Remote SOC and SOH Estimation of High Energy Lithium Batteries Based on Dual-Mode Extended Kalman Filter Algorithm

Lithium battery, as its main power source, needs to ensure the safety of drivers and passengers not only under some complex external conditions, but also under harsh use conditions, even when damaged. At some point throughout this process, it is required to evaluate the status of the battery itself in order to assure safe usage of the battery and to develop a more effective battery management plan. SOC, SOH, and condition of power are all variables that are commonly used to describe the state of a lithium battery (SOP). The ability of the battery to constantly provide or receive power, the remaining service the life cycle of the battery, and the ability of the battery to output or receive power promptly are all described by these three characteristics. In order to effectively evaluate the health status of batteries, this paper proposes a dual-mode extended Kalman filter (EKF) algorithm for the remote estimation of SOC and SOH of high-energy lithium batteries. In the estimating procedure, the open circuit voltage (OCV) is also included as a state variable in the iterative process, which allows for more accurate results. In this paper, the state space equation is established based on the first-order RC equivalent circuit model, and the battery state estimation and parameter identification are completed by using the double EKF (the dual extended Kalman filtering, DEKF) algorithm, resulting in the realization of the estimation of SOC and SOH.