Data driven analysis and forecasting of medium and heavy truck fuel consumption

2020 ◽  
pp. 1-22
Author(s):  
Thomas Bousonville ◽  
David Cheubou Kamga ◽  
Thilo Krüger ◽  
Martin Dirichs
Keyword(s):  
Fuel Consumption ◽  
Data Driven ◽  
Heavy Truck

Data-Driven Prediction Method for Truck Fuel Consumption Based on Car Networking

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Keke Long ◽  
Guanqun Wang ◽  
Zhigang Xu ◽  
Xiaoguang Yang
Keyword(s):  
Fuel Consumption ◽  
Prediction Method ◽  
Data Driven

scholarly journals Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons

2021 ◽  
Author(s):  
Hadi Meidani ◽  
◽  
Amir Kazemi ◽  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Fuel Consumption ◽  
Data Driven ◽  
Dynamics Model ◽  
Drag Forces ◽  
Consumption Reduction ◽  
Energy Economy ◽  
Computational Fluid Dynamics Cfd ◽  
Large Systems

Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncertainties such as random truck geometries, variable truck speed, random wind direction, and wind magnitude. Once trained, such a surrogate-based model can be readily employed for platoon-routing problems or the study of pavement performance.

Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case

2017 ◽  
Vol 56 ◽  
pp. 258-270 ◽  
Author(s):  
Jenny Díaz-Ramirez ◽  
Nicolas Giraldo-Peralta ◽  
Daniela Flórez-Ceron ◽  
Vivian Rangel ◽  
Christopher Mejía-Argueta ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Key Factors ◽  
Heavy Truck

scholarly journals Data-Driven Estimation of Heavy-Truck Residual Value at the Buy-Back

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 102409-102418
Author(s):  
Tania Cerquitelli ◽  
Andrea Regalia ◽  
Emanuele Manfredi ◽  
Fabrizio Conicella ◽  
Paolo Bethaz ◽  
...  
Keyword(s):  
Data Driven ◽  
Residual Value ◽  
Heavy Truck ◽  
Buy Back

scholarly journals Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods

2020 ◽  
Vol 13 (1) ◽  
pp. 226
Author(s):  
Panagiotis Fafoutellis ◽  
Eleni G. Mantouka ◽  
Eleni I. Vlahogianni
Keyword(s):  
Fuel Consumption ◽  
System Performance ◽  
Air Conditioning ◽  
Recent Literature ◽  
Fuel Efficiency ◽  
Data Driven ◽  
Route Selection ◽  
Factors Affecting ◽  
Naturalistic Driving ◽  
Multidimensional Concept

Eco-driving is a multidimensional concept that includes driving behavior, route selection and all other choices or behaviors related to the vehicles’ fuel consumption (e.g., the use of quality fuel, the use of air conditioning, driving at peak hours, etc.). The scope of this paper is to present an overview of recent literature referring to eco-driving and developed models for calculating fuel consumption, as well as the most important factors affecting it. Recent literature contains a large number of models that estimate fuel consumption, based on naturalistic driving data, which are collected using smartphones and OBDs. In this work, the existing literature is critically assessed in relation to conceptual, methodological and data related aspects. The analyses result to a set of limitations and challenges that are further discussed in the framework of system wide implementations for deriving policies that increase drivers’ awareness, but also improve system performance.

scholarly journals Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling

Aerospace ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 44
Author(s):  
Mevlut Uzun ◽  
Mustafa Umut Demirezen ◽  
Gokhan Inalhan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Supervised Learning ◽  
Fuel Consumption ◽  
Performance Model ◽  
Training Data ◽  
Data Driven ◽  
Physical Knowledge ◽  
Learning Techniques ◽  
Flight Regimes

This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations that represent fuel flow dynamics. In addition to the empirical error, we embed this physical knowledge as several extra loss terms. Results show that our proposed model accomplishes correct predictions on the labeled test set, as well as assuring physical consistency in unseen flight regimes. The results indicate that our model, while being applicable to the aircraft’s complete flight envelope, yields lower fuel consumption error measures compared to the model-based approaches and other supervised learning techniques utilizing the same training data sets. In addition, our deep learning model produces fuel consumption trends similar to the BADA4 aircraft performance model, which is widely utilized in real-world operations, in unseen and untrained flight regimes. In contrast, the other supervised learning techniques fail to produce meaningful results. Overall, the proposed methodology enhances the explainability of data-driven models without deteriorating accuracy.

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