Transfer learning for battery health estimation: a comprehensive meta-analysis of models, strategies, and domain transfer scenarios

Accurate estimation of battery state of health (SOH), capacity, and remaining useful life (RUL) is a cornerstone of reliable battery management systems, yet data scarcity, long aging durations, and strong domain shifts severely limit the generalization capability of conventional data-driven models. Transfer learning (TL) has therefore emerged as a key enabler for scalable battery diagnostics. This paper presents a comprehensive and systematic meta-analysis of 154 peer-reviewed studies on TL-based battery health estimation published between 2019 and 2025, constituting the most extensive synthesis reported to date. The reviewed literature is structured along five orthogonal dimensions: data modalities, model families, transfer learning strategies, domain transfer scenarios, and target variables. Quantitative analyses reveal that recurrent and convolutional architectures dominate early research, while attention-based Transformers and physics-informed hybrids have rapidly gained prominence in recent years. Fine-tuning remains the most widely adopted TL strategy, although explicit domain adaptation and representation-level transfer consistently demonstrate superior robustness under severe domain shifts. Despite notable progress, critical gaps persist, particularly in cross-chemistry, cross-laboratory, and real-world deployment scenarios, as well as in the limited use of physics-aware constraints and meta-learning frameworks. By consolidating fragmented findings into a unified taxonomy and identifying unresolved challenges, this review provides a structured foundation for future research and offers a clear roadmap toward robust, generalizable, and deployable transfer learning-enabled battery diagnostic systems.