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Energies
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Vehicle Engines and Powertrains: Performance, Combustion and Emission—2nd Edition
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Vehicle Engines and Powertrains: Performance, Combustion and Emission—2nd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (10 March 2026) | Viewed by 2352

Special Issue Editor

Dr. Roberto Finesso

E-Mail Website
Guest Editor
Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: internal combustion engines; hybrid powertrains; combustion and emission formation modeling and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasingly stringent regulations in terms of pollutant emissions and CO2 emission targets are pushing companies in the automotive sector to investigate innovative technological solutions for engines and powertrains, which include powertrain electrification, innovative air-path and fuel-path control, innovative combustion concepts, advanced aftertreatment systems, sensor-based and model-based control of the combustion and emission formation processes, alternative fuels, techniques for the optimization of the powertrain energy fluxes and their integration with the emerging vehicle-to-everything (V2X) systems, as well as by means of artificial intelligence.

Taking into account this scenario, this Special Issue aims to encourage both academic and industrial researchers to present their latest findings concerning the previously mentioned aspects, which can lead to a significant contribution towards the achievement of green and sustainable mobility.

The authors should provide a comprehensive and scientifically sound overview of the most recent research and methodological approaches. Both experimental and methodological contributions are welcome.

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Dr. Roberto Finesso
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 250 words) can be sent to the Editorial Office for assessment.

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. Energies is an international peer-reviewed open access semimonthly 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 2600 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
  • powertrain electrification
  • powertrain and engine optimization
  • powertrain and engine modeling and control
  • emission formation modeling and control
  • air-path and fuel-path control
  • innovative combustion concepts
  • advanced aftertreatment systems
  • alternative fuels
  • artificial intelligence systems
  • model-in-the-loop (MiL), hardware-in-the-loop (HiL), rapid prototyping (RP)
  • energy management optimization algorithms

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Related Special Issue

Published Papers (3 papers)

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Research

30 pages, 4058 KB  
Article
Dimethyl Ether as a Compression Ignition Engine Fuel for Simultaneous NOx and PM Reduction
by Matthias Rollins, Juan Felipe Rodriguez, Bret C. Windom and Daniel B. Olsen
Energies 2026, 19(10), 2439; https://doi.org/10.3390/en19102439 - 19 May 2026
Abstract
Dimethyl ether (DME) is a promising alternative fuel for compression ignition (CI) engines due to its potential to simultaneously reduce nitrogen oxides (NOx) and particulate matter (PM) emissions while maintaining diesel-equivalent power. However, its combustion behavior under varying injection timing and [...] Read more.
Dimethyl ether (DME) is a promising alternative fuel for compression ignition (CI) engines due to its potential to simultaneously reduce nitrogen oxides (NOx) and particulate matter (PM) emissions while maintaining diesel-equivalent power. However, its combustion behavior under varying injection timing and exhaust gas recirculation (EGR) conditions remains insufficiently characterized for practical calibration. This study investigates the combustion, emissions, and performance of DME relative to diesel using a fully instrumented John Deere 6068CI550 single-cylinder research engine modified for high-pressure common-rail DME operation. Baseline tests were conducted at three ISO 8178 C1 steady-state modes with matched combustion phasing, load, and EGR to isolate fuel property effects. Injection timing and EGR sweeps were then performed at 1600 rpm and 50% load. Results show that DME produces 10–35% lower NOx and orders-of-magnitude lower PM than diesel while maintaining comparable thermal efficiency. DME exhibits a single-stage premixed heat release structure with reduced peak apparent heat release rates and 4–5° shorter combustion durations than diesel. Stable combustion was sustained up to 55% EGR, beyond which incomplete combustion increased carbon monoxide (CO), total hydrocarbons (THC), and fuel consumption. Optimal low-emission operation occurred near CA50 ≈ 16° ATDC and EGR levels of 30–40%. These findings demonstrate DME’s ability to mitigate the traditional diesel NOx–PM tradeoff and support its viability as a low-emission CI fuel. Full article
(This article belongs to the Special Issue Vehicle Engines and Powertrains: Performance, Combustion and Emission—2nd Edition)
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28 pages, 2994 KB  
Article
Graph Neural Networks and Bi-Level Optimization for Equitable Electric Vehicle Charging Infrastructure Planning
by Javier Alexander Guerrero Silva, Jorge Ivan Romero Gelvez and Sebastian Zapata
Energies 2026, 19(8), 1981; https://doi.org/10.3390/en19081981 - 20 Apr 2026
Viewed by 595
Abstract
Equity-aware electric vehicle (EV) charging planning remains difficult in data-constrained cities. In this work, an integrated framework was developed by combining spatiotemporal graph neural networks (ST-GNNs), EVI-Pro Lite demand estimation, and lexicographic bi-level optimization, and was applied to Bogotá, Colombia (8.3 million inhabitants). [...] Read more.
Equity-aware electric vehicle (EV) charging planning remains difficult in data-constrained cities. In this work, an integrated framework was developed by combining spatiotemporal graph neural networks (ST-GNNs), EVI-Pro Lite demand estimation, and lexicographic bi-level optimization, and was applied to Bogotá, Colombia (8.3 million inhabitants). Household travel survey data (12,500 households across 142 zones) were used to estimate zone-level priority scores and venue-specific temporal weights. EVI-Pro Lite simulations projected a 2025 requirement of 10,870 charging ports (7352 residential, 2739 workplace, and 779 public). In the allocation stage, Level 1 preserved priority-proportional targets, while Level 2 minimized inter-zonal inequality in Hansen accessibility subject to near-optimal Level-1 compliance. The final allocation retained strong priority alignment in installed ports (Spearman ρ=0.799, p<10−31), while the priority–accessibility association was lower (Spearman ρ=0.320, p=1.04×10−4), consistent with second-stage equity redistribution. Equity outcomes also improved (Hansen Gini = 0.433; bottom-50% Lorenz share = 0.204). The mean Hansen accessibility reached 296.630 (standard deviation 248.099; minimum 1.126). These findings indicate that reproducible, equity-oriented EV infrastructure plans can be produced in cities where revealed charging microdata are limited. Full article
(This article belongs to the Special Issue Vehicle Engines and Powertrains: Performance, Combustion and Emission—2nd Edition)
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23 pages, 11115 KB  
Article
Estimation of Heat Release and In-Cylinder Pressure in Diesel Engines from Basic Testbed Data
by Roberto Finesso, Francesco Guidotti and Stefano d’Ambrosio
Energies 2025, 18(22), 5912; https://doi.org/10.3390/en18225912 - 10 Nov 2025
Viewed by 1463
Abstract
The present paper proposes a novel approach for the estimation of the in-cylinder pressure and heat release in diesel engines from basic testbed measurements (i.e., brake mean effective pressure (BMEP), gross indicated mean effective pressure (IMEP360), peak firing pressure [...] Read more.
The present paper proposes a novel approach for the estimation of the in-cylinder pressure and heat release in diesel engines from basic testbed measurements (i.e., brake mean effective pressure (BMEP), gross indicated mean effective pressure (IMEP360), peak firing pressure (PFP), crank angle at which 50% of fuel mass has burnt (MFB50) and exhaust gas temperature (Texh). The method exploits a previously developed low-throughput combustion model, based on the accumulated fuel mass approach, which has been tuned by a genetic algorithm (GA) optimizer. The latter adjusts the main combustion model parameters to minimize an objective function, which depends on the prediction errors of BMEP, IMEP360, PFP, MFB50 and Texh. Several scenarios were evaluated in which different subsets of the four previous quantities were assumed to be known from experimental activities. The proposed method is particularly useful when in-cylinder pressure traces are unavailable and only basic testbed data exist. The results show that the in-cylinder pressure and heat release profiles are estimated with a high level of accuracy, since the root mean squared error is of the order of 1–2.5 bar and 2–2.7 × 10−2 kJ, respectively, depending on the considered scenario, while requiring a modest computational effort which is of the order of 3–6 min per test. Moreover, the low-throughput nature of the method makes it straightforward for other researchers to implement and reproduce results on different engines. The approach is also fuel-independent and can be applied to engines running on alternative/zero-carbon fuels, which are currently being extensively studied as potential ways to reduce the environmental impact of internal combustion engines. Full article
(This article belongs to the Special Issue Vehicle Engines and Powertrains: Performance, Combustion and Emission—2nd Edition)
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