【百家大讲堂】第157期：Battery Management System Algorithms using Physics-Based Reduced-Order Models of Lithium-Ion Battery Cells

　　讲座题目：Battery Management System Algorithms using Physics-Based Reduced-Order Models of Lithium-Ion Battery Cells

　　报 告 人：Gregory Plett  （美国能源部电动汽车技术创新教育中心主任）

　　时　 间：2019年1月18日（周五）上午9：00

　　地　 点：中关村校区研究生教学楼101报告厅

　　主办单位：研究生院、机车学院

　　报名方式：登录bob手机在线登陆微信企业号---第二课堂---课程报名中选择“【百家大讲堂】第157期：基于物理的锂离子电池降阶模型的电池管理系统算法”

【主讲人信息】

    Gregory Plett is Professor of Electrical and Computer Engineering at the University of Colorado Colorado Springs.  He received his Ph.D. in Electrical Engineering from Stanford University in 1998 and has conducted research in battery-management topics for the past 17 years.  

    Prof. Plett and his colleague Prof. M. Scott Trimboli jointly lead a team of students who are investigating computationally efficient ways to create and implement reduced-order physics-based models of lithium-ion cells, finding methods to determine the parameter values for these cell models using simple laboratory tests, and making the models adaptive so that they capture the dynamics of the battery cell as it ages. These new methods are intended to push the performance of a battery pack to its physical limits while slowing the rate of degradation.

    Prof. Plett has authored two textbooks on battery modeling and battery management, 24 single-author U.S. patents in the area of battery controls, and other publications having a total of over 5600 citations.

【讲座摘要】   

    Battery-management systems comprise electronics and software designed to monitor the status of a battery pack, estimate its present operating state, and advise the battery load regarding the maximum amount of power that may be sourced or sunk by the load at every point in time while maintaining safety and acceptable battery-pack service life. Present battery-management algorithms base their calculations on empirical equivalent-circuit models of battery cells, which predict input–output behaviors only. Future battery management algorithms will instead use physics-based models of battery cells, which are furthermore able to predict internal cell electrochemical variables. Since it is these internal variables that are the precursors to premature aging, physics-based models are needed to maximize battery-pack performance and battery-pack life simultaneously. However, traditional physics-based models are computationally more demanding than empirical models, which has so far prevented their use in practical battery-management systems.

    This talk will discuss current research at the University of Colorado Colorado Springs to overcome the challenges to using physics-based models in algorithms for battery management. It will give an overview of physics-based models of both ideal-cell dynamics and cell aging, an approach to reduce the computational complexity of the models, methods for determining model parameter values, and state-estimation and controls approaches.