Recent Advances in Automotive Engines

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 10158

Special Issue Editor


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Guest Editor
Faculty of Science and Technology, University of Macau, Macau 999078, China
Interests: automotive engines, drive trains and chassis; intelligent automotive systems; artificial intelligence; fluid power engineering; mechanical vibration; manufacturing technology for biomedical applications
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Special Issue Information

Dear Colleagues,

Automotive engines have dominated the transportation sector and motorsports for many decades. Automotive engines are usually internal combustion (IC) engines. The ability of an IC engine to operate efficiently over a wide range of speeds and loads, as well as its good drivability, have been major contributing factors to this dominance. At present, the main directions of automotive engine research are the minimization of fuel consumption; the reduction of exhaust pollutants; and the application of artificial intelligence to engine diagnosis, analysis, control, and calibration. To this aim, various engine systems and control techniques have been developed, or are under development; various fuels, combustion process controls and exhaust gas after-treatment measures are also being examined. This Special Issue aims to create an international forum for scientists and practicing engineers throughout the world to publish the latest research findings and ideas on experimental and analytical studies of automotive engine technology.

This Special Issue solicits original research articles as well as review articles. The topics of interest include, but are not limited to:

  • Engine emission analysis and control;
  • Combustion mechanisms in spark and compression ignition engines;
  • Engine performance measurement;
  • Advances in turbocharged engines;
  • Novel combustion systems (HCCI, PCCI, and RCCI);
  • Alternative fuels and biofuels;
  • Variable valve actuation;
  • Advances in engine systems (engine management, fuel injection, cooling, lubrication, ignition, starter, intake and exhaust systems, etc.);
  • Advanced engine design, control, and analysis techniques;
  • Engine diagnosis techniques;
  • Technology for motor racing;
  • Mild hybrids;
  • Automotive engine applications of artificial intelligence.

Prof. Dr. Pak Kin Wong
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vehicles is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • internal combustion engines
  • engine systems
  • engine emissions
  • alternative fuels
  • engine control
  • automotive engine applications of artificial intelligence

Published Papers (3 papers)

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Research

18 pages, 42272 KiB  
Article
Detecting the Flame Front Evolution in Spark-Ignition Engine under Lean Condition Using the Mask R-CNN Approach
by Luca Petrucci, Federico Ricci, Roberto Martinelli and Francesco Mariani
Vehicles 2022, 4(4), 978-995; https://doi.org/10.3390/vehicles4040053 - 26 Sep 2022
Cited by 8 | Viewed by 1985
Abstract
In the wake of previous works, the authors propose a new approach for the identification and evolution of the flame front in an optical SI engine. Currently, it is an essential prerogative to characterize the capability of innovative igniters to guarantee earlier flame [...] Read more.
In the wake of previous works, the authors propose a new approach for the identification and evolution of the flame front in an optical SI engine. Currently, it is an essential prerogative to characterize the capability of innovative igniters to guarantee earlier flame development in critical operating conditions, such as ultra-lean mixture, towards which automotive research is moving to deal with the ever more stringent regulations on pollutant emissions. The core of the new approach lies in the R-CNN Mask method. The latter consists of a conceptually simple and general framework for object instance segmentation. It can efficiently detect objects contained in an image while simultaneously generating a high-quality segmentation mask for each instance. In particular, the aim this work is to develop an automatized algorithm for detecting, as objectively as possible, the flame front evolution of lean/ultra-lean mixtures ignited by low-temperature plasma-based ignition systems. The capability of the Mask R-CNN algorithm to automatically estimate the binarized area, without setting a defined binarized threshold, allows us to perform an analysis of the flame front evolution completely independent from the user interpretation. Mask R-CNN can detect the kernel in advance and can identify events as regular combustions instead of misfires or anomalies if compared to other traditional approaches. These features make the proposed method the most suitable option to analysis the real behavior of the innovative ignition systems at critical operating conditions. Full article
(This article belongs to the Special Issue Recent Advances in Automotive Engines)
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17 pages, 3257 KiB  
Article
A Study on Performance Evaluation of Biodiesel from Grape Seed Oil and Its Blends for Diesel Vehicles
by Adebayo Fadairo and Weng Fai Ip
Vehicles 2021, 3(4), 790-806; https://doi.org/10.3390/vehicles3040047 - 23 Nov 2021
Cited by 3 | Viewed by 3441
Abstract
With incessant increases in fuel prices worldwide and concerns for environmental pollution, the need for alternative sources of energy is becoming urgent. In this study, the potential of grape seed oil for biodiesel as an alternative fuel was evaluated. Refined grape seed oil [...] Read more.
With incessant increases in fuel prices worldwide and concerns for environmental pollution, the need for alternative sources of energy is becoming urgent. In this study, the potential of grape seed oil for biodiesel as an alternative fuel was evaluated. Refined grape seed oil was bought in liquid form and then subjected to an alkali-catalyzed transesterification process for biodiesel production. The physicochemical properties of the resulting biodiesel—namely, viscosity, cetane number, and heating value—were investigated. The biodiesel was blended with a conventional diesel in various proportions and combusted in a four-cylinder, four-stroke compression ignition (diesel) engine under two loading conditions. Experimental results revealed that the blend ratio of B70 (70% GS biodiesel and 30% conventional diesel) gave the best overall engine performance in terms of maximum power, minimum emissions, and fuel consumption. Furthermore, a novel neural network technique called extreme learning machine was adopted to investigate the optimal blend ratio using the dataset obtained from the experimental results. The results also indicate that the best choice of biodiesel blend ratio is approximately B73.67 (73.67% GS biodiesel and 26.33% conventional diesel). The study shows that grape seed oil could serve as a reliable source of production of quality biodiesel fuels, which could be used as an alternative to conventional diesel fuels. Full article
(This article belongs to the Special Issue Recent Advances in Automotive Engines)
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15 pages, 2506 KiB  
Article
Predictive Model of Adaptive Cruise Control Speed to Enhance Engine Operating Conditions
by Srikanth Kolachalama and Hafiz Malik
Vehicles 2021, 3(4), 749-763; https://doi.org/10.3390/vehicles3040044 - 9 Nov 2021
Cited by 5 | Viewed by 3069
Abstract
This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing the engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as the affecting parameters. This paper investigates engine operating [...] Read more.
This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing the engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as the affecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the VLV. We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived at each second. Thus, three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. A performance comparison between predicted and constant ACCSSP’s indicates that the predictive model outperforms constant ACCSSP. Full article
(This article belongs to the Special Issue Recent Advances in Automotive Engines)
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