Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Abstract

Lithium‐ion batteries (LIBs) have been dominating the markets of electric vehicles and grid energy storage. Accurate monitoring of battery health status has been one of the most critical challenges of the battery industry. Machine learning (ML) has been widely applied to battery health estimation as well as prediction. Here, by investigating the specific features and targets, we comprehensively discuss task‐oriented ML implementation in various application scenarios in the field of battery health. This review explores the tasks assisted by ML based on multi‐level cell degradation. We highlight opportunities and significance of considering the potential feature–target pair during the ML model training to identify more health information about LIBs as well as shed light into designing tasks for new application scenarios.

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

“…In practical operational processes, abundant data theoretically has the potential to unveil behaviors such as leakage, gas generation, cell inconsistency, and lithium plating in batteries. [ 188–191 ] Therefore, a data driven approach is an important part of the battery health management function. Data driven models are unlikely to lead to highly accurate predictions beyond the range of the training data, and the internal characteristics of the battery remain unknown.…”