State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model

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.