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Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization

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  • Liu, Yonggang
  • Liu, Junjun
  • Zhang, Yuanjian
  • Wu, Yitao
  • Chen, Zheng
  • Ye, Ming

Abstract

In this article, a multi-objective optimization-oriented energy management strategy is investigated for fuel cell hybrid vehicles on the basis of rule learning. The degradation of fuel cells and lithium-ion batteries are considered as the objective function and translated into the equivalent hydrogen consumption. The optimal fuel cell power sequence and state of charge trajectory, considered as the energy management input, are solved offline via the Pontryagin’s minimum principle. The K-means algorithm is employed to hierarchically cluster the optimal data set for preparation of rules extraction, and then the rules are excavated by the improved repeated incremental pruning to production error reduction algorithm and fitted by the quasi-Newton method. The simulation results highlight that the proposed rule learning-based energy management strategy can effectively save hydrogen consumption and prolong fuel cell life with real-time application potential.

Suggested Citation

  • Liu, Yonggang & Liu, Junjun & Zhang, Yuanjian & Wu, Yitao & Chen, Zheng & Ye, Ming, 2020. "Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313190
    DOI: 10.1016/j.energy.2020.118212
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    4. Zeng, Tao & Zhang, Caizhi & Zhou, Anjian & Wu, Qi & Deng, Chenghao & Chan, Siew Hwa & Chen, Jinrui & Foley, Aoife M., 2021. "Enhancing reactant mass transfer inside fuel cells to improve dynamic performance via intelligent hydrogen pressure control," Energy, Elsevier, vol. 230(C).
    5. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Waruna Maddumage & Malika Perera & Rahula Attalage & Patrick Kelly, 2021. "Power Management Strategy of a Parallel Hybrid Three-Wheeler for Fuel and Emission Reduction," Energies, MDPI, vol. 14(7), pages 1-30, March.
    7. Liu, Bo & Sun, Chao & Wang, Bo & Liang, Weiqiang & Ren, Qiang & Li, Junqiu & Sun, Fengchun, 2022. "Bi-level convex optimization of eco-driving for connected Fuel Cell Hybrid Electric Vehicles through signalized intersections," Energy, Elsevier, vol. 252(C).
    8. Binbin Sun & Tianqi Gu & Mengxue Xie & Pengwei Wang & Song Gao & Xi Zhang, 2022. "Strategy Design and Performance Analysis of an Electromechanical Flywheel Hybrid Scheme for Electric Vehicles," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    9. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    10. Xiaokai Guo & Xianguo Yan & Zhi Chen & Zhiyu Meng, 2021. "A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN," Energies, MDPI, vol. 15(1), pages 1-19, December.
    11. Mokesioluwa Fanoro & Mladen Božanić & Saurabh Sinha, 2022. "A Review of the Impact of Battery Degradation on Energy Management Systems with a Special Emphasis on Electric Vehicles," Energies, MDPI, vol. 15(16), pages 1-29, August.
    12. Min, Dehao & Song, Zhen & Chen, Huicui & Wang, Tianxiang & Zhang, Tong, 2022. "Genetic algorithm optimized neural network based fuel cell hybrid electric vehicle energy management strategy under start-stop condition," Applied Energy, Elsevier, vol. 306(PB).
    13. Jianying Liang & Yankun Li & Wenya Jia & Weikang Lin & Tiancai Ma, 2021. "Comparison of Two Energy Management Strategies Considering Power System Durability for PEMFC-LIB Hybrid Logistics Vehicle," Energies, MDPI, vol. 14(11), pages 1-24, June.
    14. Mubashir Rasool & Muhammad Adil Khan & Runmin Zou, 2023. "A Comprehensive Analysis of Online and Offline Energy Management Approaches for Optimal Performance of Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-33, April.
    15. Iqbal, Mehroze & Laurent, Julien & Benmouna, Amel & Becherif, Mohamed & Ramadan, Haitham S. & Claude, Frederic, 2022. "Ageing-aware load following control for composite-cost optimal energy management of fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 254(PA).
    16. Zhang, Caizhi & Zeng, Tao & Wu, Qi & Deng, Chenghao & Chan, Siew Hwa & Liu, Zhixiang, 2021. "Improved efficiency maximization strategy for vehicular dual-stack fuel cell system considering load state of sub-stacks through predictive soft-loading," Renewable Energy, Elsevier, vol. 179(C), pages 929-944.
    17. 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).
    18. Yang Gao & Changhong Liu & Yuan Liang & Sadegh Kouhestani Hamed & Fuwei Wang & Bo Bi, 2022. "Minimizing Energy Consumption and Powertrain Cost of Fuel Cell Hybrid Vehicles with Consideration of Different Driving Cycles and SOC Ranges," Energies, MDPI, vol. 15(17), pages 1-12, August.
    19. Chen, Huicui & Liu, Zhao & Ye, Xichen & Yi, Liu & Xu, Sichen & Zhang, Tong, 2022. "Air flow and pressure optimization for air supply in proton exchange membrane fuel cell system," Energy, Elsevier, vol. 238(PC).
    20. Zhang, Yuanjian & Liu, Yonggang & Huang, Yanjun & Chen, Zheng & Li, Guang & Hao, Wanming & Cunningham, Geoff & Early, Juliana, 2021. "An optimal control strategy design for plug-in hybrid electric vehicles based on internet of vehicles," Energy, Elsevier, vol. 228(C).

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