Battery-management systems (BMS) 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. This lecture will first discuss BMS sensing requirements imposed by these tasks. It will then give an introduction to state-of-art equivalent-circuit-model-based algorithms to estimate the battery pack's operating state: nonlinear Kalman filters for state-of-charge, recursive total-least-squares methods for state-of-health, direct computations for state-of-energy, and a bisection method for state-of-power.
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 18+ years. Prof. Plett leads 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 for use with controls 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, a specialization on "Algorithms for Battery Management Systems" on Coursera, 25 U.S. patents in the area of battery controls, and other publications having a total of over 6300 citations.