A Data-Driven Fuel Consumption Estimation Model for Airspace Redesign Analysis

Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed datalogger with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications.

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Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

Energies ◽

10.3390/en12244772 ◽

2019 ◽

Vol 12 (24) ◽

pp. 4772 ◽

Cited By ~ 2

Author(s):

Kaizhi Liang ◽

Zhaosheng Zhang ◽

Peng Liu ◽

Zhenpo Wang ◽

Shangfeng Jiang

Keyword(s):

Electric Vehicles ◽

Estimation Method ◽

Recursive Least Squares ◽

Data Driven ◽

Gradient Boosting ◽

Estimation Model ◽

Ohmic Resistance ◽

Extreme Gradient Boosting ◽

Battery Packs ◽

Mean Square Errors

Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.

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Development of a Practical Method to Estimate the Eco-Level of Driver Performance

Journal of Advanced Transportation ◽

10.1155/2020/8151720 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Jianbin Zheng ◽

Yiping Wu

Keyword(s):

Fuel Consumption ◽

Fuel Efficiency ◽

Back Propagation ◽

Practical Method ◽

Estimation Accuracy ◽

Road Transportation ◽

Estimation Model ◽

Driver Performance ◽

Study Results ◽

Propagation Network

Motor vehicle’s fuel consumption is one of the main sources of energy consumption in road transportation and is highly influenced by driver performance in the process of driving. Eco-driving behavior has been proved to be an effective way to improve the fuel efficiency of vehicles. Essential to the efforts towards saving vehicle fuel is the need to estimate the eco-level of driver performance accurately and practically. Depending on on-board diagnostics and Global Position devices, individual vehicle’s instantaneous fuel consumption, engine revolution and torque, speed, acceleration, and dynamic location were collected. Back-propagation network was adopted to explore the relationship between vehicle fuel consumption and the parameters of driver performance. Taking 700 data samples in basic segments of urban expressways as our training set and 100 data samples as validation test, we found the optimal model structure and parameters through repeated simulation experiments. In addition to the average and standard deviation value, the fluctuation frequency of driver performance data was also viewed as influence factors in eco-level estimation model. The average estimation accuracy of our developed model has been tested to be 96.37%, which is quite higher than that of linear regression model. The study results provide a practical way to evaluate drivers’ performance from the perspective of fuel consumption and thus give basis for rewarding best drivers within eco-driving programs.

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Vehicle Fuel Consumption Estimation Model for Impact Analysis of Changing Car Related Tax

INFRASTRUCTURE PLANNING REVIEW ◽

10.2208/journalip.16.455 ◽

1999 ◽

Vol 16 ◽

pp. 455-463 ◽

Cited By ~ 1

Author(s):

Kenichiro ENDO ◽

Masayoshi TANISHITA ◽

Shigeru KASHIMA

Keyword(s):

Fuel Consumption ◽

Impact Analysis ◽

Estimation Model

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

CICTP 2020 ◽

10.1061/9780784483053.055 ◽

2020 ◽

Author(s):

Keke Long ◽

Guanqun Wang ◽

Zhigang Xu ◽

Xiaoguang Yang

Keyword(s):

Fuel Consumption ◽

Prediction Method ◽

Data Driven

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Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118798286 ◽

2018 ◽

Vol 2672 (24) ◽

pp. 41-48 ◽

Cited By ~ 3

Author(s):

Jacob Holden ◽

Harrison Van Til ◽

Eric Wood ◽

Lei Zhu ◽

Jeffrey Gonder ◽

...

Keyword(s):

Energy Consumption ◽

Fuel Consumption ◽

Real World ◽

Energy Use ◽

Average Energy ◽

Estimation Model ◽

Energy Estimation ◽

Conventional Vehicle ◽

Look Up Table ◽

Wide Range

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin–destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

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

Enterprise Information Systems ◽

10.1080/17517575.2020.1856417 ◽

2020 ◽

pp. 1-22

Author(s):

Thomas Bousonville ◽

David Cheubou Kamga ◽

Thilo Krüger ◽

Martin Dirichs

Keyword(s):

Fuel Consumption ◽

Data Driven ◽

Heavy Truck

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

10.36501/0197-9191/21-036 ◽

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.

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Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

Energies ◽

10.3390/en12071273 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1273 ◽

Cited By ~ 9

Author(s):

Antonio Attanasio ◽

Marco Piscitelli ◽

Silvia Chiusano ◽

Alfonso Capozzoli ◽

Tania Cerquitelli

Keyword(s):

Decision Tree ◽

Energy Demand ◽

Building Energy ◽

Energy Performance ◽

Primary Energy ◽

Machine Learning Algorithms ◽

Data Driven ◽

Support Vector ◽

Estimation Model ◽

Primary Energy Demand

Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating ( P E D h ) and (ii) the characterization of the relationship between the P E D h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the P E D h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance.

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