磷酸铁锂电池优化多因子状态在线评估方法

An Optimized Multi-Factor Online Assessment Method of SOH for LiFePO_(4) Batteries

针对目前锂离子电池在线估计方法不准确的问题,提出了一种基于优化充电电压片段下多个健康因子的磷酸铁锂电池健康状态综合在线评估方法,将充电电压片段内所充电量估计的电池容量与实际电池容量的误差最小作为目标,利用遗传算法寻优充电电压片段。在此基础上,分别对表征电池健康状态的充入电量、充电时间以及内部阻抗三个健康因子进行在线评估,归一化处理得到各健康因子对应的健康状态,再通过最小序列优化法实时获取电池综合健康状态。最后对磷酸铁锂电池进行老化充放电实验,对比仅采用电池内阻单因子评估方法,结果表明该方法能有效减小充电过程中电池健康状态估计误差,且适用性更强。

For inaccuracy of the online estimation methods for lithium-ion batteries,this paper proposes a comprehensive online assessment method for the health status of lithium iron phosphate batteries based on multiple equivalent health factors under the optimized charging voltage segment.Taking the minimum error between the estimated battery capacity and the actual battery capacity in the charge voltage segment taken as the target,we use the genetic algorithm to optimize the charging voltage segment.On this basis,the three health factors of charge,charging time,and internal impedance,which characterize the health of lithium-ion batteries,are evaluated online.And the health status corresponding to each health factor is obtained by normalization process,and real time acquisition of battery comprehensive health status is realized through the minimum sequence optimization method.Finally,the cycle charge and discharge experiments of LiFePO_(4) battery are carried out,and the comparison with the single factor evaluation method of internal resistance is made,the results show that this method can effectively reduce the estimation error of the battery state of health during the charging process and has stronger applicability.