Model Predictive Control Using Physics-Based Models for Advanced Battery Management

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Model Predictive Control Using Physics-Based Models for Advanced Battery Management


Physics-based models (PBM) of lithium-ion batteries can describe internal cell behavior with surprising accuracy.  Recent advances in subspace-based model-order reduction enable these often highly complex, nonlinear electrochemical models to be rendered into forms no more demanding than familiar equivalent circuit models, and therefore make them candidates for embedded battery management schemes.

Model predictive control (MPC) has recently emerged as an effective real-time control strategy that employs a ‘look-ahead’ approach to foresee dynamic behaviors before they happen.   This approach – when coupled with an ability to enforce hard constraints on internal electrochemical variables that are precursors to degradation or unsafe operation – makes MPC particularly appealing for advanced battery management, where safety, lifetime and improved performance are key.

This presentation will review reduced-order modeling of physics-based battery models and illustrate the potential improvements in battery management that may be achieved by combining reduced-order PBM’s of lithium-ion cells with an MPC control strategy. 



  Date and Time

  Location

  Contact

  Registration


  • 1820 E. Big Beaver Rd
  • Troy, Michigan
  • United States 48083
  • Building: Altair Engineering, Inc.
  • Room Number: Main Auditorium
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  • We encourage you to pre-register, so as not to be disappointed, as the Main auditorium  Hall has a fixed capacity. A flyer (suitable for posting to cork boards or physical notice boards is available here)

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  Speakers

Scott Trimboli
Scott Trimboli of University of Colorado, Colorado Springs

Topic:

Model Predictive Control Using Physics-Based Models for Advanced Battery Management

Authors: M. Scott Trimboli and Gregory L. Plett


Presenter: M. Scott Trimboli


Summary:


Physics-based models (PBM) of lithium-ion batteries can describe internal cell behavior with surprising accuracy.  Recent advances in subspace-based model-order reduction enable these often highly complex, nonlinear electrochemical models to be rendered into forms no more demanding than familiar equivalent circuit models, and therefore make them candidates for embedded battery management schemes.


Model predictive control (MPC) has recently emerged as an effective real-time control strategy that employs a ‘look-ahead’ approach to foresee dynamic behaviors before they happen.   This approach – when coupled with an ability to enforce hard constraints on internal electrochemical variables that are precursors to degradation or unsafe operation – makes MPC particularly appealing for advanced battery management, where safety, lifetime and improved performance are key.


This presentation will review reduced-order modeling of physics-based battery models and illustrate the potential improvements in battery management that may be achieved by combining reduced-order PBM’s of lithium-ion cells with an MPC control strategy.


 

Biography:

Professor Trimboli is Assistant Professor of Electrical and Computer Engineering at the University of Colorado, Colorado Springs (UCCS) and is former Director of the Center for Space Studies (CSS) in the National Institute of Space, Science and Security Centers (NISSSC).

His research is conducted in collaboration with Prof. Gregory Plett where the focus is on development of control strategies for the management of high-capacity battery systems such as found in electric vehicles. He is currently UCCS principal investigator (PI) of a multi-year program with the Office of Naval Research (ONR) headed by Utah State University where he leads a team of students investigating the application of model-predictive control to improve the performance and extend the lifetime of lithium ion battery cells. He is also co-PI of an on-going battery modeling and control project with a major automotive company.

Broader research interests include system identification and modeling, as well as robust control. 

Professor Trimboli has designed and introduced a myriad of new graduate level courses including: methods of optimization, model-predictive control, and multivariable control in the frequency-domain.

Education

  • Ph.D. Control Engineering, October 1989, Oxford University, UK
  • M.S. Mechanical Eng. (Guggenheim Fellow), 1981, Columbia University, NY
  • B.S. Engr. Sci., (Distinguished Graduate), 1980, U.S. Air Force Academy, CO

Email:

Address:University of Colorado, , Colorado Springs, Colorado, United States, 80918

Scott Trimboli of University of Colorado, Colorado Springs

Topic:

Model Predictive Control Using Physics-Based Models for Advanced Battery Management

Biography:

Email:

Address:Colorado Springs, Colorado, United States





Agenda

6:30 PM Welcome to all attendees

6:45 PM Talk by Scott Trimboli

7:30 PM Q & A, General Discussions and 360 ViewPoints

7:45 PM Wrap Up



Model Predictive Control Using Physics-Based Models for Advanced Battery Management