并联式混合动力汽车能量管理的马尔可夫决策

为研究同轴并联式混合动力汽车的能量管理策略,建立了同轴并联式动力系统动态方程,分析了转矩需求无后效性的马尔可夫特性.在维持电池容量不变的条件下,以燃油消耗最小为优化目标,采用马尔可夫决策实施能量管理策略,并采用策略迭代方法求解了马尔可夫能量管理的转矩决策过程,在J1015工况和昆明工况进行了仿真,实现了能量管理的在线实施.结果表明,与基于动态规划的能量管理策略相比,马尔可夫决策的能量管理策略能在线实施,且电池容量变化更为平稳;在燃料消耗方面是全局次优的,在J1015行驶工况下100 km燃油消耗增加了1.32 L,在昆明行驶工况下100 km燃油消耗增加了1.59 L. 

Markov Decision Process for the Energy Management of Parallel Hybrid Vehicles

In order to study the energy management strategy of single-shaft parallel hybrid electric vehicles, dynamic equations of the power-train system were set up to analyze the non-aftereffect property of the required torque. To achieve the optimization objective of minimizing the oil consumption under a fixed battery capacity, Markov decision process was carried out to implement the torque allocation strategy, and the policy iteration algorithm was used to solve the Markov decision model for energy management. In addition, the Markov decision process for energy management was simulated under the condition of J1015 driving cycles and Kunming driving cycles, and executed on line. The results show that compared to the strategy based on dynamic programming, the energy management strategy based on Markov decision process, which can be implemented on line, can make the battery capacity change more smoothly, but is globally sub-optimal in oil consumption: with the new strategy, the oil consumption is increased by 1.32 L per 100 km in case of J1015 driving cycles and 1.59 L per 100 km in case of Kuming driving cycles.