Battery State-of-Health (SOH) Estimation

Module 1 introduces the primary degradation mechanisms in lithium-ion cells, covering negative and positive electrode aging processes, sensitivity of cell voltage to changes in equivalent series resistance (ESR) and total capacity, and the tradeoffs between joint and dual estimation of state and parameters.

Module 2 delves into total-least-squares battery-cell capacity estimation, exploring the limitations of ordinary least squares, computational efficiency, weighted total-least-squares solution, confidence intervals, and benchmark solutions.

Module 3 focuses on simplified total-least-squares battery-cell capacity estimates, addressing cases with proportional uncertainties, computational efficiency, defining geometry for approximate full solution to weighted total least squares, and faded memory.

Module 4 teaches learners how to write code for different total-capacity estimators using Octave, with demonstrations for hybrid electric vehicle (HEV) and battery electric vehicle (BEV) scenarios.

Module 5 covers a Kalman-filter approach to total capacity estimation, including the derivation of methods for parameter estimation, estimation of states and parameters simultaneously, and addressing issues of robustness and speed.

Module 6 involves a capstone project where learners can apply their knowledge in a practical setting, integrating the concepts and techniques learned throughout the course.