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Keywords = co-simulation model
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23 pages, 6240 KB  
Article
Efficient Anticipatory Longitudinal Control of Electric Vehicles through Machine Learning-Based Prediction of Vehicle Speeds
by Tobias Eichenlaub, Paul Heckelmann and Stephan Rinderknecht
Vehicles 2023, 5(1), 1-23; https://doi.org/10.3390/vehicles5010001 - 21 Dec 2022
Cited by 7 | Viewed by 3630
Abstract
Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of [...] Read more.
Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of automated driving. The control approach incorporates information from Vehicle-2-Everything communication to anticipate the behavior of leading vehicles and to adapt the longitudinal control of the vehicle accordingly. A supervised learning approach is derived to train a neural prediction model based on a recurrent neural network for the speed trajectories of the ego and leading vehicles. For the development, analysis and evaluation of the proposed control approach, a co-simulation environment is presented that combines a generic vehicle model with a microscopic traffic simulation. This allows for the simulation of vehicles with different powertrains in complex urban traffic environment. The investigation shows that using V2X information improves the prediction of vehicle speeds significantly. The control approach can make use of this prediction to achieve a more anticipatory driving in urban areas which can reduce the energy consumption compared to a conventional Adaptive Cruise Control approach. Full article
(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)
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21 pages, 3566 KB  
Review
Adsorption Factors in Enhanced Coal Bed Methane Recovery: A Review
by Theodora Noely Tambaria, Yuichi Sugai and Ronald Nguele
Gases 2022, 2(1), 1-21; https://doi.org/10.3390/gases2010001 - 14 Jan 2022
Cited by 22 | Viewed by 8314
Abstract
Enhanced coal bed methane recovery using gas injection can provide increased methane extraction depending on the characteristics of the coal and the gas that is used. Accurate prediction of the extent of gas adsorption by coal are therefore important. Both experimental methods and [...] Read more.
Enhanced coal bed methane recovery using gas injection can provide increased methane extraction depending on the characteristics of the coal and the gas that is used. Accurate prediction of the extent of gas adsorption by coal are therefore important. Both experimental methods and modeling have been used to assess gas adsorption and its effects, including volumetric and gravimetric techniques, as well as the Ono–Kondo model and other numerical simulations. Thermodynamic parameters may be used to model adsorption on coal surfaces while adsorption isotherms can be used to predict adsorption on coal pores. In addition, density functional theory and grand canonical Monte Carlo methods may be employed. Complementary analytical techniques include Fourier transform infrared, Raman spectroscopy, XR diffraction, and 13C nuclear magnetic resonance spectroscopy. This review summarizes the cutting-edge research concerning the adsorption of CO2, N2, or mixture gas onto coal surfaces and into coal pores based on both experimental studies and simulations. Full article
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