Abstract
The development of in-vehicle control systems such as Global Positioning Systems (GPS) provides static and dynamic road information. This widely accessible technology can be used to develop an auxiliary control sub-system to reduce vehicle fuel consumption as well as improve road safety and comfort. The raw GPS data from the receiver were processed and integrated with the past trajectory using a Neuro-fuzzy technique. The system essentially used a fuzzy logic derived relief map of the test route and this was further validated and corrected based on the past trajectory from the GPS sensor. The information was then processed and translated in order to estimate the future elevation of the vehicle. Experimental results demonstrated the feasibility and robustness of the system for potential application in vehicle control for reduced fuel consumption and emissions.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Automotive Electrics Automotive Electronics, Robert Bosch GmbH, 5th edn. Wiley, Chichester (2007)
Directorate-General Energy and Transport, Galileo European Satellite Navigation System, http://ec.europa.eu/dgs/energy_transport/galileo/index_en.htm
Kalogirou, S.A.: Applications of artificial neural-networks for energy systems. Appl. Energy 67, 17–35 (2000)
Xu, K., Luxmoore, A.R., Jones, L.M., Deravi, F.: Integration of neural networks and expert systems for microscopic wear particle analysis. Knowledge-Based Systems 11, 213–227 (1998)
Jang, J.: ANFIS: Adaptive network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics 23, 665–685 (1993)
Lee, S.H., Howlett, R.J., Walters, S.D.: An Adaptive Neuro-fuzzy Modelling of Diesel Spray Penetration, Paper No. SAE-NA 2005-24-64. In: Seventh International Conference on Engines for Automobile (ICE 2005), Capri, Napoli, Italy (September 2005) ISBN 88-900399-2-2
Hellstrom, E.: Explicit use of road topography for model predictive cruise control in heavy trucks. Master thesis. Linkoping University, Sweden (2005)
Wingren, A.: Look Ahead Cruise Control: Road Slope Estimation and Control, Linkoping University, Sweden. LiTH-ISY-EX-05/3644-SE (2005)
Lattemann, Frank, Neiss, K., Terwen, S., Connolly, T.: The predictive cruise control – a system to reduce fuel consumption of heavy duty trucks. SAE Technical paper 2004-01-2616 (2004)
Loewenau, J.P., Richter, W., Urbanczik, C., Beuk, L., Hendriks, T., Pichler, R., Artmann, K.: Real time optimization of active cruise control with map data using standardised interface. In: Proceedings of the 12th World congress on ITS, San Francisco, CA, USA, paper 2015 (2005)
Powers, W.F., Nicastri, P.R.: Automotive vehicle control challenges in the 21st century. Control Engineering Practice 8, 605–618 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, S.H., Walters, S.D., Howlett, R.J. (2008). Intelligent GPS-Based Vehicle Control for Improved Fuel Consumption and Reduced Emissions. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85567-5_87
Download citation
DOI: https://doi.org/10.1007/978-3-540-85567-5_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85566-8
Online ISBN: 978-3-540-85567-5
eBook Packages: Computer ScienceComputer Science (R0)