不同环境温度下锂离子电池的无源跨域荷电状态估计

Machine learning methods for state of charge (SOC) estimation of lithium-ion batteries (LiBs) face the problem of domain shift. Varying conditions, such as different ambient temperatures, can cause performance degradation of the estimators due to data distribution discrepancy. Some transfer learning methods have been utilized to tackle the problem. At real-time transfer, the source model is suppos...