globusjournal

The growing reliance on battery-powered systems in electric vehicles (EVs), renewable energy storage, and portable electronics necessitates efficient and reliable battery management. Fault detection in batteries is a critical aspect of ensuring safety, performance, and longevity. Traditional diagnostic methods often fall short in real-time adaptability and accuracy. This paper presents an intelligent approach to battery fault detection using machine learning (ML) techniques. By analyzing real-time data such as voltage, current, temperature, and state of charge (SoC), machine learning algorithms can learn complex patterns and accurately classify various types of faults, including overcharging, thermal runaway, and internal short circuits. The study evaluates supervised and unsupervised ML models, such as Support Vector Machines (SVM), Random Forests, and Neural Networks, for their effectiveness in early fault diagnosis. The proposed method enhances predictive maintenance strategies, reduces operational risks, and contributes to the advancement of smart Battery Management Systems (BMS). This work underscores the potential of integrating intelligent analytics with energy storage systems for safer and more efficient energy solutions.

Keywords: Battery Management System, BMS, Machine Learning, ML, Fault Detection, Predictive Maintenance, Intelligent Diagnostics, Energy Storage, Electric Vehicles.