A Systematic Literature Review of State of Health and State of Charge Estimation Methods for Batteries Used in Electric Vehicle Applications

Abstract

In recent years, artificial intelligence and machine learning have captured the attention of researchers and industrialists in order to estimate and predict the state of batteries. The quality of data must be good, and the source of data must be the same for different models’ performance comparisons. The lithium-ion battery is popularly used because of its high energy density and its compact size. Due to the non-linear and complex characteristics of lithium-ion batteries, electric vehicle users have to know about battery health conditions. Different types of state estimation methods are used, namely, electrochemical-based, equivalent circuit model (ECM) based, and data-driven approaches. This paper is a survey of electric vehicle history, different battery chemistries, battery management system (BMS) basics and key challenges and solutions in BMS, and in-depth discussions about other battery state of charge and state of health estimation methods. Research trend analysis, critical analysis of this work, limitations, and future directions of existing works are discussed. This paper also provides information on the open-access available datasets of different battery chemistry for a data-driven approach. This paper highlights the key challenges of state estimation techniques. Knowledge of accurate battery state of charge (SOC) provides critical information about remaining available energy. In comparison, battery state of health (SOH) indicates its current health condition, remaining lifetime, performance, and proper energy management of the electric vehicles.