Short circuits (SCs) in electric vehicles (EVs) with lithium-ion battery packs can be precursors to thermal runaway and fire, a significant safety concern. Early detection and quantification of micro-short circuits is a high priority for safety-conscious BMS. SC detection is especially important after an EV crash where the pack may have been damaged. Fast and accurate SC detection for a damaged EV is crucial to ensuring safety and informing decisions of first responders regarding risk mitigation. These decisions include whether to move the damaged EV from the roadway, transport it to a storage facility, store it in close proximity to other vehicles, bring it indoors for repair, charge it, and certify that it is safe to return to the road. This presentation will review the state-of-the-art in SC detection and present future directions for BMS State-of-Safety determination. The focus will be on fast, simple, and reliable SC detection using low order estimators that can be implemented for every cell in the pack without requiring excessive computation. In the case of a crashed EV, approaches will be presented that can be implemented in any EV without prior knowledge of the battery pack, making it a valuable tool for first responders to assess damaged EVs. Algorithms that use cell-to-cell comparisons to extract true SCs from voltage and current noise will be described. Lab, operating EV, and crashed EV data will be used to validate the SC detection algorithms.

Biography: Christopher D. Rahn graduated from the University of Michigan with a B.S. in 1985 and from the University of California, Berkeley with a M.S. in 1986, both in mechanical engineering. After three years as a Research and Development Engineer at Ford Aerospace, he returned to Berkeley and graduated with a Ph.D. in 1992. Dr. Rahn then joined the Department of Mechanical Engineering at Clemson University. In 2000, he moved to the Pennsylvania State University where he is now the J. 'Lee' Everett Professor of Mechanical Engineering, Director of the Mechatronics Research Laboratory, and Co-Director of the Battery and Energy Storage Technology Center. Dr. Rahn's research work on the modeling, analysis, design, and control of mechatronic systems has resulted in four books and book chapters (including Battery Systems Engineering), almost three hundred peer reviewed publications, and several patents. As a Fellow of the American Society of Mechanical Engineers (ASME), Dr. Rahn served as a Technical Editor of an ASME journal and chaired the executive committee of the ASME Design Engineering Division. He has advised over 100 Ph.D., M.S., and B.S. (honors) theses.

As electric vehicle (EV) adoption accelerates worldwide, the need for smarter battery management has never been greater. Traditional battery control strategies focus on balancing state-of-charge (SOC) to maximize immediate range, but they often sacrifice long-term battery health. In this talk, we explore a transformative shift enabled by H-bridge-based cell-level inverters, which unlock unprecedented degrees of freedom in EV battery control. I will present a novel control framework that dynamically optimizes Amp-hour throughput across cells to extend both range and lifetime — without requiring prior knowledge of battery aging models. By strategically unbalancing SOC during charging to achieve state-of-health (SOH) balancing, and intelligently rebalancing during discharge to maximize usable energy, our approach prolongs pack life by up to 30% across diverse chemistries and use cases. The talk will cover the theoretical foundations, optimization algorithm, and simulation results based on real-world EV driving and charging data.

Biography: Scott Moura is the Clare and Hsieh Wen Shen Endowed Distingiushed Professor in Civil & Environmental Engineering, Director of the Energy, Controls, & Applications Lab (eCAL), Berkeley ITS Acting Faculty Director, PATH Faculty Director, and Chair of Engineering Science at the University of California, Berkeley. He received the B.S. degree from the University of California, Berkeley, CA, USA, and the M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 2006, 2008, and 2011, respectively, all in mechanical engineering. From 2011 to 2013, he was a Post-Doctoral Fellow at the Cymer Center for Control Systems and Dynamics, University of California, San Diego and a Visiting Researcher at the Centre Automatique et Systèmes, MINES ParisTech, Paris, France in 2013. His research interests include control, optimization, and AI for batteries, electrified vehicles, and distributed energy resources.

The future of battery management for both automotive and stationary energy storage systems lies in the integration of data, physics, and machine learning. Over the past five years, significant advancements have been made in developing hardware and software infrastructures that enable seamless data collection from the field and cloud-based battery management. This emerging technology has opened up new opportunities across industries. In this talk, I will explore the potential of machine learning applications in battery management, highlight the opportunities we've encountered, and discuss the future challenges we must address in collaboration with industry leaders.

Biography: Prof. Dr.-Ing. Weihan Li is a Junior Professor in Artificial Intelligence and Digitalization for Batteries at RWTH Aachen University. Prof. Li's research focuses on leveraging artificial intelligence and physics to drive digitalization in battery technology. With over 50 peer-reviewed journal articles, numerous patents, and more than €7M in research funding since 2022 from EU, BMBF, BMWK, BMDV, and industry, his work has garnered international recognition. Prof. Li has also received prestigious honors, including the Clarivate Highly Cited Researcher 2024, the BMBF BattFutur Starting Grant (€2M+), the German Thesis Award from the Körber Foundation, the Reichart Prize, the vgbe Innovation Prize, the Battery Young Research Award, the Umbrella Award, and the RWTH Innovation Award, etc.