Assessing the impact of open-circuit voltage estimation methods on UKF performance for lithium-ion battery SOC and SOH estimation

Accurate estimation of State of Charge (SOC) and State of Health (SOH) in lithium-ion batteries is critical for effective Battery Management Systems (BMS). A key factor in enhancing estimation accuracy is the appropriate selection of the Open-Circuit Voltage (OCV-SOC) model. This study investigates two prevalent methods for OCV modeling: the Low-Current (LC) method and the Incremental Current (IC) method, and evaluates their impact on the performance of the Unscented Kalman Filter (UKF) for SOC estimation and instantaneous internal resistance (R₀) tracking. In this work, SOH is primarily assessed through R₀​, which is adopted as the main degradation indicator under dynamic operating conditions. The dynamic model employed is an Equivalent Circuit Model (ECM) of a lithium-ion battery, with estimations conducted using real-world driving data (Federal Urban Driving Schedule, FUDS). The results demonstrate that the IC method, due to its higher local resolution in the OCV modeling, achieves faster convergence and higher estimation accuracy for SOC, particularly when the initial SOC is subject to error. In contrast, the LC method, despite its experimental simplicity, exhibits significant errors in the early stages of simulation due to excessive smoothing of the OCV curve. Furthermore, a comparison of R₀ trends reveals that the IC method provides a more consistent and stable estimation, whereas the LC method shows greater short-term fluctuations and reduced stability. This difference is attributed to the higher accuracy of the OCV curve in the IC method. Ultimately, the choice of the OCV extraction method directly influences the accuracy and stability of SOC estimation and SOH analysis based on R₀ tracking.