A technique for dynamic battery model identification in automotive applications using linear parameter varying structures

In this paper, a rapid calibration procedure for identifying the parameters of a dynamic model of batteries for use in automotive applications is described. The dynamic model is a phenomenological model based on an equivalent circuit model with varying parameters that are linear spline functions of the state of charge (SoC). The model identification process is done in a layered fashion: a two step optimization process using a genetic algorithm (GA) is used to optimize the parameters of the model over an experimental data set that encompasses the operating conditions of interest for the batteries. The level of accuracy obtained with this procedure is comparable to other black/gray box techniques, while requiring very little calibration effort. The process has been applied to both lithium ion and NiMH chemistries with good results. An extension of this technique to identify a model with both SoC and temperature dependence is discussed.

[1]  Colin R. Reeves.  Genetic Algorithms: No Panacea, but a Valuable Tool for the Operations Researcher , 1997, INFORMS J. Comput..

[2]  Ralph E. White,et al.  Mathematical modeling of lithium-ion and nickel battery systems , 2002 .

[3]  E.W.C. Lo,et al.  The available capacity computation model based on artificial neural network for lead–acid batteries in electric vehicles , 2000 .

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

[5]  Lothar M. Schmitt,et al.  Theory of genetic algorithms , 2001, Theor. Comput. Sci..

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

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

[8]  Rafael Castro-Linares,et al.  Trajectory tracking for non-holonomic cars: A linear approach to controlled leader-follower formation , 2010, 49th IEEE Conference on Decision and Control (CDC).

[9]  Evgeny Verbitskiy,et al.  Modelling of rechargeable NiMH batteries , 2003 .

[10]  Andreas Jossen,et al.  Methods for state-of-charge determination and their applications , 2001 .

[11]  Bassam Bamieh,et al.  Identification of linear parameter varying models , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[12]  Sungyung Lim,et al.  Analysis and control of linear parameter-varying systems , 1999 .

[13]  D. Wheeler,et al.  Modeling of lithium-ion batteries , 2003 .

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

[15]  D. G. John,et al.  Diffusion impedance — An extended general analysis , 1980 .

[16]  M. Verbrugge,et al.  Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena , 2004 .

[17]  E. Barsoukov,et al.  Impedance spectroscopy : theory, experiment, and applications , 2005 .

[18]  Paul Dierckx,et al.  Curve and surface fitting with splines , 1994, Monographs on numerical analysis.

[19]  Lawrence C. Marsh.  Spline Regression Models , 2001 .

[20]  Michel Verhaegen,et al.  Subspace identification of multivariable linear parameter-varying systems , 2002, Autom..

[21]  Lawton H. Lee,et al.  Identification of Linear Parameter-Varying Systems Using Nonlinear Programming , 1999 .