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

Bamieh, B., & Giarre., L. (1999). Identification of linear parameter varying models. In Proceedings of the 38th IEEE conference on decision and control.

Barsoukov, 2005

Chan, 2000, The available capacity computation model based on artificial neural network for lead-acid batteries in electrical vehicles, Journal of Power Sources, 87, 201, 10.1016/S0378-7753(99)00502-9

Charbonneau, P. (2002). Release notes for PIKAIA 1.2 NCAR technical note 451+STR. Boulder: National Center for Atmospherical Research.

Dawson, 1980, Diffusion impedance—an extended general analysis, Journal of Electroanalytical Chemistry, 10, 37, 10.1016/S0022-0728(80)80363-9

Dierckx, 1994

Dudek, K. P. (2007). Application of linear splines to internal combustion engine control. US Patent No. 7,212,915 B2.

Gomadam, 2002, Mathematical modeling of lithium-ion and nickel battery systems, Journal of Power Sources, 110, 267, 10.1016/S0378-7753(02)00190-8

Kim, 2006, The novel state of charge estimation method for lithium ion battery using sliding mode observer, Journal of Power Sources, 163, 584, 10.1016/j.jpowsour.2006.09.006

Ledovskikh, 2003, Modeling of rechargeable NiMH batteries, Journal of Alloys and Compounds, 356–357, 742, 10.1016/S0925-8388(03)00082-3

Lee, 1999, Identification of linear parameter-varying systems using nonlinear programming, Journal of Dynamic Systems, Measurement, and Control, 121, 71, 10.1115/1.2802444

Lim, S. (1999). Analysis and control of linear parameter-varying systems. Ph.D. thesis, Stanford University.

Malkhandi, 2006, Fuzzy logic-based learning system and estimation of state of charge of lead-acid battery, Engineering Applications of Artificial Intelligence, 19, 479, 10.1016/j.engappai.2005.12.005

Marsh, 2001

Metcalfe, T. (n.d.). MPIKAIA—parallel genetic algorithm. Online 〈http://www.cisl.ucar.edu/css/staff/travis/mpikaia/〉.

Newman, 2003, Modeling of lithium-ion batteries, Journal of Power Sources, 119–121, 838, 10.1016/S0378-7753(03)00282-9

Pang, S., Farrell, J., Du, J., & Barth, M. (2001). Battery state-of-charge estimation. In Proceedings of the American control conference (pp. 1644–1649).

Piller, 2001, Methods for state-of-charge determination and their applications, Journal of Power Sources, 96, 113, 10.1016/S0378-7753(01)00560-2

Plett, 2004, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs, part 2. Modeling and identification, Journal of Power Sources, 134, 262, 10.1016/j.jpowsour.2004.02.032

Reeves, 1997, Genetic algorithms: no panacea, but a valuable tool for the operations researcher, INFORMS Journal on Computing, 9, 263, 10.1287/ijoc.9.3.263

Schmitt, 2001, Theory of genetic algorithm, Theoretical Computer Science, 259, 1, 10.1016/S0304-3975(00)00406-0

Serrao, L., Chehab, Z., Guezennec, Y., & Rizzoni, G. (2005). An aging model of NiMH batteries for hybrid electric vehicles. In Proceedings of the 2005 IEEE vehicle power and propulsion conference.

Shamma, 1992, Gain scheduling: potential hazards and possible remedies, IEEE Control System Magazine, 12, 101, 10.1109/37.165527

Verbrugge, 2004, Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena, Journal of Power Sources, 126, 236, 10.1016/j.jpowsour.2003.08.042

Verdult, 2002, Subspace identification of multivariable linear parameter-varying systems, Automatica, 38, 805, 10.1016/S0005-1098(01)00268-0

Wall, M. (n.d.). GAlib: AC++ library of genetic algorithm components. Online 〈http://lancet.mit.edu/ga/〉.