Automotive, Energy and Photonics engineering

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Browsing Automotive, Energy and Photonics engineering by Subject "4008 Electrical engineering"

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    Fuelling hydrogen futures? A trust-based model of social acceptance
    (Royal Society of Chemistry (RSC), 2025) Gordon, Joel A.; Balta-Ozkan, Nazmiye; Haq, Anwar U. l.; Nabavi, Seyed Ali
    Public trust plays a fundamental role in shaping national energy policies in democratic countries, as exemplified by nuclear phase-out in Germany following the Fukushima accident. While trust dynamics have been explored in different contexts of the energy transition, few studies have attempted to quantify the influence of public trust in shaping social acceptance and adoption potential. Moreover, the interaction between public trust and perceived community benefits remains underexplored in the literature, despite the relevance of each factor to facilitating social acceptance and technology uptake. In response, this quantitative analysis closes a parallel research gap by examining the antecedents of public trust and perceived community benefits in the context of deploying hydrogen heating and cooking appliances across parts of the UK housing stock. Drawing on results from a nationally representative online survey (N = 1845), the study advances insights on the consumer perspective of transitioning to ‘hydrogen homes’, which emerged as a topical and controversial aspect of UK energy policy in recent years. Partial least squares structural equation modelling and necessary condition analysis are undertaken to assess the predictive capabilities of a trust-based model, which incorporates aspects of institutional, organisational, interpersonal, epistemic, and social trust. Regarding sufficiency-based logic, social trust is the most influential predictor of public trust, whereas trust in product and service quality corresponds to the most important necessary condition for enabling public trust. Nevertheless, trust in the government, energy sector, and entities involved in research & development are needed to facilitate and strengthen public trust. Overall, this study enriches scholarly understanding of how public trust may shape prospects for trialling novel low-carbon technologies, highlights the need for segment-specific consumer engagement, and advances scholarly understanding of the innovation-decision process in the context of net-zero pathways. As policymakers approach critical decisions on the portfolio of technologies needed to support residential decarbonisation, public trust will prove fundamental to fuelling hydrogen-based energy futures.
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    Hydrogen bond enhanced electrochemical hydrogenation of benzoic acid over a bimetallic catalyst
    (Royal Society of Chemistry (RSC), 2025-06-07) Catizane, Cesar; Jiang, Ying; Sumner, Joy
    Electrochemical hydrogenation (ECH) is a sustainable alternative to traditional hydrogenation methods, offering selective reduction of organic compounds under mild conditions. This study investigates the co-hydrogenation of benzoic acid (BA) and phenol on a platinum-ruthenium on activated carbon cloth (PtRu/ACC) catalyst, with a focus on the synergistic effects arising from hydrogen bonding. Density Functional Theory (DFT) calculations reveal that the formation of a hydrogen-bonded complex between BA and phenol facilitates adsorption energy and lowers activation barrier energies compared to BA alone. Experimental results demonstrate that a 20 mM BA and 5 mM phenol mixture achieves the highest conversion rate (87.33%) and faradaic efficiency (63%), significantly outperforming single-compound systems. Notably, co-hydrogenation facilitates the reduction of BA to cyclohexanemethanol, a valuable product for biofuel applications, which has reduced corrosiveness and improved energy density. These findings underscore the potential for optimising multi-compound ECH systems through targeted catalyst design and reagent concentration tuning, thus advancing the development of efficient strategies for bio-oil upgrading and sustainable chemical production.
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    Lane centerline extraction based on surveyed boundaries: an efficient approach using maximal disks
    (MDPI, 2025-04-18) Yin, Chenhui; Cecotti, Marco; Auger, Daniel J.; Fotouhi, Abbas; Jiang, Haobin
    Maps of road layouts play an essential role in autonomous driving, and it is often advantageous to represent them in a compact form, using a sparse set of surveyed points of the lane boundaries. While lane centerlines are valuable references in the prediction and planning of trajectories, most centerline extraction methods only achieve satisfactory accuracy with high computational cost and limited performance in sparsely described scenarios. This paper explores the problem of centerline extraction based on a sparse set of border points, evaluating the performance of different approaches on both a self-created and a public dataset, and proposing a novel method to extract the lane centerline by searching and linking the internal maximal circles along the lane. Compared with other centerline extraction methods producing similar numbers of center points, the proposed approach is significantly more accurate: in our experiments, based on a self-created dataset of road layouts, it achieves a max deviation below 0.15 m and an overall RMSE less than 0.01 m, against the respective values of 1.7 m and 0.35 m for a popular approach based on Voronoi tessellation, and 1 m and 0.25 m for an alternative approach based on distance transform.
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    Optimization of dual-module floating photovoltaic arrays: layout configuration and damping mechanisms for enhanced stability and energy performance
    (Elsevier, 2025-09-01) Zheng, Zhi; Hu, Jianjian; Huang, Qiang; Jin, Peng; Yang, Yifeng; Huang, Luofeng; Zhou, Zhaomin; Zhou, Binzhen
    Floating Photovoltaic (FPV) systems are a promising solution for offshore renewable energy, with modular FPV arrays offering significant potential for large-scale deployment. However, the development of FPV systems is hindered by insufficient understanding of their hydrodynamic performance, which affects stability and energy efficiency. This study proposes a dual-module FPV array combining box-type and semi-submersible modules to improve hydrodynamic stability under mild wave conditions in the South China Sea. The effects of array layout and PTO damping are examined under various wave conditions. The system is optimized to balance energy harvesting and motion control, and its performance is further evaluated under irregular waves at selected operational sites. Results indicate that the dual-module design effectively leverages the hydrodynamic characteristics of both module types, reducing motion responses and dynamic loads. The incorporation of optimal PTO damping further enhances system stability and energy efficiency by effectively suppressing pitch and heave motions, with maximum reductions of 31.43 % and 41.56 %, respectively, under the selected operational wave conditions. While damping remains effective under head-on waves, its performance slightly decreases under oblique waves, underscoring the importance of aligning the array with the predominant wave direction. Additionally, integrating a wave energy PTO system into the FPV array enables wave power to supplement solar energy, contributing 17.04 % of the total energy output at the selected operational sites. The proposed FPV system offers a practical solution for stabilizing floater motion, enhancing solar power generation, and capturing wave energy, advancing the feasibility of FPV technology for large-scale offshore applications.
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    Revolutionizing power electronics design through large language models: applications and future directions
    (Elsevier, 2025-04) Ibrahim, Khalifa Aliyu; Luk, Patrick Chi-Kwong; Luo, Zhenhua; Ng, Seng Yim; Harrison, Lee
    The design of electronic circuits is critical for a wide range of applications, from the electrification of transportation to the Internet of Things (IoT). It demands substantial resources, is time-intensive, and can be highly intricate. Current design methods often lead to inefficiencies, prolonged design cycles, and susceptibility to human error. Advancements in artificial intelligence (AI) play a crucial role in power electronics design by increasing efficiency, promoting automation, and enhancing sustainability of electrical systems. Research has demonstrated the applications of AI in power electronics to enhance system performance, optimization, and control strategy using machine learning, fuzzy logic, expert systems, and metaheuristic methods. However, a review that includes the recent AI advancements and potential of large language models (LLMs) like generative pre-train transformers (GPT) has not been reported. This paper presents an overview of applications of AI in power electronics (PE) including the potential of LLMs. The influence of LLMs-AI on the design process of PE and future research directions is also highlighted. The development of advanced AI algorithms such as pre-train transformers, real-time implementations, interdisciplinary collaboration, and data-driven approaches are also discussed. The proposed LLMs-AI is used to design parameters of high-frequency wireless power transfer (HFWPT) using MATLAB as a first case study, and high-frequency alternating current (HFAC) inverter using PSIM as a second case study. The proposed LLM-AI driven design is verified based on a similar design reported in the literature and Wilcoxon signed-rank test was conducted to further validate the result. Results show that the LLM-AI driven design based on the OpenAI foundation model has the potential to streamline the design process of power electronics. These findings provide a good reference on the feasibility of LLMs-AI on power electronic design.
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    Thermal modelling and temperature estimation of a cylindrical lithium iron phosphate cell subjected to an automotive duty cycle
    (MDPI, 2025-03-22) Achanta, Simha Sreekar; Fotouhi, Abbas; Zhang, Hanwen; Auger, Daniel J.
    Li ion batteries are emerging as the mainstream source for propulsion in the automotive industry. Subjecting a battery to extreme conditions of charging and discharging can negatively impact its performance and reduce its cycle life. Assessing a battery’s electrical and thermal behaviour is critical in the later stages of developing battery management systems (BMSs). The present study aims at the thermal modelling of a 3.3 Ah cylindrical 26650 lithium iron phosphate cell using ANSYS 2024 R1 software. The modelling phase involves iterating two geometries of the cell design to evaluate the cell’s surface temperature. The multi-scale multi-domain solution method, coupled with the equivalent circuit model (ECM) solver, is used to determine the temperature characteristics of the cell. Area-weighted average values of the temperature are obtained using a homogeneous and isotropic assembly. A differential equation is implemented to estimate the temperature due to the electrochemical reactions and potential differences. During the discharge tests, the cell is subjected to a load current emulating the Worldwide Harmonised Light Vehicles Test Procedure (WLTP). The results from the finite element model indicate strikingly similar trends in temperature variations to the ones obtained from the experimental tests.