摘要
为了提高低温下锂离子电池的充电性能,降低其低温充电老化速率和充电时间,从而促进新能源汽车在低温地区的推广,进行了一系列锂离子电池低温充放电循环老化试验,基于大量低温充放电试验数据,分析了低温环境下不同充电条件对锂离子电池老化速率的影响。建立了用于锂离子电池低温充电老化速率估计的BP神经网络模型。在此基础上引入粒子群优化算法对传统CC-CV充电策略进行优化,将整个充电过程分为两个阶段,第一阶段,在达到充电截至电压前,采用粒子群优化算法寻找近似最优充电曲线,第二阶段采用常规的恒压充电。以低温容量衰退速率估计模型为基础,将低温充电老化速率和充电时间加权求和得到的多目标优化方程作为粒子群优化算法的适应度函数,在适应度函数中引入权值系数"g"来权衡两个优化目标的数量级,用粒子群优化算法进行迭代优化。测试结果表明所建立的低温充电老化模型对锂电池低温充电容量衰退速率具有较高的估计精度,优化后的充电策略能有效减小锂电池低温充电老化速率和充电时间。
In order to improve the low temperature charge performance of lithium-ion battery,reduce the aging rate and charging time in low temperature,thereby promoting the promotion of new energy vehicles in the low temperature region,made a series of lithium-ion battery charge and discharge cycle aging test in low temperature,based on a large number of low temperature test,charge and discharge test data under low temperature environment is analyzed under different charge conditions on the influence of lithium-ion battery aging rate.A BP neural network model for low temperature charge aging rate estimation of lithium-ion batteries was established.On this basis,particle swarm optimization algorithm is introduced to optimize the traditional CC-CV charging strategy,and the whole charging process is divided into two stages.In the first stage,particle swarm optimization algorithm is used to find the approximate optimal charging curve before reaching the charging cut-off voltage,and in the second stage,conventional constant voltage charging is used.Based on the capacity decline rate estimation model at low temperature,low temperature aging rate charging and charging time weighted summation of multi-objective optimization equation as the fitness function of particle swarm optimization algorithm,introduction of the weights coefficient"g"in the fitness function to weigh the two optimization goal,iterative optimization using particle swarm optimization algorithm.The results show that the model has high estimation accuracy for the decline rate of low temperature charging capacity,and the optimized charging strategy can effectively reduce the low temperature charging aging rate and charging time of lithium batteries.
作者
王泰华
张书杰
陈金干
WANG Taihua;ZHANG Shujie;CHEN Jin'gan(Henan Polytechnic University,Jiaozuo 454000,Henan,China;Shanghai Tongzhan New Energy Co.,Ltd.,Shanghai 201804,China)
出处
《储能科学与技术》
CAS
CSCD
2020年第6期1940-1947,共8页
Energy Storage Science and Technology
关键词
锂离子电池
BP神经网络
粒子群优化算法
低温充电
电池老化
充电策略
lithium ion batteries
BP neural network
particle swarm optimization algorithm
low temperature charge
battery aging
the charging strategy
