Publication

Abstract : Electro-thermal modeling of energy storage systems plays a crucial role in enhancing performance, safety, and lifespan. This study presents a comprehensive approach by integrating multiple modeling techniques into a unified framework using MATLAB. Specifically, the proposed model combines an equivalent circuit model (ECM) to capture the electrical characteristics and a lumped thermal model to evaluate heat dynamics within the system. The ECM accurately predicts key electrical parameters such as state of charge (SOC), open-circuit voltage, and internal resistance, while the thermal model analyzes temperature variations, heat generation, and dissipation. The methodology involves parameter extraction using experimental data and model validation against real-world scenarios. The electrical and thermal models are coupled to achieve a holistic representation of the battery behavior under different operating conditions. The study employs MATLAB's built-in battery modeling tools to improve computational efficiency, reducing simulation time by approximately 25% compared to conventional methods. Key findings demonstrate that the integrated model enhances prediction accuracy, with SOC estimation error reduced to approximately 1%, aligning with findings from similar research where SOC estimation errors were maintained within ± 1%. The proposed framework also improves thermal management strategies by identifying critical overheating conditions and suggesting optimized cooling techniques. Compared to existing models, our approach offers better scalability, faster computation, and higher fidelity in real-time applications. This research provides significant contributions to the design and optimization of energy storage systems, particularly in electric vehicles and renewable energy applications. The insights derived from this study can aid in the development of advanced battery management systems, ensuring improved safety, reliability, and operational efficiency. The results validate the effectiveness of the proposed methodology, making it a valuable tool for researchers and engineers working on next-generation energy storage solutions.