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Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

Author

Listed:
  • Lihe Xi

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, Beijing Jiaotong University, Beijing 100044, China)

  • Xin Zhang

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, Beijing Jiaotong University, Beijing 100044, China)

  • Chuanyang Sun

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, Beijing Jiaotong University, Beijing 100044, China)

  • Zexing Wang

    (Beijing Electric Vehicle Co. LTD., Beijing 102606, China)

  • Xiaosen Hou

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, Beijing Jiaotong University, Beijing 100044, China)

  • Jibao Zhang

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN) is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS) algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.

Suggested Citation

  • Lihe Xi & Xin Zhang & Chuanyang Sun & Zexing Wang & Xiaosen Hou & Jibao Zhang, 2017. "Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network," Energies, MDPI, vol. 10(11), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1871-:d:118931
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
    2. Ren, Guizhou & Wang, Jinzhong & Chen, Changlei & Wang, Haoran, 2021. "A variable-voltage ultra-capacitor/battery hybrid power source for extended range electric vehicle," Energy, Elsevier, vol. 231(C).
    3. Diming Lou & Yinghua Zhao & Liang Fang & Yuanzhi Tang & Caihua Zhuang, 2022. "Encoder–Decoder-Based Velocity Prediction Modelling for Passenger Vehicles Coupled with Driving Pattern Recognition," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    4. Jianjun Hu & Lingling Zheng & Meixia Jia & Yi Zhang & Tao Pang, 2018. "Optimization and Model Validation of Operation Control Strategies for a Novel Dual-Motor Coupling-Propulsion Pure Electric Vehicle," Energies, MDPI, vol. 11(4), pages 1-14, March.
    5. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    6. Yang Sun & He Yang & Fei Meng, 2018. "Research on an Intelligent Behavior Evaluation System for Unmanned Ground Vehicles," Energies, MDPI, vol. 11(7), pages 1-22, July.
    7. Xu Wang & Ying Huang & Jian Wang, 2023. "Study on Driver-Oriented Energy Management Strategy for Hybrid Heavy-Duty Off-Road Vehicles under Aggressive Transient Operating Condition," Sustainability, MDPI, vol. 15(9), pages 1-25, May.
    8. Paweł Krawczyk & Artur Kopczyński & Jakub Lasocki, 2022. "Modeling and Simulation of Extended-Range Electric Vehicle with Control Strategy to Assess Fuel Consumption and CO 2 Emission for the Expected Driving Range," Energies, MDPI, vol. 15(12), pages 1-41, June.
    9. Flah Aymen & Chokri Mahmoudi, 2019. "A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City," Energies, MDPI, vol. 12(5), pages 1-22, March.

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