A White-Box Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells.

Smart grids, microgrids, and pure electric powertrains are the key technologies for achieving the expected goals concerning the restraint of CO2 emissions and global warming. In this context, an effective use of electrochemical energy storage systems (ESSs) is mandatory. In particular, accurate state of charge (SoC) estimations are helpful for improving the ESS performances. To this aim, developing accurate models of electrochemical cells is necessary for implementing effective SoC estimators. Therefore, a novel neural network modeling technique is proposed in this paper. The main contribution consists in the development of a white-box neural design that provides helpful insights into the cell physics, together with a powerful nonlinear approximation capability, and a flexible system identification procedure. In order to do that, the system equations of a white-box equivalent circuit model (ECM) have been combined with computational intelligence techniques by approximating each circuit element with a dedicated neural network. The model performances have been analyzed in terms of model accuracy, SoC estimation effectiveness, and computational cost over two realistic data sets. Moreover, the proposed model has been compared with a white-box ECM and a gray-box neural network model. The results prove that the proposed modeling technique is able to provide useful improvements in the SoC estimation task with a competing computational cost.

A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells.

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.