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State of Charge (SoC) estimation of battery plays a key role in strategizing the power distribution across the vehicle in Battery Management System. In this paper, a model for SoC estimation using Extended Kalman Filter (EKF) is developed in Simulink. This model uses a 2nd order Resistance-Capacitance (2RC) Equivalent Circuit Model (ECM) of Lithium Ferrous Phosphate (LFP) cell to simulate the cell behaviour. This cell model was developed using the Simscape library in Simulink. The parameter identification experiments were performed on a new and a used LFP cell respectively, to identify two sets of parameters of ECM. The cell model parameters were identified for the range of 0% to 100% SoC at a constant temperature and it was observed that they vary as a function of SoC. Hence, variable resistance and capacitance blocks are used in the cell model so that the cell parameters can vary as a function of SoC.

Lithium-Ion batteries are popular for use in Electric vehicle (EV) applications. To improve and understand the use of Lithium-Ion batteries (LIBs) in EV application, present study focused and utilized equivalent circuit models (ECMs). Model parameters are identified using pulse charge and discharge test carried on 20Ah Lithium Iron Phosphate cell. Curve-fitting technique is utilized and detailed procedure to extract model parameters is presented. Models are validated with experimental data of pulse discharge test. Accuracy obtained using 1-RC, 2-RC, 3-RC circuit models is verified and high accuracy of 3RC circuit model can make it act as a battery emulator. Extended Kalman Filter (EKF) is utilized for estimation of State of Charge (SOC) of Lithium Iron Phosphate cell. As per our observation, a good accuracy with low computational burden can be achieved with 1RC model parameters.

Various vehicle manufacturers are launching electric vehicles, which are more sustainable and environmentally friendly. The major component in electric vehicles is the battery, and its performance plays a vital role. Usually, the end of life of a battery in the automobile sector is when the battery capacity reaches 80% of its maximum rated capacity. The capacity of a lithium-ion cell declines with the number of cycles. So, a semi-empirical model is developed for estimating the maximum stored capacity at the end of each cycle. The parameters considered in the model explain the changes in battery internal structure, like capacity losses at different conditions. The capacity estimated using the semi-empirical model is further taken as the inputs for estimating capacity using the Artificial Neural Network (ANN) and Machine Learning (ML) techniques i.e., Linear Regression (LR), Gaussian Process Regression (GPR), Support Vector Machine methods (SVM).