Online Estimation of Internal Resistance of Lithium-ion Batteries using EKF Algorithm

The performance and lifespan of lithium-ion batteries are greatly influenced by two key parameters: internal resistance (IR) and open circuit voltage (OCV). Precise estimation of these parameters is crucial for optimizing the performance of battery management system (BMS). The current parameter identification methods typically assume that the battery OCV remains constant during the observation window. However, this assumption may lead to significant errors in the estimated OCV and resistance. To address this issue, this paper proposes an online estimation algorithm based on the extended Kalman filter (EKF) for real-time estimation of IR and OCV of lithium-ion batteries. The algorithm constructs a dynamic battery model and leverages voltage, current, and other measurement data to achieve high-precision state estimation and accurate tracking of operational parameters. Experimental results demonstrate that the proposed algorithm enables rapid and accurate estimation of IR and OCV under various operating conditions. It provides real-time observation and reliable technical support for health state assessment and optimization control of lithium-ion batteries.