A new parameter estimation algorithm for an electrical analogue battery model

This paper describes a new parameter estimation algorithm for a well-recognized electrical analogue battery model. The limited bandwidth characteristic of the electrical analogue battery model is introduced and discussed, on which the new parameter estimation algorithm is built. While a real battery system is non-linear and time variant, a truncated representation of the system is provided by a commonly studied non-physical "electrical analogue" battery model. However, the limited bandwidth characteristic of the electrical analogue battery model is often overlooked. The proposed algorithm starts by assessing a desired battery application, followed by modeling the battery according to the application bandwidth, and then estimating the model parameters using the sequential quadratic programming method. This approach recognizes and makes use of the limited bandwidth of the battery model by reconciling the approximation with the limited bandwidth required by the simulation. This approach has been experimentally verified on a 6.8 Ah Ultralife UBBL10 Lithium-ion battery module. Since this electrical analogue battery model is independent of the battery chemistry it is applicable to Lithium-ion, Nickel-Metal-Hydride (NiMH), and Lead-acid batteries, among others.

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

[2]  Suleiman Abu-Sharkh,et al.  Rapid test and non-linear model characterisation of solid-state lithium-ion batteries , 2004 .

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

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

[5]  Issa Batarseh,et al.  A hysteresis model for a Lithium battery cell with improved transient response , 2011, 2011 Twenty-Sixth Annual IEEE Applied Power Electronics Conference and Exposition (APEC).

[6]  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 .

[7]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[8]  L.-A. Dessaint,et al.  A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

[9]  Hamid Sharif,et al.  An enhanced circuit-based model for single-cell battery , 2010, 2010 Twenty-Fifth Annual IEEE Applied Power Electronics Conference and Exposition (APEC).

[10]  Chao-Yang Wang,et al.  Computational battery dynamics (CBD)—electrochemical/thermal coupled modeling and multi-scale modeling , 2002 .

[11]  Georg Brasseur,et al.  Modeling of high power automotive batteries by the use of an automated test system , 2003, IEEE Trans. Instrum. Meas..

[12]  Georg Brasseur,et al.  Modelling of high power automotive batteries by the use of an automated test system , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

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

[14]  C. M. Shepherd,et al.  Design of Primary and Secondary Cells I . Effect of Polarization and Resistance on Cell Characteristics , 1965 .

[15]  Alireza Khaligh,et al.  Battery, Ultracapacitor, Fuel Cell, and Hybrid Energy Storage Systems for Electric, Hybrid Electric, Fuel Cell, and Plug-In Hybrid Electric Vehicles: State of the Art , 2010, IEEE Transactions on Vehicular Technology.