Accurate state-of-charge (SOC) estimation is essential for the safe, efficient, and long-term operation of lithium-ion batteries in electric vehicles and stationary energy storage systems. However, the inherent nonlinearities, temperature dependence, aging effects, and cell-to-cell variations make the direct SOC measurement unfeasible, presenting significant challenges for reliable estimation in practice. This survey provides a critical review of the SOC estimation methods by organizing the literature into three major categories: direct approaches, indirect techniques, and hybrid architectures. For each category, the underlying principles and typical structures are summarized, with a focus on their trade-offs in terms of accuracy, computational complexity, and data requirements. A systematic taxonomy and in-depth discussion of the hybrid SOC estimators are presented, highlighting how combinations of the direct, model-based, and data-driven components can mitigate the individual limitations and improve the overall performance. Building on this foundation, several emerging research directions are also highlighted, including the multi-state joint estimation, fractional-order modelling, and cloud-based and attack-resilient SOC estimation. Overall, this survey aims to provide a guidance on the method selection for specific applications and to identify promising avenues for future researches in advanced battery systems.
Yang et al. (Sat,) studied this question.