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Collaborative Optimization of Energy Management Strategy and Adaptive Cruise Control Based on Deep Reinforcement Learning
IEEE Transactions on Transportation Electrification ( IF 7 ) Pub Date : 2022-05-23 , DOI: 10.1109/tte.2022.3177572
Jiankun Peng 1 , Yi Fan 1 , Guodong Yin 2 , Ruhai Jiang 3
Affiliation  

Hybrid electric vehicles (HEVs) have great prospects in reducing fossil fuel consumption, and adaptive cruise control (ACC) technology provides safe and convenient travel for drivers. The fusion of the two technologies can theoretically improve the safety, comfort, and fuel economy of vehicles. Hence, the energy management strategy (EMS) of Prius, a typical HEV configuration, is studied under the car-following scenario. This optimization problem involves complex systems, inconsistent objectives, and stringent constraints, which may be challenging to conventional algorithms. Therefore, a novel deep deterministic policy gradient (DDPG)-based ecological driving strategy (DDPG-ECO) is proposed and the weights of multiple objectives are analyzed to optimize the training results. The extensive simulation experiment compares the effects of Ornstein-Uhlenbeck action noise (OUAN) and soft-max action noise (SAN), which act on the acceleration action. Simulations under different driving cycles show that the fuel economy of DDPG-ECO can achieve more than 90% of dynamic programming (DP)-based methods on the conditions of ensuring car-following performances.

中文翻译:

基于深度强化学习的能量管理策略与自适应巡航控制的协同优化

混合动力汽车(HEV)在降低化石燃料消耗方面具有广阔前景,自适应巡航控制(ACC)技术为驾驶员提供安全便捷的出行方式。两种技术的融合,理论上可以提高车辆的安全性、舒适性和燃油经济性。因此,普锐斯的能量管理策略(EMS),一种典型的 HEV 配置,在跟车场景下进行了研究。这个优化问题涉及复杂的系统、不一致的目标和严格的约束,这可能对传统算法具有挑战性。因此,提出了一种新的基于深度确定性策略梯度(DDPG)的生态驾驶策略(DDPG-ECO),并分析了多个目标的权重以优化训练结果。广泛的模拟实验比较了 Ornstein-Uhlenbeck 动作噪声 (OUAN) 和 soft-max 动作噪声 (SAN) 对加速动作的影响。不同工况下的仿真表明,在保证跟车性能的情况下,DDPG-ECO的燃油经济性可以达到基于动态规划(DP)方法的90%以上。
更新日期:2022-05-23
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