Real-Time Prediction of Fuel Consumption Based on Digital Map API
Source abstract
Abstract
At present, digital maps can estimate the travel time of each trip’s route but cannot offer a fuel consumption estimation at the same time. In this paper, we develop a fuel consumption model based on the Vehicle Specific Power (VSP) distribution, which can connect the traffic condition prediction with the fuel consumption model to predict fuel consumption. First, the traffic condition forecasting and the trip time of each route can be obtained through the digital map Application Programming Interface (API). Secondly, the users need to provide the engine displacement of their vehicles to match the fuel consumption model. Then, the fuel consumption prediction application based on Android is developed to forecast the fuel consumption by using traffic prediction data. Finally, the fuel consumption provided by the On-Board Diagnostic (OBD) data is used to verify the proposed application, and the forecasting error is less than 20%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle emissions and performance · Transportation Planning and Optimization · Traffic control and management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
References (25)
Show source-provided references
1. A tank-to-wheel analysis tool for energy and emissions studies in road vehicles10.1016/j.scitotenv.2006.02.020 ↗
2. Barth, M. (2010). The Comprehensive Modal Emission Model (CMEM) for Predicting Light-Duty Vehicle Emissions. Transportation Planning and Air Quality IV: Persistent Problems and Promising Solutions, ASCE.
3. Analytic Modeling of Vehicle Fuel Consumption10.3390/en6010117 ↗
4. Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions10.1016/S1361-9209(03)00054-3 ↗
5. Estimating Vehicle Fuel Consumption and Emissions based on Instantaneous Speed and Acceleration Levels10.1061/(ASCE)0733-947X(2002)128:2(182) ↗
6. Cappiello, A., Chabini, I., Nam, E.K., Lue, A., and Zeid, M.A. (2002, January 3–6). A statistical model of vehicle emissions and fuel consumption. Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems, Singapore.
7. Fuel consumption and emission modelling by power demand and a comparison with other models10.1016/0191-2607(84)90126-2 ↗
8. Summary of microscopic Measurement Model of fuel consumption and Emission in Road Traffic
9. Comparing real-world fuel consumption for diesel- and hydrogen-fueled transit buses and implication for emissions10.1016/j.trd.2007.03.003 ↗
10. Barth, M., An, F., Younglove, T., Scora, G., Levine, C., Ross, M., and Wenzel, T. (2000). Comprehensive Modal Emissions Model (CMEM), Version 2.0, User’s Guide, University of California.
11. Designing On-Road Vehicle Test Programs for the Development of Effective Vehicle Emission Models10.1177/0361198105194100107 ↗
12. Modelling of the fuel consumption for passenger cars regarding driving characteristics10.1016/j.trd.2008.09.002 ↗
13. U.S. Environmental Protection Agency (2009). Motor Vehicle Emission Simulator (MOVES) 2010 User Guide.
14. Distribution characteristics and model of low-speed vehicle specific power for emission quantification
15. Comprehensive Fuel Consumption Model of Light Vehicle Specific Power Based on Typical Mountainous City
16. Che, X., Yu, Q., Ren, G., and Liang, R. (2016, January 30–31). Average Fuel Consumption Calculation Model of Touring Coach Based on OBD Data- A Case Study in City X. Proceedings of the International Conference on Civil, Transportation and Environment, Guangzhou, China.10.2991/iccte-16.2016.241 ↗
17. A Methodology to Develop MaCro-Fuel Consumption Models for the Urban Transportation System
18. Instantaneous vehicle fuel consumption estimation using smartphones and recurrent neural networks10.1016/j.eswa.2018.12.006 ↗
19. A smartphone-based Intelligent Vehicle System to Minimize the Possibility of Traffic Congestion Occurrence10.3182/20130904-4-JP-2042.00125 ↗
20. The gap between certified and real-world passenger vehicle fuel consumption in China measured using a mobile phone application data10.1016/j.enpol.2018.12.039 ↗
21. Distribution Characteristics and Models of Vehicle Specific Power on Urban Expressways
22. Characteristics of VSP Distributions of Light-duty and Heavy-duty Vehicles on Freeway
23. VSP-Bin division for light-duty vehicles oriented to carbon dioxide
24. Characterizing real-world fuel consumption for light-duty passenger vehicles by using on-board diagnostic (OBD) systems
25. Real-time on-board monitoring method of gasoline vehicle fuel consumption based on OBD system