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Keywords = 4WDEV

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23 pages, 13657 KiB  
Article
Real-Time Implementation of Sensorless DTC-SVM Applied to 4WDEV Using the MRAS Estimator
by Abdelhak Boudallaa, Ahmed Belkhadir, Mohammed Chennani, Driss Belkhayat, Youssef Zidani and Karim Rhofir
Energies 2023, 16(20), 7090; https://doi.org/10.3390/en16207090 - 14 Oct 2023
Viewed by 1095
Abstract
This article presents the DTC-SVM approach for controlling a sensorless speed induction motor. To implement this approach, a practical prototype is built using a microcontroller, an embedded GPS module, and a memory card to collect real-time data during the driving route, such as [...] Read more.
This article presents the DTC-SVM approach for controlling a sensorless speed induction motor. To implement this approach, a practical prototype is built using a microcontroller, an embedded GPS module, and a memory card to collect real-time data during the driving route, such as road geographical data, speed, and time. These data are then utilized in the laboratory to implement the control law (DTC-SVM) on the electric vehicle. The d-q model of the induction motor is first presented to explain the requirements for calculating the rotor speed. Then, an adaptive model reference system speed estimator is developed based on the rotor flux, along with a controller and DTC-SVM strategy, which are implemented using the dSpace 1104 board to achieve the desired performance. The simulation results demonstrate satisfactory speed regulation with the proposed system. In this study too, an electronic differential system is modeled for the four wheels of an electric vehicle equipped with an integrated motor, all controlled by the DTC-SVM strategy. Vehicle speed and electrical vehicle steering angle variations, as well as wheel speeds estimated by code system, are verified using MATLAB/Simulink simulations. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Energy conversion chain.</p> Full article ">Figure 2
<p>Three-phase induction motor.</p> Full article ">Figure 3
<p>Forces applied to a vehicle.</p> Full article ">Figure 4
<p>Experimental model.</p> Full article ">Figure 5
<p>The itinerary between Safi and Rabat in Morocco.</p> Full article ">Figure 6
<p>PI controller applied to the vehicle’s dynamic model.</p> Full article ">Figure 7
<p>Vehicle simulation results, (<b>a</b>) Speed response; (<b>b</b>) Wheel torque; (<b>c</b>) Power transmitted to the wheels; (<b>d</b>) Motor torque.</p> Full article ">Figure 8
<p>Vehicle simulation results, (<b>a</b>) Speed response; (<b>b</b>) Wheel torque; (<b>c</b>) Power transmitted to the wheels; (<b>d</b>) Motor torque.</p> Full article ">Figure 9
<p>The voltage inverter and voltage vectors Vj with (j = 1, …, 6).</p> Full article ">Figure 10
<p>DTC control block Diagram.</p> Full article ">Figure 11
<p>(<b>a</b>) Two-level flux controller; (<b>b</b>) Three-level torque controller.</p> Full article ">Figure 12
<p>The model reference adaptive system based on rotor flux.</p> Full article ">Figure 13
<p>Simulation results, (<b>a</b>) Real and estimated speed responses with MRAS observer [rpm]; (<b>b</b>) Speed error [rpm]; (<b>c</b>) Stator currents [A]; (<b>d</b>) Estimated torque and its reference [N.m]; (<b>e</b>) Quadrature current Isq [A]; (<b>f</b>) Rotor position <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">θ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="bold-italic">θ</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> </semantics></math> estimated position; (<b>g</b>) Flux αβ [Wb]; (<b>h</b>) trajectory of flux αβ.</p> Full article ">Figure 14
<p>Simulation results, (<b>a</b>) Measured and estimated speed responses with MRAS observer [rpm]; (<b>b</b>) Speed error; (<b>c</b>) Stator currents [A]; (<b>d</b>) Estimated torque and its reference [N.m]; (<b>e</b>) Flux <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">α</mi> <mi mathvariant="bold-italic">β</mi> </mrow> </semantics></math> [wb]; (<b>f</b>) trajectory of flux <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">α</mi> <mi mathvariant="bold-italic">β</mi> </mrow> </semantics></math>.</p> Full article ">Figure 15
<p>Diagram of the experimental platform.</p> Full article ">Figure 16
<p>The test bench of the experimental setup.</p> Full article ">Figure 17
<p>Experimental results, (<b>a</b>) Real and estimated speed responses with MRAS [rpm]; (<b>b</b>) Speed error; (<b>c</b>) Stator currents [A]; (<b>d</b>) Estimated torque and its reference [N.m]; (<b>e</b>) Quadrature current Isq [A]; (<b>f</b>) Rotor position <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">θ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="bold-italic">θ</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> </semantics></math>; (<b>g</b>) Flux <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">α</mi> <mi mathvariant="bold-italic">β</mi> </mrow> </semantics></math> [Wb]; (<b>h</b>) Flux trajectory <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">α</mi> <mi mathvariant="bold-italic">β</mi> </mrow> </semantics></math>.</p> Full article ">Figure 17 Cont.
<p>Experimental results, (<b>a</b>) Real and estimated speed responses with MRAS [rpm]; (<b>b</b>) Speed error; (<b>c</b>) Stator currents [A]; (<b>d</b>) Estimated torque and its reference [N.m]; (<b>e</b>) Quadrature current Isq [A]; (<b>f</b>) Rotor position <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">θ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="bold-italic">θ</mi> </mrow> <mo stretchy="false">^</mo> </mover> </mrow> </semantics></math>; (<b>g</b>) Flux <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">α</mi> <mi mathvariant="bold-italic">β</mi> </mrow> </semantics></math> [Wb]; (<b>h</b>) Flux trajectory <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">α</mi> <mi mathvariant="bold-italic">β</mi> </mrow> </semantics></math>.</p> Full article ">Figure 18
<p>Proposed electronic differential.</p> Full article ">Figure 19
<p>Kinematic model of four-wheel drive electric vehicle.</p> Full article ">Figure 20
<p>Specified driving road topology.</p> Full article ">Figure 21
<p>Steering angle variation.</p> Full article ">Figure 22
<p>Variation in speed of the four wheels in different phases.</p> Full article ">
1297 KiB  
Article
Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization
by Cheng Lin and Zhifeng Xu
Energies 2015, 8(5), 3815-3831; https://doi.org/10.3390/en8053815 - 30 Apr 2015
Cited by 81 | Viewed by 9495
Abstract
The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs) can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to [...] Read more.
The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs) can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to improve vehicle maneuverability and reduce energy consumption. In the high-layer of the presented method, sliding mode control is used to calculate the desired yaw moment due to the model inaccuracy and parameter error. In the low-layer, mathematical programming with the penalty function consisting of the yaw moment control offset, the drive system energy loss and the slip ratio constraint is used for wheel torque control allocation. The programming is solved with the combination of off-line and on-line optimization to reduce the calculation cost, and the optimization results are sent to motor controllers as torque commands. Co-simulation based on MATLAB® and Carsim® proves that the developed strategy can both improve the vehicle maneuverability and reduce energy consumption. Full article
(This article belongs to the Special Issue Advances in Plug-in Hybrid Vehicles and Hybrid Vehicles)
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Figure 1

Figure 1
<p>Drive system structure of the nominal vehicle.</p> Full article ">Figure 2
<p>The wheel torque control strategy for 4WDEV.</p> Full article ">Figure 3
<p>Coordinates for planar motions of the 4WDEV.</p> Full article ">Figure 4
<p>Energy loss <span class="html-italic">vs</span>. motor speed and output torque.</p> Full article ">Figure 5
<p>The fitting function of energy loss <span class="html-italic">vs</span>. output torque.</p> Full article ">Figure 6
<p>The partition factor based on off-line calculation.</p> Full article ">Figure 7
<p>The input of steering wheel during the simulation</p> Full article ">Figure 8
<p>The vehicle state under large acceleration.</p> Full article ">Figure 8 Cont.
<p>The vehicle state under large acceleration.</p> Full article ">Figure 9
<p>The vehicle state under small acceleration.</p> Full article ">Figure 9 Cont.
<p>The vehicle state under small acceleration.</p> Full article ">
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