TY - JOUR
T1 - Energy-Efficient integrated thermal management for electric vehicles using evolutionary deep reinforcement learning
AU - Guo, Xin
AU - Peng, Jiankun
AU - Wu, Jingda
AU - Wu, Changcheng
AU - Ma, Chunye
N1 - Publisher Copyright:
© 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2026/3
Y1 - 2026/3
N2 - In high-temperature environments, thermal management systems are essential to improving comfort, ensuring the safe operation of electric vehicles, and reducing energy consumption. This study introduces a multi-agent control framework that leverages the Deep Deterministic Policy Gradient (MADDPG) algorithm to optimize temperature regulation and energy efficiency in an integrated thermal management system (ITMS). In recognition of the variability of air conditioning (AC) system energy efficiency under different vehicle operating conditions, the coordination agent dynamically plans target temperature trajectories for the cabin and battery, whereas the execution agent follows these trajectories and regulates motor system temperatures, ensuring coordinated energy optimization. To enhance the robustness and mitigate the risks of local optima caused by hyperparameter sensitivity, the Cross-Entropy Method (CEM) is integrated into MADDPG, forming the CEM-MADDPG control strategy. This hybrid method combines the global search capability of evolutionary algorithms with the sample efficiency of deep reinforcement learning, achieving a balance between stability and efficiency. Experimental results show that the proposed method effectively controls the temperatures of the battery, cabin, and motor, reducing the overall energy consumption of the ITMS by 19.8% and 7.2% respectively, compared to rule-based and model predictive control (MPC) methods.
AB - In high-temperature environments, thermal management systems are essential to improving comfort, ensuring the safe operation of electric vehicles, and reducing energy consumption. This study introduces a multi-agent control framework that leverages the Deep Deterministic Policy Gradient (MADDPG) algorithm to optimize temperature regulation and energy efficiency in an integrated thermal management system (ITMS). In recognition of the variability of air conditioning (AC) system energy efficiency under different vehicle operating conditions, the coordination agent dynamically plans target temperature trajectories for the cabin and battery, whereas the execution agent follows these trajectories and regulates motor system temperatures, ensuring coordinated energy optimization. To enhance the robustness and mitigate the risks of local optima caused by hyperparameter sensitivity, the Cross-Entropy Method (CEM) is integrated into MADDPG, forming the CEM-MADDPG control strategy. This hybrid method combines the global search capability of evolutionary algorithms with the sample efficiency of deep reinforcement learning, achieving a balance between stability and efficiency. Experimental results show that the proposed method effectively controls the temperatures of the battery, cabin, and motor, reducing the overall energy consumption of the ITMS by 19.8% and 7.2% respectively, compared to rule-based and model predictive control (MPC) methods.
KW - Cross-entropy method
KW - Energy consumption optimization
KW - Integrated thermal management systems
KW - Multi-agent deep deterministic policy gradient
KW - Thermal management strategies
UR - https://www.scopus.com/pages/publications/105023826852
U2 - 10.1016/j.eswa.2025.130331
DO - 10.1016/j.eswa.2025.130331
M3 - Article
AN - SCOPUS:105023826852
SN - 0957-4174
VL - 299
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -