Bandwidth based electrical-analogue battery modeling for battery modules

Abstract A technique for building a high fidelity electrical-analogue battery model by identifying the model parameters at the module level, as opposed to the cell level, is proposed in this paper. The battery model, which is represented by electrical circuit components, can be easily integrated into popular simulation environments for system level design and predictive analysis. A novel bandwidth based time-domain procedure is introduced for identifying the model parameters by selective assignment of the limited bandwidth of the battery model approximation according to the natural bandwidth of the system that uses the battery. The aim of this paper is to provide an accurate off-line electrical-analogue battery model for simulation of larger systems containing large-format batteries, as opposed to a detailed electrochemical model suitable for simulation of internal battery processes. The proposed procedure has been experimentally verified on a 6.8 Ah Ultralife UBBL10 Li-ion battery module which is a “microcosm” for a modern large-format battery pack. A maximum 0.25% error was observed during a performance test with arbitrary but bandwidth-limited charging and discharging intervals characteristic of a typical battery application.

[1]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation , 2006 .

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

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

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

[5]  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).

[6]  A. Istratov,et al.  Exponential analysis in physical phenomena , 1999 .

[7]  Matthieu Dubarry,et al.  Development of a universal modeling tool for rechargeable lithium batteries , 2007 .

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

[9]  Il-Song Kim,et al.  The novel state of charge estimation method for lithium battery using sliding mode observer , 2006 .

[10]  Stephen Yurkovich,et al.  Electro-thermal battery model identification for automotive applications , 2011 .

[11]  Chaoyang Wang,et al.  Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles , 2006 .

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

[13]  Amir Vasebi,et al.  A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended Kalman filter for hybrid electric vehicle applications , 2007 .

[14]  C. C. Chan,et al.  The state of the art of electric and hybrid vehicles , 2002, Proc. IEEE.

[15]  Stephen Yurkovich,et al.  A technique for dynamic battery model identification in automotive applications using linear parameter varying structures , 2009 .

[16]  S. Saddow,et al.  Application of the singular valve decomposition-Prony method for analyzing deep-level transient spectroscopy capacitance transients , 1998 .

[17]  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.

[18]  K. Tsang,et al.  Identification and modelling of Lithium ion battery , 2010 .

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