Trends in Renewable Energy
Not a member yet
143 research outputs found
Sort by
Development Status and Outlook of Hydrogen Internal Combustion Engine
Get PDFHydrogen energy is one of the best energy carriers for achieving carbon peak and carbon neutrality, with the characteristics of high energy and no pollution. The hydrogen internal combustion engine is one of the important forms of hydrogen energy utilization, with the significant advantages of high efficiency, high reliability, low cost and low emissions. In this paper, the characteristics of hydrogen internal combustion engines and hydrogen fuel cells were compared, and the industrialization prospects of hydrogen energy utilization in the future were analyzed. Focusing on the hydrogen internal combustion engine technology system, a comprehensive analysis was conducted on the technical issues and technical progress in hydrogen storage, combustion, NOx emissions, etc. of hydrogen internal combustion engines.Citation: Liu, M. (2024). Development Status and Outlook of Hydrogen Internal Combustion Engine. Trends in Renewable Energy, 10(3), 257-265. doi:http://dx.doi.org/10.17737/tre.2024.10.3.0017
Effect of Hydrogen Injection Flow Rate on the Performance of In-Cylinder Direct Injection Hydrogen Engines
Get PDFWhen a hydrogen internal combustion engine uses intake manifold injection to supply hydrogen, it must face the contradiction of abnormal combustion (premature combustion, backfire, etc.). The occurrence of abnormal combustion such as backfire can be avoided by using in-cylinder direct injection of hydrogen. In this paper, the In-Cylinder Direct Injection single-cylinder engine is modified, a three-dimensional simulation model is established, and simulation tests using AVL-Fire software on this basis is conducted. Through the analysis of the research results, the optimal hydrogen injection flow rate for the direct injection hydrogen engine to achieve the best power and economy under different working conditions was obtained. The results show that: under the same speed and load, the increase of hydrogen injection flow rate increases the hydrogen injection speed, which promotes the turbulent motion in the cylinder. At the same time, with the increase of hydrogen injection flow rate, the maximum pressure, temperature, indicated power and indicated thermal efficiency in the engine cylinder generally show a trend of first increasing and then decreasing, and there is an optimal hydrogen injection flow rate value.Citation: Ma, H. (2024). Effect of hydrogen injection flow rate on the performance of in-cylinder direct injection hydrogen engines. Trends in Renewable Energy, 10(3), 266-282. doi:http://dx.doi.org/10.17737/tre.2024.10.3.0017
Application and Characteristics of Hydrogen in Alternative Fuels for Internal Combustion Engines
Get PDFPetroleum has been used as the power source for internal combustion engines for hundreds of years. Nowadays, the problems of fossil energy shortage and environmental pollution are becoming increasingly serious. In response to China's carbon neutrality strategy, it is urgent to seek alternative fuels that can replace petroleum as the power source of internal combustion engines. The challenges of alternative fuels include reducing post-combustion pollutant emissions and being able to recycle them while maintaining the original engine performance. Using hydrogen as fuel can reduce automobile exhaust emissions, promote the development of hydrogen internal combustion engines, and achieve sustainable social and economic development. This article reviews the ideality of hydrogen as an alternative fuel for internal combustion engines and the combustion characteristics of hydrogen internal combustion engines. The bottleneck problems (such as abnormal combustion, NOx emission control and power recovery) that need to be solved urgently in the development of hydrogen internal combustion engines are pointed out. It’s found that these problems can be solved by the combination of software simulation and experimental verification in practice. Citation: Liu, M. (2024). Application and Characteristics of Hydrogen in Alternative Fuels for Internal Combustion Engines. Trends in Renewable Energy, 10(2), 229-238. doi:https://dx.doi.org/10.17737/tre.2024.10.2.0017
Virtual Topologies for Populating Overhead Low-Voltage Broadband over Powerlines Topology Classes by Exploiting Neural Network Topology Generator Methodology (NNTGM) - Part 2: Numerical Results
Get PDFIn [1], Neural Network Topology Generator Methodology (NNTGM) has been theoretically proposed, so that its generated overhead low-voltage broadband over power lines topologies (NNTGM OV LV BPL topologies) may populate the existing OV LV BPL topology classes. Apart from the methodology, NNTGM default operation settings and the applied performance metrics, such as the average theoretical channel attenuation (ACA) and the root mean square delay-spread (RMS-DS), have been presented in [1]. In this companion paper, the new OV LV BPL topology class maps, which are defined by the graphical combination of ACA and RMS-DS of the OV LV BPL topologies, are shown. With reference to the graphical combination of ACA and RMS-DS, NNTGM OV LV BPL topology footprints for given indicative OV LV BPL topology are demonstrated on the OV LV BPL topology class maps. The impact on the relative position and the size of the NNTGM OV LV BPL topology footprints is assessed with reference to the following factors that affect the preparation of the Topology Identification Methodology (TIM) OV LV BPL topology database being used during the NNTGM operation, namely: (i) The inclusion or not of the examined indicative OV LV BPL topology; (ii) the length of the distribution / branch line segments; and (iii) the number of the distribution / branch line segments. The performance assessment of NNTGM is supported by suitable Graphical Performance Indicators (GPIs).Citation: Lazaropoulos, A. (2024). Virtual Topologies for Populating Overhead Low-Voltage Broadband over Powerlines Topology Classes by Exploiting Neural Network Topology Generator Methodology (NNTGM) - Part 2: Numerical Results. Trends in Renewable Energy, 10(3), 315-334. doi:http://dx.doi.org/10.17737/tre.2024.10.3.0018
Exploring Cutting-Edge Approaches to Reduce Africa's Carbon Footprint through Innovative Technology Dissemination
Get PDFThis paper investigates the possibility of revolutionizing Africa's carbon footprint through innovative technology dissemination strategies for GHG emission reduction. It highlights the importance of harnessing renewable energy sources to mitigate climate change and promote sustainable development in Africa. This paper also examined several technology diffusion theories in order to unleash Africa's climate-smart potential by tying them to the recommended techniques for dealing with technological diffusion concerns. These theories varied from diffusion of innovation theory to planned behaviour theory. By analysing these theories, it was found that the most appropriate technology diffusion theory for the assessment of innovative technology dissemination strategies for GHG emission reduction in Africa would be the Diffusion of Innovations Theory. This is due to the theory's emphasis on the dissemination and adoption of new ideas, technologies, or innovations by people or groups within a social system. It would give useful insights into the variables influencing the adoption and dissemination of novel technology for reducing GHG emissions in Africa. The paper also discusses the challenges and barriers faced in the diffusion of renewable energy technologies across the continent while proposing innovative strategies to overcome these obstacles and unlock Africa's untapped climate-smart potential. These strategies include promoting policy and regulatory frameworks that incentivize investment in renewable energy, fostering partnerships between governments, private sector entities, and international organizations to support technology transfer and capacity building, and implementing financial mechanisms such as green bonds and carbon pricing to mobilize funding for renewable energy projects. These proposed strategies were also used to develop seven policies required for innovative technology dissemination strategies for GHG emission reduction in Africa. These policies aim to address the unique challenges faced by African countries in adopting and implementing innovative technologies for GHG emission reduction. By focusing on capacity building, financial incentives, and knowledge sharing, these strategies seek to promote the widespread adoption of sustainable technologies across the continent. They emphasize the importance of collaboration between governments, private sector entities, and international organizations to ensure the successful implementation and long-term sustainability of these policies.Citation: Nwokolo, S., Eyime, E., Obiwulu, A., & Ogbulezie, J. (2023). Exploring Cutting-Edge Approaches to Reduce Africa's Carbon Footprint through Innovative Technology Dissemination. Trends in Renewable Energy, 10(1), 1-29. doi:http://dx.doi.org/10.17737/tre.2024.10.1.0016
Big Data and Neural Networks in Smart Grid - Part 2: The Impact of Piecewise Monotonic Data Approximation Methods on the Performance of Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Powerlines Networks
Get PDFΤhe impact of measurement differences that follow continuous uniform distributions (CUDs) of different intensities on the performance of the Neural Network Identification Methodology for the distribution line and branch Line Length Approximation (NNIM-LLA) of the overhead low-voltage broadband over powerlines (OV LV BPL) topologies has been assessed in [1]. When the αCUD values of the applied CUD measurement differences remain low and below 5dB, NNIM-LLA may internally and satisfactorily cope with the CUD measurement differences. However, when the αCUD values of CUD measurement differences exceed approximately 5dB, external countermeasure techniques against the measurement differences are required to be applied to the contaminated data prior to their handling by NNIM-LLA. In this companion paper, the impact of piecewise monotonic data approximation methods, such as L1PMA and L2WPMA of the literature, on the performance of NNIM-LLA of OV LV BPL topologies is assessed when CUD measurement differences of various αCUD values are applied. The key findings that are going to be discussed in this companion paper are: (i) The crucial role of the applied numbers of monotonic sections of the L1PMA and L2WPMA for the overall performance improvement of NNIM-LLA approximations as well as the dependence of the applied numbers of monotonic sections on the complexity of the examined OV LV BPL topology classes; and (ii) the performance comparison of the piecewise monotonic data approximation methods of this paper against the one of more elaborated versions of the default operation settings in order to reveal the most suitable countermeasure technique against the CUD measurement differences in OV LV BPL topologies.Citation: Lazaropoulos, A., & Leligou, H. (2023). Big Data and Neural Networks in Smart Grid - Part 2: The Impact of Piecewise Monotonic Data Approximation Methods on the Performance of Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Powerlines Networks. Trends in Renewable Energy, 10(1), 67-97. doi:http://dx.doi.org/10.17737/tre.2024.10.1.0016
Big Data and Neural Networks in Smart Grid - Part 1: The Impact of Measurement Differences on the Performance of Neural Network Identification Methodologies of Overhead Low-Voltage Broadband over Power Lines Networks
Get PDFUntil now, the neural network identification methodology for the branch number identification (NNIM-BNI) and the neural network identification methodology for the distribution line and branch line length approximation (NNIM-LLA) have approximated the number of branches and the distribution line and branch line lengths given the theoretical channel attenuation behavior of the examined overhead low-voltage broadband over powerlines (OV LV BPL) topologies [1], [2]. The impact of measurement differences that follow continuous uniform distribution (CUDs) of different intensities on the performance of NNIM-BNI and NNIM-LLA is assessed in this paper. The countermeasure of the application of OV LV BPL topology databases of higher accuracy is here investigated in the case of NNIM-LLA. The strong inherent mitigation efficiency of NNIM-BNI and NNIM-LLA against CUD measurement differences and especially against those of low intensities is the key finding of this paper. The other two findings that are going to be discussed in this paper are: (i) The dependence of the approximation Root-Mean-Square Deviation (RMSD) stability of NNIM-BNI and NNIM-LLA on the applied default operation settings; and (ii) the proposal of more elaborate countermeasure techniques from the literature against CUD measurement differences aiming at improving NNIM-LLA approximations.Citation: Lazaropoulos, A., & Leligou, H. (2024). Big Data and Neural Networks in Smart Grid - Part 1: The Impact of Measurement Differences on the Performance of Neural Network Identification Methodologies of Overhead Low-Voltage Broadband over Power Lines Networks. Trends in Renewable Energy, 10(1), 30-66. doi:http://dx.doi.org/10.17737/tre.2024.10.1.0016
Assessing the Impact of Soiling, Tilt Angle, and Solar Radiation on the Performance of Solar PV Systems
Get PDFThis research examined the observed datasets and a theoretically derived model for estimating yearly optimum tilt angle (β), maximum incident solar radiation (Hmax), clean gain indicator (CGI), and soiling loss indicator (SLI) at Mumbwa, Zambia, the Mediterranean Region, and low latitude locations across the globe. The cleaned tilted collector emerged as the best performing collector due to Hmax and much higher energy gains compared with the soiled collector. CGI showed an appreciable performance of 0.4737% over -0.4708% on the SLI, indicating that soiling on the surface of photovoltaic (PV) modules significantly depreciates the overall performance of PV modules. Two established empirical models obtained from the literature were compared with the established theoretical model (β=φ). The result revealed that the two models overestimated the observed annual optimum tilt angle in this paper, simply because the models were developed with high latitude location datasets from the Asia continent. However, the newly established monthly and yearly global radiation indicator (GRI) models by the authors in their previous paper performed excellently in the selected representative cities in the Mediterranean region.Citation: Nwokolo, S., Obiwulu, A., Amadi, S., & Ogbulezie, J. (2023). Assessing the Impact of Soiling, Tilt Angle, and Solar Radiation on the Performance of Solar PV Systems. Trends in Renewable Energy, 9(2), 120-136. doi:http://dx.doi.org/10.17737/tre.2023.9.2.0015
Research Progress of Nanofluid Heat Pipes in Automotive Lithium-ion Battery Heat Management Technology
Get PDFPower batteries are a crucial component of electric vehicles and other electric equipment. Their long-term high-rate discharge generates a lot of heat, which can lead to battery failure, shortened battery life, and even safety accidents if not managed properly. Due to its high thermal conductivity, the heat pipe can quickly conduct heat away from the battery and separate the heat source from the heat sink. In addition, due to its excellent isothermal performance, the heat pipe can also achieve the characteristics of low-temperature preheating and high-temperature cooling of the power battery by reducing the inhomogeneity of the battery temperature field to reduce the temperature difference. In this paper, we review the current state of the art in thermal management of automotive lithium-ion battery, and highlight the current state of thermal management of batteries based on the combination of nanofluids and heat pipes. Finally, the development of nanofluidic heat pipes in lithium-ion battery heat management systems is prospected.Citation: Wang, X., Zhao, Y., & Jin, Y. (2023). Research Progress of Nanofluid Heat Pipes in Automotive Lithium-ion Battery Heat Management Technology. Trends in Renewable Energy, 9(2), 137-156. doi:http://dx.doi.org/10.17737/tre.2023.9.2.0015
Optimized Lightweight Frame for Intelligent New-energy Vehicles
Get PDFIn this paper, a joint optimization method based on multi-objective response surface approximation model and finite element simulation program is proposed to realize the lightweight optimization of new-energy vehicle frames. Under the premise of satisfying the constraints of strength, frequency and vibration, the thickness of different important parts is optimized to achieve the goal of minimizing the quality of intelligent vehicles. In order to obtain the stress distribution of each part and the vibration frequency of the frame, various finite element analyses of the intelligent vehicle frame are analyzed. In order to achieve optimization, this paper adopts the response surface method for multi-objective optimization. Sample data was generated by the central composite design, and the response surface optimization method was used to filter out 5 design variables that had a large impact on the frame. As a result, the weight of the frame was reduced from 25.05 kg to 19.86 kg, a weight reduction of 20.7%, achieving a significant weight reduction effect. This method provides important reference value and guiding significance for the optimization of frame and its lightweight. In this way, the design of the frame can be better optimized to make it lighter, thereby improving the performance of the smart car. At the same time, this method can also be applied to optimization problems in other fields to achieve more efficient and accurate optimization goals. Citation: Wu, P. (2023). Optimized Lightweight Frame for Intelligent New-energy Vehicles. Trends in Renewable Energy, 9(2), 157-166. doi:http://dx.doi.org/10.17737/tre.2023.9.2.0015