基于工况识别的混合动力汽车能量管理策略优化

Energy Management Strategy Optimization of HEV based on Driving Pattern Recognition

针对现有基于工况识别的能量管理策略在车辆行驶过程中未全面考虑动力电池荷电状态(SOC)在某些工况片段下降过快的问题,从ADVISOR中选取23个典型的循环工况,用聚类分析方法将其划分为五类,以燃油消耗最小为目标,采用模拟退火粒子群算法对各类工况下能量管理策略中的关键参数进行离线优化,并建立优化参数数据库,提出了一种基于工况识别的能量管理策略优化方法。利用构建的综合测试工况对所制定的能量管理策略进行仿真分析。结果表明,所制定的基于工况识别的能量管理策略与未采用工况识别的能量管理策略相比,综合油耗降低了12.77%;同时,所制定的基于工况识别的能量管理策略可使汽车在行驶过程中动力电池SOC下降速度大为减小。

The existing energy management strategy which is based on driving pattern recognition failed to fully consider the battery state of charge（ SOC） falling fast in some driving cycle into consideration in the progress of driving. The 23 typical driving cycles are chosen from ADVISOR software and five categories are divided by using clustering analysis method,the key parameters of each category are optimized by using particle swarm algorithm,with the goal of reducing fuel consumption,relevant optimized results are saved in database,an energy management strategy optimization method of HEV based on driving pattern recognition is proposed.Finally,the simulation analysis for the energy management is carried out under a comprehensive test cycle,simulation results show that vehicle fuel consumption is cut down 12. 77%,and the deviation of the battery SOC have greatly decrease compare with other energy management strategy which based on driving pattern recognition.