Lithium Ion Battery Modeling and State of Charge Estimation using Kalman Filter based Observer

Fossil fuels are decreasing day by day in this contemporary world. So, the demand for Electric Vehicle increases rapidly. As the battery is the heart of the Electric Vehicle. There is wide competition among the batteries. In the present era, Lithium-Ion Batteries are popularly used in Electric Vehicles due to their higher energy density, longer life span, lower self-discharge and good temperature performance in comparison with other rechargeable batteries. A rechargeable battery pack is managed by the Battery Management System, an electrical device that operates outside the range of a battery’s safe operation. The Battery Management System monitors the key states of batteries. Key states are the State of energy, State of charge, State of power and State of health. The estimation of key states of the battery requires an accurate battery model. Battery modeling is a great technique to predict and improve several fundamental battery characteristics, such as charge level, battery life, and charge-discharge characteristics. And to study the batteries’ performance there is a need for an estimation of the State of Charge. State of Charge estimation plays a vital role and it acts as a fuel gauge in Electric Vehicles. Accurate State of charge estimation can offer an effective result of charging and discharging which helps to operate lithium - Ion Batteries under safe conditions. Hence, MATLAB simulations are done to observe the state of charge using the Kalman filter method and coulomb counting method.