Modeling the unobserved heterogeneity effects of the factors influencing the fuel consumption of heavy-duty diesel trucks under real road conditions: A preliminary investigation in China

Zhang, Changjian; Gong, Jian; He, Jie; Bai, Chunguang; Yan, Xintong; Wang, Chenwei; Ye, Yuntao; Wang, Haifeng

doi:10.1016/j.egyr.2022.07.121

Cited by 4 publications

(1 citation statement)

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“…In the paper [12] suggest methods for modelling fuel consumption with driving behaviour. The authors have suggested a novel energy consumption index obtained by calibrating a semi-physical model and a data-driven approach obtained by training a Generalised Regression Neural Network model to represent the dynamic relationship between truck fuel consumption and driving behaviour.…”

Section: Introductionmentioning

confidence: 99%

We Prefer to Stay Grounded!

Gupta,

Kameswaran,

Singh

2024

NMI

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We have proposed a three-pronged approach to identify sustainable practices during road construction: (1) Route segmentation and identification of drive phases, including loading and dumping stages; (2) Use of Digital Elevation Models (DEM) and digital terrain models(DTM) acquired by UAVs to measure progress of construction from volumetric changes; and (3) Use of vegetation indices derived from satellite imagery to calculate changes in carbon stock. The paper introduces a novel technique for identifying driving phases for heavy trucks based only on GPS data. We also delve into how these approaches can serve as the foundational elements in the creation of digital twins tailored to the construction industry.

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Section: Introductionmentioning

confidence: 99%

We Prefer to Stay Grounded!

Gupta,

Kameswaran,

Singh

2024

NMI

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Instantaneous CO2 emission modelling for a Euro 6 start-stop vehicle based on portable emission measurement system data and artificial intelligence methods

Mądziel

2023

Environ Sci Pollut Res

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One of the increasingly common methods to counteract the increased fuel consumption of vehicles is start-stop technology. This paper introduces a methodology which presents the process of measuring and creating a computational model of CO2 emissions using artificial intelligence techniques for a vehicle equipped with start-stop technology. The method requires only measurement data of velocity, acceleration of vehicle, and gradient of road to predict the emission of CO2. In this paper, three methods of machine learning techniques were analyzed, while the best prediction results are shown by the gradient boosting method. For the developed models, the results were validated using the coefficient of determination, the mean squared error, and based on visual evaluation of residual and instantaneous emission plots and CO2 emission maps. The developed models present a novel methodology and can be used for microscale environmental analysis.

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