power batteries. Specifically, we investigated the impact of varying coolant flow rates and the contact radius between flow channels on the thermal performance of the bionic battery modules. Our findings indicate that a liquid flow rate of 0.6 m/s achieves a stable maximum surface temperature and temperature differential across the bionic battery liquid cooling module, with a relatively low overall system power consumption, suggesting room for further enhancement of heat transfer performance. By augmenting the contact radius between flow channels, we observed an initial increase in the maximum surface temperature, temperature differential, and inlet–outlet pressure differential at a flow rate of 0.2 m/s. However, at flow rates equal to or exceeding 0.4 m/s, these parameters stabilized across different design Scenarios. Notably, the pump power consumption remained consistent across various scenarios and flow rates. This study’s outcomes offer valuable insights for the development of liquid-cooled battery thermal management systems that are energy-efficient and offer superior heat transfer capabilities.
Full article
(This article belongs to the Special Issue Electrochemical and Thermal Modeling of Batteries for Electric Vehicle)
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<p>Experimental process.</p> Full article ">Figure 2
<p>Overall voltage curve of HPPC experiment.</p> Full article ">Figure 3
<p>Fitting curve of battery internal resistance and <span class="html-italic">SOC</span>.</p> Full article ">Figure 4
<p>The fitting curve of temperature entropy coefficient and state of charge.</p> Full article ">Figure 5
<p>Thermal model verification of single cells.</p> Full article ">Figure 6
<p>Liquid cooling geometric model of battery module.</p> Full article ">Figure 7
<p>Liquid cooling plate model structure of biomimetic organism.</p> Full article ">Figure 8
<p>Effect of liquid flow rate on system performance: (<b>a</b>) pressure difference and pump power; (<b>b</b>) max temperature and temperature difference.</p> Full article ">Figure 9
<p>Effect of angular arcs between runners on system performance: (<b>a</b>) maximum surface temperature; (<b>b</b>) temperature difference; (<b>c</b>) pressure difference; and (<b>d</b>) pump power.</p> Full article ">Figure 10
<p>Pressure and temperature cloud images of the three Scenarios.</p> Full article ">Figure 10 Cont.
<p>Pressure and temperature cloud images of the three Scenarios.</p> Full article ">
<p>Experimental process.</p> Full article ">Figure 2
<p>Overall voltage curve of HPPC experiment.</p> Full article ">Figure 3
<p>Fitting curve of battery internal resistance and <span class="html-italic">SOC</span>.</p> Full article ">Figure 4
<p>The fitting curve of temperature entropy coefficient and state of charge.</p> Full article ">Figure 5
<p>Thermal model verification of single cells.</p> Full article ">Figure 6
<p>Liquid cooling geometric model of battery module.</p> Full article ">Figure 7
<p>Liquid cooling plate model structure of biomimetic organism.</p> Full article ">Figure 8
<p>Effect of liquid flow rate on system performance: (<b>a</b>) pressure difference and pump power; (<b>b</b>) max temperature and temperature difference.</p> Full article ">Figure 9
<p>Effect of angular arcs between runners on system performance: (<b>a</b>) maximum surface temperature; (<b>b</b>) temperature difference; (<b>c</b>) pressure difference; and (<b>d</b>) pump power.</p> Full article ">Figure 10
<p>Pressure and temperature cloud images of the three Scenarios.</p> Full article ">Figure 10 Cont.
<p>Pressure and temperature cloud images of the three Scenarios.</p> Full article ">
Open AccessArticle
Dynamic Wireless Charging of Electric Vehicles Using PV Units in Highways
by
Tamer F. Megahed, Diaa-Eldin A. Mansour, Donart Nayebare, Mohamed F. Kotb, Ahmed Fares, Ibrahim A. Hameed and Haitham El-Hussieny
World Electr. Veh. J. 2024, 15(10), 463; https://doi.org/10.3390/wevj15100463 - 12 Oct 2024
Abstract
Transitioning from petrol or gas vehicles to electric vehicles (EVs) poses significant challenges in reducing emissions, lowering operational costs, and improving energy storage. Wireless charging EVs offer promising solutions to wired charging limitations such as restricted travel range and lengthy charging times. This
[...] Read more.
Transitioning from petrol or gas vehicles to electric vehicles (EVs) poses significant challenges in reducing emissions, lowering operational costs, and improving energy storage. Wireless charging EVs offer promising solutions to wired charging limitations such as restricted travel range and lengthy charging times. This paper presents a comprehensive approach to address the challenges of wireless power transfer (WPT) for EVs by optimizing coupling frequency and coil design to enhance efficiency while minimizing electromagnetic interference (EMI) and heat generation. A novel coil design and adaptive hardware are proposed to improve power transfer efficiency (PTE) by defining the optimal magnetic resonant coupling WPT and mitigating coil misalignment, which is considered a significant barrier to the widespread adoption of WPT for EVs. A new methodology for designing and arranging roadside lanes and facilities for dynamic wireless charging (DWC) of EVs is introduced. This includes the optimization of transmitter coils (TCs), receiving coils (RCs), compensation circuits, and high-frequency inverters/converters using the partial differential equation toolbox (pdetool). The integration of wireless charging systems with smart grid technology is explored to enhance energy distribution and reduce peak load issues. The paper proposes a DWC system with multiple segmented transmitters integrated with adaptive renewable photovoltaic (PV) units and a battery system using the utility main grid as a backup. The design process includes the determination of the required PV array capacity, station battery sizing, and inverters/converters to ensure maximum power point tracking (MPPT). To validate the proposed system, it was tested in two scenarios: charging a single EV at different speeds and simultaneously charging two EVs over a 1 km stretch with a 50 kW system, achieving a total range of 500 km. Experimental validation was performed through real-time simulation and hardware tests using an OPAL-RT platform, demonstrating a power transfer efficiency of 90.7%, thus confirming the scalability and feasibility of the system for future EV infrastructure.
Full article
(This article belongs to the Special Issue Wireless Power Transfer Technology for Electric Vehicles)
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<p>DWC station block diagram.</p> Full article ">Figure 2
<p>Equivalent circuit of PV.</p> Full article ">Figure 3
<p>Station control loop flowchart.</p> Full article ">Figure 4
<p>Types of coil systems.</p> Full article ">Figure 5
<p>Magnetically coupled ideal coils.</p> Full article ">Figure 6
<p>Compensation circuit types: (<b>a</b>) series–series “SS”; (<b>b</b>) parallel–parallel “PP”; (<b>c</b>) series–parallel “SP”; (<b>d</b>) parallel–series “PS”.</p> Full article ">Figure 7
<p>Design procedure for DWC for EVB.</p> Full article ">Figure 8
<p>The spiral coil arrangement design using pdetool.</p> Full article ">Figure 9
<p>The spiral coil arrangement design using pdetool.</p> Full article ">Figure 10
<p>The dominant magnetic field component.</p> Full article ">Figure 11
<p>Two identical resonators for transmitter and receiver coils modeled as linear arrays at a specific distance.</p> Full article ">Figure 12
<p>Changing the frequency with different S21 values.</p> Full article ">Figure 13
<p>Changing the frequency against different S21 values and distance.</p> Full article ">Figure 14
<p>Prototype setup layout.</p> Full article ">Figure 15
<p>Prototype operation process.</p> Full article ">Figure 16
<p>PV power generated on testing days.</p> Full article ">Figure 17
<p>Source voltage and current.</p> Full article ">Figure 18
<p>System battery voltage.</p> Full article ">Figure 19
<p>DC link voltage and current.</p> Full article ">Figure 20
<p>Roadside winding voltage and current.</p> Full article ">Figure 21
<p>Vehicle side winding voltage and current.</p> Full article ">Figure 22
<p>Car battery SOC.</p> Full article ">Figure 23
<p>Sending and receiving power.</p> Full article ">Figure 24
<p>Consumed active and reactive power.</p> Full article ">
<p>DWC station block diagram.</p> Full article ">Figure 2
<p>Equivalent circuit of PV.</p> Full article ">Figure 3
<p>Station control loop flowchart.</p> Full article ">Figure 4
<p>Types of coil systems.</p> Full article ">Figure 5
<p>Magnetically coupled ideal coils.</p> Full article ">Figure 6
<p>Compensation circuit types: (<b>a</b>) series–series “SS”; (<b>b</b>) parallel–parallel “PP”; (<b>c</b>) series–parallel “SP”; (<b>d</b>) parallel–series “PS”.</p> Full article ">Figure 7
<p>Design procedure for DWC for EVB.</p> Full article ">Figure 8
<p>The spiral coil arrangement design using pdetool.</p> Full article ">Figure 9
<p>The spiral coil arrangement design using pdetool.</p> Full article ">Figure 10
<p>The dominant magnetic field component.</p> Full article ">Figure 11
<p>Two identical resonators for transmitter and receiver coils modeled as linear arrays at a specific distance.</p> Full article ">Figure 12
<p>Changing the frequency with different S21 values.</p> Full article ">Figure 13
<p>Changing the frequency against different S21 values and distance.</p> Full article ">Figure 14
<p>Prototype setup layout.</p> Full article ">Figure 15
<p>Prototype operation process.</p> Full article ">Figure 16
<p>PV power generated on testing days.</p> Full article ">Figure 17
<p>Source voltage and current.</p> Full article ">Figure 18
<p>System battery voltage.</p> Full article ">Figure 19
<p>DC link voltage and current.</p> Full article ">Figure 20
<p>Roadside winding voltage and current.</p> Full article ">Figure 21
<p>Vehicle side winding voltage and current.</p> Full article ">Figure 22
<p>Car battery SOC.</p> Full article ">Figure 23
<p>Sending and receiving power.</p> Full article ">Figure 24
<p>Consumed active and reactive power.</p> Full article ">
Open AccessArticle
Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks
by
Christina V. Grammenou, Magdalini Dragatsika and Aggelos S. Bouhouras
World Electr. Veh. J. 2024, 15(10), 462; https://doi.org/10.3390/wevj15100462 - 11 Oct 2024
Abstract
In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in
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In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction.
Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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Figure 1
Figure 1
<p>Flowchart of critical node selected.</p> Full article ">Figure 2
<p>Flowchart of scheduling EV charging and PV siting.</p> Full article ">Figure 3
<p>LV network topology.</p> Full article ">Figure 4
<p>Loads demand: (<b>a</b>) loads 1–12; (<b>b</b>) loads 13–25.</p> Full article ">Figure 5
<p>Daily PV generation.</p> Full article ">Figure 6
<p>Daily energy losses in 14 scenarios for all 6 cases.</p> Full article ">Figure 7
<p>Controlled and uncontrolled EV charging for Sc#5, cases A and D.</p> Full article ">Figure 8
<p>Controlled and uncontrolled EV charging for Sc#13, cases A and D.</p> Full article ">Figure 9
<p>Installation of PVs in critical nodes for 14 scenarios, cases B and C.</p> Full article ">Figure 10
<p>Optimal PV siting: (<b>a</b>) Sc#5, case E; (<b>b</b>) Sc#5, case F; (<b>c</b>) Sc#13, case E; (<b>d</b>) Sc#13, case F.</p> Full article ">Figure 11
<p>Voltage profile for Sc#5: (<b>a</b>) case A, (<b>b</b>) case B, (<b>c</b>) case C, (<b>d</b>) case D, (<b>e</b>) case E, and (<b>f</b>) case F.</p> Full article ">Figure 12
<p>Voltage profile for Sc#13: (<b>a</b>) case A, (<b>b</b>) case B, (<b>c</b>) case C, (<b>d</b>) case D, (<b>e</b>) case E, and (<b>f</b>) case F.</p> Full article ">Figure 13
<p>Current profile for Sc#5: (<b>a</b>) case A, (<b>b</b>) case B, (<b>c</b>) case C, (<b>d</b>) case D, (<b>e</b>) case E, and (<b>f</b>) case F.</p> Full article ">Figure 14
<p>Current profile for Sc#13: (<b>a</b>) case A, (<b>b</b>) case B, (<b>c</b>) case C, (<b>d</b>) case D, (<b>e</b>) case E, and (<b>f</b>) case F.</p> Full article ">
<p>Flowchart of critical node selected.</p> Full article ">Figure 2
<p>Flowchart of scheduling EV charging and PV siting.</p> Full article ">Figure 3
<p>LV network topology.</p> Full article ">Figure 4
<p>Loads demand: (<b>a</b>) loads 1–12; (<b>b</b>) loads 13–25.</p> Full article ">Figure 5
<p>Daily PV generation.</p> Full article ">Figure 6
<p>Daily energy losses in 14 scenarios for all 6 cases.</p> Full article ">Figure 7
<p>Controlled and uncontrolled EV charging for Sc#5, cases A and D.</p> Full article ">Figure 8
<p>Controlled and uncontrolled EV charging for Sc#13, cases A and D.</p> Full article ">Figure 9
<p>Installation of PVs in critical nodes for 14 scenarios, cases B and C.</p> Full article ">Figure 10
<p>Optimal PV siting: (<b>a</b>) Sc#5, case E; (<b>b</b>) Sc#5, case F; (<b>c</b>) Sc#13, case E; (<b>d</b>) Sc#13, case F.</p> Full article ">Figure 11
<p>Voltage profile for Sc#5: (<b>a</b>) case A, (<b>b</b>) case B, (<b>c</b>) case C, (<b>d</b>) case D, (<b>e</b>) case E, and (<b>f</b>) case F.</p> Full article ">Figure 12
<p>Voltage profile for Sc#13: (<b>a</b>) case A, (<b>b</b>) case B, (<b>c</b>) case C, (<b>d</b>) case D, (<b>e</b>) case E, and (<b>f</b>) case F.</p> Full article ">Figure 13
<p>Current profile for Sc#5: (<b>a</b>) case A, (<b>b</b>) case B, (<b>c</b>) case C, (<b>d</b>) case D, (<b>e</b>) case E, and (<b>f</b>) case F.</p> Full article ">Figure 14
<p>Current profile for Sc#13: (<b>a</b>) case A, (<b>b</b>) case B, (<b>c</b>) case C, (<b>d</b>) case D, (<b>e</b>) case E, and (<b>f</b>) case F.</p> Full article ">
Open AccessArticle
Optimal Scheduling of Integrated Energy System Considering Virtual Heat Storage and Electric Vehicles
by
Yinjun Liu, Yongqing Zhu, Shunjiang Yu, Zhibang Wang, Zhen Li, Changming Chen, Li Yang and Zhenzhi Lin
World Electr. Veh. J. 2024, 15(10), 461; https://doi.org/10.3390/wevj15100461 - 11 Oct 2024
Abstract
Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility
[...] Read more.
Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility of power systems by considering the participation of IESs in distribution network optimization scheduling. Therefore, the optimal scheduling of IESs considering virtual heat storage and electric vehicles (EVs) is proposed in this paper. Firstly, the basic structure of IESs and mathematical models for the operation of the relevant equipment are presented. Then, an optimal scheduling strategy of an IES considering virtual heat storage and electric vehicles is proposed. Finally, an IES with an IEEE 33-node distribution network, 20-node Belgian natural gas network, and 44-node heating network topologies is selected to validate the proposed strategy. The proposed models of integrated demand response (IDR), EV orderly charging participation, virtual heat storage, and actual multitype energy storage devices play the role of peak shaving and valley filling, which also helps to reduce the scheduling cost from CNY 11,253.0 to CNY 11,184.4. The simulation results also demonstrate that the proposed model can effectively improve the operational economy of IESs, and the scheduling strategy can promote the consumption of renewable energy, with the wind curtailment rate decreasing from 63.62% to 12.50% and the solar curtailment rate decreasing from 56.92% to 21.34%.
Full article
(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
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Figure 1
Figure 1
<p>Schematic diagram of integrated energy system structure.</p> Full article ">Figure 2
<p>Vertical section of a supply pipeline <span class="html-italic">p</span> in heating networks.</p> Full article ">Figure 3
<p>Distribution network structure diagram.</p> Full article ">Figure 4
<p>Renewable resource output, electricity price, and load curve: (<b>a</b>) EV charged in an orderly manner; (<b>b</b>) EV charged in a disorderly manner.</p> Full article ">Figure 5
<p>Electric power optimization scheduling result.</p> Full article ">Figure 6
<p>Thermal power optimization scheduling result.</p> Full article ">Figure 7
<p>Gas power optimization scheduling result.</p> Full article ">Figure 8
<p>Participation of IDR of multitype energy in IES.</p> Full article ">Figure 9
<p>Participation of EVs in electricity demand response.</p> Full article ">Figure 10
<p>Electric power scheduling of charge and discharge.</p> Full article ">Figure 11
<p>Thermal power scheduling of charge and discharge.</p> Full article ">Figure 12
<p>Electric power optimization scheduling result of M-ENEVD.</p> Full article ">Figure 13
<p>Comparisons of wind and solar curtailment rate before and after IES participation in distribution network scheduling.</p> Full article ">
<p>Schematic diagram of integrated energy system structure.</p> Full article ">Figure 2
<p>Vertical section of a supply pipeline <span class="html-italic">p</span> in heating networks.</p> Full article ">Figure 3
<p>Distribution network structure diagram.</p> Full article ">Figure 4
<p>Renewable resource output, electricity price, and load curve: (<b>a</b>) EV charged in an orderly manner; (<b>b</b>) EV charged in a disorderly manner.</p> Full article ">Figure 5
<p>Electric power optimization scheduling result.</p> Full article ">Figure 6
<p>Thermal power optimization scheduling result.</p> Full article ">Figure 7
<p>Gas power optimization scheduling result.</p> Full article ">Figure 8
<p>Participation of IDR of multitype energy in IES.</p> Full article ">Figure 9
<p>Participation of EVs in electricity demand response.</p> Full article ">Figure 10
<p>Electric power scheduling of charge and discharge.</p> Full article ">Figure 11
<p>Thermal power scheduling of charge and discharge.</p> Full article ">Figure 12
<p>Electric power optimization scheduling result of M-ENEVD.</p> Full article ">Figure 13
<p>Comparisons of wind and solar curtailment rate before and after IES participation in distribution network scheduling.</p> Full article ">
Open AccessArticle
Physics-Informed Neural Network-Based Nonlinear Model Predictive Control for Automated Guided Vehicle Trajectory Tracking
by
Yinping Li and Li Liu
World Electr. Veh. J. 2024, 15(10), 460; https://doi.org/10.3390/wevj15100460 - 10 Oct 2024
Abstract
This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Traditional physical models often face the challenges of computational inefficiency and
[...] Read more.
This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Traditional physical models often face the challenges of computational inefficiency and insufficient control precision when dealing with complex dynamic systems. However, by integrating physical laws directly into the training process of neural networks, PINNs can effectively learn and capture the kinematic characteristics of vehicles, replacing traditional nonlinear ordinary differential equation models and thus significantly enhancing computational efficiency and control performance. During the model-training phase, this study further incorporates the Theory of Functional Connections (TFC) and adaptive loss balancing strategies to efficiently solve ODE problems without relying on numerical integration and optimize the control strategy. This combined approach not only reduces computational complexity, but also improves the robustness and precision of the control strategy in varying environments. Numerical simulations demonstrate that this method offers significant advantages in AGV trajectory-tracking tasks, manifested in higher computational efficiency and precise control performance. The proposal of the PINN-MPC method provides new theoretical support and innovative methods for real-time complex system control, with important research and application potential, and is expected to play a key role in future intelligent control systems.
Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
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Figure 1
Figure 1
<p>Configuration of the AGV platform.</p> Full article ">Figure 2
<p>A schematic diagram of the LB-TFC-PINN framework.</p> Full article ">Figure 3
<p>LB-TFC-PINN-based nonlinear MPC scheme.</p> Full article ">Figure 4
<p>Training Performance.</p> Full article ">Figure 5
<p>Simulation results from LB-TFC-PINN, PINN, and RK4.</p> Full article ">Figure 6
<p>Trajectory tracking simulation results (Task 1).</p> Full article ">Figure 7
<p>Control variables change over time (Task 1).</p> Full article ">Figure 8
<p>Error over time of the solution of LB-TFC-PINN-based nonlinear MPC for the tracking problem (Task 1).</p> Full article ">Figure 9
<p>Trajectory tracking simulation results (Task 2).</p> Full article ">Figure 10
<p>Control variable changes over time (Task 2).</p> Full article ">Figure 11
<p>Error over time of the solution of LB-TFC-PINN-based nonlinear MPC for the tracking problem (Task 2).</p> Full article ">
<p>Configuration of the AGV platform.</p> Full article ">Figure 2
<p>A schematic diagram of the LB-TFC-PINN framework.</p> Full article ">Figure 3
<p>LB-TFC-PINN-based nonlinear MPC scheme.</p> Full article ">Figure 4
<p>Training Performance.</p> Full article ">Figure 5
<p>Simulation results from LB-TFC-PINN, PINN, and RK4.</p> Full article ">Figure 6
<p>Trajectory tracking simulation results (Task 1).</p> Full article ">Figure 7
<p>Control variables change over time (Task 1).</p> Full article ">Figure 8
<p>Error over time of the solution of LB-TFC-PINN-based nonlinear MPC for the tracking problem (Task 1).</p> Full article ">Figure 9
<p>Trajectory tracking simulation results (Task 2).</p> Full article ">Figure 10
<p>Control variable changes over time (Task 2).</p> Full article ">Figure 11
<p>Error over time of the solution of LB-TFC-PINN-based nonlinear MPC for the tracking problem (Task 2).</p> Full article ">
Open AccessArticle
Investigation of Drive Performance of Motors in Electric Loaders with Unequal Transmission Ratios—A Case Study
by
Xiaotao Fei, Shaw Voon Wong, Muhammad Amin Azman, Peng Liu and Yunwu Han
World Electr. Veh. J. 2024, 15(10), 459; https://doi.org/10.3390/wevj15100459 - 10 Oct 2024
Abstract
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Research on electric wheel loaders (EWLs) has predominantly focused on battery management, hybrid technologies, and energy recovery. However, the influence of motor types and drivetrains on the drive performance of EWLs has received little attention in previous studies. This case study addresses this
[...] Read more.
Research on electric wheel loaders (EWLs) has predominantly focused on battery management, hybrid technologies, and energy recovery. However, the influence of motor types and drivetrains on the drive performance of EWLs has received little attention in previous studies. This case study addresses this gap by examining different EWL configurations and analyzing the drive theory and force requirements by integrating classic vehicle theory with EWL-specific characteristics. The study compares an original EWL, equipped with Permanent Magnet Synchronous Motors (PMSMs) on both the front and rear axles with identical transmission ratios of 22.85, to a modified EWL, which features a Switched Reluctance Motor (SRM) on the front axle and a transmission ratio of 44.05. Walking and shoveling tests were conducted to evaluate performance. The walking test results reveal that, at motor speeds of 200 rpm, 400 rpm, and 600 rpm, energy consumption in R-drive mode is 68.56%, 71.88%, and 74.87% of that in F-drive mode when two PMSMs are used. When an SRM is applied with a transmission ratio of 44.05, these values shift to 73.90%, 70.35%, and 67.72%, respectively. This demonstrates that using the rear motor alone for driving under walking conditions can yield greater energy savings. The shoveling test results indicate that distributing torque according to wheel load reduces rear wheel slippage, and the SRM with a transmission ratio of 44.05 delivers sufficient drive force while operating within a high-efficiency speed range for the EWL.
Full article
Figure 1
Figure 1
<p>Typical schematic diagrams of EWL mechanism.</p> Full article ">Figure 2
<p>Speed and torque for two drive motors in shoveling condition.</p> Full article ">Figure 3
<p>Tire sliding during shoveling process.</p> Full article ">Figure 4
<p>Test program for EWL in (<b>a</b>) walking conditions and (<b>b</b>) shoveling conditions.</p> Full article ">Figure 5
<p>Torque distribution control strategies in shoveling tests [<a href="#B33-wevj-15-00459" class="html-bibr">33</a>].</p> Full article ">Figure 6
<p>Settings of data acquisition tool CANDTU-200R.</p> Full article ">Figure 7
<p>Data processing software CANoe.</p> Full article ">Figure 8
<p>Curves of motors in F-drive mode at speed of 200 rpm in walking condition.</p> Full article ">Figure 9
<p>The speed and torque curves of continuous shoveling test for the original EWL.</p> Full article ">Figure 9 Cont.
<p>The speed and torque curves of continuous shoveling test for the original EWL.</p> Full article ">Figure 10
<p>The speed and torque curves of three shoveling tests for the modified EWL under evenly distributed drive force strategy.</p> Full article ">Figure 11
<p>The speed and torque curves of three shoveling tests for the modified EWL under drive force distributed by wheel load strategy.</p> Full article ">Figure 12
<p>Speed and force of drive wheels in shoveling tests under an evenly distributed drive force control method.</p> Full article ">Figure 13
<p>Speed and force of drive wheels in shoveling tests under the drive force distributed by wheel load control method.</p> Full article ">Figure A1
<p>Speed, torque, and current curves of motor when the original EWL is driven by the front PMSM at 200 rpm.</p> Full article ">Figure A2
<p>Speed, torque, and current curves of motor when the original EWL is driven by the front PMSM at 400 rpm.</p> Full article ">Figure A3
<p>Speed, torque, and current curves of motor when the original EWL is driven by the front PMSM at 600 rpm.</p> Full article ">Figure A4
<p>Speed, torque, and current curves of motor when the original EWL is driven by the rear PMSM at 200 rpm.</p> Full article ">Figure A5
<p>Speed, torque, and current curves of motor when the original EWL is driven by the rear PMSM at 400 rpm.</p> Full article ">Figure A6
<p>Speed, torque, and current curves of motor when the original EWL is driven by the rear PMSM at 600 rpm.</p> Full article ">Figure A7
<p>Speed, torque, and current curves of motor when the modified EWL is driven by SRM at 400 rpm.</p> Full article ">Figure A8
<p>Speed, torque, and current curves of motor when the modified EWL is driven by SRM at 800 rpm.</p> Full article ">Figure A9
<p>Speed, torque, and current curves of motor when the modified EWL is driven by SRM at 1200 rpm.</p> Full article ">Figure A10
<p>Speed, torque, and current curves of motor when the modified EWL is driven by PMSM at 200 rpm.</p> Full article ">Figure A11
<p>Speed, torque, and current curves of motor when the modified EWL is driven by PMSM at 400 rpm.</p> Full article ">Figure A12
<p>Speed, torque, and current curves of motor when the modified EWL is driven by PMSM at 600 rpm.</p> Full article ">
<p>Typical schematic diagrams of EWL mechanism.</p> Full article ">Figure 2
<p>Speed and torque for two drive motors in shoveling condition.</p> Full article ">Figure 3
<p>Tire sliding during shoveling process.</p> Full article ">Figure 4
<p>Test program for EWL in (<b>a</b>) walking conditions and (<b>b</b>) shoveling conditions.</p> Full article ">Figure 5
<p>Torque distribution control strategies in shoveling tests [<a href="#B33-wevj-15-00459" class="html-bibr">33</a>].</p> Full article ">Figure 6
<p>Settings of data acquisition tool CANDTU-200R.</p> Full article ">Figure 7
<p>Data processing software CANoe.</p> Full article ">Figure 8
<p>Curves of motors in F-drive mode at speed of 200 rpm in walking condition.</p> Full article ">Figure 9
<p>The speed and torque curves of continuous shoveling test for the original EWL.</p> Full article ">Figure 9 Cont.
<p>The speed and torque curves of continuous shoveling test for the original EWL.</p> Full article ">Figure 10
<p>The speed and torque curves of three shoveling tests for the modified EWL under evenly distributed drive force strategy.</p> Full article ">Figure 11
<p>The speed and torque curves of three shoveling tests for the modified EWL under drive force distributed by wheel load strategy.</p> Full article ">Figure 12
<p>Speed and force of drive wheels in shoveling tests under an evenly distributed drive force control method.</p> Full article ">Figure 13
<p>Speed and force of drive wheels in shoveling tests under the drive force distributed by wheel load control method.</p> Full article ">Figure A1
<p>Speed, torque, and current curves of motor when the original EWL is driven by the front PMSM at 200 rpm.</p> Full article ">Figure A2
<p>Speed, torque, and current curves of motor when the original EWL is driven by the front PMSM at 400 rpm.</p> Full article ">Figure A3
<p>Speed, torque, and current curves of motor when the original EWL is driven by the front PMSM at 600 rpm.</p> Full article ">Figure A4
<p>Speed, torque, and current curves of motor when the original EWL is driven by the rear PMSM at 200 rpm.</p> Full article ">Figure A5
<p>Speed, torque, and current curves of motor when the original EWL is driven by the rear PMSM at 400 rpm.</p> Full article ">Figure A6
<p>Speed, torque, and current curves of motor when the original EWL is driven by the rear PMSM at 600 rpm.</p> Full article ">Figure A7
<p>Speed, torque, and current curves of motor when the modified EWL is driven by SRM at 400 rpm.</p> Full article ">Figure A8
<p>Speed, torque, and current curves of motor when the modified EWL is driven by SRM at 800 rpm.</p> Full article ">Figure A9
<p>Speed, torque, and current curves of motor when the modified EWL is driven by SRM at 1200 rpm.</p> Full article ">Figure A10
<p>Speed, torque, and current curves of motor when the modified EWL is driven by PMSM at 200 rpm.</p> Full article ">Figure A11
<p>Speed, torque, and current curves of motor when the modified EWL is driven by PMSM at 400 rpm.</p> Full article ">Figure A12
<p>Speed, torque, and current curves of motor when the modified EWL is driven by PMSM at 600 rpm.</p> Full article ">
Open AccessArticle
An Approach for Estimating the Contributions of Various Real-World Usage Conditions towards the Attained Utility Factor of Plug-In Hybrid Electric Vehicles
by
Karim Hamza, Kenneth Laberteaux and Kang-Ching Chu
World Electr. Veh. J. 2024, 15(10), 458; https://doi.org/10.3390/wevj15100458 - 9 Oct 2024
Abstract
Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners.
[...] Read more.
Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners. PHEVs also relieve range anxiety through seamless switching to hybrid driving—an efficient mode of fuel-powered operation—whenever the battery reaches a low state of charge. Stemming from the perception that PHEVs are a well-rounded solution to reducing greenhouse gas (GHG) emissions, various metrics exist to infer the effectiveness of GHG reduction, with utility factor (UF) being prominent among such metrics. Recently, articles in the literature have called into question whether the theoretical values of UF agree with the real-world performance of PHEVs, while also suggesting that infrequent charging was the likely cause for observed deviations. However, it is understood that other reasons could also be responsible for UF mismatch. This work proposes an approach that combines theoretical modeling of UF under progressively relaxed assumptions (including the statistical distribution of daily traveled distance, charging behavior, and attainable electric range), along with vehicle data logs, to quantitatively infer the contributions of various real-world factors towards the observed mismatch between theoretical and real-world UF. A demonstration of the proposed approach using data from three real-world vehicles shows that all contributing factors could be significant. Although the presented results (via the small sample of vehicles) are not representative of the population, the proposed approach can be scaled to larger datasets.
Full article
(This article belongs to the Special Issue Design, Modelling and Control Strategies for Hybrid and Electric Vehicles)
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Figure 1
<p>Utility factor curves from select standards.</p> Full article ">Figure 2
<p>Illustration of the main categories of reasons for utility factor mismatch.</p> Full article ">Figure 3
<p>Scatter plot and piecewise linear regression fit of the attained EDR for the sample of 3 PHEVs.</p> Full article ">Figure 4
<p>UF mismatch indices and fraction of the distance traveled at the vehicle and fleet levels.</p> Full article ">
<p>Utility factor curves from select standards.</p> Full article ">Figure 2
<p>Illustration of the main categories of reasons for utility factor mismatch.</p> Full article ">Figure 3
<p>Scatter plot and piecewise linear regression fit of the attained EDR for the sample of 3 PHEVs.</p> Full article ">Figure 4
<p>UF mismatch indices and fraction of the distance traveled at the vehicle and fleet levels.</p> Full article ">
Open AccessCorrection
Correction: El Hafdaoui et al. Energy and Environmental National Assessment of Alternative Fuel Buses in Morocco. World Electr. Veh. J. 2023, 14, 105
by
Hamza El Hafdaoui, Faissal Jelti, Ahmed Khallaayoun and Kamar Ouazzani
World Electr. Veh. J. 2024, 15(10), 457; https://doi.org/10.3390/wevj15100457 - 9 Oct 2024
Abstract
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In the original publication [...]
Full article
Figure 2
Open AccessArticle
Impact of Mixed-Vehicle Environment on Speed Disparity as a Measure of Safety on Horizontal Curves
by
Tahmina Sultana and Yasser Hassan
World Electr. Veh. J. 2024, 15(10), 456; https://doi.org/10.3390/wevj15100456 - 9 Oct 2024
Abstract
Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and
[...] Read more.
Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and automation is to improve traffic safety, negative safety impacts may persist in the mixed-vehicle environment. Speed disparity measures have been shown in the literature to be related to safety performance. Therefore, speed disparity measures are derived from the expected speed distributions of different vehicle technologies and are used as surrogate measures to assess the safety of mixed-vehicle environments and identify the efficacy of prospective countermeasures. This paper builds on speed models in the literature to predict the speed behavior of CVs, AVs, and DVs on horizontal curves on freeways and major arterials. The paper first proposes a methodology to determine speed disparity measures on horizontal curves without any control in terms of speed limit. The impact of speed limit or advisory speed, as a safety countermeasure, is modeled and assessed using different strategies to set the speed limit. The results indicated that the standard deviation of the speeds of all vehicles ( ) in a mixed environment would increase on arterial roads under no control compared to the case of DV-only traffic. This speed disparity can be reduced using an advisory speed as a safety countermeasure to decrease the adverse safety impacts in this environment. Moreover, it was shown that compared to the practice of a constant speed limit based on road classification, the advisory speed is more effective when it is based on the speed behavior of various vehicle types.
Full article
(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)
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Show Figures
Figure 1
Figure 1
<p>Schematic of AV speed distributions in scenarios 2 and 3 (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 2
<p>Schematic of compliant and non-compliant vehicles of a specific subpopulation (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 3
<p>Mean and 85th percentile speeds for each vehicle type (no CM) (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 4
<p>Combined speed and speed disparity measures (no CM) for a sample of vehicle share combinations (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 5
<p>Maximum values of speed disparity measures (no CM) for different vehicle share combinations (adopted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 6
<p>Maximum values of speed disparity measures for two countermeasures (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 7
<p>Maximum <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> on arterial roads for the case of no CM and all countermeasures (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 8
<p>Heatmap of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>C</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>D</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> in CM4b (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 9
<p>Heatmap of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>C</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>D</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> in CM6 (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">
<p>Schematic of AV speed distributions in scenarios 2 and 3 (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 2
<p>Schematic of compliant and non-compliant vehicles of a specific subpopulation (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 3
<p>Mean and 85th percentile speeds for each vehicle type (no CM) (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 4
<p>Combined speed and speed disparity measures (no CM) for a sample of vehicle share combinations (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 5
<p>Maximum values of speed disparity measures (no CM) for different vehicle share combinations (adopted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 6
<p>Maximum values of speed disparity measures for two countermeasures (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 7
<p>Maximum <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> on arterial roads for the case of no CM and all countermeasures (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 8
<p>Heatmap of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>C</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>D</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> in CM4b (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">Figure 9
<p>Heatmap of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>C</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>D</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> in CM6 (reprinted from [<a href="#B16-wevj-15-00456" class="html-bibr">16</a>]).</p> Full article ">
Open AccessArticle
Design of a Low-Cost AI System for the Modernization of Conventional Cars
by
Wilver Auccahuasi, Kitty Urbano, Sandra Meza, Luis Romero-Echevarria, Arlich Portillo-Allende, Karin Rojas, Jorge Figueroa-Revilla, Giancarlo Sanchez-Atuncar, Sergio Arroyo and Percy Junior Castro-Mejia
World Electr. Veh. J. 2024, 15(10), 455; https://doi.org/10.3390/wevj15100455 - 8 Oct 2024
Abstract
Artificial intelligence techniques are beginning to be implemented in most areas. In the particular case of automobiles, new cars include integrated applications, such as cameras in different configurations, including in the rear of the car to provide assistance while reversing, as well as
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Artificial intelligence techniques are beginning to be implemented in most areas. In the particular case of automobiles, new cars include integrated applications, such as cameras in different configurations, including in the rear of the car to provide assistance while reversing, as well as front and side cameras; these applications also include different configurations of sensors that provide information to the driver, such as objects approaching from different directions, such as from the front and sides. In this paper, we propose a practical and low-cost methodology to provide solutions using artificial intelligence techniques, as is the purpose of YOLO architecture, version 3, using hardware based on Nvidia’s Jetson TK1 architecture, and configurations in conventional cars. The results that we present demonstrate that these technologies can be applied in conventional cars, working with independent power to avoid causing problems in these cars, and we evaluate their application in the detection of people and cars in different situations, which allows information to be provided to the driver while performing maneuvers. The methodology that we provide can be replicated and scaled according to needs.
Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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Show Figures
Figure 1
Figure 1
<p>Block diagram of the methodological proposal.</p> Full article ">Figure 2
<p>Configuration of the hardware architecture of the proposal.</p> Full article ">Figure 3
<p>Jetson TK1 hardware view.</p> Full article ">Figure 4
<p>Touch screen view.</p> Full article ">Figure 5
<p>Front view camera setup, for demonstration of proposal.</p> Full article ">Figure 6
<p>Rear view camera configuration, for demonstration of the proposal.</p> Full article ">Figure 7
<p>Internal location of the Jetson TK1 board with the display, in use mode.</p> Full article ">Figure 8
<p>Detection of people working in gardens very close to the road.</p> Full article ">Figure 9
<p>Detection of people on bicycles.</p> Full article ">Figure 10
<p>Detection of cleaning personnel.</p> Full article ">Figure 11
<p>Detection of people on sidewalks.</p> Full article ">Figure 12
<p>Car and truck detection.</p> Full article ">Figure 13
<p>Rearview mirror blind spots.</p> Full article ">Figure 14
<p>Final hardware configuration.</p> Full article ">
<p>Block diagram of the methodological proposal.</p> Full article ">Figure 2
<p>Configuration of the hardware architecture of the proposal.</p> Full article ">Figure 3
<p>Jetson TK1 hardware view.</p> Full article ">Figure 4
<p>Touch screen view.</p> Full article ">Figure 5
<p>Front view camera setup, for demonstration of proposal.</p> Full article ">Figure 6
<p>Rear view camera configuration, for demonstration of the proposal.</p> Full article ">Figure 7
<p>Internal location of the Jetson TK1 board with the display, in use mode.</p> Full article ">Figure 8
<p>Detection of people working in gardens very close to the road.</p> Full article ">Figure 9
<p>Detection of people on bicycles.</p> Full article ">Figure 10
<p>Detection of cleaning personnel.</p> Full article ">Figure 11
<p>Detection of people on sidewalks.</p> Full article ">Figure 12
<p>Car and truck detection.</p> Full article ">Figure 13
<p>Rearview mirror blind spots.</p> Full article ">Figure 14
<p>Final hardware configuration.</p> Full article ">
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