Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System

Abstract

Electric Vehicles (EVs) reliance on batteries, which currently have lower energy and power densities than liquid fuels and are prone to aging and performance degradation over time, restricts their mainstream adoption. With applications like electric vehicles and grid-scale energy storage, effective management of lithium-ion batteries is a vital enabler for a low-carbon future. Monitoring the battery’s condition of health and charge over the lifetime of an EV is, therefore, a highly pertinent issue. Battery Management Systems (BMS) are used during the operation of EVs to monitor, estimate and control battery states to ensure that batteries can function effectively and safely. Additionally, the materials composition, system design, and operating circumstances substantially impact a battery’s usable life, making it more challenging to govern and maintain battery systems. This work proposes the structure of a battery digital twin-based battery for the electronic vehicle, which has the potential to enhance BMS situational awareness greatly and enable the optimal functioning of battery storage units. Digitalization and Artificial Intelligence (AI) present an opportunity and offer a Digital Twin (DT) of the EV battery as a solution to the difficulty of onboard computation for the incremental State Of Health (SOH) and State Of Charge (SOC) based on Extreme Gradient Boost (XGBoost) and Extended Kalman Filter (EKF) to predict the state estimate. The battery’s condition has been determined by using the EKF, which can provide vital information for maintenance. The battery’s usable life can be extended with an accurate estimate of the SOC to continue then a learning-based prediction approach to gauge the battery’s health state is suggested in order to increase battery life. A SOC model is frequently retrained to depict the effects of aging, and a SOH model is often performed to foretell the reduction in the highest battery capacity. According to a result, DT models are useful for managing batteries, and full life cycle statistics are important for planning the battery’s upgrade path.