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Technology Applications in Sustainable Energy and Power Engineering

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3613

Special Issue Editors

Dr. Huaiyu Wang

E-Mail Website
Guest Editor
College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, PR China
Interests: hybrid electric vehicle; Zero-carbon engine; hydrogen; artificial intelligence
Dr. Cheng Shi

E-Mail Website
Guest Editor
School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China
Interests: rotary engine; hydrogen; combustion; emissions control; ignition; sustainability; vehicle engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the context of dual carbon and sustainable development, renewable energy-related technologies are vigorously developing. The application of renewable energy has brought new challenges to current energy and power engineering. In the case of the most commonly used power machine, the internal combustion engine, the adoption of renewable energy sources such as hydrogen, ammonia, and methanol requires not only the modification of the original engine but also, more importantly, the calibration of the control strategy. Hybrid power systems can reduce overall vehicle emissions, and the use of renewable energy sources also requires the optimization of control strategies for the internal combustion engine and the power battery. With the sustainable development of artificial intelligence technology, its application in sustainable energy and power engineering is becoming increasingly widespread. Taking the field of internal combustion engines as an example, machine learning has achieved important results in fault diagnosis, intelligent calibration, and performance prediction. Machine learning combined with intelligent algorithms can also be used for the multi-objective optimization of performance and emissions. In addition, in the field of civil engineering, deep learning can also be used for predicting concrete strength.

However, in the existing research, there are various modeling methods in a certain field, and there is no unified conclusion. In response to the current situation, Sustainability aims to provide a platform for sharing research on technology applications of related technologies in sustainable energy and power engineering.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Sustainable energy management;
  • Sustainable development of deep learning and machine learning models;
  • The combination of sustainable energy and artificial intelligence;
  • The technology applications in energy and power engineering.

Dr. Huaiyu Wang
Dr. Cheng Shi
Guest Editors

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. Sustainability 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 2400 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

  • renewable energy
  • sustainable power engineering
  • artificial intelligence application
  • sustainability

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Published Papers (3 papers)

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Research

15 pages, 2006 KiB  
Article
Sensitivity Analysis Study of Engine Control Parameters on Sustainable Engine Performance
by Bingfeng Huang, Wei Hong, Kun Shao and Heng Wu
Sustainability 2024, 16(24), 11107; https://doi.org/10.3390/su162411107 - 18 Dec 2024
Cited by 1 | Viewed by 538
Abstract
With the increasing global concern for environmental protection and sustainable resource utilization, sustainable engine performance has become the focus of research. This study conducts a sensitivity analysis of the key parameters affecting the performance of sustainable engines, aiming to provide a scientific basis [...] Read more.
With the increasing global concern for environmental protection and sustainable resource utilization, sustainable engine performance has become the focus of research. This study conducts a sensitivity analysis of the key parameters affecting the performance of sustainable engines, aiming to provide a scientific basis for the optimal design and operation of engines to promote the sustainable development of the transportation industry. The performance of an engine is essentially determined by the combustion process, which in turn depends on the fuel characteristics and the work cycle mode suitability of the technical architecture of the engine itself (oil-engine synergy). Currently, there is a lack of theoretical support and means of reference for the sensitivity analysis of the core parameters of oil–engine synergy. Recognizing the problems of unclear methods of defining sensitivity parameters, unclear influence mechanisms, and imperfect model construction, this paper proposes an evaluation method system composed of oil–engine synergistic sensitivity factor determination and quantitative analysis of contribution. The system contains characteristic data acquisition, model construction and research, and sensitivity analysis and application. In this paper, a hierarchical SVM regression model is constructed, with fuel physicochemical characteristics and engine control parameters as input variables, combustion process parameters as an intermediate layer, and diesel engine performance as output parameters. After substituting the characteristic data into the model, the following results were obtained, R2 > 0.9, MSE < 0.014, MAPE < 3.5%, indicating the model has high accuracy. On this basis, a sensitivity analysis was performed using the Sobol sensitivity analysis algorithm. It was concluded that the load parameters had the highest influence on the ID (ignition delay time), combustion duration (CD), and combustion temperature parameters of the combustion elements, reaching 0.24 and above. The influence weight of the main spray strategy was greater than that of the pre-injection strategy. For the sensitivity analysis of the premix ratio, the injection timing, EGR (exhaust gas recirculation) rate, and load have significant influence weights on the premix ratio, while the influence weights of the other parameters are not more than 0.10. In addition, the combustion temperature among the combustion elements has the highest influence weights on the NOx, PM (particulate matter) concentration, and mass, as well as on the BTE (brake thermal efficiency) and BSFC (brake specific fuel consumption). The ID has the highest influence weight on HC and CO at 0.35. Analysis of the influence weights of the index parameters shows that the influence weights of the fuel physicochemical parameters are much lower than those of the engine control parameters, and the influence weights of the fuel CN (cetane number) are about 5% greater than those of the volatility, which is about 3%. From the analysis of the proportion of index parameters, the engine control parameter influence weights are in the following order: load > EGR > injection timing > injection pressure > pre-injection timing> pre-injection ratio. Full article
(This article belongs to the Special Issue Technology Applications in Sustainable Energy and Power Engineering)
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18 pages, 10244 KiB  
Article
Improvement of Engine Combustion and Emission Characteristics by Fuel Property Modulation
by Kaijie Liang, Jinguang Liang, Guowei Li, Zhengri Shao, Zhipeng Jiang and Jincheng Feng
Sustainability 2024, 16(23), 10764; https://doi.org/10.3390/su162310764 - 8 Dec 2024
Cited by 1 | Viewed by 1158
Abstract
The sustainability of diesel engines has come to the forefront of research with the growing global interest in reducing greenhouse gas emissions and improving energy efficiency. The aim of this paper is to support the goal of sustainable development by improving the volatile [...] Read more.
The sustainability of diesel engines has come to the forefront of research with the growing global interest in reducing greenhouse gas emissions and improving energy efficiency. The aim of this paper is to support the goal of sustainable development by improving the volatile properties of diesel fuel to promote cleaner combustion in engines. In order to study the effect of diesel fuel volatility on spraying, combustion, and emission, the tests were carried out with the help of the constant volume chamber (CVC) test rig and an engine test rig, respectively. CVC test: A high-speed video camera recorded the spray characteristics of different volatile fuels in a constant-volume combustion bomb. The effects of different rail pressures and ambient back pressures on the spray characteristics were investigated. Engine test: The combustion and emission characteristics of different volatile diesel fuels under different load conditions (25%, 50%, 75%) were investigated in a four-stroke direct-injection diesel engine with the engine speed fixed at 2000 rpm. The test results show that as the rail pressure increases and the ambient pressure decreases, the spray characteristics of the fuels tend to increase; for the more volatile fuels, although reducing the spray tip penetration, the spray projected area and spray cone angle increase, which is conducive to improving the homogeneity of the fuel and air mixing in the cylinder. The improvement of fuel volatility can form more and better-quality mixtures within the ignition delay time (ID), resulting in a 1–2% increase in peak cylinder pressure and a 2–4% increase in peak heat release. For different loads, pre-injection heat release is generated to redefine the ID and combustion duration (CD). Improved fuel volatility effectively reduces carbon monoxide (CO) emissions by about 8–10% and hydrocarbon (HC) emissions by about 13–16%, but it increases nitrogen oxide (NOx) emissions by about 8–11%. Analyzing from the perspective of particulate matter (PM), combined with the aromatic content of volatile fuels, it is recommended to use fuels with moderate volatility and aromatic content under low load conditions, and at medium to large loads, the volatility of the fuel has less weight on particulates and more weight on aromatics, so it is desirable to use the fuel with the lowest volatility and lowest aromatic content of the fuel selected. Full article
(This article belongs to the Special Issue Technology Applications in Sustainable Energy and Power Engineering)
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15 pages, 6579 KiB  
Article
Impact of Shortening Real Driving Emission (RDE) Test Trips on CO, NOX, and PN10 Emissions from Different Vehicles
by Haiguang Zhao, Laihua Shi, Xiaoliu Xu, Jinshan Qiu, Lan Li, Junfang Wang, Wenhan Yu and Yunshan Ge
Sustainability 2024, 16(21), 9453; https://doi.org/10.3390/su16219453 - 30 Oct 2024
Viewed by 908
Abstract
The real driving emission (RDE) test is the test for vehicle type approval in the China VI emission standard and is one of the most important indicators for assessing the environmental performance of vehicles. To investigate the feasibility of shortening the RDE test [...] Read more.
The real driving emission (RDE) test is the test for vehicle type approval in the China VI emission standard and is one of the most important indicators for assessing the environmental performance of vehicles. To investigate the feasibility of shortening the RDE test trip, we measured emissions of CO, NOX, and PN10 (i.e., the number of particles above 10 nm in diameter) from gasoline, diesel, and hybrid electric vehicles based on portable emission measurement systems (PEMSs) and analyzed the influence of shortening test trips on pollutant emissions. The results indicated that the CO and PN10 emission factors of the gasoline vehicle increased by about two times during short trips compared with standard trips, while the NOX emission factor changed insignificantly. The diesel vehicle showed a two-fold increase in NOX and PN10 emission factors during short trips compared with standard trips, with CO emissions remaining largely unchanged. The short trips of the hybrid electric vehicle doubled CO and PN10 emission factors and slightly increased NOX emission factors compared with standard trips. The study can aid in improving RDE test efficiency, reducing RDE test cost, and controlling pollutant emissions from newly produced and in-use vehicles, which is crucial for air pollution management and sustainable development. Full article
(This article belongs to the Special Issue Technology Applications in Sustainable Energy and Power Engineering)
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