OSE: On-Site State-of-Health Estimation for Li-Ion Battery Using Real-Time Field Data

Abstract

On-site lithium-ion batteries’ state-of-health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low quality of unlabeled real-time field data, diverse operating environments of in-service EVs, and limited computational capability of onboard devices, existing techniques established on data from well-controlled experimental environments are not practical for real-world EVs’ SoH estimation. Accurate and rapid SoH estimation based on field data of in-service EVs still remains quite challenging. To tackle this challenge, we present an on-site SoH estimation (OSE) method using in-service EV field data through a new knowledge-embedded deep transfer learning (DTL) model. Initially, a universal data preprocessing approach integrating mechanism knowledge is designed to process low-quality data under diverse operating environments. Then, we develop a domain-adaptive hybrid deep neural network (DAHDNN) model suitable for unlabeled field data, which can be deployed via an edge cloud collaborative framework to meet actual computational capability. We demonstrate the superiority of our method across four real datasets, where OSE’s estimation error is decreased by up to 78.5% compared with the state-of-the-art methods. The results indicate that the proposed method has good generalizability and reliability for SoH estimation on real-time field data.