A Health Prediction Method for Electrochemical Energy Storage Systems Based on the BP-Adaboost Model

The prediction and timely identification of the health status of electrochemical energy storage (EES) systems is of great importance to the prompt formulation of precautions and the prevention of potential failures. In light of the characteristics of EES systems, the research proposes a health prediction method for these systems based on the BP-Adaboost model, which can identify key parameters affecting the health status of EES systems. The technique relies on the back propagation (BP) neural network algorithm and the adaptive boosting (AdaBoost) algorithm to construct a strong predictor comprising multiple weak predictors derived from the BP neural network, aiming to predicate the health status of EES systems. The simulation experiments show that the strong predictor developed based on the BP_Adaboost model has notably lower prediction errors compared to the weak predictors, demonstrating superior predictive performance.