Open Access
Issue |
E3S Web Conf.
Volume 500, 2024
The 1st International Conference on Environment, Green Technology, and Digital Society (INTERCONNECTS 2023)
|
|
---|---|---|
Article Number | 03005 | |
Number of page(s) | 11 | |
Section | Engineering and Technology | |
DOI | https://doi.org/10.1051/e3sconf/202450003005 | |
Published online | 11 March 2024 |
- Uslu, S. ; Celik, M.B. Performance and Exhaust Emission Prediction of a SI Engine Fueled with I amyl Alcohol-Gasoline Blends: An ANN Coupled RSM Based Optimization. Fuel 2020, 265, 116922. https://doi.org/10.1016/j.fuel.2019.116922, doi:10.1016/j.fuel.2019.116922. [CrossRef] [Google Scholar]
- Karagiorgis, S. ; Glover, K. ; Collings, N. Control Challenges in Automotive Engine Management. European Journal of Control 2007, 13, 92–104, doi:10.3166/ejc.13.92-104. [CrossRef] [Google Scholar]
- Singh, D. Russia-Ukraine war: How rising crude oil prices impact us in ways we don’t quite notice. [Google Scholar]
- Turner, B. Energy crisis: Europeans ’must lower thermostats to prepare for Russia turning off gas supplies. [Google Scholar]
- Al-fattah, S.M. Non-OPEC conventional oil: Production decline, supply outlook and key implications. Journal of Petroleum Science and Engineering 2020, 189, 107049. https://doi.org/10.1016/j.petrol.2020.1070, doi:10.1016/j.petrol.2020.107049. [CrossRef] [Google Scholar]
- Kutlu, O. Global oil production declines in June 2020. [Google Scholar]
- Chen, Z. ; Zhang, H. ; Xiong, R. ; Shen, W. ; Liu, B. Energy management strategy of connected hybrid electric vehicles considering electricity and oil price fluctuations: A case study of ten typical cities in China. Journal of Energy Storage 2021, 36. [Google Scholar]
- Biswal, A. ; Gedam, S. ; Balusamy, S. ; Kolhe, P. Effects of using ternary gasoline-ethanol-LPO blend on PFI engine performance and emissions. Fuel 2020, 281, 118664. https://doi.org/10.1016/j.fuel.2020.118664, doi:10.1016/j.fuel.2020.118664. [CrossRef] [Google Scholar]
- Mehra, R.K. ; Duan, H. ; Luo, S. ; Rao, A. ; Ma, F. Experimental and arti fi cial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios. Applied Energy 2018, 228, 736-754. https://doi.org/10.1016/j.apenergy.2018.0, doi:10.1016/j.apenergy.2018.06.085. [CrossRef] [Google Scholar]
- Xiong, H. ; Liu, H. ; Zhang, R. ; Yu, L. An energy matching method for battery electric vehicle and hydrogen fuel cell vehicle based on source energy consumption rate. International Journal of Hydrogen Energy 2019, 44, 29733-29742. https://doi.org/10.1016/j.ijhydene.20, doi:10.1016/j.ijhydene.2019.02.169. [CrossRef] [Google Scholar]
- Loveday, S. How Long Does It Take to Charge an Electric Car? [Google Scholar]
- Poullikkas, A. Sustainable options for electric vehicle technologies. Renewable and Sustainable Energy Reviews 2015, 41, 1277–1287, doi:10.1016/j.rser.2014.09.016. [CrossRef] [Google Scholar]
- Canepa, K. ; Hardman, S. ; Tal, G. An early look at plug-in electric vehicle adoption in disadvantaged communities in California. Transport Policy 2019, 78, 19–30, doi:10.1016/j.tranpol.2019.03.009. [CrossRef] [Google Scholar]
- Qiao, D. ; Wei, X. ; Fan, W. ; Jiang, B. ; Lai, X. ; Zheng, Y. ; Dai, H. ; Tang, X. Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles. Applied Energy 2022, 317, 119168, doi:https://doi.org/10.1016/j.apenergy.2022.119168. [CrossRef] [Google Scholar]
- Reed, S. Why Europe’s Electricity Prices Are Soaring. [Google Scholar]
- Irina, J. ; Benjamin, P. ; Benjamin, P. ; Benjamin, P. ; Benjamin, P. ; Irina, P.J. Model-based residual gas fraction control Model-based residual gas fraction control with spark advance optimization. IFAC PapersOnLine 2021, 54, 108–113, doi:10.1016/j.ifacol.2021.10.149. [Google Scholar]
- Liu, W. ; Safdari, M. ; Tlili, I. ; Maleki, A. ; Bach, Q. The effect of alcohol – gasoline fuel blends on the engines’ performances and emissions. Fuel 2020, 276, 117977, doi:10.1016/j.fuel.2020.117977. [CrossRef] [Google Scholar]
- Hunicz, J. ; Mikulski, M. ; Kosza, G. ; Ignaciuk, P. Detailed analysis of combustion stability in a spark-assisted compression ignition engine under nearly stoichiometric and heavy EGR conditions. Applied Energy 2020, 280, 115955, doi:10.1016/j.apenergy.2020.115955. [CrossRef] [Google Scholar]
- Li, Y. ; Khajepour, A. ; Devaud, C. ; Liu, K. Power and fuel economy optimizations of gasoline engines using hydraulic variable valve actuation system. Applied Energy 2017, 206, 577–593, doi:10.1016/j.apenergy.2017.08.208. [CrossRef] [Google Scholar]
- Wang, Y. ; Shi, Y. ; Cai, M. ; Xu, W. Predictive control of air-fuel ratio in aircraft engine on fuel-powered unmanned aerial vehicle using fuzzy-RBF neural network. Journal of the Franklin Institute 2020, 357, 8342-8363. https://doi.org/10.1016/j.jfranklin.202, doi:https://doi.org/10.1016/j.jfranklin.2020.03.016. [CrossRef] [Google Scholar]
- Deng, B. ; Li, Q. ; Chen, Y. ; Li, M. ; Liu, A. ; Ran, J. ; Xu, Y. ; Liu, X. ; Fu, J. ; Feng, R. The effect of air/fuel ratio on the CO and NOx emissions for a twin-spark motorcycle gasoline engine under wide range of operating conditions. Energy 2019, 169, 1202-1213. https://doi.org/10.1016/j.energy.2018.1, doi:10.1016/j.energy.2018.12.113. [CrossRef] [Google Scholar]
- Gianfranco Gagliardi ; Mari, D. ; Tedesco, F. ; Casavola, A. An Air-to-Fuel ratio estimation strategy for turbocharged spark-ignition engines based on sparse binary HEGO sensor measures and hybrid linear observers. Control Engineering Practice 2021, 107, 104694, doi:https://doi.org/10.1016/j.conengprac.2020.104694. [CrossRef] [Google Scholar]
- Ahmed, S. ; Al, F. Analyzing and predicting the relation between air – fuel ratio (AFR), lambda (λ) and the exhaust emissions percentages and values of gasoline – fueled vehicles using versatile and portable emissions measurement system tool. SN Applied Sciences 2019, 1, 1-12. https://doi.org/10.1007/s42452-019-1392–5, doi:10.1007/s42452-019-1392-5. [Google Scholar]
- Wong, P.K. ; Gao, X.H. ; Wong, K.I. ; Vong, C.M. ; Yang, Z.X. Initial-training-free online sequential extreme learning machine based adaptive engine air–fuel ratio control. International Journal of Machine Learning and Cybernetics 2019, 10, 2245–2256, doi:10.1007/s13042-018-0863-0. [CrossRef] [Google Scholar]
- Sardarmehni, T. ; Aghili Ashtiani, A. ; Menhaj, M.B. Fuzzy model predictive control of normalized air-to-fuel ratio in internal combustion engines. Soft Computing 2019, 23, 6169-6182. https://doi.org/10.1007/s00500-018–3270, doi:https://doi.org/10.1007/s00500-018-3270-2. [CrossRef] [Google Scholar]
- Xing, Y. ; Lv, C. ; Cao, D. ; Lu, C. Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling. Applied Energy 2020, 261, 114471.https://doi.org/10.1016/j.apenergy.2019.114, doi:10.1016/j.apenergy.2019.114471. [CrossRef] [Google Scholar]
- Zou, P. ; Liu, J. ; Zhou, X. ; Chen, Z. ; Luo, B. ; Shen, D. ; Duan, X. ; Fu, J. Effect of a novel mechanical CVVL system on economic performance of a turbocharged spark-ignition engine fuelled with gasoline and ethanol blend. Fuel 2020, 263, 116697, doi:10.1016/j.fuel.2019.116697. [CrossRef] [Google Scholar]
- Sharma, A. ; Zheng, Z. ; Bhaskar, A. ; Haque, M. Modelling car-following behaviour of connected vehicles with a focus on driver compliance. Transportation Research Part B 2019, 126, 256-279.https://doi.org/10.1016/j.trb.2019.06.008, doi:https://doi.org/10.1016/j.trb.2019.06.008. [CrossRef] [Google Scholar]
- Grove, K. ; Soccolich, S. ; Engström, J. ; Hanowski, R. Driver visual behavior while using adaptive cruise control on commercial motor vehicles q. Transportation Research Part F: Psychology and Behaviour 2019, 60, 343-352. https://doi.org/10.1016/j.trf.2018.10.013, doi:10.1016/j.trf.2018.10.013. [CrossRef] [Google Scholar]
- Fung, K.C. ; Dick, T.J. System and method for responding to driver behavior 2016, 2. [Google Scholar]
- Hong, Z. ; Chen, Y. ; Wu, Y. A driver behavior assessment and recommendation system for connected vehicles to produce safer driving environments through a “ follow the leader ” approach. Accident Analysis and Prevention 2020, 139, 105460. https://doi.org/10.1016/j.aap.2020.105460, doi:10.1016/j.aap.2020.105460. [CrossRef] [PubMed] [Google Scholar]
- Martinelli, F. ; Mercaldo, F. ; Orlando, A. ; Nardone, V. ; Santone, A. ; Kumar, A. Human behavior characterization for driving style recognition in vehicle system R. Computers and Electrical Engineering 2020, 83, 102504. https://doi.org/10.1016/j.compeleceng.2017, doi:https://doi.org/10.1016/j.compeleceng.2017.12.050. [CrossRef] [Google Scholar]
- Silver, A. ; Lewis, L. Automatic identification of a vehicle driver based on driving behavior 2015, 2. [Google Scholar]
- Yuan, Y. ; Lu, Y. ; Wang, Q. Adaptive forward vehicle collision warning based on driving behavior. Neurocomputing 2020, 408, 64-71. https://doi.org/10.1016/j.neucom.2019.11.02, doi:10.1016/j.neucom.2019.11.024. [CrossRef] [Google Scholar]
- Ashkrof, P. ; Homem, G. ; Correia, D.A. ; Arem, B. Van Analysis of the effect of charging needs on battery electric vehicle drivers ’ route choice behaviour: A case study in the Netherlands. Transportation Research Part D 2020, 78, 102206. https://doi.org/10.1016/j.trd.2019.102206, doi:10.1016/j.trd.2019.102206. [CrossRef] [Google Scholar]
- Hongbo, G. ; Guotao, X. ; Hongzhe, L. ; Xinyu, Z. Lateral control of autonomous vehicles based on learning driver behavior via cloud model. The Journal of China Universities of Posts and Telecommunications 2017, 24, 10-17. http://dx.doi.org/10.1016/S1005-8885(17)601, doi:10.1016/S1005-8885(17)60194-8. [CrossRef] [Google Scholar]
- Yansong, R. ; O’Gorman, L. ; Wood, T.L. Driver behavior monitoring systems and methods for driver behavior monitoring 2019, 2. [Google Scholar]
- Stogios, C. ; Kasraian, D. ; Roorda, M.J. ; Hatzopoulou, M. Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions. Transportation Research Part D 2019, 76, 176-192. https://doi.org/10.1016/j.trd.2019.09.020, doi:10.1016/j.trd.2019.09.020. [CrossRef] [Google Scholar]
- Kohl, J. ; Gross, A. ; Henning, M. ; Baumgarten, T. Driver glance behavior towards displayed images on in-vehicle information systems under real driving conditions. Transportation Research Part F: Psychology and Behaviour 2020, 70, 163-174. https://doi.org/10.1016/j.trf.2020.01.017, doi:10.1016/j.trf.2020.01.017. [CrossRef] [Google Scholar]
- Zhao, X. ; Wang, Z. ; Xu, Z. ; Wang, Y. ; Li, X. ; Qu, X. Field experiments on longitudinal characteristics of human driver behavior following an autonomous vehicle. Transportation Research Part C 2020, 114, 205-224. https://doi.org/10.1016/j.trc.2020.02.018, doi:10.1016/j.trc.2020.02.018. [CrossRef] [Google Scholar]
- Fadhloun, K. ; Rakha, H. A novel vehicle dynamics and human behavior car-following model: Model development and preliminary testing. International Journal of Transportation Science and Technology 2020, 9, 14-28.https://doi.org/10.1016/j.ijtst.2019.05.004., doi:10.1016/j.ijtst.2019.05.004. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.