针对车辆低速行驶的制动能量回收率低,频繁充放电影响动力电池寿命的问题,提出以电池荷电状态(state of charge,SOC)、制动强度、车速和制动间隔时间为输入,再生制动力分配系数为输出的纯电动汽车模糊控制再生制动策略.同时,采用遗传算法对控制参数进行优化.在Simulink中搭建控制策略模型,并在不同测试工况下与CarSim联合进行仿真,结果表明,相比于仅以电池SOC、制动强度和车速为输入的模糊控制再生制动策略,所提策略减少了制动能量回收次数,提高了制动能量回收率.该策略不仅可以改善对动力电池的损害情况,而且可以获得更多的制动能量.
Regenerative Energy Recovery Technology of Pure Electric Vehicle Based on Fuzzy Control
To solve the problems of low braking energy recovery rate and frequent charging and discharging affecting the life of the power battery,a regenerative braking strategy of pure electric vehicles based on the fuzzy control with the battery state of charge(SOC),braking intensity,vehicle speed and braking interval as input and the regenerative braking force distribution coefficient as output is proposed.At the same time,genetic algorithm is used to optimize the control parameters.The control strategy is modelled in Simulink,and the simulation is carried out jointly with CarSim under different test conditions.The results show that compared with the regenerative braking strategy based on the fuzzy control which only takes battery(SOC),braking strength and vehicle speed as input,the proposed strategy reduces the number of braking energy recovery times and improves the braking energy recovery rate.This strategy can reduce the damage to the power battery and obtain more braking energy.
Pure electric vehicleregenerative brakingfuzzy controlgenetic algorithmCarSim
万方数据
维普
