State of Health Estimation of LiFePO4Battery based on Probability Density Function

Abstract

Battery Management System (BMS) is very important for most of the electric vehicle (EV) and battery energy storage system (BESS), BMS can guarantee the safety, operation and even the life of the battery system. Checking and controlling the status of battery within their specified safe operating conditions is exactly the major function of BMS. The state of health (SOH) is a critical parameter of a Li-ion battery, an accurate on-line estimation algorithm of the SOH is important for forecasting the EV driving range and BESS power dispatching. A widely used method to estimate SOH is based on battery capacity, due to the uncertainty, including unit-to-unit variation, measurement noise, operational uncertainties, and model inaccuracy, it's difficult to estimate the SOH by using battery capacity. In this paper, a new method, probability density function to estimate the capacity of LiFePO4 battery by analyzing the charge and discharge data is presented. A comparison of the probability density function and differential voltage analysis (DVA) is provided, shows that the mathematical basis of the algorithm and DVA are in agreement, then present the relationship of dQ/dV vs V, synthesize derivation curve of anode and cathode. Further, in order to get the relationship between derivation curve and capacity of LiFePO4 battery over the lifecycle, the peak intensity, peak voltage, peak number and peak shift is analyzed. Finally, by utilizing the actual operation data, experiments and numerical analysis were conducted, show that this capacity estimation algorithm based on probability density function has better robust performance of the practical application of LiFePO4 battery, and the superiority of this method is verified.