The accurate estimation of State of Health (SoH) for lithium-ion batteries in real-world electric vehicles (EVs) is critical for ensuring safety, reliability, optimal energy management, and lifecycle sustainability. Unlike laboratory-controlled conditions, real-world EV batteries operate under highly dynamic loads, irregular charging behaviors, diverse environmental conditions, and user-dependent driving patterns. This review provides a comprehensive and structured overview of recent progress in SoH estimation for real-world EV applications. The fundamentals of battery aging mechanisms are summarized, with a clarification of key SoH definitions, metrics, and influencing factors under practical operating conditions. Subsequently, existing methodologies are systematically categorized into physics-based models, data-driven approaches, hybrid/model-assisted frameworks, and uncertainty-aware probabilistic methods, with a focus on their strengths and limitations in real-world deployment. Key challenges, including domain shift, computational constraints, explainability, thermal variability, and data heterogeneity, are critically and systematically analyzed. Finally, future research directions are outlined, emphasizing transfer learning, foundation models, physics-informed AI, self-supervised learning, digital twins, and the need for standardized benchmarks. This review aims to provide researchers and practitioners with a clear roadmap toward reliable, scalable, and trustworthy SoH estimation for next-generation intelligent battery management systems in electric vehicles.
Zhu et al. (Sat,) studied this question.