Experimental parameter identification of scalable white and grey box jounce bumper models for ride comfort simulation

References

• Huber W. Industrie 4.0 in der Automobilproduktion. Wiesbaden: Springer Vieweg; 2016. doi:10.1007/978-3-658-12732-9.
   Google Scholar
• Wolf-Monheim F. CAE-based driving comfort optimization for passenger cars. In: Pfeffer PE, editor. 5th international Munich chassis symposium. Wiesbaden: Springer Vieweg; 2014. p. 133–149. doi:10.1007/978-3-658-05978-1_11.
   Google Scholar
• Badiru I, Cwycyshyn WB. Customer focus in ride development. SAE technical paper series. Proceedings; 2013.
   Google Scholar
• Wang Y, Ma Z, Wang L. A finite element stratification method for a polyurethane jounce bumper. Proc Inst Mech Eng D J Automob Eng. 2016;230:983–992.
   Web of Science ®Google Scholar
• Röski K. Eine Methode zur simulationsbasierten Grundauslegung von PKW-Fahrwerken mit Vertiefung der Betrachtungen zum Fahrkomfort. München: Technische Universität München; 2012.
   Google Scholar
• Reimpell J, Betzler JW. Fahrwerktechnik: Grundlagen. 5th ed. Würzburg: Vogel; 2005.
   Google Scholar
• Kortüm W, Lugner P. Systemdynamik und Regelung von Fahrzeugen: Einführung und Beispiele. Springer eBook Collection Computer Science and Engineering. Berlin: Springer; 1994.
   Google Scholar
• Heissing B, Brandl HJ. Subjektive Beurteilung des Fahrverhaltens. Würzburg: Vogel fachbuch; 2002.
   Google Scholar
• Heißing B, Ersoy M, Gies S. Fahrwerkhandbuch: Grundlagen, Fahrdynamik, Komponenten, Systeme, Mechatronik, Perspektiven. 4th ed. Wiesbaden: Springer Vieweg; 2013.
   Google Scholar
• Wong JY. Theory of ground vehicles. 3rd ed. New York: John Wiley; 2001.
   Google Scholar
• Haigermoser A, Luber B, Rauh J, et al. Road and track irregularities: measurement, assessment and simulation. Veh Syst Dyn. 2015;53:878–957.
   Web of Science ®Google Scholar
• ISO. Mechanical vibration – road surface profiles – reporting of measured data. Berlin: Beuth Verlag; 1995.
   Google Scholar
• Braun H. Messergebnisse von Straßenunebenheiten. VDI-Berichte Nr. 877. 1991:47–80.
   Google Scholar
• Karamihas S, Sayers M. The little book of profiling. Michigan: University of Michigan, Ann Arbor, Transportation Research Institute, Engineering Research Division; 1998.
   Google Scholar
• Agostinacchio M, Ciampa D, Olita S. The vibrations induced by surface irregularities in road pavements – a Matlab® approach. Eur Transp Res Rev. 2014;6:267–275.
   Web of Science ®Google Scholar
• Rabiner LR, Schafer RW. Digital processing of speech signals. Prentice-Hall Signal Processing Series. Englewood Cliffs (NJ): Prentice-Hall; 1978.
   Google Scholar
• Brandt A. Noise and vibration analysis. Chichester: Wiley; 2011.
   Google Scholar
• Gillespie TD. Fundamentals of vehicle dynamics. 4th ed. Warrendale (PA): Society of Automotive Engineers; 1992.
   Google Scholar
• Eze MC, Eze VHU, Chidume C, et al. Review of the effects of standard deviation on time and frequency response of Gaussian filter. International Journal of Scientific & Engineering Research. 2016;7(9):749.
   Google Scholar
• Fornberg B. Generation of finite difference formulas on arbitrarily spaced grids. Math Comput. 1988: 699–706.
   Web of Science ®Google Scholar
• Dahl PR. A solid frition model. El Segundo (CA): Aerospace Corp; 1968.
   Google Scholar
• Franklin GF, Powell JD. Digital control of dynamic systems. Reading (MA): Addison-Wesley; 1980.
   Google Scholar
• Butcher JC. Numerical methods for ordinary differential equations. Chichester: Wiley Blackwell; 2016.
   Google Scholar
• Dessort R, Meissner M, Kolmeder S, et al. Experimental nonlinear system identification of a shock absorber focusing on secondary ride comfort. In: Pfeffer P, editor. 12th international Munich chassis symposium 2021. Proceedings. Berlin: Springer Vieweg. doi:10.1007/978-3-662-64550-5_13
   Google Scholar
• Nelles O. Nonlinear system identification. Berlin: Springer; 2001.
   Google Scholar
• Nelles O. Nonlinear system identification with local linear neuro-fuzzy models. Aachen: Automatisierungstechnik, Shaker; 1999.
   Google Scholar
• Halfmann O, Nelles O, Holzmann H. Semi-physical modeling of the vertical vehicle dynamics. Proceedings of the American Control Conference; 1999.
   Google Scholar
• Rehrl J, Schwingshackl D, Horn M. A modeling approach for HVAC systems based on the LoLiMoT algorithm. IFAC Proc Vol. 2014;47(3):10862–10868.
   Google Scholar
• Nelles O, Fink A, Isermann R. Local linear model trees (LOLIMOT) toolbox for nonlinear system identification. IFAC Proc Vol. 2000;33(15):845–850.
   Google Scholar
• Lunze J. Regelungstechnik 1: Systemtheoretische Grundlagen, Analyse und Entwurf einschleifiger Regelungen. Berlin: Springer Vieweg; 2020. doi:10.1007/978-3-662-60746-6.
   Google Scholar
• Penrose R. A generalized inverse for matrices. Math Proc Cambridge Philos Soc. 1955;51(3):406–413.
   Google Scholar
• Brunton SL, Proctor JL, Kutz NJ. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc Natl Acad Sci USA; 113(15):3932–3937.
   PubMed Web of Science ®Google Scholar
• Audet C, Dennis JE. Mesh adaptive direct search algorithms for constrained optimization. SIAM J Optim. 2006;17(1):188–217.
   Web of Science ®Google Scholar
• Nocedal J, Wright SJ. Numerical optimization. Springer Series in Operations Research and Financial Engineering. New York: Springer; 2006. doi:10.1007/978-0-387-40065-5.
   Google Scholar