基于效率优化的混合动力车辆强化学习能量管理策略研究

Research on Efficiency Optimization Based Energy Management Strategy for a Hybrid Electric Vehicle with Reinforcement Learning

以功率分流混联式混合动力车辆为对象,建立了系统综合效率计算模型,并提出了以效率优化为目标基于强化学习的能量管理策略。首先建立了关键部件的效率模型与耦合机构效率模型,并基于复合传动无级调速通用结构,分析电功率分流系数对效率的影响规律,进一步构建了系统综合效率计算模型。而后以效率优化为目标,提出了基于强化学习的能量管理策略。并进行仿真对比,结果显示:相比于基于规则的方法,所提出的策略能在实现优秀的燃油经济性的同时维持电池SOC处于更小的波动范围内。最后搭建了试验台架,试验结果证明了所建立的效率模型的正确性以及提出的能量管理策略的有效性。

Taking the power split hybrid electric vehicle as the object,this paper establishes the model for calculating the system comprehensive efficiency and proposes an efficiency optimization based energy management strategy with reinforcement learning.Firstly,the efficiency model of key components and the efficiency model of coupling mechanism are established.Based on the general structure of stepless speed regulation of composite transmission,the influence law of power splitting coefficient on the efficiency is analyzed,and the system comprehensive efficiency model is further constructed.Then with efficiency optimization as the goal,an energy management strategy based on reinforcement learning is proposed.Simulation comparisons are implemented,and the results show that the proposed strategy can achieve excellent fuel economy while maintaining battery SOC within a smaller fluctuation range.Finally,a test bench is built and the test results prove the correctness of the established efficiency model and the effectiveness of the proposed energy management strategy.