Detection on SOC of VRLA battery with EIS

In this paper, one of the nondestructive detection techniques, electrochemical impedance spectroscopy (EIS), is adopted to detect the state of charge (SOC) of valve regulated lead acid (VRLA) battery under different conditions. Those conditions include off-line (open circuit) and on-line (discharging). Battery which is discharged with 0.1C is measured off-line in different SOC. As for the on-line group, batteries are discharged with different proportions of rated capacity which are 0.1 C, 0.5C and 1C, sequentially. The measurement results and the built equivalent electrical model can verify each other with curve fitting analysis and information hidden in the measurement results can be discovered with curve fitting analysis. Value and error % of all electrochemical elements can be compared after analysis so that observation on the relations between electrochemical elements and SOC can be implemented. Parameters belong to impedance category are inversely proportional to SOC; however, parameters belong to capacitance category are approximately proportional to SOC. In addition to variations of every electrochemical element, how position of battery change under different circumstances can be observed easily. The feasibility of detection on the SOC of battery with EIS can be ensured after series experiments and analysis.

[1]  Mohammad Farrokhi,et al.  State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF , 2010, IEEE Transactions on Industrial Electronics.

[2]  Shuo Pang,et al.  Battery state-of-charge estimation , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[3]  Souradip Malkhandi,et al.  Fuzzy logic-based learning system and estimation of state-of-charge of lead-acid battery , 2006, Eng. Appl. Artif. Intell..

[4]  J. Randles Kinetics of rapid electrode reactions , 1947 .

[5]  Chunbo Zhu,et al.  State-of-Charge Determination From EMF Voltage Estimation: Using Impedance, Terminal Voltage, and Current for Lead-Acid and Lithium-Ion Batteries , 2007, IEEE Transactions on Industrial Electronics.

[6]  Ruth Latham,et al.  Algorithm development for electrochemical impedance spectroscopy diagnostics in PEM fuel cells , 2004 .

[7]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

[8]  A. I. Harrison,et al.  Conductance measurements in relation to battery state of charge , 1999, 21st International Telecommunications Energy Conference. INTELEC '99 (Cat. No.99CH37007).

[9]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

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

[11]  Abbas Shoulaie,et al.  A practical approach to measure battery's internal impedance , 2010, 2010 1st Power Electronic & Drive Systems & Technologies Conference (PEDSTC).

[12]  Chin-Sien Moo,et al.  State-of-charge estimation for lead-acid batteries based on dynamic open-circuit voltage , 2008, 2008 IEEE 2nd International Power and Energy Conference.

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

[14]  Arnaud Delaille,et al.  Study of the "coup de fouet" of lead-acid cells as a function of their state-of-charge and state-of-health , 2006 .