Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Abstract

Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input.

Equivalent Electrical Circuits and Their Use Across Electrochemical Impedance Spectroscopy Application Domains

“…For this work, the performance of a variety of objective functions from the literature (mainly differing in their weighing method) [15], [16], [23], [31]- [35] were compared on simulated data from several different equivalent circuits. This comparison was done by evaluating the fits on the basis of the parameter values resulting from the optimization of the tested objective functions.…”

“…Frequency Hz] ] (c) Several recently developed parameter optimization methods make use of a two-step parameter identification, where a first optimization method provides the initial values of a second parameter optimization method for further fine-tuning of the parameter values. An effective two-step parameter identification strategy described in [16], [17] consists of a differential evolution step that does not require initial parameter guesses, followed by a Nelder-Mead simplex finetuning step. This parameter identification approach is also adopted in the software accompanying this work (see Section II).…”

Electrochemical Impedance Spectrum Equivalent Circuit Parameter Identification Using a Deep Learning Technique

“…The variables defined in the mathematical model are presented in Table 3. To compare the results of this new algorithm, we propose using the same equivalent circuit and tests presented in the work by Olarte et al [22].…”

“…A two-step parameter identification algorithm to monitor EEC parameter variations was reported in a publication by Olarte et al [22]. The authors' goal with the proposed method was to automatically monitor the variations in the EEC parameters in operation with impedance sensors to infer the variations in the battery health state or failure modes from the electrochemical interpretation of these values, as in the study by Chun et al [18].…”

Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy

“…In a previous work, the authors already reported a two-step parameter identification algorithm [32] in order to monitor variations of the EEC parameters, whereas, in this new work, the target is to make a more rigorous comparison with other known methods while identifying new algorithms that offer low computational costs. Additionally, the objective motivating this new hybrid approach is to find a new and more accurate automatic identification of EEC parameters from raw EIS data.…”