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Just Accepted – McCarthy et al. – Electrochemical impedance correlation analysis for the estimation of Li-ion battery state of charge, state of health and internal temperature – Journal of Energy Storage, Volume 50, June 2022, 104608

June 1, 2022 – 10:50 am – Comments Off on Just Accepted – McCarthy et al. – Electrochemical impedance correlation analysis for the estimation of Li-ion battery state of charge, state of health and internal temperature – Journal of Energy Storage, Volume 50, June 2022, 104608

Kieran McCarthya, Hemtej Gullapallib, Kevin M. Ryana, Tadhg Kennedya
aBernal Institute and Department of Chemical Sciences, University of Limerick, V94 T9PX Limerick, Ireland
bAnalog Devices, 125 Summer St., MA 02110, Boston, United States
Received 3 November 2021, Revised 16 March 2022, Accepted 2 April 2022, Available online 13 April 2022, Version of Record 13 April 2022.

Link to Paper: https://doi.org/10.1016/j.est.2022.104608

a) Typical Nyquist plot of a Li-ion battery. b) Equivalent circuit model used to interpret impedance spectra.

Abstract

Electrochemical impedance spectroscopy (EIS) is an effective characterization tool for a multitude of battery states including state of charge (SoC), state of health (SoH) and internal temperature (IT). The intrinsic relationship between equivalent circuit elements and components of an impedance spectra (frequency, real, imaginary and phase) could be exploited to estimate the battery state at a given point of time without the need of continuous historical tracking information. Identification and analysis of battery state sensitive impedance variables is paramount for the development of any impedance-based battery management system (BMS). In this paper, correlation analysis between equivalent circuit elements and impedance spectra of multiple commercial Li-ion polymer batteries at varying SoC, SoH and IT levels was performed to identify and quantify the degree of dependence. Curve fitting techniques were used to fit the measured Impedance spectra on to an equivalent circuit model (ECM) to extract the circuit elements. Pearson’s r correlation matrix was employed for quantifying the degree of correlation between each impedance variable and state parameter. Optimal impedance variables that demonstrated high dependence with SoC, SoH and IT are then proposed in this paper. Knowledge of this information is of high value to develop a direct impedance-based state estimation models for real time battery management systems.