Bias-Compensated State Estimation Algorithm for Lithium Iron Phosphate Batteries with Flat OCV-SOC Curves

The estimation of the state of charge (SOC) and state of health (SOH) for lithium-ion batteries is essential to ensure their safety and reliable operation. Although the current model-based estimation algorithms can mitigate the impact of measurement noise, they often struggle to address voltage measurement bias that can significantly undermine the estimation accuracy. This challenge is particularly pronounced in the case of Lithium Iron Phosphate (LFP) batteries that exhibit flat open-circuit voltage curves within the middle SOC range, rendering them highly susceptible to bias and noise. In this work, the effect of voltage measurement bias on the joint SOC/SOH estimation is quantitatively analyzed. Moreover, a bias-compensated algorithm is proposed based on the Extended Kalman Filter (EKF) to achieve accurate and robust state estimation for LFP batteries by estimating and compensating the bias in voltage measurements. Simulation results demonstrate the effectiveness of the proposed approach, which outperforms the conventional method that continuously estimates states without considering the effects of voltage measurement bias. Finally, the proposed algorithm is also experimentally verified, further validating its real-world applicability and advantages.