State of Charge Estimation for Lithium Polymer Battery using Kalman Filter under Varying Internal Resistance

Authors:
Publisher website
Google Scholar
Abstract
Battery Management System (BMS) is necessary in order the batteries to work properly. One important item in BMS is State of Charge (SOC), which indicates charge level of the batteries and belongs to internal states of battery. Practically speaking, the internal states of battery can not be measured directly. Thus, SOC has to be estimated. Moreover, it is possible that its internal resistance changes while the battery is being used. In this paper, nominal value of the internal resistance is acquired from parameter identification by utilizing experimental setup for lithium polymer battery. Subsequently, through simulation, the internal resistance is set to vary around its nominal value and Kalman Filter is utilized to estimate the SOC. The estimated SOC from Kalman Filter is then compared to one from Observer and Coulomb Counting under the same condition for verification purpose. Simulation verifies that Kalman Filter performs better than Observer and Coulomb Counting for SOC estimation under varying internal resistance.

This publication has 19 references indexed in Scilit: