A Whale Optimization Algorithm-Enhanced CNN–TCN Model with Temporal Attention for Lithium-Ion Battery State-of-Health Estimation

Abstract

Reliable state of health (SOH) estimation plays an important role in the safe and stable operation of lithium-ion battery energy storage systems. Nevertheless, the nonlinear degradation characteristics and complex aging behaviors of batteries hinder accurate SOH estimation. This study proposes a Whale Optimization Algorithm (WOA)-optimized Convolutional Neural Network (CNN)–Temporal Convolutional Network (TCN)–Temporal Pattern Attention (TPA) framework for lithium-ion battery SOH estimation. Multiple health factors are extracted from charge–discharge curves to characterize battery degradation behaviors. Neighborhood-based imputation and Hampel-Median Absolute Deviation (MAD) correction are employed to handle missing values and local outliers, while Pearson correlation analysis is applied to evaluate the relevance of extracted features. CNN module captures local degradation patterns, TCN module learns long-term aging dependencies, and TPA mechanism enhances the representation of critical degradation stages. Furthermore, WOA adaptively optimizes key hyperparameters to improve model performance and robustness. The proposed framework is validated using NASA and CALCE battery datasets. Experimental results demonstrate that, compared with the CNN-TCN model, the proposed method reduces RMSE by 7.49–50.81% on NASA datasets and 15.83–60.38% on CALCE datasets, achieving higher estimation accuracy and stability.