The state of health (SOH) of lithium-ion (Li⁺) battery prediction plays significant roles in battery management and the determination of the durability of the battery in service. This study used segmentation-type anomaly detection, the Levenberg–Marquardt (LM) algorithm, and multiphase exponential regression (MER) model to determine SOH of the Li⁺ batteries. By determining the changepoint boundaries using the characteristic values such as voltage transition rate (VTR), temperature transition rate (TTR), and charge capacities of the Li⁺ battery at the changepoint timestamps, we determined the parametric values of the biphasic MER. The characteristic transition rate values, which depend on the transition probabilities of the rolling standard deviations of the measured voltage and temperature, were later utilized with the matching charge capacities to model various training–testing dataset combinations. This helped to estimate the SOH of the battery at different life-cycle phases. This study also developed a technique for real-time estimation of the remaining useful life of the battery by using the MER model parameters, VTR, and TTR which were previously unseen parametric values of the Li⁺ battery. The result obtained from the proposed model indicates that our technique will be effective for online SOH estimation of Li⁺ batteries.

锂离子（Li ⁺）电池预测的健康状态（SOH）在电池管理和确定使用中的电池耐久性方面起着重要作用。本研究使用分段类型异常检测，Levenberg-Marquardt（LM）算法和多相指数回归（MER）模型来确定Li ⁺电池的SOH 。通过使用电压跃迁速率（VTR），温度跃迁速率（TTR）和Li ^+的充电容量等特征值确定变化点边界在变化点时间戳记电池，我们确定了双相MER的参数值。取决于所测电压和温度的滚动标准偏差的转换概率的特征转换速率值，随后与匹配的电荷容量一起用于对各种训练-测试数据集组合进行建模。这有助于估算电池在不同生命周期阶段的SOH。这项研究还开发了一种通过使用MER模型参数，VTR和TTR实时估算电池剩余使用寿命的技术，这些参数以前是Li ⁺电池的未知参数值。从提出的模型中获得的结果表明，我们的技术将对Li ^+的在线SOH估计有效 电池。