Physics-informed Gaussian Process Regression Model for Battery Management

Sara Kohtz, Pingfeng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The accurate estimation of battery SoH has become a significant challenge due to the wide-spread usage of lithium-ion batteries for high-impact utilization such as electric vehicles and aerospace applications, as well as operation in everyday life: computers, cell phones, and other small electronics. Physics models and data-driven approaches have shown success for this application of health monitoring. However, singular data-driven approaches often require substantial amounts of reliable data and necessitates access to the full history of the system of interest; in addition, purely data-driven techniques, particularly machine learning, can be prone to overfitting. Hybrid methodologies that combine physics models and data-driven approaches have shown some success in recent literature, however these studies are relatively scarce. In this study, a physics-informed machine learning (PIML) technique is employed to estimate SoH of LIBs using only a partial charging segment. Essentially, the machine learning model is trained by generated data from a 1D finite-element physics model. This addresses the problem of requiring an abundance of data to train the model, as the data used to train the model is obtained by simulation. The machine learning model for estimation consists of a nested Gaussian Process Regression (GPR) model, where temperature and current are inputted, and a projected voltage charging curve for each SoH level are outputted; then the most similar curve is chosen for SoH estimation. Overall, the results show the method performs well in terms of accuracy of capacity estimation; and this work provides a new baseline model for general health management applications.

Original languageEnglish (US)
Title of host publicationIISE Annual Conference and Expo 2022
EditorsK. Ellis, W. Ferrell, J. Knapp
PublisherInstitute of Industrial and Systems Engineers, IISE
ISBN (Electronic)9781713858072
StatePublished - 2022
EventIISE Annual Conference and Expo 2022 - Seattle, United States
Duration: May 21 2022May 24 2022

Publication series

NameIISE Annual Conference and Expo 2022


ConferenceIISE Annual Conference and Expo 2022
Country/TerritoryUnited States


  • Gaussian Process Regression
  • Lithium ion batteries
  • Machine learning
  • Physics model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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