基于温度相关扩展卡尔曼滤波与深度学习的锂离子电池荷电状态与健康状态联合估计方法

Accurate estimations of the state of charge (SOC) and state of health (SOH) are crucial for improving battery management techniques. However, batteries are affected by temperature and aging, leading to nonlinear relationships that are more difficult to be characterized. This article proposes an SOC–SOH joint estimation method of lithium-ion battery based on temperature-dependent extended Kalman filter (EKF) and deep learning. First, the battery model state, control, and observation matrices with temperature and capacity variables are created for real-time SOC estimation by using EKF at the local end. Second, battery aging features are extracted and weighted using convolutional neural networks (CNNs) and attention mechanisms and are combined with a gated unit to solve long time series memory problem for SOH estimation at remote computing platform. Finally, the dual time-scale joint model is realized by real-time SOC estimation on the local controller, and the SOH can be calculated on the remote computing platform to correct the available capacity to further update SOC at the end of the discharge. Through 1C discharge rate cycle experimental validation, the root mean square errors of SOC and SOH estimation were within 1%. Therefore, the proposed joint SOC–SOH estimation method can be achieved with local and remote computation.