Vehicle parameter identification using population based algorithms

Year 2015, Volume: 3 Issue: 2, 31 - 38, 08.07.2015

Hakan Gökdağ

Abstract

This work deals with parameter identification of a vehicle using population based algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC) and Genetic Algorithm (GA). Full vehicle model with seven degree of freedom (DoF) is employed, and two objective functions based on reference and computed responses are proposed. Solving the optimization problem vehicle mass, moments of inertia and vehicle center of gravity parameters, which are necessary for later applications such as vehicle control and performance analysis, are obtained. It is demonstrated the proposed approach achieves to determine unknown parameters with negligible relative errors in spite of noise interference. 

Keywords

Optimization; vehicle parameter identification; particle swarm, artificial bee colony

References

• Rozyn, M. and Zhang, N., “A method for estimation of vehicle inertial parameters”, Vehicle System Dynamics: Mechanics and Mobility, 48:5, 547-565, (2010).
• Venture, G., Bodson, P., Gautier, M., and Khalil, W., “Identification of the dynamic parameters of a car”, SAE Technical Paper, doi:10.4271/2003-01-1283.
• Furukawa, T, and Dissanayake, G., “Parameter identification of autonomous vehicles using multi- objective optimization”, Engineering Optimization, 34:4, 369-395, (2002).
• Wesemeier, D., and Isermann, R., “Identification of vehicle parameters using stationary maneuvers”, Control Engineering Practice, 17, 1426-1431, (2009).
• Khaknejad, M. B., Kazemi, R., Azadi, Sh., and Keshavaraz, A., “Identification of vehicle parameters using modified least square method in ADAMS/Car”, Proceedings of 2011 International Conference on Modelling, Identification and Control, Shanghai, China, 98-103, (2011).
• Wilhelm, E., Bornatico, R., Widmer, R., Rodgers, L., and identification”, EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, Los Angeles, California, 1-10, (2012).
• Kidambi, N., Harne, R.L., Fuji, Y., and Pietron, G.M. “Methods in vehicle mass and road grade estimation”, SAE International Journal of Passanger Cars-Mechanical 0111.
• doi:4271/2014-01- [8] Jazar, R.N. Vehicle
• Application, Springer, New York, (2008). Theory and
• Kennedy, J., and Eberhart, R. “Particle swarm optimization”, Proceedings of the 4th IEEE International Conference on Neural Networks, 4, 1942-1948, (1995).
• Clerc, M., and Kennedy, J., “The particle swarm- explosion, multidimensional Transactions on Evolutionary Computation 6(1), 58- 73, (2002). convergence space”, IEEE
• Parsopoulos, K. E., and Vrahatis, M. N., Particle Swarm Optimization and Intelligence: Advances and Applications. IGI Global, Hershey PA, (2010).
• Baştürk, B., and Karaboğa, D., “An artificial bee colony (ABC) algorithm for numeric function optimization”, Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis (USA), May (2006).
• Narasimhan, H., “Parallel artificial bee colony (PABC) algorithm”, IEEE World Congress Nature Coimbatore, 306-311, (2009). on & Inspired Computing,
• Karaboğa, D., and Akay, B. “A comparative study of artificial bee colony algorithm”, Mathematics and Computation, 214, 108-132, (2008). Applied
• http://mf.erciyes.edu.tr/abc/software.htm.