论文标题
使用深钢筋学习的坡道合并和插电式混合动力电动汽车动力分开的竞选
Co-Optimization of On-Ramp Merging and Plug-In Hybrid Electric Vehicle Power Split Using Deep Reinforcement Learning
论文作者
论文摘要
当前关于自动加固学习（DRL）合并忽略车辆动力总成和动态的研究。这项工作考虑了使用DRL的2015年Toyota Prius插件（使用DRL）进行自动化的坡道合并，以进行动力插入式插件混合动力汽车（PHEV）。在越来越多的合并控制和PHEV能源管理合作的情况下，DRL策略直接输出了发动机和电动机之间的功率。测试结果表明，DRL可以成功地用于选拔，从而导致无碰撞的坡道合并。与顺序的方法相比，在该方法中，较高的坡道合并控制和下层PHEV能源管理是独立执行的，并且按顺序进行了，我们发现合作式化导致经济化但繁琐的跨度合并，而顺序的方法可能会导致由于忽略了在设计高层上层稳定层面上的电动机上的电源限制因素而导致的碰撞，从而导致碰撞。
Current research on Deep Reinforcement Learning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics. This work considers automated on-ramp merging for a power-split Plug-In Hybrid Electric Vehicle (PHEV), the 2015 Toyota Prius Plug-In, using DRL. The on-ramp merging control and the PHEV energy management are co-optimized such that the DRL policy directly outputs the power split between the engine and the electric motor. The testing results show that DRL can be successfully used for co-optimization, leading to collision-free on-ramp merging. When compared with sequential approaches wherein the upper-level on-ramp merging control and the lower-level PHEV energy management are performed independently and in sequence, we found that co-optimization results in economic but jerky on-ramp merging while sequential approaches may result in collisions due to neglecting powertrain power limit constraints in designing the upper-level on-ramp merging controller.
