Modeling of lithium-ion battery and state of charge estimation using MATLAB/SIMULINK

Abstract

Due to a lack of sustainable energy sources and the effects of climate change, the development of electric vehicles (EVs) have accelerated during the past years. One of the major technologies used in EVs, the battery, likewise contributes to the growth of EVs being constrained. Due to its high energy density, extended lifespan, high efficiency, quick charging capability, and minimal self-discharge, lithium ferro phosphate (LiFePO4) is among the lithium-ion batteries that is widely utilised. The state of charge (SOC) assessment of the battery is a crucial characteristic that must be carefully taken into account for battery management systems (BMS). To monitor how the battery pack is being charged and discharged, optimise performance, and increase battery life, it is essential that the SOC estimation be accurate. The SOC calculation gets exceedingly complicated because the battery stores energy in a chemical state that cannot be immediately accessed. Additionally, there are several uncertainties and disturbances that make judging the accuracy of a SOC estimation difficult. This project's objectives concentrate on creating a LiFePO4 battery model utilising an Equivalent Circuit Model (ECM) to forecast SOC using the Unscented Kalman Filter (UKF) technique. Two different types of battery ECM modules with two RC pairs and three RC pairs were studied to compare the model's accuracy. Using the dynamic behaviours of a LiFePO4 battery from an experimental data, the battery ECM parameters were calculated using the MATLAB Parameter Estimation Tool. Constant Discharge Test (CDT), Pulse Discharge Test (PDT), and Random Charge and Discharge Test (RCDT) have all been used in experiments to examine the dynamic properties of the LiFePO4 battery. Battery ECMs with two RC pair and three RC pairs were used to achieve the SOC estimation using the UKF block algorithm in MATLAB. Then, using error analysis tools including Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), the accuracy of the battery ECMs was analysed. The most precise battery ECM was chosen to be used in the UKF method to predict the SOC of a LiFePO4 battery based on the findings of the error analysis. After that, the simulation's result is verified by comparison to the actual SOC using the Coulomb Counting technique. Then, using error analysis like MAE, MSE, and RMSE, the performance of a UKF algorithm was compared to an Extended Kalman Filter (EKF). The most accurate method for estimating value of SOC is chosen depend on the results of the error analysis.