Dual Layer Warning Control Method for Energy Storage Batteries Based on Multidimensional Parameters

Early warning is critical for safe operation of energy storage batteries, this study proposes a dual layer warning control method for energy storage batteries based on multidimensional parameters. First, the state of charge (SOC) and temperature are selected as characteristic parameters for fault behavior warning, and the state of safety (SOS) is established to reflect the safety performance of energy storage batteries. Second, the warning levels are determined based on the SOS value intervals, and the Pelican Optimization Algorithm (POA) is utilized to optimize the Long Short-Term Memory Neural Network (LSTM) model for predicting SOC, temperature, and SOS. Finally, a dual layer warning control method for energy storage batteries is constructed based on the predicted SOS values and the absolute error of SOS prediction to provide warnings of hazardous behaviors. Simulation results show that the warning control method improves the time efficiency of warnings by 2.36% while ensuring reliability, providing theoretical and technical support for the fault warning of energy storage batteries.