Title: Enhancing Electric Vehicle Battery Reliability Through AI and Machine Learning Innovations

Abstract:

Advancements in Artificial Intelligence (AI) and Machine Learning (ML) methodologies present transformative potentials for improving electric vehicle (EV) battery reliability. The research talk delves into the intricacies of battery health assessment and anomaly detection, employing AI to predict and mitigate degradation phenomena, thus extending the operational life and safety of EV batteries. By integrating data-driven insights with battery modeling, the study offers novel approaches for real-time battery management systems, enhancing the performance and reliability of EVs. Through extensive experimentation and validation, the findings contribute to the sustainable advancement of electric mobility, emphasizing the critical role of AI in advancing battery technology and fostering the adoption of clean transportation solutions.

Biography:

Dr. Huzaifa Rauf, a distinguished academic with a B.S. and M.S. in Electrical Engineering from FAST-NUCES and LUMS respectively, earned his Ph.D. degree in Electrical Engineering from LUMS, specializing in renewable energy systems and the application of AI and ML to enhance battery storage reliability and efficiency. His doctoral research focused on developing advanced ML algorithms for predicting battery degradation, aimed at extending the lifespan and ensuring the safety of battery systems in electric vehicles and renewable energy storage. Dr. Rauf furthered his expertise as a faculty assistant at the Center for Advanced Life Cycle Engineering in the University of Maryland, College Paark, United States where he worked under with world’s renowned professor in the field of reliability, Prof Micheal Pecht. Currently, Dr Rauf is at the forefront of the USAID eMobility Research and Development Center at LUMS, spearheading projects in electric vehicles, battery energy storage, and motor testing technologies. His relentless pursuit of sustainability and innovation is evidenced by his extensive portfolio of high-impact publications, emphasizing his contributions to energy data analytics, battery safety, and energy management.