A Novel On-line Self-learning State-of-charge Estimation of Battery Management System for Hybrid Electric Vehicle

摘要

State-of-charge (SOC) estimation is the most difficult problem in battery management system, which is one of the key component of electric vehicle and hybrid electric vehicle. Suffered from the non-zero mean noises in practice, the conventional current integral and Kalman filter estimation methods can not achieve the required accuracy, even causing nonconvergent results. According to the SOC truth value obtained by Open-circuit-voltage Vs. SOC curve at each vehicle start time, we deduce a mathematic formula to calculate the mean values of system noises and then a self-learning strategy is proposed to improve the current integral and Kalman filter methods in colored noise environment. The simulation experiment based on a typical battery model verifies the availability and efficiency of proposed strategy.