Battery Management System Health Monitoring Using Artificial Intelligence

A battery-management system solves these typical limitations and improves the efficiency of battery modular handling. A battery state of health (SOH) examination is a vital step in preventing potential battery problems and ensuring safety. This paper presents a systematic literature analysis on battery health estimate methodologies in order to provide beneficial data to the scholarly field. Thus, the incorporation of innovations and emerging technologies allows for the implementation of continuous surveillance with an emphasis on Industry 4.0. Employing Artificial Intelligence technologies, the present research investigates several techniques and methods for calculating the SOH of Li-ion battery. The study demonstrated that AI approaches such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy Logic are capable of correctlyenvisaging the status of Li-ion batteries. At last, the paper makes important suggestions, like integrating edge computing with AI prototype-based products.