Heavy duty vehicle fuel consumption modeling using artificial neural networks

Wysocki, Oskar; Deka, Lipika; Elizondo, David

doi:10.23919/iconac.2019.8895072

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…However, it should be noted that the model they proposed was trained only on a local dataset, which implies its strong regional bias and limited applicability in other regions. As a result, Wysocki et al [13] trained a fuel consumption model using heavy-duty truck driving data collected over a five-year span. They employed an ANN model and achieved an RMSE of 0.32.…”

Section: Neural Network Modelmentioning

confidence: 99%

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

Zhao

Hou

et al. 2023

Energies

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Accurately and efficiently predicting the fuel consumption of vehicles is the key to improving their fuel economy. This paper provides a comprehensive review of data-driven fuel consumption prediction models. Firstly, by classifying and summarizing relevant data that affect fuel consumption, it was pointed out that commonly used data currently involve three aspects: vehicle performance, driving behavior, and driving environment. Then, from the model structure, the predictive energy and the characteristics of the traditional machine learning model (support vector machine, random forest), the neural network model (artificial neural network and deep neural network), and this paper point out that: (1) the prediction model of fuel consumption based on neural networks has a higher data processing ability, higher training speed, and stable prediction ability; (2) by combining the advantages of different models to build a hybrid model for fuel consumption prediction, the prediction accuracy of fuel consumption can be greatly improved; (3) when comparing the relevant indicts, both the neural network method and the hybrid model consistently exhibit a coefficient of determination above 0.90 and a root mean square error below 0.40. Finally, the summary and prospect analysis are given based on various models’ predictive performance and application status.

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Section: Neural Network Modelmentioning

confidence: 99%

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

Zhao

Hou

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…Neural Network has good nonlinear mapping ability, adaptive learning ability, and parallel information processing ability (Illahi et al, 2019;Tran, 2019), and thus it has been widely used in prediction (Wysocki et al, 2019;Yuan et al, 2019). Neural Network is the most typical BBM which has good performance in ship energy consumption prediction (Kim et al, 2021;Zheng et al, 2019;Gkerekos and Lazakis, 2020), and it mainly includes BPNN, MLPN, LSTM, DNN, and CNN.…”

Section: Prediction Of Ship Energy Consumption Based On Neural Networkmentioning

confidence: 99%