Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS)
© 2020 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited. Optimal operation of lithium-ion batteries requires robust battery models for advanced battery management systems (ABMS). A nonlinear model predictive control strategy is proposed that directly employ...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
The Electrochemical Society
2021
|
| Online Access: | https://hdl.handle.net/1721.1/135937 |
| _version_ | 1826194758267043840 |
|---|---|
| author | Kolluri, Suryanarayana Aduru, Sai Varun Pathak, Manan Braatz, Richard D Subramanian, Venkat R |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Kolluri, Suryanarayana Aduru, Sai Varun Pathak, Manan Braatz, Richard D Subramanian, Venkat R |
| author_sort | Kolluri, Suryanarayana |
| collection | MIT |
| description | © 2020 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited. Optimal operation of lithium-ion batteries requires robust battery models for advanced battery management systems (ABMS). A nonlinear model predictive control strategy is proposed that directly employs the pseudo-Two-dimensional (P2D) model for making predictions. Using robust and efficient model simulation algorithms developed previously, the computational time of the nonlinear model predictive control algorithm is quantified, and the ability to use such models for nonlinear model predictive control for ABMS is established. |
| first_indexed | 2024-09-23T10:01:39Z |
| format | Article |
| id | mit-1721.1/135937 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:01:39Z |
| publishDate | 2021 |
| publisher | The Electrochemical Society |
| record_format | dspace |
| spelling | mit-1721.1/1359372023-02-28T20:42:14Z Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS) Kolluri, Suryanarayana Aduru, Sai Varun Pathak, Manan Braatz, Richard D Subramanian, Venkat R Massachusetts Institute of Technology. Department of Chemical Engineering © 2020 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited. Optimal operation of lithium-ion batteries requires robust battery models for advanced battery management systems (ABMS). A nonlinear model predictive control strategy is proposed that directly employs the pseudo-Two-dimensional (P2D) model for making predictions. Using robust and efficient model simulation algorithms developed previously, the computational time of the nonlinear model predictive control algorithm is quantified, and the ability to use such models for nonlinear model predictive control for ABMS is established. 2021-10-27T20:30:02Z 2021-10-27T20:30:02Z 2020 2021-06-09T13:30:58Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135937 en 10.1149/1945-7111/AB7BD7 Journal of the Electrochemical Society Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf The Electrochemical Society The Electrochemical Society |
| spellingShingle | Kolluri, Suryanarayana Aduru, Sai Varun Pathak, Manan Braatz, Richard D Subramanian, Venkat R Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS) |
| title | Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS) |
| title_full | Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS) |
| title_fullStr | Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS) |
| title_full_unstemmed | Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS) |
| title_short | Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS) |
| title_sort | real time nonlinear model predictive control nmpc strategies using physics based models for advanced lithium ion battery management system bms |
| url | https://hdl.handle.net/1721.1/135937 |
| work_keys_str_mv | AT kollurisuryanarayana realtimenonlinearmodelpredictivecontrolnmpcstrategiesusingphysicsbasedmodelsforadvancedlithiumionbatterymanagementsystembms AT adurusaivarun realtimenonlinearmodelpredictivecontrolnmpcstrategiesusingphysicsbasedmodelsforadvancedlithiumionbatterymanagementsystembms AT pathakmanan realtimenonlinearmodelpredictivecontrolnmpcstrategiesusingphysicsbasedmodelsforadvancedlithiumionbatterymanagementsystembms AT braatzrichardd realtimenonlinearmodelpredictivecontrolnmpcstrategiesusingphysicsbasedmodelsforadvancedlithiumionbatterymanagementsystembms AT subramanianvenkatr realtimenonlinearmodelpredictivecontrolnmpcstrategiesusingphysicsbasedmodelsforadvancedlithiumionbatterymanagementsystembms |