Modeling and online parameter identification of Li-Polymer battery cells for SOC estimation

Finding an accurate and easily to implement model of batteries is an essential step in properly estimating the state of charge (SOC) of the battery in real-time. In this paper, an equivalent circuit based battery model with nonlinear relationship between the open circuit voltage (VOC) and the SOC is projected into several piece-wise linear functions. Moving window Least Squares (LS) parameter identification technique is then utilized to estimate and update the parameters of the battery model in each sampling time. The continuously updated parameters are fed to a linear observer to estimate the SOC of the battery. The effectiveness of the proposed modeling and estimation approach are verified experimentally on Lithium Polymer batteries.

[1]  Katsuhiko Ogata,et al.  Discrete-time control systems , 1987 .

[2]  Mo-Yuen Chow,et al.  A Survey on the Electrification of Transportation in a Smart Grid Environment , 2012, IEEE Transactions on Industrial Informatics.

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

[4]  Katsuhiko Ogata,et al.  Discrete-time control systems (2nd ed.) , 1995 .

[5]  D. Sauer,et al.  Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries , 2011 .

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

[7]  Mo-Yuen Chow,et al.  On-line PHEV battery hysteresis effect dynamics modeling , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[8]  IL-Song Kim,et al.  A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer , 2010, IEEE Transactions on Power Electronics.

[9]  F. Baronti,et al.  Enhanced model for Lithium-Polymer cells including temperature effects , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[10]  David A. Stone,et al.  New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observer Techniques , 2009, IEEE Transactions on Vehicular Technology.

[11]  Wencong Su,et al.  Sensitivity analysis on battery modeling to large-scale PHEV/PEV charging algorithms , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[12]  V. Battaglia,et al.  Electrochemical modeling of lithium polymer batteries , 2002 .

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