摘要
针对现有混合动力汽车能量管理策略存在的局限性,以某型并联混合动力汽车为研究对象,基于ADVISOR车辆仿真软件中24种标准工况构建组合工况,选定车辆行驶过程中的平均速度、平均绝对加速度以及怠速时间比3个参数为工况识别特征量,利用K均值聚类算法得到4种典型行驶工况,建立整车能耗优化目标函数,采用粒子群算法对电量消耗-电量维持型(CD-CS)规则控制策略主要参数离线寻优,得到4种典型行驶工况下的功率分配权重,且在MATLAB/Simulink平台上建立整车仿真模型验证。仿真结果表明,所采用控制策略能够准确识别行驶工况,与未采用工况识别的能量管理策略相比,电机与发动机间的能量分配更加合理,车辆综合油耗下降5.45%,电池荷电状态变化更加平稳。
In view of limitations of current energy management strategies for hybrid electric vehicle(HEV), take a certain type of parallel hybrid electric vehicle(PHEV) as the research object, the synthetic cycle was built based on 24 standard driving cycles of ADVISOR vehicle simulation software, three parameters, namely mean speed,mean absolute acceleration and idle time ratio of the combined cycle were selected as the features of driving cycles recognition. 4 typical driving cycles were obtained by K means clustering algorithm. An optimization function of vehicle energy consumption was established, and the particle swarm optimization(PSO) algorithm was implemented to optimize the main control parameters of charge depletion-charge sustaining(CD-CS) rule-based control strategy, power distribution weights of 4 typical driving cycles were determined. The vehicle simulation model was built based on the MATLAB/Simulink platform, the simulation results show that the proposed control strategy can accurately identify driving cycle. Compared with the CD-CS rule-based control strategy without driving cycles recognition, the energy allocation between motor and engine is more reasonable, and the vehicle fuel consumption is reduced by 5.45%, besides, the battery state of changel is more stable.
引文
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