Model-Based Control of Torque and Nitrogen Oxide Emissions in a Euro VI 3.0 L Diesel Engine through Rapid Prototyping
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- Fabio Cococcetta & Roberto Finesso & Gilles Hardy & Omar Marello & Ezio Spessa, 2019. "Implementation and Assessment of a Model-Based Controller of Torque and Nitrogen Oxide Emissions in an 11 L Heavy-Duty Diesel Engine," Energies, MDPI, vol. 12(24), pages 1-19, December.
- Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
- Edgar Talavera & Alberto Díaz-Álvarez & Felipe Jiménez & José E. Naranjo, 2018. "Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation," Energies, MDPI, vol. 11(1), pages 1-17, January.
- Stefano d’Ambrosio & Alessandro Ferrari & Alessandro Mancarella & Salvatore Mancò & Antonio Mittica, 2019. "Comparison of the Emissions, Noise, and Fuel Consumption Comparison of Direct and Indirect Piezoelectric and Solenoid Injectors in a Low-Compression-Ratio Diesel Engine," Energies, MDPI, vol. 12(21), pages 1-16, October.
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- Alessandro Mancarella & Omar Marello, 2022. "Effect of Coolant Temperature on Performance and Emissions of a Compression Ignition Engine Running on Conventional Diesel and Hydrotreated Vegetable Oil (HVO)," Energies, MDPI, vol. 16(1), pages 1-27, December.
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Keywords
torque; nitrogen oxide emissions; model-based control; diesel engines;All these keywords.
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