基于等效电路模型的云端动力电池寿命估计OA北大核心CSTPCD

The current power battery management system (BMS)has such problems as small storage and low arithmetic power,causing capacity errors with increasing cumulative State of Charge (SOC)errors of batteries.To realize the accurate estimation of power battery life,this paper proposes a power battery capacity estimation model based on Equivalent Circuit Model (ECM).The model is based on the relationship between Open Circuit Voltage (OCV)and SOC,linking the first-order RC model directly to capacity.The cloud data will be data augmented,the representative charging data in the data will be screened out,the capacity will be recognized by substituting it into the first-order RC model and outputting the simulated end voltage,and the results of parameter recognition will be evaluated by the RMSE between the simulated end voltage and the actual end voltage.Based on the Particle Swarm Optimization (PSO) algorithm,the minimum RMSE identification result for initial capacity estimation is optimized. Our identification results are then optimized by combining Polynomial Curve Fitting (PCF)controlled Kalman filter (KF).Our results show the inclusion of filtering effectively improves the stability of the estimation results.Finally cloud data from the power packs of five vehicles are employed to validate the methodology. The root-mean-square error (RMSE)for both is less than 3％ and the maximum absolute error less than 2Ah,demonstrating the method accurately estimates the capacity of the power batteries.