Joint Estimation of State of Charge and State of Health of Lithium Ion Battery

Abstract

Abstract Overcharge and discharge of power battery not only increase the battery loss but also lead to fire and other accidents under harsh environmental conditions. Accurate estimation of battery parameters and status is an important reference in the battery management system to prevent battery overcharge and discharge. In this article, the following studies are carried out by focusing on the time separation scale and estimating parameters and state values online based on the improved particle filter: (1) The unscented transform and multi-innovation were applied to the particle filter to optimize the particle distribution and update the status value from the historical information, and the multi-innovation unscented particle filter was formed to estimate the state of battery charge. (2) Considering the influence of parameter variation on the estimation of battery state of charge (SOC). Due to the slow change characteristics of parameters and fast change characteristics of states, the parameters and states are jointly estimated from macro and micro time scales, respectively. The capacity change estimated by the unscented particle filter is used to characterize the battery health state, and finally, the joint estimation of battery SOC and state of health (SOH) is formed; (3) Three different working conditions are used to verify the algorithm. The joint algorithm accurately estimates the real-time changes of SOC and SOH, and the average error of SOC is less than 0.5%, which confirms the high accuracy of the joint algorithm.