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Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS

Author

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  • Yuping Zeng

    (Jiangxi Province Key Laboratory of Precision Drive & Control, Nanchang Institute of Technology, Nanchang 330099, China
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Yang Cai

    (Jiangxi Province Key Laboratory of Precision Drive & Control, Nanchang Institute of Technology, Nanchang 330099, China)

  • Guiyue Kou

    (Jiangxi Province Key Laboratory of Precision Drive & Control, Nanchang Institute of Technology, Nanchang 330099, China)

  • Wei Gao

    (Jiangxi Province Key Laboratory of Precision Drive & Control, Nanchang Institute of Technology, Nanchang 330099, China)

  • Datong Qin

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

Abstract

When searching for the optimal solution, Equivalent Consumption Minimum Strategy (ECMS) has to calculate and compare the total equivalent fuel rate of huge candidates covered all over the control domain for each time instant. Therefore, this strategy still has a heavy computation burden problem; it is a challenge for ECMS to be implemented online for real-time control. To reduce ECMS’s calculation load, this paper proposes an adaptive Simplified-ECMS-based strategy for a parallel plug-in hybrid electric vehicle (PHEV). A convex piecewise function is applied to fit the total equivalent fuel rate with respect to the motor torque, which is the control variable. Then, the ECMS problem is simplified to calculate and compare only five candidates’ total equivalent fuel rate to determine the optimal torque distribution. Particle swarm optimization (PSO) algorithm is applied to optimize the equivalent factor, and the MAPs of this factor under different driving cycles, driving distances and initial SOC are obtained. Based on this, the adaptive Simplified-ECMS-based strategy is proposed. Simulations were performed, and the results show that the Simplified-ECMS-based strategy can obviously shorten the calculation time compared to ECMS-based strategy, and the adaptive Simplified-ECMS-based strategy can decrease fuel consumption of plug-in hybrid electric vehicle by 16.43% under the testing driving cycle, compared to CD-CS-based strategy. A road test on the prototype vehicle is conducted and the effectiveness of the Simplified-ECMS-based strategy is validated by the test data.

Suggested Citation

  • Yuping Zeng & Yang Cai & Guiyue Kou & Wei Gao & Datong Qin, 2018. "Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS," Sustainability, MDPI, vol. 10(6), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:2060-:d:153032
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    References listed on IDEAS

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    Cited by:

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    3. Li, Weihan & Cui, Han & Nemeth, Thomas & Jansen, Jonathan & Ünlübayir, Cem & Wei, Zhongbao & Feng, Xuning & Han, Xuebing & Ouyang, Minggao & Dai, Haifeng & Wei, Xuezhe & Sauer, Dirk Uwe, 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning," Applied Energy, Elsevier, vol. 293(C).
    4. Tobias Frambach & Ralf Kleisch & Ralf Liedtke & Jochen Schwarzer & Egbert Figgemeier, 2022. "Environmental Impact Assessment and Classification of 48 V Plug-in Hybrids with Real-Driving Use Case Simulations," Energies, MDPI, vol. 15(7), pages 1-21, March.
    5. Wang, Siyang & Lin, Xianke, 2020. "Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios," Applied Energy, Elsevier, vol. 271(C).
    6. Shi, Dehua & Xu, Han & Wang, Shaohua & Hu, Jia & Chen, Long & Yin, Chunfang, 2024. "Deep reinforcement learning based adaptive energy management for plug-in hybrid electric vehicle with double deep Q-network," Energy, Elsevier, vol. 305(C).
    7. Wu, Yitao & Zhang, Yuanjian & Li, Guang & Shen, Jiangwei & Chen, Zheng & Liu, Yonggang, 2020. "A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks," Energy, Elsevier, vol. 208(C).
    8. Liu, Yonggang & Wu, Yitao & Wang, Xiangyu & Li, Liang & Zhang, Yuanjian & Chen, Zheng, 2023. "Energy management for hybrid electric vehicles based on imitation reinforcement learning," Energy, Elsevier, vol. 263(PC).
    9. Giyeon Hwang & Kyungmin Lee & Jongmyung Kim & Kyu-Jin Lee & Sangyul Lee & Minjae Kim, 2020. "Energy Management Optimization of Series Hybrid Electric Bus Using an Ultra-Capacitor and Novel Efficiency Improvement Factors," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    10. Yangyang Ma & Pengyu Wang & Tianjun Sun, 2021. "Research on Energy Management Method of Plug-In Hybrid Electric Vehicle Based on Travel Characteristic Prediction," Energies, MDPI, vol. 14(19), pages 1-17, September.
    11. Naga Kavitha Kommuri & Andrew McGordon & Antony Allen & Dinh Quang Truong, 2020. "Evaluation of a Modified Equivalent Fuel-Consumption Minimization Strategy Considering Engine Start Frequency and Battery Parameters for a Plugin Hybrid Two-Wheeler," Energies, MDPI, vol. 13(12), pages 1-26, June.
    12. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    13. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    14. Jin Li & Feng Wang & Yu He, 2020. "Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    15. Naga Kavitha Kommuri & Andrew McGordon & Antony Allen & Dinh Quang Truong, 2022. "A Novel Adaptive Equivalence Fuel Consumption Minimisation Strategy for a Hybrid Electric Two-Wheeler," Energies, MDPI, vol. 15(9), pages 1-19, April.
    16. 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.
    17. Zhou, Yujie & Huang, Yin & Mao, Xuping & Kang, Zehao & Huang, Xuejin & Xuan, Dongji, 2024. "Research on energy management strategy of fuel cell hybrid power via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 293(C).
    18. Da Huo & Peter Meckl, 2022. "Power Management of a Plug-in Hybrid Electric Vehicle Using Neural Networks with Comparison to Other Approaches," Energies, MDPI, vol. 15(15), pages 1-19, August.
    19. Hsiu-Ying Hwang & Tian-Syung Lan & Jia-Shiun Chen, 2020. "Optimization and Application for Hydraulic Electric Hybrid Vehicle," Energies, MDPI, vol. 13(2), pages 1-17, January.

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