The estimation of the state of health (SOH) of lithium-ion batteries is a critical technology for enhancing battery lifespan and safety. When estimating SOH, it is essential to select representative features, commonly referred to as health indicators (HIs). Most existing studies primarily focus on HIs related to capacity degradation and internal resistance increase. However, due to the complexity of lithium-ion battery degradation mechanisms, the relationships between these mechanisms and health indicators remain insufficiently explored. This paper provides a comprehensive review of core methodologies for SOH estimation, with a particular emphasis on the classification and extraction of health indicators, direct measurement techniques, model-based and data-driven SOH estimation approaches, and emerging trends in battery management system applications. The findings indicate that capacity, internal resistance, and temperature-related indicators significantly impact SOH estimation accuracy, while machine learning models demonstrate advantages in multi-source data fusion. Future research should further explore composite health indicators and aging mechanisms of novel battery materials, and improve the interpretability of predictive models. This study offers theoretical support for the intelligent management and lifespan optimization of lithium-ion batteries.

Lithium-ion batteries gradually lose capacity and show increased internal resistance during repeated cycling, so reliable state-of-health (SOH) estimation is important for safe and dependable battery operation. In this study, an online SOH estimation method is developed using a convolutional neural network–long short-term memory (CNN-LSTM) model combined with a multi-head Squeeze-and-Excitation (SE) attention mechanism. Four health-related features are obtained from charge–discharge measurements: constant-current charging time, constant-voltage charging time, the maximum value of the incremental capacity curve, and its corresponding voltage. The CNN is used to identify local degradation patterns, whereas the LSTM describes the temporal evolution of battery aging. The SE module further adjusts the contribution of different feature channels. Tests on the Xi’an Jiaotong University (XJTU) dataset indicate that the proposed model gives the lowest mean absolute error (MAE) and root mean square error (RMSE) for Battery-1 and Battery-7 and comparable results for Battery-3. These findings support the effectiveness of the proposed method for online SOH estimation of the tested batteries.