CAREER: Intelligent Battery Management with Safe, Efficient, Fast-Adaption Reinforcement Learning and Physics-Inspired Machine Learning: From Cells to Packs

Texas Tech University, Lubbock TX

Investigators

Abstract

Electric vehicles (EVs) are critically important to the decarbonization of the transportation sector. However, major bottlenecks still exist that limit the wider adoption of EVs, including mileage anxiety due to the slow charging speed of batteries and safety concerns associated with battery degradation. To address these challenges, fast charging strategies are needed to reduce charging time and reliable monitoring mechanisms must be developed to provide early prognostics of battery health. This proposal aims to address these challenges by developing intelligent battery management systems with novel reinforcement learning (RL) and machine learning methods. The proposed methodologies will enable safe, efficient, and adaptive fast-charging strategies, as well as reliable and accurate health prognostics for batteries. This program also will advance knowledge on the management of battery packs by considering cell-to-cell interactions and inconsistencies. Both graduate and undergraduate student research will be supported in this project. Research results from this project will be tightly integrated into the existing curriculum and in the creation of a new machine learning course for chemical engineering students. Through integrated research, education, and outreach activities, this project also will train students to use data science knowledge and programming skills to meet future engineering challenges. This project aims to address challenges related to the development of the next-generation intelligent battery management systems. For the optimization of fast-charging protocols, existing methods often depend on overly complex battery electrochemical models, many of which fail to adapt to battery operating conditions. For battery health prognostics, such as capacity estimation and useful-life prediction, current methods often require expensive manual feature extraction. Moreover, cell-to-cell interactions and inconsistencies must be considered for fast-charging and health prognostics of battery packs. This research will address these knowledge gaps by studying: (1) deep RL-based methods, based on safe, hierarchical, and meta RL, to enable safe and adaptive fast-charging protocols; (2) efficient and physics-informed transformer-based health prognostics that incorporate battery aging physics for capacity estimation and lifetime prediction; and (3) the extension of proposed methods to battery packs while considering the inconsistencies among individual cells. Both open-source battery simulation platforms and battery experimental testbeds will be employed to validate the proposed methods. Software developed in implementing these algorithms will be made publicly available for a broader audience to further advance research in this field and to educate next-generation battery engineers. Both graduate and undergraduate student research will be supported in this program. Outreach activities, such as incorporating research findings of this project into the chemical engineering curriculum, educating K-12 students about decision-making, and training college students about programming, will widely cultivate a “data thinking” mindset across the entire STEM pipeline. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.