Hardware implementation of EKF based SOC estimate for lithium-ion batteries in electric vehicle applications

In the rechargeable batteries lithium-ion batteries are now being utilized extensively in a variety of industries, including electric vehicles, drones, and portable electronics. When it comes to such batteries, it is extremely challenging to accurately monitor the state of charge (SOC). In this instance, the EKF method has been implemented with software and hardware demonstration, and the measured value and estimated value have both achieved an error that is within 2% of each other. As a result of executing the static capacity test under dV/dt with the discharging current set to constant and hybrid pulse power characterization test, the figures that are becoming available are being acquired. The approach of optimization assigns a SOC of 90% and 10% for two reference points in the V_cell equation. Additionally, the technique updates the electrical model of the cell by using the derivative of the terminal voltage that was recorded for the cell. With the use of the covariance matrices in the Extended Kalman Filtering equations, the SOC of the battery may be reliably predicted with a level of accuracy that exceeds 98% when compared to conventional techniques of estimation such as coulomb counting. As part of this research, an adaptive model approach for evaluating the state of charge of Li-ion batteries that are becoming older is being developed.