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Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor

Author

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  • Xixue Liu

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

  • Datong Qin

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

  • Shaoqian Wang

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

Abstract

A parallel hybrid electric vehicle (PHEV) is used to investigate the fuel economy effect of the equivalent fuel consumption minimization strategy (ECMS) with the equivalent factor as the core, where the equivalent factor is the conversion coefficient between fuel thermal energy and electric energy. In the conventional ECMS strategy, the battery cannot continue to discharge when the state of charge (SOC) is lower than the target value. At this time, the motor mainly works in the battery charging mode, making it difficult to adjust the engine operating point to the high-efficiency zone during the acceleration process. To address this problem, a relationship model of the battery SOC, vehicle acceleration a , and equivalent factor S was established. When the battery SOC is lower than the target value and the vehicle demand torque is high, which makes the engine operating point deviate from the high-efficiency zone, the time that the motor spends in the power generation mode during the driving process is reduced. This enables the motor to drive the vehicle at the appropriate time to reduce the engine output torque, and helps the engine operate in the high-efficiency zone. The correction function under US06 condition was optimized by genetic algorithm (GA). The best equivalent factor MAP was obtained with acceleration a and battery SOC as independent variables, and the improved global optimal equivalent factor of ECMS was established and simulated offline. Simulation results show that compared with conventional ECMS, the battery still has positive power output even when the SOC is less than the target value. The SOC is close to the target value after the cycle condition, and fuel economy improved by 1.88%; compared with the rule-based energy management control strategies, fuel economy improved by 10.17%. These results indicate the effectiveness of the proposed energy management strategy.

Suggested Citation

  • Xixue Liu & Datong Qin & Shaoqian Wang, 2019. "Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor," Energies, MDPI, vol. 12(11), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2076-:d:235832
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    References listed on IDEAS

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    1. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    2. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
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    Citations

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    Cited by:

    1. Gao, Sichen & Zong, Yuhua & Ju, Fei & Wang, Qun & Huo, Weiwei & Wang, Liangmo & Wang, Tao, 2024. "Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving," Energy, Elsevier, vol. 304(C).
    2. Aimin Du & Yaoyi Chen & Dongxu Zhang & Yeyang Han, 2021. "Multi-Objective Energy Management Strategy Based on PSO Optimization for Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 14(9), pages 1-18, April.
    3. Aleš Hace, 2019. "The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft," Energies, MDPI, vol. 12(15), pages 1-31, August.
    4. Laeun Kwon & Dae-Seung Cho & Changsun Ahn, 2021. "Degradation-Conscious Equivalent Consumption Minimization Strategy for a Fuel Cell Hybrid System," Energies, MDPI, vol. 14(13), pages 1-14, June.
    5. Zeng, Tao & Zhang, Caizhi & Zhang, Yanyi & Deng, Chenghao & Hao, Dong & Zhu, Zhongwen & Ran, Hongxu & Cao, Dongpu, 2021. "Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle," Energy, Elsevier, vol. 227(C).
    6. Wang, Hao & He, Hongwen & Bai, Yunfei & Yue, Hongwei, 2022. "Parameterized deep Q-network based energy management with balanced energy economy and battery life for hybrid electric vehicles," Applied Energy, Elsevier, vol. 320(C).
    7. Chien-Hsun Wu & Yong-Xiang Xu, 2019. "The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation," Energies, MDPI, vol. 13(1), pages 1-16, December.
    8. Liu, Yonggang & Huang, Bin & Yang, Yang & Lei, Zhenzhen & Zhang, Yuanjian & Chen, Zheng, 2022. "Hierarchical speed planning and energy management for autonomous plug-in hybrid electric vehicle in vehicle-following environment," Energy, Elsevier, vol. 260(C).
    9. Piotr Bera, 2019. "Development of Engine Efficiency Characteristic in Dynamic Working States," Energies, MDPI, vol. 12(15), pages 1-14, July.
    10. Pei Zhang & Xianpan Wu & Changqing Du & Hongming Xu & Huawu Wang, 2020. "Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization," Energies, MDPI, vol. 13(20), pages 1-20, October.
    11. Andyn Omanovic & Norbert Zsiga & Patrik Soltic & Christopher Onder, 2021. "Optimal Degree of Hybridization for Spark-Ignited Engines with Optional Variable Valve Timings," Energies, MDPI, vol. 14(23), pages 1-21, December.
    12. Zhang, Yang & Li, Qingxin & Wen, Chengqing & Liu, Mingming & Yang, Xinhua & Xu, Hongming & Li, Ji, 2024. "Predictive equivalent consumption minimization strategy based on driving pattern personalized reconstruction," Applied Energy, Elsevier, vol. 367(C).

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