Next-Generation Battery Management Systems: Leveraging AI Beyond Traditional Modeling

#battery-management-systems #AI #modeling

--Technical Webinar Series by IEEE Benelux Section Transportation Electrification Chapter--

Topic: Next-Generation Battery Management Systems: Leveraging AI Beyond Traditional Modeling

Speaker: Dr. Farshid Naseri, Associate Professor, Aalborg University

Bio: Dr. Farshid Naseri is an Associate Professor in the Department of Energy at Aalborg University (AAU Energy), Denmark, and the Principal Investigator of the ERC-funded REVIVE project. He received his B.S.E.E. in Control Engineering in 2013, and his M.Sc. and Ph.D. in Electrical Power Engineering in 2015 and 2019, respectively, with a focus on electric vehicle (EV) drivetrains, including motor drives, power electronics, and energy storage. Following his doctorate, Dr. Naseri was awarded multiple competitive fellowships, including from the National Elites Foundation (INEF) and the European Union’s Marie Skłodowska-Curie Postdoctoral Fellowship. During three postdoctoral appointments exclusively devoted to battery research, he developed expertise across the full battery design pipeline—from cell to module to pack—covering testing and characterization, advanced electrochemical modeling, digital twinning, battery informatics (SoX estimation), optimization, sizing, and system integration across TRLs 2–7. His research spans lithium-ion batteries, supercapacitors, lithium-ion capacitors, and solid-state cells. Dr. Naseri has contributed to numerous European and national R&D programs, including HELIOS, HEROES, DeepBMS, S4Mile, iBattMan, and REVIVE, focusing on high-performance battery and power electronics systems for EVs. He has received several prestigious awards, including the Marie Skłodowska-Curie Fellowship (2023–2025), ERC grant (2025), EU Seal of Excellence (2022), and INEF Excellence Award (2020). He is an active member of IEEE Young Professionals, the IEEE Vehicular Technology Society, and the IEEE Industrial Electronics Society, where he has served as an organizer, reviewer, and contributor to multiple conferences and journals. He has also guest-edited for several journals, including Batteries, and serves on the board of the Vehicle Engineering Section in Machines.

SUMMARY: In this talk, we’ll explore the fascinating complexity of lithium-ion batteries in EVs. These systems are nonlinear, time-varying, and degrade over time, making their modeling a significant challenge. Traditional battery management approaches rely on model-based techniques, but as battery behavior constantly shifts, accurate predictions become difficult. This is where we introduce an innovative approach that combines AI with model-driven methods to enhance the accuracy of battery state predictions. This hybrid approach leverages AI to identify patterns and trends in battery behavior that traditional models may overlook, allowing for more effective monitoring and management of battery health. Such advancements are critical not only for prolonging battery life but also for enhancing the efficiency and safety of EVs. I'll also provide a brief overview of key EU-funded projects and the broader European research landscape on battery technologies, showcasing how Europe is investing in this crucial field. Additionally, we’ll discuss hardware-in-the-loop prototyping, which plays a vital role in developing and refining battery management software—from data generation and modeling to software development, integration, testing, and final prototyping.

Contact: Dr. Zian Qin, z.qin-2@tudelft.nl