Learning physics based models of Lithium-ion Batteries. Issue 3 (2021)

• Record Type: Journal Article
• Authors: Mckay, Maricela Best; Wetton, Brian; Gopaluni, R. Bhushan
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 3(2021)
• Page Start: 97
• Page End: 102
• Publication Date: 2021
• Subjects: Time series modelling -- Nonlinear model reduction -- Application of power electronics -- Optimal operation -- control of power systems -- Real time simulation -- dispatching -- Modeling -- simulation of power systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.08.225
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Lithium-ion (Li-ion) batteries are increasingly pervasive and important in daily life. We present a surrogate modeling approach that uses synthetic data generated by an electrochemical model to approximate Li-ion battery dynamics using a Deep Neural Network. Elechtrochemical models are needed to describe high current operation but are computationally costly. As an initial study, we prototype our approach for the Single Particle Model. We use a battery use-cycle and observations of concentrations and voltages to predict future battery behavior. Given only the use cycle and knowledge that the battery is fully charged, the surrogate model can accurately forecast observations of Li-ion concentrations and voltages for the entire use cycle, as well as give a window of time for battery depletion.