Accurate modeling of electrochemical cells is nowadays mandatory for achieving effective upgrades in the fields of energetic efficiency and sustainable mobility. Indeed, these models are often used for performing accurate State-of-Charge (SoC) estimations in energy storage systems used in microgrids or powering pure electric and hybrid cars. To this aim, a novel neural networks ensemble approach for modeling electrochemical cells is proposed in this paper. Herein, the system identification has been faced by means of a gray box technique, in which different and specialized neural networks are used for identifying the unknown internal behaviors of the cell. In particular, the a priori knowledge on the system dynamic is used for defining the network architecture. Specifically, each nonlinear function appearing in the system equations is approximated by a distinct neural network. The proposed model has been validated upon three different data sets both in terms of model accuracy and effectiveness in the SoC estimation task. The achieved performances have been compared with those of other computational intelligence approaches proposed in the literature. The results prove the effectiveness of the gray box scheme, achieving very promising performances in both the system identification accuracy and the SoC estimation task.

A novel neural networks ensemble approach for modeling electrochemical cells / Luzi, Massimiliano; Paschero, Maurizio; Rizzi, Antonello; Maiorino, Enrico; Frattale Mascioli, Fabio Massimo. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. - ISSN 2162-237X. - STAMPA. - (2019), pp. 343-354. [10.1109/TNNLS.2018.2827307]

A novel neural networks ensemble approach for modeling electrochemical cells

Luzi, Massimiliano
;
Paschero, Maurizio;Rizzi, Antonello;Maiorino, Enrico;Frattale Mascioli, Fabio Massimo
2019

Abstract

Accurate modeling of electrochemical cells is nowadays mandatory for achieving effective upgrades in the fields of energetic efficiency and sustainable mobility. Indeed, these models are often used for performing accurate State-of-Charge (SoC) estimations in energy storage systems used in microgrids or powering pure electric and hybrid cars. To this aim, a novel neural networks ensemble approach for modeling electrochemical cells is proposed in this paper. Herein, the system identification has been faced by means of a gray box technique, in which different and specialized neural networks are used for identifying the unknown internal behaviors of the cell. In particular, the a priori knowledge on the system dynamic is used for defining the network architecture. Specifically, each nonlinear function appearing in the system equations is approximated by a distinct neural network. The proposed model has been validated upon three different data sets both in terms of model accuracy and effectiveness in the SoC estimation task. The achieved performances have been compared with those of other computational intelligence approaches proposed in the literature. The results prove the effectiveness of the gray box scheme, achieving very promising performances in both the system identification accuracy and the SoC estimation task.
2019
Battery management systems (BMSs); electrochemical cell modeling; gray box modeling; neural networks ensemble (NNE); State-of-Charge (SoC) estimation; system identification
01 Pubblicazione su rivista::01a Articolo in rivista
A novel neural networks ensemble approach for modeling electrochemical cells / Luzi, Massimiliano; Paschero, Maurizio; Rizzi, Antonello; Maiorino, Enrico; Frattale Mascioli, Fabio Massimo. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. - ISSN 2162-237X. - STAMPA. - (2019), pp. 343-354. [10.1109/TNNLS.2018.2827307]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1117756
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