Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries
The increasing computational complexity of Model Predictive Control (MPC) in battery systems limits its practical adoption, despite its potential for optimizing performance under dynamic operating conditions. To address this challenge, this study introduces an Artificial Neural Network-based MPC fra...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/4/231 |
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| author | Joris Jaguemont Ali Darwiche Fanny Bardé |
| author_facet | Joris Jaguemont Ali Darwiche Fanny Bardé |
| author_sort | Joris Jaguemont |
| collection | DOAJ |
| description | The increasing computational complexity of Model Predictive Control (MPC) in battery systems limits its practical adoption, despite its potential for optimizing performance under dynamic operating conditions. To address this challenge, this study introduces an Artificial Neural Network-based MPC framework (MPCANN) tailored for VTC6 3Ah lithium-ion cells, aiming to reduce computational burdens while retaining predictive accuracy. The framework synergizes MPC’s predictive capabilities with the daptive learning of Artificial Neural Network (ANN) by training the ANN offline using MPC-derived input–output data. Validation against prior MPC results demonstrates MPCANN’s ability to replicate MPC behavior across temperatures, achieving strong alignment in current and temperature predictions. While state of charge (SoC) estimation accuracy requires refinement at elevated temperatures, the framework reduces computation time by 94% compared to traditional MPC, highlighting its efficiency. These results underscore MPCANN’s potential to enable real-time implementation of advanced battery control strategies, offering a pathway to balance computational efficiency with performance in adaptive energy systems. |
| format | Article |
| id | doaj-art-ebbfb54dbfd943b38a1f00288cbe6e60 |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-ebbfb54dbfd943b38a1f00288cbe6e602025-08-20T02:18:21ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-04-0116423110.3390/wevj16040231Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion BatteriesJoris Jaguemont0Ali Darwiche1Fanny Bardé2Solithor, 3800 Sint-Truiden, BelgiumSolithor, 3800 Sint-Truiden, BelgiumSolithor, 3800 Sint-Truiden, BelgiumThe increasing computational complexity of Model Predictive Control (MPC) in battery systems limits its practical adoption, despite its potential for optimizing performance under dynamic operating conditions. To address this challenge, this study introduces an Artificial Neural Network-based MPC framework (MPCANN) tailored for VTC6 3Ah lithium-ion cells, aiming to reduce computational burdens while retaining predictive accuracy. The framework synergizes MPC’s predictive capabilities with the daptive learning of Artificial Neural Network (ANN) by training the ANN offline using MPC-derived input–output data. Validation against prior MPC results demonstrates MPCANN’s ability to replicate MPC behavior across temperatures, achieving strong alignment in current and temperature predictions. While state of charge (SoC) estimation accuracy requires refinement at elevated temperatures, the framework reduces computation time by 94% compared to traditional MPC, highlighting its efficiency. These results underscore MPCANN’s potential to enable real-time implementation of advanced battery control strategies, offering a pathway to balance computational efficiency with performance in adaptive energy systems.https://www.mdpi.com/2032-6653/16/4/231battery modelingfast-chargingMPCNMC-based technologythermal behavior |
| spellingShingle | Joris Jaguemont Ali Darwiche Fanny Bardé Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries World Electric Vehicle Journal battery modeling fast-charging MPC NMC-based technology thermal behavior |
| title | Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries |
| title_full | Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries |
| title_fullStr | Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries |
| title_full_unstemmed | Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries |
| title_short | Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries |
| title_sort | model predictive control using an artificial neural network for fast charging lithium ion batteries |
| topic | battery modeling fast-charging MPC NMC-based technology thermal behavior |
| url | https://www.mdpi.com/2032-6653/16/4/231 |
| work_keys_str_mv | AT jorisjaguemont modelpredictivecontrolusinganartificialneuralnetworkforfastcharginglithiumionbatteries AT alidarwiche modelpredictivecontrolusinganartificialneuralnetworkforfastcharginglithiumionbatteries AT fannybarde modelpredictivecontrolusinganartificialneuralnetworkforfastcharginglithiumionbatteries |