Abstract: The state of health(SOH) of electrochemical energy storage batteries is an important indicator for evaluating the degree of battery performance degradation and safety. In order to improve the engineering adaptability and accuracy of the model,this study takes 20 Ah lithium iron phosphate batteries as the research object and proposes an evaluation method that integrates multiple feature parameters: partial charge and discharge data and characteristic frequency points of the electrochemical impedance spectrum are used as input features, a BP neural network model optimized based on the whale algorithm is constructed, and the two types of feature parameters are further fused and input into the model for SOH evaluation. The experimental results show that the evaluation accuracy of the model with fused features is significantly improved: its mean absolute percentage error(MAPE) reaches 1.09%. Compared with the single feature input model, MAPE decreased by 39.8%(compared to impedance method) and 43.5%(compared to voltage method). This study provides an effective method for performance evaluation and system management of lithium iron phosphate batteries for energy storage.

Key words: energy storage batteries, state estimation, discharge data, AC impedance