Multisource Domain Adaption for Health Degradation Monitoring of Lithium-Ion Batteries

Battery health monitoring is crucial to reduce unexpected maintenance and realize maximum productivity. However, batteries generally work under dynamic and complex working conditions (i.e., different domains) in industrial applications. The distribution difference exists not only between source and target domain but also between multisource domains, it is a challenging problem to monitor battery health deterioration under multiple working conditions. This study proposes a novel transfer learning-based method, multisource domain adaption network (MSDAN), to address the problem of battery health degradation monitoring under different working conditions. First, a battery health degradation monitoring method is proposed based on multisource transfer learning. The knowledge learned from multiple operational conditions is transferred to the target condition. Second, multisource domain adaption is proposed to address the problem of distribution discrepancy of different domains, where unsupervised feature alignment metric and maximum mean discrepancy (MMD) are utilized to evaluate the difference between target domain and each source domain. Multisource adversarial learning is adopted to guide feature generator to provide domain-invariant features of different domains. Third, a feature generator is proposed to extract common and domain-specific features of different domains. The experimental results illustrate that MSDAN has a better performance of battery health prognostics than other single-source domain adaption methods.