Connected Vehicle Based Distributed Meta-Learning for Online Adaptive Engine/Powertrain Fuel Consumption Modeling

A microscopic fuel consumption model with respect to comprehensive fuel consumption key impact factors that can reflect dynamic engine operations and various real-world driving conditions is essential for fuel efficient model-based vehicle and powertrain control (MB-VPC). Existing microscopic fuel c...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2020-09, Vol.69 (9), p.9553-9565
Main Authors: Ma, Xin, Shahbakhti, Mahdi, Chigan, Chunxiao
Format: Article
Language:English
Subjects:
Adaptation
Adaptation models
Adaptive fuel consumption modeling
Algorithms
Computational modeling
Connected vehicles
Data centers
Data mining
Data models
distributed meta-learning
Driving conditions
Engines
Fuel consumption
Fuels
Impact factors
Mathematical models
Model accuracy
Powertrain
Steady state models
Training
Vehicle dynamics
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Summary:A microscopic fuel consumption model with respect to comprehensive fuel consumption key impact factors that can reflect dynamic engine operations and various real-world driving conditions is essential for fuel efficient model-based vehicle and powertrain control (MB-VPC). Existing microscopic fuel consumption models are mostly steady state models with limited vehicle parameters as dynamic variables. Data-driven solutions can make the model comprehensive by introducing broad impact factors. However, without the required accessible online computational capability and fast model adaptation mechanisms, those data-driven solutions cannot quickly adapt to unseen driving conditions and engine condition changes based on real-world vehicle data to support MB-VPC. In this paper, a connected vehicle-based data mining (CV-DM) framework is proposed to achieve online adaptive dynamic fuel consumption modeling through knowledge sharing over CVs and the CV remote data center. Based on the CV-DM framework, CV-supported Distributed Meta-regression (CV-DMR) algorithms are developed to realize a fast few-shot adaptation with limited training data. Extensive proof-of-concept experiments are conducted with steady-state and transient vehicle engine data. Compared to the baseline physical model and the existing non-adaptive grey-box model, prediction accuracy is improved by 47%-85% and 38%-80% respectively with only limited training data needed for the subject vehicle. Accordingly, a fuel savings of up to 9.4% can be achieved owing to the improvement of prediction accuracy.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.3002491