增程式混合动力汽车的分段式能量管理策略研究

Research on Segmented Energy Management Strategy for Range Extender Hybrid Electric Vehicles

分析了增程式混合动力汽车的开关式能量管理策略,提出了一种兼顾提高发动机效率和减少充放电损失的分段式能量管理策略。为进一步降低油耗,在分段式策略基础上引入自学习算法,自动在线调整分段式控制阈值。建立混合动力汽车仿真模型,对开关式能量管理策略和分段式能量管理策略进行了仿真比较,同时对具有自学习功能的分段式能量管理策略进行了仿真分析。某车型的仿真研究案例表明,与开关式策略相比,分段式能量管理策略能使油耗降低3.2%,自学习策略则在充电周期内路线较为固定的情况下可以自动调节到最优的控制阈值。

The traditional energy management strategy for range extender hybrid electric vehicles was analyzed. A segmented energy management strategy was proposed,which aims to improve the engine operation efficiency and reduces the losses during both charging and discharging. To further reduce the fuel consumption,the auto-learning was implemented to adjust the SOC threshold in the segmented management strategy. A model was built for range extender hybrid electric vehicles and simulations were conducted to compare the results of the traditional strategy and the segmented management strategy. The effects of the auto-learning method were also analyzed. The simulation results show that the segmented energy management strategy reduces the fuel consumption by 3.2%. By using the auto-learning method,the optimum values of the thresholds in the strategy are obtained for the fixed driving conditions during the charging cycle.