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A novel EMS for FCV combined with condition-adaptive search ranges of decision variables based on the spatial and temporal traffic characteristics

Author

Listed:
  • Wu, Huiduo
  • Min, Haitao
  • Zhao, Honghui
  • Sun, Weiyi

Abstract

Condition-adaptive energy management strategies (EMSs) have been widely used in fuel cell vehicles (FCVs). There are two main issues that need to be further addressed: classification or recognition methods for driving conditions and adaptive strategies under these conditions. To address these issues, a novel MPC–based EMS for FCV combined with condition-adaptive search ranges based on traffic characteristics is proposed. The real urban road segments are further classified into several conditions based on the proposed spatial-temporal traffic characteristics, then the fuel cell system power intervals under these conditions are obtained using an efficiency-probability principle. A novel optimization algorithm, artificial protozoa optimizer is used to solve the short-term optimization problem under the novel MPC framework, which searches the optimal decision variables within the condition-adaptive fuel cell system power intervals. The results show that the proposed EMS combined with condition-adaptive fuel cell system power intervals and artificial protozoa optimizer–based solver reduces the total cost by 4.49 % and 4.30 % under real urban cycles compared to the conventional MPC–based EMS. In addition, the novel MPC framework with condition–adaptive fuel cell system power intervals reduce the computational burden of the short–term optimization problem, and the artificial protozoa optimizer–solver reduces the FCS power fluctuations compared to the solvers based on other algorithms.

Suggested Citation

  • Wu, Huiduo & Min, Haitao & Zhao, Honghui & Sun, Weiyi, 2025. "A novel EMS for FCV combined with condition-adaptive search ranges of decision variables based on the spatial and temporal traffic characteristics," Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225037600
    DOI: 10.1016/j.energy.2025.138118
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    References listed on IDEAS

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    1. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information," Energy, Elsevier, vol. 290(C).
    2. Ganesh, Akhil Hannegudda & Xu, Bin, 2022. "A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    3. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    4. Chen, Zheng & Gu, Hongji & Shen, Shiquan & Shen, Jiangwei, 2022. "Energy management strategy for power-split plug-in hybrid electric vehicle based on MPC and double Q-learning," Energy, Elsevier, vol. 245(C).
    5. Guo, Lingxiong & Liu, Hui & Han, Lijin & Yang, Ningkang & Liu, Rui & Xiang, Changle, 2023. "Predictive energy management strategy of dual-mode hybrid electric vehicles combining dynamic coordination control and simultaneous power distribution," Energy, Elsevier, vol. 263(PA).
    6. Wu, Gang & Wang, Chunyan & Zhao, Wanzhong & Meng, Qikang, 2023. "Integrated energy management of hybrid power supply based on short-term speed prediction," Energy, Elsevier, vol. 262(PB).
    7. Chen, Xu & Li, Mince & Chen, Zonghai, 2023. "Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    8. Wang, Yichun & Zhang, Yuanzhi & Zhang, Caizhi & Zhou, Jiaming & Hu, Donghai & Yi, Fengyan & Fan, Zhixian & Zeng, Tao, 2023. "Genetic algorithm-based fuzzy optimization of energy management strategy for fuel cell vehicles considering driving cycles recognition," Energy, Elsevier, vol. 263(PF).
    9. Huang, Xuejin & Zhang, Jingyi & Ou, Kai & Huang, Yin & Kang, Zehao & Mao, Xuping & Zhou, Yujie & Xuan, Dongji, 2024. "Deep reinforcement learning-based health-conscious energy management for fuel cell hybrid electric vehicles in model predictive control framework," Energy, Elsevier, vol. 304(C).
    10. Zheng, Weiguang & Ma, Mengcheng & Xu, Enyong & Huang, Qibai, 2024. "An energy management strategy for fuel-cell hybrid electric vehicles based on model predictive control with a variable time domain," Energy, Elsevier, vol. 312(C).
    11. Wu, Jinglai & Zhang, Yunqing & Ruan, Jiageng & Liang, Zhaowen & Liu, Kai, 2023. "Rule and optimization combined real-time energy management strategy for minimizing cost of fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    12. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    13. Zhou, Yang & Ravey, Alexandre & Péra, Marie-Cecile, 2020. "Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer," Applied Energy, Elsevier, vol. 258(C).
    14. Lu, Dagang & Yi, Fengyan & Hu, Donghai & Li, Jianwei & Yang, Qingqing & Wang, Jing, 2023. "Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy," Applied Energy, Elsevier, vol. 348(C).
    15. Zhang, Yahui & Wang, Zimeng & Tian, Yang & Wang, Zhong & Kang, Mingxin & Xie, Fangxi & Wen, Guilin, 2024. "Pre-optimization-assisted deep reinforcement learning-based energy management strategy for a series–parallel hybrid electric truck," Energy, Elsevier, vol. 302(C).
    16. Min, Haitao & Wu, Huiduo & Zhao, Honghui & Sun, Weiyi & Yu, Yuanbin, 2024. "Research on energy management strategy for fuel cell hybrid electric vehicles based on multi-scale information fusion," Applied Energy, Elsevier, vol. 368(C).
    17. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control," Applied Energy, Elsevier, vol. 355(C).
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