Intelligent Battery Management System Using Machine Learning and Dynamic Capacitor Techniques

This paper details on the design of an IBMS an artificial neural network and fuzzy systems, dynamic capacitor techniques using variable duty cycle Pulse Width Modulation (PWM) is integrated to enhance battery performance and its lifespan duration. The special part of IBMS architecture is a capacitor circuit, which controls energy storage components by a variable duty cycle of PWM The PWM contains the main elements, including the inductor, capacitor, and switches (S1 and S2). Rich algorithms such as SVM, RF, DT, Bagging, and XGBoost use battery previous performance history data to predict near real-time duty cycles for Battery Management to effect the equalization in the batteries accordingly. The used technique included data gathering and preparation that included voltage, current, SoC and SoH – historical as well as real-time. Simulations in MATLAB/Simulink showed significant improvements: with the screw operating at 20% duty cycle, 95. 5% equalization was done in 500 seconds and at 50% a 30mV voltage difference was acquired in 500 seconds, while at 80% a 2. The condition of the experiment whereby 2mV voltage difference was obtained was done in 125 seconds. These results showed the expected performance to be better than the traditional fixed switched-capacitor designs. In practice, the resource confirmed the focus of the system and its versatility for use in renewable energy systems and electric vehicles. This work provides a solution that is flexible, robust, and capable of being implemented in contemporary energy storage demands and proves the significant enhancement in battery management techniques.