This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Generic Modeling Approach for Automotive Power Net Consumers
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 16, 2012 by SAE International in United States
Annotation ability available
The integration of safety-critical and major power-consuming electrical systems presents a challenge for the development of future automotive electrical networks. Both reliability and performance must be enhanced in order to guarantee the power supply to essential electrical consumers at a sufficient degree of power quality. Often, in order to cope with these requirements, merely an upgrade of the existing wiring harness design is used, resulting in additional complexity, weight, and cost .
A characterization of the wiring harness and its electrical consumers facilitates a systematic optimization approach aimed at designing new automotive power networks [1, 5]. Measurement and analysis methods to characterise the thermal behaviour of the wiring harness have been presented and discussed in a previous paper 
This paper presents and compares two methods aimed at modeling the electrical behavior of consumers at various voltages and temperatures. The first, conventional method relies on physics-based, analytical models. The second method is based on a generic representation of non-linear dynamic behavior using a common model frame for various electrical consumers. A dedicated parameterization strategy for the data-driven models is developed. The two modeling approaches are compared in terms of representation, flexibility and generalization capabilities. The models obtained are integrated into a modular and scalable HiL test platform used to characterize and test automotive electrical power nets in an early phase of the development process.
The methodology and tools used, as well as the results obtained, are presented and discussed in this paper.
|Technical Paper||Modeling and Simulation of Future Vehicle Powernets|
|Technical Paper||Compact Starter-Alternator Systems with High Efficiency for 42V and 12V|
CitationAyeb, M., Brabetz, L., Jilwan, G., and Graebel, P., "A Generic Modeling Approach for Automotive Power Net Consumers," SAE Technical Paper 2012-01-0924, 2012, https://doi.org/10.4271/2012-01-0924.
- Ayeb, M. Brabetz, L. Graebel, P. Jilwan, G. et al. “Characterization and Test of Automotive Electrical Power Networks,” SAE Technical Paper 2009-01-1093 2009 10.4271/2009-01-1093
- Ayeb, M. Lichtenthäler, D. Winsel, T. Theuerkauf, H. “SI Engine Modeling Using Neural Networks,” SAE Technical Paper 980790 1998 10.4271/980790
- Brabetz, L. Mechatronics in Electrical Distribution Systems 25 Forum Vehicle Electrical Systems Architecture Sci-Worx, Hannover 2004
- Brabetz, L. Ayeb, M. Neumeier, H. “A New Approach to the Thermal Analysis of Electrical Distribution Systems,” SAE Technical Paper 2011-01-1437 2011 10.4271/2011-01-1437
- Brabetz, L. Ayeb, M. Tellmann, D. “Efficient Vehicle Power Supply by Adaptive Energy, Charge and Heat Management of an Alternator - Super Capacitor System,” SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 2 1 359 366 2009 10.4271/2009-01-1094
- Gerke, T. Boulos, A. “Model Based Design of Robust Vehicle Power Networks,” SAE Technical Paper 2008-01-0898 2008 10.4271/2008-01-0898
- Gerke, T. Petsch, C. “Analysis of Vehicle Power Supply Systems Using System Simulation,” SAE Technical Paper 2006-01-0299 2006 10.4271/2006-01-0299
- Giri, F. Chaoui, F.Z. Haloua, M. Rochdi, Y. Naitali, A. “Hammerstein Model Identification” Proceedings of the 10th Mediterranean Conference on Control and Automation - MED2002 Lisbon, Portugal July 9 12 2002
- Hagan, M. T. Menjaj, M. B. Training feedforward networks with the marquardt algorithm IEEE-Transactions on Neural Networks 5 6 989 993 November 1994
- Hamady, A. “Modeling of dynamic and static behavior of actors on electrical distribution systems” Diploma-Thesis University of Kassel 2011
- Hornik, K. Approximation capabilities of multilayer feedforward networks Neural Networks 4 251 257 1991
- Jin, L. Nikiforuk, P. N. Gupta Madan, M. Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks IEEE Transactions on Automatic Control 40 7 July 1995
- Krzyzak, A. ‘Identification of discrete Hammerstein systems by the Fourier series regression estimate’ Int. J. Systems Sciences 20 1729 1744 1989
- Ku, Chao-Chee Lee, Kwang Y. Diagonal recurrent neural networks for dynamic systems Control IEEE Transactions on Neural Networks 6 1 January 1995
- Kung, F.C. Shinh, D.H. ‘Analysis and identification of Hammerstein model nonlinear systems using block-pulse function expansion’ Int. J. Control 43 139 147 1986
- Marquardt, D. An algorithm for least squares estimation of non-linear parameters J. Soc. Ind. Appl. Math 431 441 1963
- Reinbold, I. “Measurement and Simulation of electrical consumers on electrical distribution systems” Diploma-Thesis University of Kassel 2011