SOH estimation method for lithium-ion batteries based on charging and discharging features

State of health estimation of lithium-ion batteries is essential for ensuring operational safety, extending service life, and optimizing production processes. To address the limitation of relying solely on features from either the charging or discharging process, this study proposes a health state estimation method based on features extracted from both charging and discharging phases. Specifically, three features are extracted from the battery’s charging and discharging curves and then evaluated for their correlation with SOH using the spearman correlation coefficient to ensure the effectiveness. A particle swarm optimization-optimized gate recurrent unit-based model is constructed to estimate state of health, and the proposed method is validated using the CALCE dataset and NASA dataset. Experimental results show that the root mean square error is controlled to around 1%, demonstrating that the method can effectively and accurately assess the SOH of lithium-ion batteries, providing valuable guidance for practical applications in battery management systems.