Lithium-ion batteries are complex systems that require suitable management strategies to work properly, achieve fast charging, mitigate ageing mechanisms and guarantee safety. Among the different model-based charging strategies, the use of predictive control has shown promising results, due to its ability to deal with nonlinear systems subject to safety constraints. However, although many implementations have been proposed in the literature, little attention has been paid to their practical feasibility, which is limited by the high computational cost required online. In this paper, we exploit, for the first time in the batteries field, an approximation of predictive control obtained through the use of a deep neural network. The proposed solution is suitable for real-time battery charging, due to the fact that most of the computational burden is addressed offline. The results highlight the effectiveness of the presented methodology in approximating a standard model predictive control solution.

Pozzi, A., Moura, S., Toti, D., A Neural Network-Based Approximation of Model Predictive Control for a Lithium-Ion Battery with Electro-Thermal Dynamics, Paper, in IEEE International Conference on Control and Automation, ICCA, (Italia, 27-30 June 2022), IEEE Computer Society, Washington, DC 2022:<<... IEEE INTERNATIONAL CONFERENCE ON CONTROL &amp; AUTOMATION>>,2022 160-165. 10.1109/ICCA54724.2022.9831878 [http://hdl.handle.net/10807/214126]

A Neural Network-Based Approximation of Model Predictive Control for a Lithium-Ion Battery with Electro-Thermal Dynamics

Pozzi, A.
Primo
;
Toti, D.
Ultimo
2022

Abstract

Lithium-ion batteries are complex systems that require suitable management strategies to work properly, achieve fast charging, mitigate ageing mechanisms and guarantee safety. Among the different model-based charging strategies, the use of predictive control has shown promising results, due to its ability to deal with nonlinear systems subject to safety constraints. However, although many implementations have been proposed in the literature, little attention has been paid to their practical feasibility, which is limited by the high computational cost required online. In this paper, we exploit, for the first time in the batteries field, an approximation of predictive control obtained through the use of a deep neural network. The proposed solution is suitable for real-time battery charging, due to the fact that most of the computational burden is addressed offline. The results highlight the effectiveness of the presented methodology in approximating a standard model predictive control solution.
Inglese
IEEE International Conference on Control and Automation, ICCA
17th IEEE International Conference on Control and Automation, ICCA 2022
Italia
Paper
27-giu-2022
30-giu-2022
978-1-6654-9572-1
IEEE Computer Society
Pozzi, A., Moura, S., Toti, D., A Neural Network-Based Approximation of Model Predictive Control for a Lithium-Ion Battery with Electro-Thermal Dynamics, Paper, in IEEE International Conference on Control and Automation, ICCA, (Italia, 27-30 June 2022), IEEE Computer Society, Washington, DC 2022:<<... IEEE INTERNATIONAL CONFERENCE ON CONTROL &amp; AUTOMATION>>,2022 160-165. 10.1109/ICCA54724.2022.9831878 [http://hdl.handle.net/10807/214126]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/214126
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