Abstract：Under the dual pressures of the global energy crisis and environmental pollution, electric vehicles have emerged as an important development direction in the transportation sector, thanks to their zero-emission and high energy-efficiency characteristics. However, electric vehicles still face challenges, including high energy consumption, limited driving range, and reduced comfort in dynamic, complex environments. Traditional energy efficiency optimization methods, such as rule-based control strategies, are computationally simple but lack flexibility. Optimization-based approaches can approximate global optimality but struggle with real-time adaptability due to their high computational complexity. Existing deep reinforcement learning methods can handle high-dimensional state spaces. However, these methods often suffer from energy waste and driving instability due to inadequate environmental perception or abrupt changes in action. This study proposes an innovative framework that integrates environmental perception with deep reinforcement learning to achieve efficient energy conservation and enhanced driving comfort in complex road conditions. To address the challenge of processing high-dimensional traffic data, a city road-condition perception module based on a World Model is proposed. The encoder is trained using both contrastive learning and coupled reinforcement learning, enabling the agent to perceive real-time traffic changes and generate control strategies aligned with energy-saving objectives. In addition, the study incorporates an active learning framework that uses a KL-divergence-based strategy to select more difficult training samples. Periodic offline training is then employed to optimize the policy. This intelligent approach significantly enhances the energy efficiency of electric vehicles under urban road conditions. The proposed method has been validated on the CARLA simulation platform. The results demonstrate that it can effectively extract key environmental features and accelerate model convergence. Under real-world driving conditions, vehicle energy consumption, SOC variation, and battery degradation rate are improved. Meanwhile, statistical analyses of real-time acceleration and jerk data show that the proposed approach yields smooth control behavior and substantially reduces the frequency of sudden acceleration and deceleration events. In conclusion, this study proposes an efficient, smooth, and adaptive energy efficiency optimization framework for electric vehicles. By deeply integrating environmental perception with active reinforcement learning, the framework demonstrates innovation in low-dimensional traffic representation and achieves breakthroughs in optimizing multi-objective reward functions. The results confirm that the method achieves significant energy savings and improves driving comfort in dynamic, complex environments, providing strong theoretical support for the practical application of intelligent driving systems. Future research will focus on optimizing perception models for multimodal traffic scenarios, thereby enabling fully intelligent, energy-efficient driving technologies. Additionally, drivers' personalized driving habits significantly impact vehicle energy consumption and battery lifespan. Future work will integrate driving behavior modeling with energy-saving strategies by learning from drivers' operational preferences, aiming to achieve intelligent control strategies that balance energy efficiency with personalized adaptability. Meanwhile, hardware-in-the-loop（HIL）platforms will be used to systematically evaluate the algorithm's real-time performance, stability, and reliability. By deploying the policy network on embedded controllers and testing its response latency and fault-recovery capabilities in dynamic traffic environments, the method can be ensured to provide sufficient safety and robustness for engineering implementation.
彭自然, 范泽宇. 融合环境感知强化学习的电动汽车能效优化控制[J]. 电工技术学报, 2026, 41(16): 5712-5728.
Peng Ziran, Fan Zeyu. Energy-Efficient Control for Electric Vehicles Incorporating Environment-Aware Reinforcement Learning. Transactions of China Electrotechnical Society, 2026, 41(16): 5712-5728.
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2026, Vol. 41 
