Review of Management System and State-of-Charge Estimation Methods for Electric Vehicles

Abstract

Energy storage systems (ESSs) are critically important for the future of electric vehicles. Due to the shifting global environment for electrical distribution and consumption, energy storage systems (ESS) are amongst the electrical power system solutions with the fastest growing market share. Any ESS must have the capacity to regulate the modules from the system in the case of abnormal situations as well as the ability to monitor, control, and maximize the performance of one or more battery modules. Such a system is known as a battery management system (BMS). One parameter that is included in the BMS is the state-of-charge (SOC) of the battery. The BMS is used to enhance battery performance while including the necessary safety measures in the system. SOC estimation is a key BMS feature, and precise modelling and state estimation will improve stable operation. This review discusses the current methods used in BEV LIB SOC modelling and estimation. It also efficiently monitors all of the electrical characteristics of a battery-pack system, including the voltage, current, and temperature. The main function of a BMS is to safeguard a battery system for machine electrification and electric propulsion. The major responsibility of the BMS is to guarantee the trustworthiness and safety of the battery cells coupled to create high currents at high voltage levels. This article examines the advancements and difficulties in (i) cutting-edge battery technology and (ii) cutting-edge BMS for electric vehicles (EVs). This article’s main goal is to outline the key characteristics, benefits and drawbacks, and recent technological developments in SOC estimation methods for a battery. The study follows the pertinent industry standards and addresses the functional safety component that concerns BMS. This information and knowledge will be valuable for vehicle manufacturers in the future development of new SOC methods or an improvement in existing ones.

Short-Term Charging Load Prediction of Electric Vehicles with Dynamic Traffic Information Based on a Support Vector Machine

“…m is the total number of road nodes. T ij is the element in the OD matrix to be solved [19,20]. A a − ij is the probability of passing through section a for vehicles departing from point i to point j.…”