AI Summary & Key Takeaways
AI InsightFuture progress depends on transparent validation, uncertain-aware estimation, chemistry transfer, compact edge implementation, multimodal sensing, and auditable life-cycle data under emerging battery regulations.
📌 3 Key Takeaways
Abstract
Lithium-ion battery state of health (SOH) cannot be measured directly and must be inferred from capacity, resistance, voltage, current, temperature, and usage history. This structured narrative review integrates degradation mechanisms with feature-based, model-based, data-driven, and physics-informed/hybrid estimation methods, and evaluates them against validation design, chemistry dependence, uncertainty, computational burden, and on-board feasibility. The literature was updated through targeted searches of Crossref-indexed publisher platforms, IEEE Xplore, ScienceDirect, SpringerLink/Nature, Wiley Online Library, and MDPI; 117 unique publications, institutional reports, and regulatory documents were retained after duplicate removal and relevance screening. Reported errors are treated as study-specific results rather than universal characteristics of method families. The synthesis shows that no single estimator is uniformly superior: feature-based diagnostics provide physical insight but require informative and low-noise operating segments; equivalent-circuit observers are compatible with embedded implementation but depend on model adaptation; data-driven models can achieve low error within their validation domain but require rigorous cross-cell and cross-condition testing; and physics-informed or hybrid approaches offer a promising compromise when their added complexity is justified. Future progress depends on transparent validation, uncertain-aware estimation, chemistry transfer, compact edge implementation, multimodal sensing, and auditable life-cycle data under emerging battery regulations.