This work investigates the effectiveness of deep neural networks within the realm of battery charging. This is done by introducing an innovative control methodology that not only ensures safety and optimizes the charging current, but also substantially reduces the computational complexity with respect to traditional model-based approaches. In addition to their high computational costs, model-based approaches are also hindered by their need to accurately know the model parameters and the internal states of the battery, which are typically unmeasurable in a realistic scenario. In this regard, the deep learning-based methodology described in this work was been applied for the first time to the best of the authors' knowledge, to scenarios where the battery's internal states cannot be measured and an estimate of the battery's parameters is unavailable. The reported results from the statistical validation of such a methodology underline the efficacy of this approach in approximating the optimal charging policy.

Pozzi, A., Barbierato, E., Toti, D., Optimizing Battery Charging Using Neural Networks in the Presence of Unknown States and Parameters, <<SENSORS>>, 2023; 23 (9): N/A-N/A. [doi:10.3390/s23094404] [https://hdl.handle.net/10807/235911]

Optimizing Battery Charging Using Neural Networks in the Presence of Unknown States and Parameters

Pozzi, Andrea
;
Barbierato, Enrico;Toti, Daniele
2023

Abstract

This work investigates the effectiveness of deep neural networks within the realm of battery charging. This is done by introducing an innovative control methodology that not only ensures safety and optimizes the charging current, but also substantially reduces the computational complexity with respect to traditional model-based approaches. In addition to their high computational costs, model-based approaches are also hindered by their need to accurately know the model parameters and the internal states of the battery, which are typically unmeasurable in a realistic scenario. In this regard, the deep learning-based methodology described in this work was been applied for the first time to the best of the authors' knowledge, to scenarios where the battery's internal states cannot be measured and an estimate of the battery's parameters is unavailable. The reported results from the statistical validation of such a methodology underline the efficacy of this approach in approximating the optimal charging policy.
2023
Inglese
Pozzi, A., Barbierato, E., Toti, D., Optimizing Battery Charging Using Neural Networks in the Presence of Unknown States and Parameters, <<SENSORS>>, 2023; 23 (9): N/A-N/A. [doi:10.3390/s23094404] [https://hdl.handle.net/10807/235911]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/235911
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