Development of a novel engine power model to estimate heavy-duty truck fuel consumption

References

• Barth, M., G. Scora, and T. Younglove. 2004. “Modal Emissions Model for Heavy-Duty Diesel Vehicles.” Transportation Research Record: Journal of the Transportation Research Board 1880: 10–20.
   Google Scholar
• Bengio, Y., P. Simard, and P. J. I. t. o. n. n. Frasconi. 1994. “Learning Long-Term Dependencies with Gradient Descent is Difficult.” IEEE transactions on neural networks. 5 (2): 157–166.
   Google Scholar
• Chen, Y.-y., Y. Lv, Z. Li, and F.-Y. Wang. 2016. Long Short-Term Memory Model for Traffic Congestion Prediction with Online Open Data. (Ed.), ∧(Eds.). Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on.
   Google Scholar
• Connor, J. T., R. D. Martin, and L. E. Atlas. 1994. “Recurrent Neural Networks and Robust Time Series Prediction.” IEEE Transactions on Neural Networks 5 (2): 240–254.
   Google Scholar
• Edwardes, W., and H. Rakha. 2014. “Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Modeling Diesel and Hybrid Buses.” Transportation Research Record: Journal of the Transportation Research Board 2428: 1–9.
   Google Scholar
• EPA. 2015. Exhaust Emission Rates for Heavy-Duty On-road Vehicles in MOVES2014.
   Google Scholar
• FHWA. 2018. FHWA Forecasts of Vehicle Miles Traveled (VMT): Spring 2018.
   Google Scholar
• Gers, F. A., N. N. Schraudolph, and J. Schmidhuber. 2002. “Learning Precise Timing with LSTM Recurrent Networks.” Journal of Machine Learning Research 3 (1): 115–143.
   Google Scholar
• Giannelli, R. A., E. Nam, K. Helmer, T. Younglove, G. Scora, and M. Barth. 2005. Heavy-Duty Diesel Vehicle Fuel Consumption Modeling Based on Road Load and Power Train Parameters (No. 0148-7191).
   Google Scholar
• Hausberger, S., M. Rexeis, and R. Luz. 2013. PHEM Passenger car and Heavy Duty Emissions Model User Guide for Version 11, Graz University of Technology.
   Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. “LSTM Can Solve Hard Long Time lag Problems.” Advances in Neural Information Processing Systems 473–479.
   Google Scholar
• Huang, X., J. Sun, and J. Sun. 2018. “A car-Following Model Considering Asymmetric Driving Behavior Based on Long Short-Term Memory Neural Networks.” Transportation Research Part C-Emerging Technologies 95: 346–362.
   Google Scholar
• Kanarachos, S., J. Mathew, and M. E. J. E. S. w. A. Fitzpatrick. 2019. “Instantaneous Vehicle Fuel Consumption Estimation Using Smartphones and Recurrent Neural Networks.” Expert Systems with Applications  120: 436–447.
   Google Scholar
• Kingma, D., and J. Ba. 2014. Adam: A Method for Stochastic Optimization. Computer Science.
   Google Scholar
• Kingma, D. P., and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:.
   Google Scholar
• Lechner, G., and H. Naunheimer. 1999. Automotive Transmissions: Fundamentals, Selection, Design and Application. Berlin, New York: Springer Science & Business Media.
   Google Scholar
• Ma, X., Z. Tao, Y. Wang, H. Yu, and Y. Wang. 2015. “Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data.” Transportation Research Part C: Emerging Technologies 54: 187–197.
   Google Scholar
• McAuliffe, B., M. Lammert, X.-Y. Lu, S. Shladover, M.-D. Surcel, and A. Kailas. 2018. Influences on Energy Savings of Heavy Trucks Using Cooperative Adaptive Cruise Control (No. 0148-7191).
   Google Scholar
• MOVES-Technical-Report-a. 2015. Greenhouse Gas and Engery Consumption Rates for on-road Vehicles: Updates for MOVES 2014. http://neipis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100NNUQ.TXT.
   Google Scholar
• Rakha, H. A., K. Ahn, K. Moran, B. Saerens, and E. Van den Bulck. 2011. “Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model Development and Testing.” Transportation Research Part D: Transport Environment 16 (7): 492–503.
   Google Scholar
• Rakha, H., I. Lucic, S. H. Demarchi, J. R. Setti, and M. V. Aerde. 2001. “Vehicle Dynamics Model for Predicting Maximum Truck Acceleration Levels.” Journal of Transportation Engineering 127 (5): 418–425.
   Google Scholar
• Ramezani, H., S. E. Shladover, X.-Y. Lu, and F.-C. Chou. 2018. Microsimulation Framework to Explore Impact of Truck Platooning on Traffic Operation and Energy Consumption: Development and Case Study.
   Google Scholar
• Ramnezani, H., X.-Y. Lu, and S. E. Shladover. 2019. Calibration of Motor Vehicle Emission Simulator (MOVES) Using Real Heavy-Duty Truck Data.
   Google Scholar
• Smit, R., R. Smokers, and E. Rabé. 2007. “A new Modelling Approach for Road Traffic Emissions: VERSIT+.” Transportation Research Part D: Transport Environment 12 (6): 414–422.
   Google Scholar
• Teresa Pamula, W. P. 2020. “Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning.” Energies 13 (9): 2340. https://www.mdpi.com/1996-1073/13/9/2340#cite
   Google Scholar
• Wang, J., and H. A. Rakha. 2017. “Fuel Consumption Model for Heavy Duty Diesel Trucks: Model Development and Testing.” Transportation Research Part D: Transport and Environment 55: 127–141. doi:10.1016/j.trd.2017.06.011.
   Google Scholar
• Wong, J. Y. 2001. Theory of Ground Vehicles. New York: John Wiley & Sons.
   Google Scholar