
Driving Behavior Modelling Framework for Intelligent Powertrain Health Management
Journal Article
03-16-04-0026
ISSN: 1946-3936, e-ISSN: 1946-3944
Sector:
Topic:
Citation:
Doikin, A., Campean, F., Priest, M., Angiolini, E. et al., "Driving Behavior Modelling Framework for Intelligent Powertrain Health Management," SAE Int. J. Engines 16(4):2023, https://doi.org/10.4271/03-16-04-0026.
Language:
English
Abstract:
The implementation of an intelligent powertrain health management relies on
robust prognostics modelling. However, prognostic capability is often limited
due to unknown future operating conditions, which vary with duty cycles and
individual driver behaviors. On the other hand, the growing availability of data
pertaining to vehicle usage allows advanced modelling of usage patterns and
driver behaviors, bringing optimization opportunities for powertrain operation
and health management. This article introduces a methodology for driving
behavior modelling, underpinned by Machine Learning (ML) classification
algorithms, generating model-based predictive insight for intelligent powertrain
health management strategies. Specifically, the aim is to learn the patterns of
driving behavior and predict characteristics for the short-term future operating
conditions as a basis for enhanced control strategies to optimize 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 ML deployed on real-world trip behavior data, in conjunction with a
reliability-based model of the operational behavior 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 setup with a
representative sample of real-world vehicle driving data.