A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor
<p>Structure of compound structure permanent-magnet motor.</p> "> Figure 2
<p>Equivalent diagram of compound-structure permanent-magnet motor.</p> "> Figure 3
<p>Hybrid mode and the related power flow diagram.</p> "> Figure 4
<p>Starter mode and the related power flow diagram.</p> "> Figure 5
<p>Generating mode and the related power flow diagram.</p> "> Figure 6
<p>Motor mode and the related power flow diagram.</p> "> Figure 7
<p>Regenerative braking mode and the related power flow diagram.</p> "> Figure 8
<p>Simulation model of CSPM-HEV.</p> "> Figure 9
<p>ICE group block diagram.</p> "> Figure 10
<p>BP neural network energy management controller structure diagram.</p> "> Figure 11
<p>Levenberg-Marquardt algorithm flowchart.</p> "> Figure 12
<p>Power (<b>a</b>) and speed (<b>b</b>) of ICE during UDDS driving cycle.</p> "> Figure 13
<p>Power (<b>a</b>) and speed (<b>b</b>) of ICE during NEDC driving cycle.</p> "> Figure 14
<p>Power (<b>a</b>) and speed (<b>b</b>) of ICE during US06 driving cycle.</p> "> Figure 14 Cont.
<p>Power (<b>a</b>) and speed (<b>b</b>) of ICE during US06 driving cycle.</p> "> Figure 15
<p>State of charge of battery during UDDS driving cycle.</p> ">
Abstract
:1. Introduction
2. CSPM-HEV Operating Mode Analysis
3. CSPM-HEV Model and Parameters
4. Instantaneous Energy Management Strategy Based on BP Neural Network
5. Neural Network Controller Design
5.1. BP Neural Network Training
- (1)
- The sample should be widely representative and reflect the working characteristics of all possible operating conditions of the HEV.
- (2)
- The neural network after the sample learning has good generalization ability.
- (3)
- Do not have too many samples, otherwise it may lead to over-fit of the network.
5.2. Analysis of Simulation Results
6. Conclusions
- (1)
- The transmission ratio of the CSPM iCSPM was defined with reference to the traditional mechanical transmission, furthermore, the CSPM-HEV power distribution coefficient was raised for analyzing the system. Afterwards, the relationship about the power loss of the vehicle and (iCSPM, f1) was derived, as a result, and the instantaneous optimization energy management based on the principle of “minimum power loss” is established. The strategy calculates the engine power and speed at the current time according to the optimal combination of iCSPM and f1, so that achieve the instantaneous optimal control of the vehicle.
- (2)
- According to the simulation results of the instantaneous optimization strategy under various working conditions, the learning samples were made. The vehicle power, speed and battery SOC were input variables, the engine power and speed were output variables, afterwards, the neural network controller was established, so the real-time energy management strategy based on a BP neural network is fulfilled.
- (3)
- The real-time energy management strategy based on a BP neural network was simulated and compared with the results of a traditional instantaneous optimization strategy. The results show that the BP-EMS can greatly improve the running speed and optimize the control effect, and it also realizes the nonlinear mapping between engine output and drive axle demand power, speed and battery SOC, as a result, the instantaneous optimal control of CSPM-HEV is completed. In spite of benefits of BP-EMS, since the training sample of the BP neural network controller is the result of the instantaneous optimization strategy in the paper, it is unable to reach the global optimization. In the future, the authors will train samples based on the results of the global optimization algorithm, and employ a hybrid vehicle working condition recognition technology, which is expected to further improve the fuel economy of CSPM-HEV.
Author Contributions
Funding
Conflicts of Interest
References
- Hoeijmakers, M.J.; Ferreira, J.A. The electrical variable transmission. In Proceedings of the 39th IAS Annual Meeting, Seattle, WA, USA, 3–7 October 2004; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2004. [Google Scholar]
- Abdrakhmanov, R.; Adouane, L. Dynamic programming resolution and database knowledge for online predictive energy management of hybrid vehicles. In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017), Madrid, Spain, 26–28 July 2017; Volume 1, pp. 132–143. [Google Scholar]
- Johannesson, L.; Asbogard, M.; Egardt, B. Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming. IEEE Trans. Intell. Transp. Syst. 2007, 8, 71–83. [Google Scholar] [CrossRef]
- Ouddah, N.; Adouane, L.; Abdrakhamanov, R.; Kamal, E. Optimal energy management strategy of plug-in hybrid electric bus in urban conditions. In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017), Madrid, Spain, 26–28 July 2017; Volume 1, pp. 304–311. [Google Scholar]
- Amini, M.H.; Karabasoglu, O. Optimal operation of interdependent power systems and electrified transportation networks. Energies 2018, 11, 196. [Google Scholar] [CrossRef]
- Qi, X.; Wu, G.; Boriboonsomsin, K.; Barth, M.J. An on-line energy management strategy for plug-in hybrid electric vehicles using an estimation distribution algorithm. In Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 8–11 October 2014; pp. 2480–2485. [Google Scholar]
- Qi, X.; Wu, G.; Boriboonsomsin, K.; Barth, M.J. Development and evaluation of an evolutionary algorithm-based online energy management system for plug-in hybrid electric vehicles. IEEE Trans. Intell. Transp. Syst. 2017, 18, 2181–2191. [Google Scholar] [CrossRef]
- Peng, J.; Fan, H.; He, H.; Pan, D. A rule-based energy management strategy for a plug-in hybrid school bus based on a controller area network bus. Energies 2015, 8, 5122–5142. [Google Scholar] [CrossRef]
- Hofman, T.; van Druten, R.M.; Serrarens, A.F.A.; Steinbuch, M. Rule-based energy management strategies for hybrid vehicles. Int. J. Elect. Hybrid Veh. 2007, 1, 71–94. [Google Scholar] [CrossRef]
- Hofman, T.; Steinbuch, M.; van Druten, R.M.; Serrarens, A.F.A. Rule-based energy management strategies for hybrid vehicle drivetrains: A fundamental approach in reducing computation time. In Proceedings of the 4th IFAC Symposium on Mechatronic Systems, Heidelberg, Germany, 12–14 September 2006; pp. 1–6. [Google Scholar]
- Adel, B.; Youtong, Z.; Shua, S. Parallel HEV hybrid controller modeling for power management. World Electr. Veh. J. 2010, 4, 190–196. [Google Scholar] [CrossRef]
- Hajizadeh, A.; Golkar, M.A. Intelligent power management strategy of hybrid distributed generation system. Electr. Power Energy Syst. 2007, 29, 783–795. [Google Scholar] [CrossRef]
- Lihao, Y.; Youjun, W.; Congmin, Z. Study on fuzzy energy management strategy of parallel hybrid vehicle based on quantum PSO algorithm. Int. J. Multimedia Ubiquitous Eng. 2016, 11, 147–158. [Google Scholar] [CrossRef]
- Denis, N.; Dubois, M.R.; Desrochers, A. Fuzzy-based blended control for the energy management of a parallel plug-in hybrid electric vehicle. Intell. Transp. Syst. 2015, 9, 30–37. [Google Scholar] [CrossRef]
- Martnez, C.M.; Hu, X.; Cao, D.; Velenis, E.; Gao, B.; Weller, M. Energy management in plug-in hybrid electric vehicles: Recent progress and a connected vehicles perspective. IEEE Trans. Veh. Technol. 2017, 66, 4534–4549. [Google Scholar] [CrossRef]
- Dai, X.; Li, C.K.; Rad, A.B. An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control. IEEE Trans. Intell. Transp. Syst. 2005, 6, 285–293. [Google Scholar] [CrossRef]
- Qi, X.; Wu, G.; Boriboonsomsin, K.; Barth, M.J.; Gonder, J. Data driven reinforcement learning-based real-time energy management system for plug-in hybrid electric vehicles. Transp. Res. Rec. J. Transp. Res. Board 2016, 2572, 1–8. [Google Scholar] [CrossRef]
- Qi, X.; Luo, Y.; Wu, G.; Boriboonsomsin, K.; Barth, M.J. Deep reinforcement learning-based vehicle energy efficiency autonomous learning system. In Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, 11–14 June 2017; pp. 1228–1233. [Google Scholar]
- Zhang, X.; Liu, Y.; Zhang, J. A Fuzzy Neural Network Energy Management Strategy for Parallel Hybrid Electric Vehicle. In Proceedings of the 9th International Conference on Modelling, Identification and Control (ICMIC 2017), Kunming, China, 10–12 July 2017. [Google Scholar]
- Xu, Q.; Cui, S.; Song, L.; Zhang, Q. Research on the Power Management Strategy of Hybrid Electric Vehicles Based on Electric Variable Transmissions. Energies 2014, 7, 934–960. [Google Scholar] [CrossRef] [Green Version]
- Xu, Q.; Sun, J.; Luo, L.; Cui, S.; Zhang, Q. A Study on Magnetic Decoupling of Compound-Structure Permanent-Magnet Motor for HEVs Application. Energies 2016, 9, 819. [Google Scholar] [CrossRef]
- Wang, X. Introduction of Neural Networks; Science Press of China: Beijing, China, 2017. [Google Scholar]
Parameter | Data | Parameter | Data |
---|---|---|---|
Curb weight | 1360 kg | Main reducer efficiency | 0.95 |
Frontal area | 1.746 m2 | Rolling resistance coefficient | 0.01 |
Air resistance coefficient | 0.3 | Tire Rolling radius | 0.2928 m |
Final drive ratios | 3.905 |
Component | Parameter | Data |
---|---|---|
ICE | Power | 43 kW |
Torque | 100 Nm | |
CSPM | Type | Double magnet type |
External motor power | 30 kW | |
Internal motor power | 20 kW | |
Torque peak of external motor | 305 Nm | |
Torque peak of internal motor | 100 Nm | |
Battery | Rated voltage | 300 V |
Cycle Condition | Parameter | Mean Square Error |
---|---|---|
UDDS | Power of Engine | 3.6 |
Speed of Engine | 0.063 | |
US06 | Power of Engine | 5.5 |
Speed of Engine | 0.135 | |
NEDC | Power of Engine | 9.8 |
Speed of Engine | 0.536 |
Cycle Condition | BP-EMS | IO-EMS | ||
---|---|---|---|---|
Fuel Consumption (L/100 km) | Operation Time (s) | Fuel Consumption (L/100 km) | Operation Time (s) | |
UDDS | 3.71 | 20 | 3.70 | 1230 |
NEDC | 3.69 | 18 | 3.64 | 1059 |
US06 HWY | 4.39 | 11 | 4.31 | 356 |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Xu, Q.; Mao, Y.; Zhao, M.; Cui, S. A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor. Energies 2018, 11, 2212. https://doi.org/10.3390/en11092212
Xu Q, Mao Y, Zhao M, Cui S. A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor. Energies. 2018; 11(9):2212. https://doi.org/10.3390/en11092212
Chicago/Turabian StyleXu, Qiwei, Yunqi Mao, Meng Zhao, and Shumei Cui. 2018. "A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor" Energies 11, no. 9: 2212. https://doi.org/10.3390/en11092212