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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。
In view of the existing energy management strategy based on condition recognition, the state of charge (SOC) of power battery has not been fully considered in some working conditions during the running process of the vehicle. 23 typical cycling conditions were selected from ADVISOR , Using cluster analysis method to divide them into five categories, using the simulated annealing particle swarm optimization algorithm to optimize the key parameters of energy management strategy under various conditions and establishing the optimized parameter database An Energy Management Strategy Optimization Method Based on Condition Identification. The constructed energy management strategy is simulated and analyzed by using the integrated test conditions. The results show that compared with the energy management strategy without working condition identification, the comprehensive fuel consumption reduction is 12.77%. At the same time, the energy management strategy based on condition identification can make the vehicle In the course of driving power battery SOC decline greatly reduced.