Implementation of an Intelligent Battery Management System using Fuzzy Logic and Adaptive Neural Networks

The rapid advancements in battery-powered technologies, particularly for electric vehicles (EVs) and renewable energy applications, necessitate the development of intelligent and accurate battery management systems (BMS) to ensure safety, reliability, and extended performance. This paper proposes an advanced framework for predicting the State of Health (SOH) by integrating Fuzzy Logic with Adaptive Neural Networks (ANN). The hybrid approach leverages Fuzzy Logic to address uncertainties in critical battery parameters such as internal resistance, capacity degradation, and temperature, while ANN enhances precision by modeling complex nonlinear relationships among these variables. The system combines the outputs of Fuzzy Logic and ANN dynamically, ensuring robust and adaptive SOH estimation under varying operational conditions. Comprehensive MATLAB simulations demonstrate the effectiveness of the proposed framework, achieving superior accuracy with a mean squared error (MSE) and robust adaptability to diverse battery aging profiles. Results highlight the hybrid system's significant performance improvements over standalone methods, showcasing its potential for real-world implementation in next-generation BMS. This novel methodology lays the groundwork for future research and practical deployment, enhancing battery lifespan and system efficiency in sustainable energy technologies.