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All Journal JOIV : International Journal on Informatics Visualization International Journal of Renewable Energy Development
Nguyen, Duc Chuan
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Using hydrogen as potential fuel for internal combustion engines: A comprehensive assessment Long Huynh, Diep Ngoc; Nguyen, Thanh Hai; Nguyen, Duc Chuan; Vo, Anh Vu; Nguyen, Duy Tan; Nguyen, Van Quy; Le, Huu Cuong
International Journal of Renewable Energy Development Vol 14, No 1 (2025): January 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60707

Abstract

This comprehensive review explores the feasibility and potential of using hydrogen gas as a fuel for internal combustion engines, a topic of growing importance in the context of global efforts to reduce greenhouse gas emissions and transition towards sustainable energy sources. Hydrogen, known for its high energy content and clean combustion properties, presents a promising alternative to traditional fossil fuels. This paper examines the chemical properties of hydrogen and its benefits over conventional fuels, particularly focusing on the technological advancements and modifications required for compression ignition and spark ignition engines to efficiently utilize hydrogen. The review delves into the necessary engine design modification, fuel injection systems, combustion characteristics, and emission control technologies specific to both compression ignition and spark ignition engines. Furthermore, it addresses the environmental impacts, including reductions in greenhouse gases and other pollutants, and evaluates the economic implications, such as production costs and feasibility compared to other energy solutions. Key challenges associated with the storage, distribution, and safety of hydrogen are discussed, along with potential solutions and innovations currently under investigation. This paper aims to provide a thorough understanding of the current state of hydrogen as a promising fuel for internal combustion engines, guiding future research and development in this vital field.
Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy Nguyen, Tien Han; Paramasivam, Prabhu; Dong, Van Huong; Le, Huu Cuong; Nguyen, Duc Chuan
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2637

Abstract

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.
Towards self-diagnostic solar farms: Leveraging EfficientNet and class activation mapping for predictive maintenance Nguyen, Du; Nguyen, Thi Bich Ngoc; Nguyen, Duc Chuan; Chau, Thanh Hieu; Duong, Minh Thai; Dang, Thanh Nam
International Journal of Renewable Energy Development Vol 15, No 2 (2026): March 2026
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.62298

Abstract

The high rate of utility photovoltaic (PV) system development has increased the demand for stable, automated, and interpretable fault diagnostic systems that can be utilised in real-world environments. Solar farms with a large size are increasingly making conventional manual inspection methods impractical, and triggering the use of intelligent data-driven solutions. This paper presents a justifiable deep learning model for automated fault classification of solar panels based on the EfficientNet-B2 architecture combined with Gradient-weighted Class Activation Mapping (Grad-CAM). A six-class image dataset made of clean panels and five prevalent fault types is used. The two stages of transfer learning used to train the model include a warm-up phase and selective fine-tuning of upper network layers. Data augmentation is also performed extensively to make it more robust to changing illumination, viewing angles, and environmental noise. The experimental findings reveal consistent convergence and excellent generalization ability, and a high level of classification accuracy of all types of faults, as it achieved high classification accuracy, macro-averaged F1-scores exceeding 0.90 for most fault classes, and a macro-averaged ROC–AUC of approximately 0.981, highlighting the robustness and reliability of the proposed diagnostic model. The suggested structure will provide a scalable, interpretable, and realistic predictive maintenance of solar farms of the next generation with self-diagnostic capabilities.
Co-Authors Chau, Thanh Hieu Dang, Thanh Nam Dong, Van Huong Duong, Minh Thai Le, Huu Cuong Long Huynh, Diep Ngoc Nguyen, Du Nguyen, Duy Tan Nguyen, Thanh Hai Nguyen, Thi Bich Ngoc Nguyen, Tien Han Nguyen, Van Quy Paramasivam, Prabhu Vo, Anh Vu