Fusion of stress and electrical signals for high-accuracy joint estimation of SOC and SOH in lithium-ion batteries

The state of charge (SOC) and state of health (SOH) are critical parameters in battery management systems (BMS) for lithium-ion batteries. Traditional state estimation methods, which rely primarily on electrical signals such as current and voltage, often fail to capture the complex internal aging characteristics of batteries. This study addresses this limitation by investigating the relationship between battery states and mechanical stress, proposing a novel joint estimation method for SOC and SOH that integrates electrical and mechanical signals to enhance estimation accuracy and robustness. First, the relationship between mechanical stress and SOC under various operating conditions and aging levels is analyzed, and a stress-based SOC estimation model is developed. Concurrently, SOC is estimated using an equivalent circuit model and extended Kalman filtering based on voltage and current signals. The two SOC estimates are then fused using Kalman filtering to improve accuracy. Second, a mapping between battery capacity and stress characteristics is established, enabling a SOH estimation method based on the stress curve during constant-current charging. This method updates the battery capacity in real time for SOC estimation, achieving joint SOC and SOH estimation. Experimental results demonstrate that the proposed method achieves a root-mean-square error of less than 1.3% for SOC estimation across different aging levels and dynamic conditions, along with an SOH estimation error below 2.05%. This study advances battery state estimation by leveraging multi-dimensional signals, significantly improving estimation precision and reliability.