SOC and SOH state estimation of lithium battery based on ffrls and kalman filter

In the energy storage System, the Battery Management System (BMS) is responsible for the charge and discharge control and energy balance control of the lithium battery, and the accuracy of lithium battery state estimation is directly affected by the control effect of BMS. In order to ensure the normal operation of lithium batteries, this paper selects the State of Charge (SOC) and State of Health (SOH) of lithium batteries as the research object and accurately estimates the SOC and SOH of lithium batteries as the research goal. The equivalent circuit model based on lithium battery is established, and the identification model parameters are based on the recursive least squares method with forgetting factor. Based on the Thevenin equivalent circuit model, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to estimate the SOC of the battery, respectively. FFRLS and Double Extended Kalman Filter (DEKF) are used to estimate the ohm internal resistance in the equivalent model based on lithium battery, and then the SOH is estimated.