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    Driving Behaviour Modelling Framework for Intelligent Powertrain Health Management

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    Publication date
    2022
    Author
    Doikin, Aleksandr
    Campean, I. Felician
    Priest, Martin
    Angiolini, E.
    Lin, C.
    Agostinelli, E.
    Keyword
    Intelligent health management
    Prognostics
    Driving behaviour
    Machine learning
    Diesel particulate filter
    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.
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
    Metadata
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    Abstract
    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.
    URI
    http://hdl.handle.net/10454/19092
    Version
    Published version
    Citation
    Doikin A, Campean I, Felician I.F. et al (2022) Driving Behaviour Modelling Framework for Intelligent Powertrain Health Management. SAE International Journal of Engines. 16(4).
    Link to publisher’s version
    https://doi.org/10.4271/03-16-04-0026
    Type
    Article
    Collections
    Engineering and Informatics Publications

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