CAREER: Reconfigurable and Predictive Control with Reinforcement Learning Supervisor for Active Battery Cell Balancing

Oakland University, Rochester MI

Investigators

Abstract

This project will support research that will contribute novel methodologies related to battery electric vehicles, promoting the progress of science and advancing national health and prosperity by enabling greener electric vehicles. Due to battery cell variations, significant amount of electricity in electric vehicle remain unused, which raises concerns on efficiency and sustainability. Active battery cell balancing control aims to address these concerns, but existing methods have limited success in battery electric vehicles due to concerns in real-time constraints and limited sensing capability inherent to many automotive systems. This project supports fundamental research that addresses the major challenges in control of large-scale systems, control fusion, intelligent control, and battery management. The new designs and methodologies will offer a transformative framework in active battery cell balancing control that seamlessly integrate vehicle level information into battery management to improve the energy efficiency and driving range of electric vehicles. This research is synergistic with key societal goals related to developing efficient transportation systems for combating the climate change. Therefore, results from this research will benefit the U.S. economy, life quality, and health. This research involves several disciplines including control theory, reinforcement learning, sensing and estimation, and battery management. The multi-disciplinary approach also facilitates the participation of underrepresented groups in research and positively impacts engineering education and automotive workforce. The active battery cell balancing control is expected to greatly enhance the efficiency of battery electric vehicles, increase the driving range, and improve public acceptance. In pursuit of this goal, four closely integrated research objectives are planned: 1) Develop an efficient cell level estimation framework to estimate cell state-of-charge and capacity under limited measurements without incurring heavy computation; 2) Develop a novel predictive cell balancing control framework to seamlessly integrate vehicle speed preview with fast and robust adaptation to preview errors; 3) Develop a reconfigurable control framework to scale up cell balancing control algorithm for EV batteries without degrading overall balancing control performance; and 4) Evaluate and validate the proposed framework through extensive simulations and experiments. Collectively, advances from these research endeavors are expected to make electric vehicles more acceptable and more affordable, and it will create new computationally-efficiency control mechanism for large scale dynamical systems. 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.