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Intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario

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  • Cao, Jianfei
  • He, Hongwen
  • Wei, Dong

Abstract

At present, plug-in hybrid electric vehicle has got widely concerned and studied for its special advantages of fuel-saving and emission-reduction potential. In order to improve the electric energy efficiency in power battery and meet the practical requirements of energy management strategies, an intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario is proposed in this paper. Generated by random combination of the selected driving cycle units, the variable scene could reflect the driving situation of commercial vehicles performing transportation tasks at some extent. The strategy is trained and generated by a reinforcement learning framework. Firstly, a power-flow analysis model is constructed. Then, a dynamic programming description that could solve the optimal power-flow is defined. Furthermore, the optimizations for different initial conditions were performed repeatedly. With the solution results, an energy-conversion model is generated to describes the conversion of fuel and electricity, and reward in reinforcement learning was defined and used to guide the agent to improve and enhance the required SOC-allocation strategy. The trained strategy performed a reasonable SOC-allocation according to the cycle unit and residual SOC; and fuel consumption under average condition is significantly lower than that of two normally used SOC-allocation strategies.

Suggested Citation

  • Cao, Jianfei & He, Hongwen & Wei, Dong, 2021. "Intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario," Applied Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920313994
    DOI: 10.1016/j.apenergy.2020.115942
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    References listed on IDEAS

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    Cited by:

    1. Li, Jie & Wu, Xiaodong & Xu, Min & Liu, Yonggang, 2022. "Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections," Energy, Elsevier, vol. 251(C).
    2. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    3. Choi, Mingi & Cha, Junepyo & Song, Jingeun, 2024. "Analysis of fuel economy reduction factors of hybrid electric vehicles in winter using on-road driving data," Energy, Elsevier, vol. 289(C).

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