Accurate State-of-Health (SOH) estimation is a key technology for ensuring battery safety, optimizing energy management, and enhancing lifecycle value. This paper proposes a novel SOH estimation method for lithium-ion batteries, utilizing incremental energy features and a hybrid deep learning model that combines Convolutional Neural Network (CNN), Kolmogorov–Arnold Network (KAN), and Bidirectional Long Short-Term Memory (BiLSTM) (CNN-KAN-BiLSTM). First, the battery’s voltage, current, temperature, and other data during the charging stage were measured and recorded through experiments. Incremental Energy Analysis (IEA) was conducted on the charging data to extract various incremental energy characteristics. The Pearson correlation method was used to verify the strong correlation between the proposed characteristics and the battery SOH. This paper includes experimental verification of the method for both battery cells and battery pack. For the battery cell, a complete multi-feature sequence was formed based on the incremental energy curve characteristics combined with temperature characteristics. For the battery pack, the characteristics of the incremental energy curve were supplemented with Variance of Voltage Means (VVM) as an inconsistent feature, combined with Standard Deviation of Temperature Means (SDTM), to create a complete multi-feature sequence. The features were then input into the CNN-KAN-BiLSTM deep learning model developed in this study for training, successfully estimating the SOH of lithium batteries. The results demonstrate that the proposed method can accurately estimate the SOH of lithium batteries, even though the SOH degradation of lithium batteries has significant nonlinear characteristics. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for the lithium battery pack were 0.3910 and 0.4797, respectively, with an average coefficient of determination (R²) exceeding 99%. The final SOH estimation MAE values for battery cells at different charging rates of 0.1 C (250 mA), 0.2 C (500 mA), and 0.5 C (1250 mA) were 0.2728, 0.3301, and 0.2094. The RMSE were 0.3792, 0.4494, and 0.2699, respectively. The corresponding R² values were 98.76%, 97.07%, and 99.37%, respectively. Finally, the effectiveness and universality of the method proposed in this paper were verified using the NASA battery dataset and the CALCE battery dataset.

Lithium-ion batteries are the primary power source for new energy vehicles, making accurate estimation of their state of health (SOH) essential for ensuring the safe operation of battery systems. This paper proposes a Capacity–Power Analysis (CPA) method that incorporates temperature features to enhance feature extraction across a broader range. Additionally, we introduce an SOH estimation method for lithium batteries based on a Convolutional Neural Network (CNN) and a Kolmogorov–Arnold Network (KAN). By extracting the capacity–power curve and average temperature features during constant-current and constant-voltage charging, the CNN-KAN model establishes a nonlinear mapping relationship between the extracted features and SOH, enabling high-precision SOH estimation for lithium-ion batteries. Four 18650 batteries were tested under various charging and discharging conditions in a laboratory setting. The coefficient of determination (R²) exceeded 96.4%, the root mean square error (RMSE) was below 0.86%, and the mean absolute error (MAE) was under 0.7%, confirming that the proposed method demonstrates excellent estimation performance.

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R²) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis.