Name
Title | ||
|---|---|---|
On modeling microscopic vehicle fuel consumption using radial basis function neural network |
Abstract | ||
|---|---|---|
Energy conservation is one of the central challenges for the transportation system today. A variety of microscopic vehicle fuel consumption models have been developed to support eco-friendly transport strategies. However, most existing models are regression based and are sensitive to the vehicle-specific parameters and the operating conditions, therefore, an expensive and time-consuming calibration procedure is always indispensable in these models’ application. In this paper, we propose an artificial neural network-based model to avoid the calibration problem. The main works include: (1) collect extensive field datasets such as large-scale controller area network bus to reflect the local transportation environment’s fuel consumption characteristics; (2) conduct correlational analysis to identify the key fuel consumption influence factors; (3) develop a radial basis function neural network-based learning model to capture the nonlinear relationship between the key factors and the corresponding fuel consumption values based on the collected training datasets. The proposed model can give a reasonable prediction of instantaneous fuel consumption without calibration. The effectiveness of the proposed model is validated from a combination of both in-lab and field experiments. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00500-015-1676-7 | Soft Comput. |
Keywords | Field | DocType |
Microscopic vehicle fuel consumption estimation, RBF neural network, Regression-based model, RBF neural network center selection | CAN bus,Mathematical optimization,Energy conservation,Nonlinear system,Regression,Computer science,Radial basis function neural,Artificial intelligence,Fuel efficiency,Artificial neural network,Machine learning,Calibration | Journal |
Volume | Issue | ISSN |
20 | 7 | 1433-7479 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| jian huang | 1 | 0 | 0.34 |
| yanni wang | 2 | 0 | 0.34 |
| Zheli Liu | 3 | 356 | 28.79 |
| boyuan guan | 4 | 0 | 0.34 |
| dan long | 5 | 0 | 0.34 |
| Xiaoping Du | 6 | 17 | 8.22 |