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

Abstract:

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.

Key words: lithium ion batteries, BP neural network, particle swarm optimization algorithm, low temperature charge, battery aging, the charging strategy