State of charge estimation for lithium-ion batteries using gated recurrent unit recurrent neural network and adaptive Kalman filter

The state of charge (SOC) is one of the most important monitoring states for the battery management system. It is still a challenge to estimate the battery SOC accurately and stably. The conventional model-based filtering methods may cause inaccurate SOC estimation in application due to the high dependence on accurate battery model. And the emerging methods based on machine learning often have the problem of estimated SOC fluctuation when the current fluctuates greatly. To solve these problems, this paper proposes a robust and efficient combined SOC estimation method, GRU-AKF, which combines the gated recurrent unit recurrent neural network (GRU-RNN) and the adaptive Kalman filter (AKF). Firstly, the GRU-RNN is used to establish a mapping model between the battery measured variables and SOC in the full temperature range, and to achieve the SOC pre-estimation. Then, an AKF is employed to filter the output SOC of the GRU-RNN for reducing the fluctuation in pre-estimated SOC. Finally, the accurate and stable estimated SOC is obtained. In the experiments, the LiFePO 4 battery datasets at various temperatures are used to validate the SOC estimation performance and generalization ability. Specifically, the root mean square error is less than 1.3% and 5.8%, and the maximum error is less than 2.2% and 7.7% for the unknown data at positive and negative temperatures, respectively. By comparing with other methods of the same type, the proposed method is demonstrated to be superior in SOC estimation performance and computation efficiency, especially it has excellent performance in initial SOC convergence ability. • A method combining neural network and Kalman filter is proposed for SOC estimation. • A GRU-RNN is used to achieve the pre-estimation of SOC. • An AKF is employed to reduce the fluctuation in pre-estimated SOC. • Comparative experiments between the method and other combined methods are designed. • The method exhibits accurate SOC estimation and excellent generalization ability.