TY - JOUR
T1 - Electrochemical impedance correlation analysis for the estimation of Li-ion battery state of charge, state of health and internal temperature
AU - Mc Carthy, Kieran
AU - Gullapalli, Hemtej
AU - Ryan, Kevin M.
AU - Kennedy, Tadhg
N1 - Publisher Copyright:
© 2022
PY - 2022/6
Y1 - 2022/6
N2 - 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.
AB - 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.
KW - Correlation analysis
KW - Electrochemical impedance spectroscopy
KW - Internal temperature
KW - State of charge
KW - State of health
UR - http://www.scopus.com/inward/record.url?scp=85128203731&partnerID=8YFLogxK
U2 - 10.1016/j.est.2022.104608
DO - 10.1016/j.est.2022.104608
M3 - Article
AN - SCOPUS:85128203731
SN - 2352-152X
VL - 50
JO - Journal of Energy Storage
JF - Journal of Energy Storage
M1 - 104608
ER -