Estimation of battery state-of-charge for electric vehicles using an MCMC-based auxiliary particle filter

摘要

The estimation of battery state-of-charge (SOC) is crucial for the safety and reliability of electric vehicles. This paper develops an auxiliary particle filter based on a Markov-chain Monte-Carlo (MCMC) method. Compared with the standard particle filter, it improves the estimation accuracy by incorporating auxiliary sampling and enhances its robustness by using MCMC resampling. Simulation results demonstrate that the proposed filter is more accurate and robust.