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.