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Integrated energy-thermal management strategy for range extended electric vehicles based on soft actor-critic under low environment temperature

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  • Sun, Ziyi
  • Guo, Rong
  • Luo, Maohui

Abstract

The integrated energy-thermal management system is significant in improving vehicle energy utilization efficiency under low temperatures. An integrated energy-thermal management strategy based on Gaussian mixture model (GMM) and soft actor-critic (SAC) that fully utilizes the waste heat from the engine and electric motor to heat the battery is proposed. The GMM model classifies the driving conditions into 5 clusters featuring vehicle velocity and required power and is used as a state input to the SAC. SAC agent is trained to achieve battery heating, SOC maintenance, and equivalent energy saving. The performance of the GMM-SAC based strategy is compared with the rule based and SAC based strategies according to the average battery temperature rise rate and equivalent fuel consumption. The GMM-SAC based strategy improves the average battery temperature rise rate (from environment temperature to 10 °C) by up to 58.22 % over the rule-based strategy, better than 21.03 % of the SAC-based strategy. The GMM-SAC based strategy saves up to 5.52 % equivalent fuel consumption over the rule-based strategy, superior to 4.51 % of the SAC-based strategy.

Suggested Citation

  • Sun, Ziyi & Guo, Rong & Luo, Maohui, 2025. "Integrated energy-thermal management strategy for range extended electric vehicles based on soft actor-critic under low environment temperature," Energy, Elsevier, vol. 330(C).
    • Handle: RePEc:eee:energy:v:330:y:2025:i:c:s0360544225025101
    DOI: 10.1016/j.energy.2025.136868
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    References listed on IDEAS

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    1. Huang, Ruchen & He, Hongwen, 2025. "UpdatingEMS: An online updating framework for deep reinforcement learning-based energy management of fuel cell hybrid electric bus with integrated transfer learning," Applied Energy, Elsevier, vol. 402(PA).
    2. C. Treesatayapun & A. J. Munoz-Vazquez & S. K. Korkua & B. Srikarun & C. Pochaiya, 2025. "Electric Vehicle Energy Management Under Unknown Disturbances from Undefined Power Demand: Online Co-State Estimation via Reinforcement Learning," Energies, MDPI, vol. 18(15), pages 1-20, July.

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