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Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle

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  • Lin, Xinyou
  • Xu, Xinhao
  • Wang, Zhaorui

Abstract

The driving trip pattern is of great significance in hydrogen consumption and battery Longevity of the plug-in fuel cell hybrid electric vehicles (PFCHEV). However, the traditional energy management strategy failed to consider the uncertainty of driving patterns. To overcome this drawback, a deep Q-learning network based trip pattern adaptive (DQN-TPA) battery longevity-conscious strategy is proposed in this study. To begin with, the trip pattern recognition based Learning Vector Quantization Neural Network is devised for pattern identification, and the adaptive-equivalent consumption minimizes strategy (A-ECMS) is conducted to improve the hydrogen consumption. Then, a TPA longevity-conscious strategy is developed and compared with the conventional multi-criteria (MC) optimization strategy to investigate the discrepancy brought by the pattern adaptation. And finally, in combination with the above efforts, an improved DQN-TPA based battery longevity-conscious strategy has been established accordingly. The advances are confirmed by the validation results that, the A-ECMS makes an 11.76% promotion in fuel economy by taking the deviation among different driving patterns into concern. The TPA strategy shows more adaptiveness than the MC optimization strategy in which, the effective Ah-throughput is 5.17% lower than MC-based while keeping the same economy. Further improvement can be achieved by the modified DQN-TPA based approach by remedying the imperfection of TPA-based recognition delay and performing the economy and durability conscious actions with 5.87% further reduction of effective Ah-throughput without observably sacrificing the fuel economy. Furthermore, the effectiveness and adaptiveness of the proposed strategy are validated by the Hardware-in-the-Loop experiments. Both the numerical validation and semi-physical validation results indicate that the DQN-TPA based approach made it possible to develop the battery longevity-conscious strategy capable of significantly adapting various driving patterns and improving the hydrogen consumption and battery durability performance of the PFCHEV.

Suggested Citation

  • Lin, Xinyou & Xu, Xinhao & Wang, Zhaorui, 2022. "Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle," Applied Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:appene:v:321:y:2022:i:c:s0306261922007206
    DOI: 10.1016/j.apenergy.2022.119378
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    2. 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).
    3. Ren, Xiaoxia & Ye, Jinze & Xie, Liping & Lin, Xinyou, 2024. "Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 286(C).
    4. Liu, Huimin & Lin, Cheng & Yu, Xiao & Tao, Zhenyi & Xu, Jiaqi, 2024. "Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle," Applied Energy, Elsevier, vol. 365(C).
    5. 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).
    6. Hossein Pourrahmani & Majid Siavashi & Adel Yavarinasab & Mardit Matian & Nazanin Chitgar & Ligang Wang & Jan Van herle, 2022. "A Review on the Long-Term Performance of Proton Exchange Membrane Fuel Cells: From Degradation Modeling to the Effects of Bipolar Plates, Sealings, and Contaminants," Energies, MDPI, vol. 15(14), pages 1-30, July.

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