Using transient electrical measurements for real-time monitoring of battery state-of-charge and state-of-health

This paper describes a transient-based approach for estimating the state-of-charge (SOC) and state-of-health (SOH) of a lithium-ion battery. In this methodology, a small test signal is superimposed on top of the battery load to trigger its transient dynamics. The resulting terminal voltage and current are measured, and a non-linear least-squares routine is used to estimate the impedance parameters of the battery model. These parameter values are then passed to an H∞ filter that estimates the open-circuit voltage while the battery is under load. Experimental results are presented. The approach requires minimal hardware and could be used to form the basis of a robust on-line monitoring system.

[1]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[2]  Rik W. De Doncker,et al.  Impedance measurements on lead–acid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles , 2005 .

[3]  R. S. Robinson On-line battery testing: a reliable method for determining battery health? , 1996, Proceedings of Intelec'96 - International Telecommunications Energy Conference.

[4]  A. Vervaet,et al.  A new method for the measurement of the double layer capacitance for the estimation of battery capacity , 2003, The 25th International Telecommunications Energy Conference, 2003. INTELEC '03..

[5]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[6]  J. D. Kozlowski Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[7]  Roger A. Dougal,et al.  Dynamic lithium-ion battery model for system simulation , 2002 .

[8]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background , 2004 .

[9]  S. Rodrigues,et al.  A review of state-of-charge indication of batteries by means of a.c. impedance measurements , 2000 .

[10]  Xuezhe Wei,et al.  Internal Resistance Identification in Vehicle Power Lithium-Ion Battery and Application in Lifetime Evaluation , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[11]  Giovanni Fiengo,et al.  Lithium-ion battery state of charge estimation with a Kalman Filter based on a electrochemical model , 2008, 2008 IEEE International Conference on Control Applications.

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

[13]  B. Fahimi,et al.  A novel battery identification method based on pattern recognition , 2008, 2008 IEEE Vehicle Power and Propulsion Conference.

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

[15]  Li Ran,et al.  Prediction of state of charge of Lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[16]  Lalit Prakash Mandal Methodology for on-line battery health monitoring , 2012 .

[17]  John Chiasson,et al.  Estimating the state of charge of a battery , 2003, Proceedings of the 2003 American Control Conference, 2003..

[18]  Steven R. Shaw,et al.  A Method for Nonlinear Least Squares With Structured Residuals , 2006, IEEE Transactions on Automatic Control.

[19]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[20]  D. Linden Handbook Of Batteries , 2001 .

[21]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .