In the realm of battery charging, several complex aspects demand meticulous attention, including thermal management, capacity degradation, and the need for rapid charging while maintaining safety and battery lifespan. By employing the imitation learning paradigm, this manuscript introduces an innovative solution to confront the inherent challenges often associated with conventional predictive control strategies for constrained battery charging. A significant contribution of this study lies in the adaptation of the Dataset Aggregation (DAGGER) algorithm to address scenarios where battery parameters are uncertain, and internal states are unobservable. Results drawn from a practical battery simulator that incorporates an electrochemical model highlight substantial improvements in battery charging performance, particularly in meeting all safety constraints and outperforming traditional strategies in computational processing.

Espin, J., Zhang, D., Toti, D., Pozzi, A., Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging, in Proceedings of the American Control Conference, (Toronto, Canada, 10-12 July 2024), Institute of Electrical and Electronics Engineers Inc., Stevenage Hertfordshire 2024:<<PROCEEDINGS OF THE AMERICAN CONTROL CONFERENCE>>, 2224-2229. [10.23919/ACC60939.2024.10644739] [https://hdl.handle.net/10807/292436]

Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging

Toti, Daniele;Pozzi, Andrea
2024

Abstract

In the realm of battery charging, several complex aspects demand meticulous attention, including thermal management, capacity degradation, and the need for rapid charging while maintaining safety and battery lifespan. By employing the imitation learning paradigm, this manuscript introduces an innovative solution to confront the inherent challenges often associated with conventional predictive control strategies for constrained battery charging. A significant contribution of this study lies in the adaptation of the Dataset Aggregation (DAGGER) algorithm to address scenarios where battery parameters are uncertain, and internal states are unobservable. Results drawn from a practical battery simulator that incorporates an electrochemical model highlight substantial improvements in battery charging performance, particularly in meeting all safety constraints and outperforming traditional strategies in computational processing.
2024
Inglese
Proceedings of the American Control Conference
2024 American Control Conference, ACC 2024
Toronto, Canada
10-lug-2024
12-lug-2024
Institute of Electrical and Electronics Engineers Inc.
Espin, J., Zhang, D., Toti, D., Pozzi, A., Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging, in Proceedings of the American Control Conference, (Toronto, Canada, 10-12 July 2024), Institute of Electrical and Electronics Engineers Inc., Stevenage Hertfordshire 2024:<<PROCEEDINGS OF THE AMERICAN CONTROL CONFERENCE>>, 2224-2229. [10.23919/ACC60939.2024.10644739] [https://hdl.handle.net/10807/292436]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/292436
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