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Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles

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  • Chen, Xu
  • Li, Mince
  • Chen, Zonghai

Abstract

Driving pattern recognition (DPR) is widely used to improve the robustness of rule-based energy management strategies (EMS). However, the number and quality of preset patterns limit further performance improvements. This paper proposes a meta rule-based EMS to replace the role of DPR, wherein the meta rule refers to a rule that determines the parameters of the energy management rule. Firstly, the power distribution rule pattern is derived through the analysis of dynamic programming results. Then, the value of the parameters is obtained through clustering. Subsequently, a feature screening method based on mutual information is proposed to select useful features of driving conditions and system states. The seagull optimization algorithm is then employed to find the mathematical expression of the meta rule. Finally, simulation results demonstrate the effectiveness of the meta rule-based EMS. Compared with DPR-based and long short-term memory-based EMS, the meta rule-based EMS can reduce the Ampere-hour throughput of Li-ion batteries by 17.0% and 9.7% respectively under the China light-duty vehicle test cycle, and by 3.4% and 16.6% respectively under a real-world driving condition. The meta rule-based EMS requires only dozens of milliseconds for each moment and is suitable for real-time systems.

Suggested Citation

  • Chen, Xu & Li, Mince & Chen, Zonghai, 2023. "Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027597
    DOI: 10.1016/j.energy.2023.129365
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    References listed on IDEAS

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    1. Shi, Junzhe & Xu, Bin & Shen, Yimin & Wu, Jingbo, 2022. "Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition," Energy, Elsevier, vol. 243(C).
    2. Dharmalingam Marimuthu & Ieva Meidute-Kavaliauskiene & Ghanshaym S. Mahapatra & Renata Činčikaitė & Pratik Roy & Aidas Vasilis Vasiliauskas, 2022. "Sustainable Urban Conveyance Selection through MCGDM Using a New Ranking on Generalized Interval Type-2 Trapezoidal Fuzzy Number," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
    3. Wei, Xiaodong & Wang, Jiaqi & Sun, Chao & Liu, Bo & Huo, Weiwei & Sun, Fengchun, 2023. "Guided control for plug-in fuel cell hybrid electric vehicles via vehicle to traffic communication," Energy, Elsevier, vol. 267(C).
    4. Quan, Shengwei & He, Hongwen & Chen, Jinzhou & Zhang, Zhendong & Han, Ruoyan & Wang, Ya-Xiong, 2023. "Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method," Energy, Elsevier, vol. 278(PA).
    5. Yin, Linfei & Qiu, Yao, 2022. "Neural network dynamic differential control for long-term price guidance mechanism of flexible energy service providers," Energy, Elsevier, vol. 255(C).
    6. Song, Ziyou & Hou, Jun & Xu, Shaobing & Ouyang, Minggao & Li, Jianqiu, 2017. "The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses," Energy, Elsevier, vol. 135(C), pages 91-100.
    7. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
    8. Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
    9. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    10. Giuseppe De Lorenzo & Francesco Piraino & Francesco Longo & Giovanni Tinè & Valeria Boscaino & Nicola Panzavecchia & Massimo Caccia & Petronilla Fragiacomo, 2022. "Modelling and Performance Analysis of an Autonomous Marine Vehicle Powered by a Fuel Cell Hybrid Powertrain," Energies, MDPI, vol. 15(19), pages 1-21, September.
    11. Guo, Hongqiang & Hou, Daizheng & Du, Shangye & Zhao, Ling & Wu, Jian & Yan, Ning, 2020. "A driving pattern recognition-based energy management for plug-in hybrid electric bus to counter the noise of stochastic vehicle mass," Energy, Elsevier, vol. 198(C).
    12. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
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    1. Zhao, Junwei & Xu, Xiangyang & Dong, Peng & Liu, Xuewu & Wang, Shuhan & Qi, Hongzhong & Liu, Yanfang, 2024. "A novel EMS design framework for SPHTs based on instantaneous layer, driving event layer, and driving cycle layer," Energy, Elsevier, vol. 307(C).
    2. Chen, Yifan & Yang, Liuquan & Yang, Chao & Wang, Weida & Zha, Mingjun & Gao, Pu & Liu, Hui, 2024. "Real-time analytical solution to energy management for hybrid electric vehicles using intelligent driving cycle recognition," Energy, Elsevier, vol. 307(C).

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