基于粒子群算法的动力电池组充放电优化

Optimization of Charging and Discharging of Power Battery Pack Based on Particle Swarm Optimization Algorithm

为了提高动力电池组的充放电性能,降低其充电老化速率和充电时间,降低充放电成本,提出基于粒子群算法的动力电池组充放电优化方法。根据电池负载特性,将电池负载设定为随机微粒,采用微粒群算法求解电池负载特性,并引入自适应学习因子,实现电池负载特性的优化。实验结果显示,该设计方法所对应的动力电池组的使用寿命明显延长,充电耗时减少,充放电费用有所降低,有较大的实用价值。

In order to improve the charging and discharging performance of power battery,reduce its charging aging rate and charging time,and reduce the charging and discharging cost,an optimization method of charging and discharging of power battery based on particle swarm optimization is proposed.According to the battery load characteristics,the battery load is set as random particles,and the battery load characteristics are solved by particle swarm optimization,and the adaptive learning factor is introduced to optimize the battery load characteristics.The experimental results show that the service life of the power battery pack corresponding to this design method is obviously prolonged,the charging time is reduced,and the charging and discharging costs are reduced,which has great practical value.