State-of-charge for battery management system via Kalman filter

Battery Management System (BMS) requires an indefinite accurate model. With an aging model, the lifetime of a battery can be precisely predicted with respect to the State-of-Charge (SoC) of a battery. The mathematical model in terms of state variables involving smart BMS is presented in this work. The state space model is crucial as an accurate model and is able to represent the complex dynamic behavior of a battery system. A numerical case study is done to verify the model obtained through mathematical derivations by adopting the prominent RC battery model from literature. Furthermore, the well-known Kalman filter (KF) is applied to estimate the SoC of a battery system. With accurate prediction of SoC of battery system, its lifetime could be prolonged, and thereby saving us substantial cost.