Abstract: In order to improve the safety of retired lithium-ion batteries of electric vehicles in cascade utilization, it is necessary to obtain the state of charge (SOC) parameter values of retired batteries in use accurately and timely. Because the aging of retired batteries will cause capacity decay faster and seriously affect the estimation accuracy of SOC parameters, this paper proposes an online SOC estimation method considering the capacity decay of retired lithium-ion batteries. Through the test of hybrid plulse power characteristic (HPPC) and dymanic stress test (DST) cycle conditions, it is proved that the proposed method can estimate the SOC of retired lithium-ion batteries under different operating conditions accurately and realtimely, and the maximum estimation error is less than 2%.

Key words: Retired lithium-ion batteries; SOC estimation; Maximum usable capacity; Dual extended kalman filter

摘要： 为提高电动汽车退役锂电池在梯次利用中的安全性，需要准确及时获取退役电池在使 用过程中的荷电状态（SOC）参数值。由于退役电池不断老化会引起容量较快衰减，严重影 响 SOC 参数的估计精度，所以文章提出了一种考虑退役锂电池容量衰减的 SOC 在线估计方法。 经过混合脉冲功率（HPPC）和动态应力测试（DST）循环工况的试验测试，证明了所提方法 能够在不同使用工况下准确和实时估计退役锂电池的 SOC，并且最大估计误差均小于 2%。

关键词: 退役锂电池；SOC 估计；最大可用容量；双扩展卡尔曼滤波