Online Estimation of Electrochemical Impedance Spectra for Lithium-Ion Batteries via Discrete Fractional Order Model

An electrochemical impedance spectrum is one critical non-destructive approach to indicate the health status of lithium-ion batteries. This paper presents an online model-based method of estimating the electrochemical impedance spectra based on discrete fractional order model. Firstly, a discrete fractional order model (FOM) is employed to model the dynamic behavior of the lithium-ion battery, especially the diffusion kinetics. In addition, another highlight of FOM lay on its significant performance in the impedance modeling for Li-ion battery over a wide range of frequency domain. Secondly, the Levenberg-Marquardt algorithm is adopted to identify parameters of FOM recursively. Based on identification results, the electrochemical impedance spectra can be obtained by simulation. Finally, a verifying experiment is carried out based on hybrid pulse power characterization test (HPPC) mixed by EIS test. The first order and second order equivalent circuits (short as, EC1 & EC2) have been imported here as the comparison with the fractional order model. The simulation results reveal that the fractional order model can ensure an acceptable accuracy of the RMS of impedance spectra, with a maximum error being less than 0.1mohm.

[1]  Dominik Sierociuk,et al.  Time domain validation of ultracapacitor fractional order model , 2010, 49th IEEE Conference on Decision and Control (CDC).

[2]  Aziz Naamane,et al.  Battery Models for Estimation of State of Charge by Sliding Mode Observer , 2012 .

[3]  Ralph E. White,et al.  Capacity Fade Mechanisms and Side Reactions in Lithium‐Ion Batteries , 1998 .

[4]  I. Podlubny.  Fractional differential equations , 1998 .

[5]  Alain Oustaloup,et al.  Fractional system identification for lead acid battery state of charge estimation , 2006, Signal Process..

[6]  D. Aurbach.  Review of selected electrode–solution interactions which determine the performance of Li and Li ion batteries , 2000 .

[7]  S. Venugopalan,et al.  Impedance parameters and the state-of charge. I. Nickel-cadmium battery , 1979 .

[8]  P. Singh,et al.  Fuzzy logic-based state-of-health determination of lead acid batteries , 2002, 24th Annual International Telecommunications Energy Conference.

[9]  L. S. Kanevskii,et al.  Degradation of Lithium-Ion batteries and how to fight it: A review , 2005 .

[10]  J. C. Wang.  Realizations of Generalized Warburg Impedance with RC Ladder Networks and Transmission Lines , 1987 .

[11]  Hongwen He,et al.  Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach , 2011 .

[12]  Jean-Paul Diard,et al.  Constant load vs constant current EIS study of electrochemical battery discharge , 1997 .

[13]  Marc Thele,et al.  Hybrid modeling of lead-acid batteries in frequency and time domain , 2005 .

[14]  Alain Oustaloup,et al.  A fractional order model for lead-acid battery crankability estimation , 2010 .

[15]  Yi-Hsien Chiang,et al.  Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electr , 2011 .

[16]  Hongwen He,et al.  Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles , 2012 .

[17]  Dominik Sierociuk,et al.  Comparison and validation of integer and fractional order ultracapacitor models , 2011 .

[18]  F. Huet.  A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries , 1998 .

[19]  Hongjie Wu,et al.  A Lithium-Ion Battery Fractional Order State Space Model and its Time Domain System Identification , 2013 .

[20]  Bo-Hyung Cho,et al.  Li-Ion Battery SOC Estimation Method Based on the Reduced Order Extended Kalman Filtering , 2006 .

[21]  Ivo Petras,et al.  Fractional-Order Nonlinear Systems , 2011 .

[22]  Rik W. De Doncker,et al.  Impedance-based non-linear dynamic battery modeling for automotive applications , 2003 .

[23]  Hongwen He,et al.  State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model , 2011, IEEE Transactions on Vehicular Technology.

[24]  I. Petráš.  Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation , 2011 .

[25]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[26]  K. Amine,et al.  High-temperature storage and cycling of C-LiFePO4/graphite Li-ion cells , 2005 .

[27]  Jean-Michel Vinassa,et al.  Embedded Fractional Nonlinear Supercapacitor Model and Its Parametric Estimation Method , 2010, IEEE Transactions on Industrial Electronics.

[28]  Kang Xu,et al.  EIS study on the formation of solid electrolyte interface in Li-ion battery , 2006 .

[29]  Impedance parameters and the state-of-charge , 2022 .