An ANFIS Based System Identification Procedure for Modeling Electrochemical Cells

The development of electrochemical cell models and of the related system identification procedures are of utmost importance for achieving effective management of electrochemical Energy Storage Systems. Specifically, accurate models are mandatory for performing effective estimation of the State of Charge (SoC) by means of Kalman Filtering approaches. Currently, some of the most promising models are those based on the equivalent circuit technique. However, these models are based on the standard definition of the SoC, which is related to the integral of the input current. The main drawback of this approach is that it is not related to any physical characteristic of the cell. A first implementation of an equivalent circuit model based on a novel mechanical inspired definition of the SoC is proposed in this paper. Herein, the cell is assimilated to a liquid tank so that the SoC, can be defined with respect to the reservoir shape. In particular, the charge reservoir has been modeled by means of an ANFIS estimator. A flexible system identification procedure has been defined by formulating a fitting problem upon generic sequences of the measured current and voltage. Specifically, a customized Particle Swarm optimization is in charge both of training the ANFIS and of identifying the other model parameters. The proposed modeling approach has been tested upon the Randomized Battery Usage Data Set and the obtained results proved its effectiveness both in emulating the cell behavior and in the SoC estimation task.