Peer-to-peer Collaborative Vehicle Health Management – the Concept and an Initial Study
Advanced vehicle diagnostics and prognostics (D&P) technology enhances ownership experience and reduces corporate warranty cost. D&P performance optimization requires significant algorithm tuning and a large amount of test data collection, which is resource consuming.
In this paper, we propose a novel D&P framework called Collaborative Vehicle Health Management (CVHM) to automatically optimize the D&P algorithms on a host vehicle, using the field data collected from peer vehicles encountered on the road. The carefully designed system architecture and learning algorithms enhance D&P performance without costly human intervention. The experimental results on battery remaining useful life prediction show the effectiveness of the proposed framework. This proposed framework has been implemented in a small test fleet as a proof-of-concept prototype.
How to Cite
battery management systems, remote diagnosis, Remaining Useful Life Estimation, adaptive optimization
Brockwell, P. J., & Davis, R. A. (1991). Time Series: Theory and Methods (Second ed.). New York: Springer-Verlag Inc.
Byttner, S., Rögnvaldsson, T., Svensson, M., Bitar, G., & Chominsky, W. (2009). Networked vehicles for automated fault detection. Proceedings of IEEE International Symposium on Circuits and Systems . Taipei, Taiwan.
Carr, B. J. (2005). Practical application of remote diagnostics. SAE World Congress. Detroit, Michigan.
Edwin, G. R., Chiang, Y.-M., Carter, W. C., Limthongkul, P., & Bishop, C. M. (2005). Microstructural Modeling and Design of Rechargeable Lithium-Ion Batteries. Journal of The Electrochemical Society, 152(1), A255-A263.
Kuschel, J. O. (2004). Presenting a conceptual framework for remote vehicle diagnostics. IRIS 27.
Millstein, S. (2002). vRM (vehicle Relationship Management). Convergence: Transportation Electronics Conference. Detroit, Michigan.
Rasmussen, C. E., & Williams, C. K. (2006). Gaussian Process for Machine Learning. Cambridge, Massachusetts: The MIT Press.
Saha, B. G., Poll, S., & Christophersen, J. (2009, February). Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 58(2 ), 291-296.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., et al. (2008). Metrics for Evaluating Performance of Prognostics Techniques. Proceedings of 1st International Conference on Prognostics and Health Management (PHM08). Denver, CO.
Schiffer, J., Sauer, D. U., Bindner, H., Cronin, T., Lundsager, P., & Kaiser, R. (2007). Model prediction for ranking lead-acid batteries according
to expected lifetiime in renewable energy systems and autonomous power-supply systems. Journal of Power Sources, 168, 66-78.
Shin, K., & Salman, M. (2010). Evidence Theory Based Automotive Battery Health Monitoring. SAE Int. J. Passeng. Cars - Electron. Electr. Syst., pp. 10-16.
Spiegel, M. R., Schiller, J. J., & Srinivasan, R. A. (2009). Probability and Statistics. New York: McGraw- Hill Companies Inc. .
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Hoboken, New Jersey: John Wiley & Sons, Inc.
Vapnik, V. N. (1998). Statistical Learning Theory. New York, US: John Wiley & Sons, Inc.
You, S., Krage, m., & Jalics, L. (2005). Overview of remote diagnosis and maintenance for automotive systems. SAE World Congress. Detroit, MI.
Zhang, X., Grube, R., Shin, K., & Salman, M. (2008). Automotive Battery State-of-Health Monitoring: a Battery Cranking Voltage based Approach. Proceedings of the 2008 Integrated
System Health Management Conference. Covington, KY.
Zhang, X., Grube, R., Shin, K., & Salman, M. (2009). A parity-relation based approach to starting, lighting and ignition battery state-of-health monitoring: algorithm development. Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS). Barcelona, Spain.
Zhang, Y., Grantt, G. W., Rychlinski, M. J., Edwards, R. M., Correia, J. J., & Wolf, C. E. (2009). Connected vehicle diagnostics and prognostics, concept and initial practice. IEEE Transactions on Reliability, 58, 286-294.
Zoia, D. E. (2006). OnStar e-mail service hits million mark.
[Online] wardauto.com,http://wardsauto.com/ar/onstar_em ail_million/.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.