储能工况下磷酸铁锂电池动态辨识方案研究

Dynamic parameter identification method of lithium iron phosphate cell for BESS

锂离子电池的动态性能受温度、电流及老化等多种因素制约,限制了电池储能系统的大规模推广和应用,同时由于传统的参数辨识方法只能准确辨识电池开路电压,而复杂的储能电池工况却对储能电池组的性能参数辨识提出了更高要求。以储能用大容量磷酸铁锂电池为研究对象,分析了传统电池参数辨识对不同温度、不同电流倍率下电池动态性能的估计误差,综合利用一阶和二阶等效电路模型研究了参数辨识在不同使用区间的精度,结合典型储能工况提出了复合脉冲序列条件下的粒子群参数辨识方法。实验结果表明该方法对于准确评估单体和串联电池组的动态电压及性能表征参数具有较高的精度,为大规模储能系统电池参数的在线辨识和电池评估提供依据。

The Dynamic performance of lithium ion battery was influenced by various factors, such as temperature, current and ageing, which restricts large-scale application and promotion of battery energy storage system （BESS）. And the traditional parameter identification method could only accurately identify the open circuit voltage, but the complex working condition was in high demand of evaluation of battery performance. The high-capacity lithium iron phosphate （LiFePO4） battery used in BESS was taken as the research objective. Dynamic performance estimation error of conventional parameter identification method under different temperatures and current rates was analyzed. The first-order and second-order RC equivalent circuit model precision under different SOC was studied. Particle Swarm Optimization （PSO） parameter identification method based on multiple plus combined with typical storage condition was put forward. The results show that this parameter identification method has high precision for voltages and performance measurement parameters evaluation of single celland series batteries, and can provid basis for online identification parameters for storage battery management system.