The course focuses on modeling and estimation methods as necessary tools to extract the full potential from Lithium-ion batteries, specifically used in electrified vehicles. The complex nature of a battery system requires that a physics-based approach, in the form of electrochemical models, be used as a modeling platform to develop system-level control algorithms to allow designer to maximize batteries performance and longevity while guaranteeing safety operations. In this course, we will cover 1) first-principles methods to model battery dynamics, 2) electrochemical and control-oriented models, 3) estimation algorithms for real-time application. A formal exposure to state space analysis and estimation of dynamical systems will be given. Previously ENERGY ENERGY 294. Prerequisites: Equivalent coursework in linear systems and control. Prior working knowledge of Matlab/Simulink tools is assumed.
3 units · Letter or Credit/No Credit
The course focuses on modeling and estimation methods as necessary tools to extract the full potential from Lithium-ion batteries, specifically used in electrified vehicles. The complex nature of a battery system requires that a physics-based approach, in the form of electrochemical models, be used as a modeling platform to develop system-level control algorithms to allow designer to maximize batteries performance and longevity while guaranteeing safety operations. In this course, we will cover 1) first-principles methods to model battery dynamics, 2) electrochemical and control-oriented models, 3) estimation algorithms for real-time application. A formal exposure to state space analysis and estimation of dynamical systems will be given. Previously ENERGY 294. Prerequisites: Equivalent coursework in linear systems and control. Prior working knowledge of Matlab/Simulink tools is assumed.
Offered in Winter 2026 at Stanford University.