锂离子电池低温充电老化建模及其充电策略优化

Low temperature charging aging modeling and optimization of charging strategy for lithium batteries

为了促进新能源汽车在寒冷地区的推广,对锂离子电池低温充电老化及其充电控制策略的研究具有重要意义。本工作基于大量低温充电实验数据,建立了多应力低温充电老化模型。以温度为主要影响因素,同时考虑充电截止电压和充电倍率及充电循环次数对电池老化的影响。引入衰退加速度因子,将多个充电应力相结合作用于整体模型,并对模型的估计精度进行了仿真测试。在此基础上引入遗传算法对充电控制策略进行优化,以充电电压为基准,将达到充电截止电压前的充电过程均分为多个充电阶段,将各阶段充电电流作为遗传算法的基因序列,以充电老化速率和充电时间作为优化目标,进行迭代优化。仿真结果表明所建立的低温充电老化模型具有较高的参数估计精度,充电控制策略能够有效较少电池老化并节约充电时间。通过设计的充电控制器对充电策略进行了实验测试,测试结果与仿真结果相同。对电池低温充电进行的实验,摸索了低温充电对电池寿命衰退影响的规律,实验数据、老化模型和充电策略优化方法有较为直接的参考价值。

The aging of lithium-ion batteries in case of low-temperature charging and a control strategy for low-temperature charging should be investigated to promote the use of new energy vehicles in cold regions.Further,a multi-stress low-temperature charging aging model was established using a large number of low-temperature charging experimental data.By considering temperature as the main influencing factor,the influence of the charging cut-off voltage,rate,and cycle times with respect to battery aging was considered.A decay acceleration factor is introduced,and several charging stresses are combined to measure their effects on the model.By introducing the genetic algorithm to optimize the charging control strategy based on the charging voltage,charging to the cut-off voltage is divided into several stages.Each stage's charging current becomes the genetic sequence of a genetic algorithm.The charge rate of aging and charging time are considered to be the optimization objectives,creating an iterative optimization procedure.The simulation results show that the low-temperature charging aging model exhibits high parameter estimation accuracy and that the charging control strategy can effectively reduce battery aging and the charging time.The charging strategy is verified using the designed charging controller,and the test results are identical to the simulation results.These experiments explore the law of influence of low-temperature charging on the battery-life decline,and the data,the aging model,and the charging strategy optimization method offer direct reference value.