Power Capability Prediction of Lithium-Ion Batteries Using Physics-Based Model and NMPC

Yang Li, Zhongbao Wei, Mahinda Vilathgamuwa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A model-based battery power capability prediction method is reported to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power, but also the internal constraints such as lithium plating and thermal runaway, can be readily taken into account. The online prediction of maximum power is accomplished by formulating and successively solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, an accurate and computationally efficient scheme based on nonlinear model predictive control is designed.

Original languageEnglish
Title of host publication1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350398069
DOIs
Publication statusPublished - 2022
Event1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022 - Kharagpur, India
Duration: 9 Dec 202211 Dec 2022

Publication series

Name1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

Conference

Conference1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
Country/TerritoryIndia
CityKharagpur
Period9/12/2211/12/22

Keywords

  • lithium-ion batteries
  • model predictive control
  • power capability

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