State of health (SOH) is critical to evaluate the life expectancy of lithium-ion battery (LIB), thus should be estimated accurately in practical applications. This article proposes a computationally efficient model-based method for SOH estimation of LIB. A revised Lorentzian function-based voltage–capacity (VC) (RL-VC) model is exploited to accurately capture the voltage plateaus of LIB which reflect the material-level phase transition phenomenon. A full set of new features of interest (FOIs) is extracted by simply fitting the RL-VC model leveraging data collected from the constant-current charging process. Correlation analysis is then performed for the captured FOIs, based on which linear models are calibrated to estimate the battery SOH. The proposed method is validated with experimental data from different battery chemistries. The results show that the extracted FOIs have high linearities with the battery capacity, suggesting a good potential for SOH estimation and better feasibility over traditionally used methods. The proposed method shows a high accuracy for battery SOH estimation and an expected robust performance against the initial aging status and practical cycling condition.

中文翻译：

---

基于电压-容量模型的增量容量分析锂离子电池健康状态估计

健康状态（SOH）对于评估锂离子电池（LIB）的预期寿命至关重要，因此在实际应用中应准确估计。本文提出了一种计算高效的基于模型的 LIB SOH 估计方法。改进的基于洛伦兹函数的电压-容量（VC）（RL-VC）模型被用来准确捕捉反映材料级相变现象的LIB的电压平台。利用从恒流充电过程中收集的数据，通过简单地拟合 RL-VC 模型，提取了一整套感兴趣的新特征 (FOI)。然后对捕获的 FOI 执行相关分析，根据校准线性模型来估计电池 SOH。所提出的方法通过来自不同电池化学成分的实验数据进行了验证。结果表明，提取的 FOI 与电池容量具有较高的线性关系，表明 SOH 估计具有良好的潜力，并且比传统使用的方法具有更好的可行性。所提出的方法显示出电池 SOH 估计的高精度和针对初始老化状态和实际循环条件的预期稳健性能。