A simplified fractional order impedance model and parameter identification method for lithium-ion batteries.

Qingxia Yang,Xiuqing Li,Jun Xu,Binggang Cao

doi:10.1371/journal.pone.0172424

Qingxia Yang, Xiuqing Li + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0172424

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Abstract

Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries.

Highlights

In recent years, with the rapid development of electric vehicle (EV) technology, lithium-ion batteries have been attracting much attention because of their superior performance [1]
The obtained voltage at the end of rest is regarded as the open circuit voltage (OCV)
As can be seen from Fig 6(D), the error distribution of the fractional order impedance model (FIM)&least square genetic algorithm (LSGA)-based method is mostly restricted to the region between –0.015 V and 0.02 V, which corresponds to an error lower than 0.5%; this indicates that the tracing error is small enough for our method to be effectively applied in EV battery management systems

Summary

Introduction

With the rapid development of electric vehicle (EV) technology, lithium-ion batteries have been attracting much attention because of their superior performance [1]. Wang et al [22] presented a FOM for lithium-ion batteries that showed higher accuracy for voltage tracing under different conditions compared with the commonly used 1-RC models. Joel et al [26] proposed a parameter identification method based on a genetic algorithm (GA) for a LiFePO4 cell electrochemical model.

Results

Conclusion