Yes / Implementation of an intelligent powertrain health management relies on robust prognostics modelling. However, prognostics capability is often limited due to unknown future operating conditions, which varies with duty cycles and individual driver behaviours. On the other hand, the growing availability of data pertaining to vehicle usage allows advanced modelling of usage patterns and driver behavious, bringing optimisation opportunities for powertrain operation and health management. This paper introduces a methodology for driving behaviour modelling, underpinned by Machine Learning classification algorithms, generating model-based predictive insight for intelligent powertrain health management strategies. Specifically, the aim is to learn the patterns of driving behaviour and predict characteristics for the short-term future operating conditions as a basis for enhanced control strategies to optimise energy efficiency and system reliability. A case study of an automotive emissions aftertreatment system is used to comprehensively demonstrate the proposed framework. The case study illustrates the approach for integrating predictive insight from machine learning deployed on real world trip behaviour data, in conjunction with a reliability-based model of the operational behaviour of a particulate filter, to propose an intelligent active regeneration control strategy for improved efficiency and reliability performance. The effectiveness of the proposed strategy was demonstrated on an industry standard model-in-the-loop set up with a representative sample of real-world vehicle driving data. / The authors acknowledge funding for the research presented in this article from Jaguar Land Rover under a research collaboration with the University of Bradford on “Intelligent Personalised Powertrain Healthcare”, and the Institute of Digital Engineering who have provided funding for proof of concept – the aiR-FORCE project.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19092 |
| Date | 26 July 2022 |
| Creators | Doikin, Aleksandr, Campean, Felician, Priest, Martin, Angiolini, E., Lin, C., Agostinelli, E. |
| Publisher | SAE International |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | © 2023 The Authors. Published by SAE International. This Open Access article is published under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits distribution, and reproduction in any medium, provided that the original author(s) and the source are credited., CC-BY |
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