Enhanced Battery Management System and Comparative Analysis using FLC and ANN

Battery Management Systems (BMS) play a vital role in ensuring the reliability and lifespan of batteries, particularly in electric vehicles and renewable energy systems [2]. This project focuses on developing advanced methods for accurately estimating key metrics like State of Charge (SoC) and State of Health (SoH) using MATLAB simulations [1]. Two innovative approaches were implemented: a Fuzzy Logic Controller (FLC) and a Neural Network Controller (NNC) [2]. The FLC effectively handled uncertainties in input data, providing reliable real-time SoC estimation in dynamic and noisy environments [7]. Meanwhile, the NNC utilized a data-driven approach, analyzing voltage, current, and temperature data to deliver precise SoC and SoH predictions, especially with sufficient training data [9]. These findings offer valuable insights for advancing predictive models and adaptive BMS technologies.