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A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor

Author

Listed:
  • Qiwei Xu

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Yunqi Mao

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Meng Zhao

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Shumei Cui

    (Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150080, China)

Abstract

A dynamic optimization energy management strategy called Hybrid Electric Vehicle Based on Compound Structured Permanent-Magnet Motor (CSPM-HEV) is investigated in this paper. CSPM-HEV has obvious advantages in power density, heat dissipation efficiency, torque performance and energy transmission efficiency. This paper describes the topology and working principle of the CSPM-HEV, and analyzes its operating mode and corresponding energy flow laws. On this basis, the relationship about the power loss of the vehicle, the CSPM transmission ratio i CSPM and the CSPM-HEV power distribution coefficient f 1 were derived. According to the optimal combination of ( i CSPM , f 1 ), the engine power and speed which minimize the power loss of the vehicle, were calculated, thus realizing the instantaneous optimal control of the vehicle. In addition, in order to improve the instantaneously optimized control processing speed, a neural network controller was established. The drive axle demand power, speed and battery State of Charge (SOC), were taken as input variables. Then, the engine power and speed were taken as output variables. The simulation results show that the average speed of the instantaneous optimization strategy after BP neural network optimization is increased by 98.1%, the control effect is significant, and it has high application value.

Suggested Citation

  • Qiwei Xu & Yunqi Mao & Meng Zhao & Shumei Cui, 2018. "A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor," Energies, MDPI, vol. 11(9), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2212-:d:165455
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    References listed on IDEAS

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    1. Jiankun Peng & Hao Fan & Hongwen He & Deng Pan, 2015. "A Rule-Based Energy Management Strategy for a Plug-in Hybrid School Bus Based on a Controller Area Network Bus," Energies, MDPI, vol. 8(6), pages 1-21, June.
    2. Qiwei Xu & Shumei Cui & Liwei Song & Qianfan Zhang, 2014. "Research on the Power Management Strategy of Hybrid Electric Vehicles Based on Electric Variable Transmissions," Energies, MDPI, vol. 7(2), pages 1-27, February.
    3. Qiwei Xu & Jing Sun & Lingyan Luo & Shumei Cui & Qianfan Zhang, 2016. "A Study on Magnetic Decoupling of Compound-Structure Permanent-Magnet Motor for HEVs Application," Energies, MDPI, vol. 9(10), pages 1-16, October.
    4. M. Hadi Amini & Orkun Karabasoglu, 2018. "Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks," Energies, MDPI, vol. 11(1), pages 1-25, January.
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    Cited by:

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    2. Massimiliano Passalacqua & Mauro Carpita & Serge Gavin & Mario Marchesoni & Matteo Repetto & Luis Vaccaro & Sébastien Wasterlain, 2019. "Supercapacitor Storage Sizing Analysis for a Series Hybrid Vehicle," Energies, MDPI, vol. 12(9), pages 1-15, May.
    3. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
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