A Closed-loop SOC Estimator for LiFePO4 Batteries Using Improved Neural Network and Particle Filter

Accurate estimation of the state of charge (SOC) is vital for the safe operation of battery systems. Traditional SOC estimation methods encounter challenges with LiFePO4 batteries due to their flat open circuit voltage characteristic in the middle range of SOC. Therefore., this paper proposes a closed-loop SOC estimation algorithm which improves the self-attention mechanism to build a neural network for preliminary SOC estimation and uses a particle filter to adaptively fuse the outputs of the neural network and the ampere-hour counting method for the final estimation results. The algorithm also provides a 95% confidence interval for the estimate., facilitating uncertainty assessment. Experimental results show that the proposed algorithm exhibits good performance for different temperatures and different cells within a battery pack., with a mean absolute error of less than 0.9% across the entire SOC range. Two extreme test cases demonstrate the algorithm's strong robustness to unknown initial SOC., sensor anomalies., and noise.