Design and Development of Intelligent Controller for EV BMS Application

Abstract

The necessity for a cutting-edge Battery Management System (BMS) driven by Artificial Intelligence (AI) to guarantee the best possible battery safety, efficiency, and longevity has increased due to the quick uptake of electric vehicles (EVs). Traditional BMS systems use preset algorithms to estimate the State of Health (SOH) and State of Charge (SOC), but these techniques are not flexible enough to adjust to changing battery conditions. By employing predictive modeling and intelligent data analysis to increase the accuracy of SOC/SOH estimates, AI-driven BMS gets beyond these restrictions. Furthermore, AI-based fault detection systems may identify early indicators of battery deterioration, lowering failure risk and enhancing safety. Predictive maintenance techniques and anomaly detection driven by AI guarantee pre-emptive diagnostics, averting unplanned battery failures. Higher efficiency and longer battery life result from the inclusion of AI-enhanced SOC estimation, which optimizes charge cycles and reduces energy losses. Additionally, cloud-based AI analytics greatly enhance performance management by offering remote battery diagnostics, adaptive control, and real-time monitoring. Additionally, self-learning algorithms that continually improve energy distribution and cell balance based on real-world data are implemented by the suggested AI-driven BMS. Preventive maintenance and economical battery use are made possible by AI-based failure prediction models, which enable early identification of possible problems. The solution guarantees data-driven optimization, remote updates, and dynamic charge control by fusing AI with cloud computing and IoT connection. The scalable, AI-powered BMS controller shown in this study improves battery longevity, performance, and dependability, making it a crucial part of next-generation EV technology.