A thermal-aware and self-adapting method for accurate state of charge estimation in lithium-ion batteries

Accurate state-of-charge (SOC) estimation remains a key challenge for lithium-ion batteries (LIBs) operating under varying thermal and load conditions, particularly in electric vehicles and grid-scale energy storage systems. Conventional model-based estimators such as EKF and UKF typically assume fixed parameters and neglect coupled electro-thermal dynamics, which degrades estimation accuracy under temperature fluctuations and aging effects. To address these limitations, this paper proposes a Thermal-Aware Hierarchical Self-Tuning Square Root Unscented Kalman Filter (TA-HST-SRUKF) for real-time SOC estimation. The proposed framework explicitly incorporates a lumped thermal model into the SOC estimation loop and employs a hierarchical self-tuning mechanism to adapt process and measurement noise covariances online. In addition, the square-root formulation enhances numerical stability and reduces computational burden, making the approach suitable for embedded battery management systems. Compared with conventional EKF- and UKF-based methods, the proposed TA-HST-SRUKF demonstrates improved robustness and accuracy across a wide temperature range and varying operating conditions. Simulation results confirm lower estimation errors and better adaptability without increasing computational complexity. The proposed method provides an effective and practical solution for highaccuracy SOC estimation in thermally dynamic and aging-prone battery applications.