An online calibration algorithm of SOC for LiFePO4 battery by using characteristic curve

Abstract

Battery Monitoring is very important for most of the electric vehicle(EV) and battery energy storage system (BESS) since the safety, operation and even the life of the passenger/operator depends on the battery system. Checking and controlling the status of battery within their specified safe operating conditions is exactly the major function of battery management system (BMS). The state of charge (SOC) is a critical parameter of a Li-ion battery, an accurate on-line estimation of the SOC is important for forecasting the EV driving range and BESS power dispatching. A widely used method to estimate SOC is based on coulomb counting, due to the uncertainty, including unit-to-unit variation, measurement noise, operational uncertainties, and model inaccuracy, it's difficult to estimate the SOC by using coulomb counting, an online inference of open-circuit voltage (OCV) is used to calibrate the SOC, however, a relatively flat OCV curve of LiFePO4 will lead to the error of SOC is large. In this paper, an algorithm using the derivation curve (dV/dQ vs Q) to calibrate the SOC on-line is bring out. The SOC estimation has been implemented using coulomb counting and derivation curve methods thereby eliminating the limitation of the stand-alone coulomb counting method. Finally, by utilizing the actual operation data, experiments and numerical table were conducted, show that this SOC calibrate algorithm based on characteristic curve has better robust performance of the practical application of LiFePO4 battery energy storage system.