Joint estimation of SOC and SOH of lithium ion battery

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 battery management system to prevent battery overcharge and discharge. In this paper, 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 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. 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 unscented particle filter is used to characterize the battery health state, and finally the joint estimation of battery SOC and 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.