This two-part talk will examine two problems in the battery management systems domain. The first problem is the optimization of battery test trajectories for parameter identifiability. There is an extensive existing literature showing that electrochemical batteries suffer from poor identifiability, and proposing different trajectory optimization algorithms for improving identifiability. The ideas in this literature have been validated experimentally for multiple battery chemistries, including both the lithium-ion and lithium-sulfur chemistries. This talk pushes this topic further by examining an interesting dilemma: when battery parameter identifiability is poor, it is difficult to quantify, and therefore difficult to optimize, especially for machine learning-based battery models. The talk will examine a new approach for addressing this challenge. The second problem is ensuring battery safety, especially in the context of cascading thermal runaway events. Control barrier function theory provides a rigorous foundation that can be used for battery temperature control, with the goal of preventing thermal runaway. However, once thermal runaway ensues in a given battery cell, the substantial resulting heat generation makes it futile to attempt to stop it in that specific cell. This creates a need for battery pack safety control algorithms that gracefully degrade from focusing on thermal runaway prevention to focusing on stopping thermal runaway propagation once it ensues. The talk will examine a novel mathematical approach for achieving such graceful degradation in the context of barrier function-based battery thermal safety control.

Biography: Hosam K. Fathy is currently a Mechanical Engineering at The University of Maryland. His research focuses on the optimal control and estimation of both healthcare and energy systems. This includes electrochemical battery control, with a focus on both lithium-ion and lithium-sulfur batteries.

Estimation of battery parameters are important for ensuring the fidelity of battery models and the efficacy of model-based battery management. Estimation typically involves 3 basic elements, i.e. model, algorithm, and data (e.g. current, voltage, and temperature measurements). We have been interested in exploring how the structure of measurement data affects the accuracy of estimation. In a series of works, we first derive the general form for the estimation error under the common least squares formulation, which reveals fundamentally how errors are induced by system uncertainties, including parameter mismatch, random measurement noises, and unmodeled dynamics. The results give insights and interpretation on the criteria for evaluating the quality of data, including the popular Fisher information, and also inspire new ones that relate to the propagation of uncertainties. We then proceed to explore the optimization of data to improve the accuracy of estimation, including design of input excitation to generate data when there is input control authority, and selection of data from existing database or stream when lack thereof. The efforts lead to the discovery of unique optimal current excitation patterns for estimating different battery electrochemical parameters. It is also found that the input optimization problem suffers from an intrinsic challenge caused by parameter uncertainty. Different approaches have been pursued to address the issue, including reinforcement learning, and a recently proposed nondimensionalization-based reformulation technique.

Biography: Xinfan Lin is currently an Associate Professor with the Department of Mechanical and Aerospace Engineering at the University of California, Davis, since 2017. He received his B.S. and M.S. degrees in Automotive Engineering from Tsinghua University, Beijing, China in 2007 and 2009, and Ph.D. in Mechanical Engineering from University of Michigan in 2014. Prior to his appointment at UC Davis, he was a research engineer at the Ford Motor Company from 2014 to 2016. His research interests include dynamic systems modeling, estimation, and control, data analytics, and machine learning with applications in energy, automotive, and aerospace systems. He is a recipient of the NSF CAREER Award (2021), LG Global Innovation Award (2019), and LG Battery Innovation Award (2017). His research has been funded by NSF, Office of Naval Research (ONR), NASA, California Climate Action Initiative, and industry. He has also been serving in different positions in the ASME Energy Systems Technical Committee (ESTC) since 2018, including Secretary, Publicity Chair, and Award Chair.

Extreme fast charging (XFC), which enables batteries to charge up to 80% in just 10 minutes, is vital for the wider adoption of electric vehicles (EVs) using lithium-ion (Li-ion) batteries. The ideal XFC should support rapid charging at rates between 4C to 6C while minimizing capacity degradation. Present methods to achieve XFC include reducing electrode thickness and increasing porosity, but these often reduce energy density, which is crucial for long-mileage EVs. Consequently, research is shifting towards developing high-performance electrolytes that improve mass transport and charge transfer within batteries, addressing XFC challenges. However, it is time-consuming due to the vast types of electrolyte materials and extensive testing required. Moreover, while many studies focus on enhancing ionic conductivity, they often overlook other crucial parameters like the lithium-ion transference number and diffusion coefficient. Research shows that conductivity is not the sole determinant of electrolyte transport ability; instead, other parameters often play a larger role, such as the Li-ion transference number. To address these limitations, this talk will introduce a novel learning framework to streamline the development of efficient electrolytes by optimizing all key properties, thus enhancing the battery’s charging capabilities while alleviating capacity degradation. Specifically, Bayesian optimization, i.e., a widely employed active learning method designed to efficiently pinpoint optimal solutions in high-cost targets, is used to explore high-performance electrolytes from diverse combinations of lithium salts and solvents to mitigate polarization, thereby improving fast charging performance.

Biography: Dr. Ziyou Song is an Assistant Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS). He earned his bachelor’s degree with honors and Ph.D. degree with the highest honors in Automotive Engineering from Tsinghua University, China, in 2011 and 2016, respectively. After graduation, he served as a Research Scientist at Tsinghua University from 2016 to 2017, and a Research Fellow at the University of Michigan, Ann Arbor, from 2017 to 2019, where he was also an Assistant Research Scientist and Lecturer from 2019 to 2020. Before his tenure at NUS, Dr. Song worked as a Battery Algorithm Engineer at Apple, Cupertino, US. In this role, he was responsible for overseeing battery management systems for audio products such as AirPods Pro 2. Dr. Song's research focuses on modeling, estimation, optimization, and control of energy storage systems, especially for the electrified transportation sector. Dr. Song has received several paper awards, including Automotive Innovation Best Paper Award, Applied Energy Highly Cited Paper Award, Applied Energy Award for Most Cited Energy Article from China, NSK Outstanding Paper Award of Mechanical Engineering, and IEEE VPPC Best Student Paper Award. Dr. Song serves as an Associate Editor and Editorial Member for IEEE Transactions on Transportation Electrification, IEEE Transactions on Intelligent Vehicles, Applied Energy, and eTransportation, among others.

I will summarize how control engineering can make a big difference in the entire battery lifetime, from autonomous mining to precursor synthesis, smart manufacturing, robotic assembly, and by securing their performance in various applications with onboard diagnostics, estimation, prognostics, and all the way to their next lives.

Biography: Dr. Anna G. Stefanopoulou, the William Clay Ford Professor of Technology at the University of Michigan, has served as the Director of the Automotive Research Center, a multi-university U.S. Army Center of Excellence, and the Michigan Energy Institute.
She has mentored and taught a generation of engineers in control of advanced powertrains through classroom, online, and asynchronous courses. She has been an advisor of new curricula, training needs, and research in modeling, estimation, and control for engines, fuel cells, and batteries, with findings documented in a book, 21 US patents, and 400 publications.
She has been recognized by many prestigious awards and is a Fellow of the ASME, IEEE, and SAE. She has served on two US National Academy committees (2015 and 2020) formed upon request by the US Congress to report on vehicle fuel economy standards and the transition to electrification.