A Comprehensive Review on Optimization and Artificial Intelligence Algorithms for Effective Battery Management in EVs

Abstract

Globally, research on battery technology to be utilized in electric vehicle applications is rapidly expanding to solve the problems of greenhouse emissions and global warming. The efficiency of Electric Vehicles (EVs) are highly depends on the precise measurement of significant factors, as well as on the appropriate operation and analysis of the battery storage system. Unfortunately, inadequate battery storage system monitoring and safety measures can result in serious problems such battery over-charging, over-discharging, overloading, imbalanced cells, heat explosion, and combustion hazards. The quantity of a battery’s energy in respect to its capability is described to as the state of charge (SOC). SOC is measured in percentage points and is estimated as the distance between the battery’s maximum possible output and its average energy at a specific time under the same issues. State of health (SOH) is the evaluation of a battery’s maximum charge amount compared to its starting value when it is first discharged. SOH is calculated using percentage points as its variables. An efficient battery management system, which includes tailored to the content, charging-discharging control, thermal regulation, battery protection and security, is essential for addressing these issues. This paper’s objective is to provide a thorough analysis of various intelligent control strategies and battery management system methodologies used in the EV applications. Also, the review assesses the smart algorithms for estimating battery state in terms of their attributes, customization, arrangement, accuracy, benefits, and drawbacks. Finally, prospects and directions for developing a successful sophisticated algorithm and controller are presented in order to create an enhanced battery management system for applications in future, eco-friendly EV technology.