Search Results (606)

Green hydrogen is a potential enabler of deep decarbonisation for industrial process heat. We assess its role in Aotearoa New Zealand using a bottom-up, least-cost energy-system model based on the integrated MARKAL-EFOM system (TIMES), which includes hydrogen production electrolysis, storage, and delivery of end-use technologies for process heat, as well as alternative low-carbon options. Drawing on detailed data on industrial energy use by sector and temperature band, we simulate pathways to 2050 under varying assumptions for electrolyser and fuel prices, technology efficiencies, electricity decarbonisation and carbon prices. In most scenarios, the least-cost pathway involves widespread electrification of low- and medium-temperature heat, with green hydrogen playing a targeted role where high-temperature requirements and process constraints limit direct electrification. Sensitivity analysis reveals that hydrogen uptake increases under higher carbon prices, lower electrolyser capital expenditure, and when grid connection or peak capacity constraints are binding. These results suggest that policy should prioritise rapid industrial electrification while focusing hydrogen support on hard-to-electrify, high-temperature processes, such as primary metals and mineral products, alongside enabling infrastructure and standards for hydrogen production, transport, and storage. Full article

The transition to electric aircraft for zero-emission transport requires integrating thermal management systems for high-performance batteries without incurring significant weight, balance, or aerodynamic penalties. This study focuses on the aerodynamic penalties associated with air-cooling systems that can compound the presently unavoidable reduction in endurance imposed by current battery energy density limitations. Building on previous research into battery installation layouts and internal cooling flows, this study is the first to investigate the lift-to-drag (L/D) optimisation for the multiple wing-mounted inlets and outlets necessary for air-cooling batteries in the wing of an electrified aircraft. Wing leading-edge inlets and NACA (National Advisory Committee for Aeronautics) ducts were analysed by systematically varying their layout, number, and dimensions. The analysis evaluated their effects on the wing’s lift, drag, and moment to maximise the L/D. Multiple highly efficient simulation test designs were developed to screen for the main factors to identify the best inlet and outlet configuration, resulting in 66 different Computational Fluid Dynamics (CFD) simulations in Ansys Fluent. Following this, three CFD verification cases of the best configuration were conducted to verify the cooling effect by combining both internal and external flow simulations with heat generation. Compared to the baseline wing of the carbon combustion aircraft, the best configuration caused a 1.75% reduction in L/D, range, and endurance. While the aerodynamic penalty is now minimised, the internal battery pack layout requires further optimisation to re-establish uniform cooling across the battery pack. Designers may still be able to separate the CFD analysis of the internal and external flow regimes with idealised inlets and outlets; however, more whole-field CFD iterations are needed to guide such subdivision to a viable and safe design for wing-mounted batteries. Further, the margins are such that wing-mounted electrification warrants careful instrumented validation in an aircraft. These findings provide crucial design guidance for sustainable aviation, particularly to enable after-market electrification projects. Full article

The development of robust and efficient wireless charging systems is essential for the widespread adoption of electrification in the transport sector, e.g., Electric Vehicles (EVs). Capacitive Wireless Power Transfer (CWPT) has emerged as a promising alternative to inductive methods, offering advantages such as lower cost, lighter structure, and reduced electromagnetic interference. However, the performance of practical CWPT systems, particularly systems employing simple L-type compensation networks, is severely affected by coupling plate misalignment, which causes variations in coupling capacitance. These variations give rise to a pseudo-resonance phenomenon, wherein conventional controllers, such as traditional Sliding Mode Control, mistakenly regulate reactive power to zero at an off-resonant frequency, leading to a drastic collapse in active power transfer. To overcome this limitation, this paper introduces a novel Adaptive Sliding Mode Control (ASMC) framework augmented with an online Recursive Least Squares (RLS) observer for real-time estimation of the time-varying coupling capacitance. The proposed dual-loop control structure integrates an inner adaptive loop that accurately tracks capacitance changes and an outer sliding mode loop that dynamically adjusts the inverter switching frequency to sustain true resonant operation. A rigorous Lyapunov-based stability analysis confirms global convergence and robustness of the closed-loop system. Comprehensive MATLAB/Simulink R2025a simulations validate the proposed approach, demonstrating its capability to maintain zero reactive power and stable 35 kW power transfer with over 95% efficiency under dynamic misalignment conditions of up to 30%. In contrast, a conventional SMC approach experiences severe pseudo-resonant collapse, with output power degrading below 1 kW. These results conclusively highlight the effectiveness and necessity of the proposed ASMC-RLS strategy for achieving robust, misalignment-tolerant CWPT in high-power EV charging applications. Full article

The electrification of public transport with battery electric buses (BEBs) poses technical, regulatory, and environmental challenges. This paper analyzes the impact of BEB charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility GIS data into OpenDSS simulation models. The study utilizes Geographic Database of the Distribution Company (BDGD) data from the Brazilian Electricity Regulatory Agency (ANEEL) and OpenDSS simulations. Motivated by Cuiabá’s proposal to electrify its public bus fleet, four realistic scenarios were simulated, incorporating distributed photovoltaic (PV) generation and vehicle-to-grid (V2G) operation. Results show that up to 118 BEBs can be charged simultaneously without voltage violations. However, thermal overload occurs beyond 56 units, requiring conductor upgrades or load redistribution. PV systems can supply up to 64% of the daily energy demand but introduce reverse power flows and overvoltages, indicating the need for dynamic control. V2G operation enables peak shaving but also leads to overvoltages when more than 33 buses inject power concurrently. The findings suggest that while the current infrastructure partially supports fleet electrification, future scalability depends on integrating smart grid features and reinforcing the system. Although focused on Cuiabá, the methodology offers a replicable approach for low-carbon urban mobility planning in similar developing regions. Full article

With the continuous expansion in China’s vehicle fleet, emissions of CO₂ and air pollutants from the on-road transportation sector are widely projected to be rising, posing a challenge to realizing China’s targets of carbon peaking in 2030 and carbon neutrality in 2060, as well as the national target for air quality improvement. Therefore, vehicle electrification in the on-road transportation sector is urgently needed to reduce emissions of CO₂ and air pollutants, as it serves as a key pathway to align transportation development with sustainability goals. While vehicle electrification is supposed to be the primary solution, there is a research gap in quantifying the provincial, environmental, and economic impacts of implementing such a policy in China. To bridge this gap, we projected the provincial-level ownership of different types of vehicles based on historical trends, assessed the emission reduction potential for CO₂ and air pollutants using the LEAP model from 2021 to 2060, and predicted the provincial marginal abatement costs at different mitigation stages under various scenarios with different strategies of vehicle electrification and development patterns of electricity structure. Our results show that the implementation of vehicle electrification lowers the national carbon peak by 0.2–0.6 Gt yr⁻¹ and advances its achievement by 1–3 years ahead of 2030. The marginal abatement cost ranges from $532 to $3466 per ton CO₂ (tCO₂⁻¹) in 2025 and from −$180 to −$113 tCO₂⁻¹ in 2060 across scenarios. The provincial marginal abatement cost curves further indicate that China’s vehicle electrification should be prioritized in cost-effective regions (e.g., Shanghai and Guangdong), while concurrently advancing nationwide grid decarbonization to guarantee the supply of low-carbon electricity across the country. This optimized pathway ensures that transportation decarbonization aligns with both environmental and economic requirements, providing actionable support for China’s sustainable development strategy. Full article

Transport is among the fastest-growing contributors to carbon dioxide (CO₂) emissions in the Gulf Cooperation Council (GCC) region, where rapid urbanization, population growth, and high mobility demand continue to shape energy use. This study aims to quantify the extent to which economic growth and urbanization drive transport-related CO₂ emissions across Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates between 2012 and 2022. Using sector-specific data from the International Energy Agency and World Bank, we apply panel and country-level log–log regression models to estimate long-run and short-run elasticities of transport CO₂ emissions with respect to GDP and urban population. The analysis also includes robustness checks excluding the COVID-19 pandemic year to isolate structural effects from temporary shocks. Results show that transport emissions remain strongly correlated with GDP in most countries, indicating emissions-intensive growth, while the influence of urbanization varies: positive in Kuwait and Saudi Arabia, where expansion is car-dependent, and negative in Oman and Qatar, where compact urban forms and transit investments mitigate emissions. The findings highlight the importance of differentiated policy responses—fuel-pricing reform, vehicle efficiency standards, electrification, and transit-oriented planning—to advance low-carbon mobility. By integrating elasticity-based diagnostics with decoupling analysis, this study provides the first harmonized empirical framework for the GCC to assess progress toward transport-sector decarbonization. Full article

The transportation sector is responsible for over 20% of Europe’s CO₂ emissions, significantly worsening urban air quality and compromising public health. Electric vehicles (EVs)—namely BEVs and PHEVs—offer some relief by lowering noise and pollution in urban settings. Nevertheless, their effectiveness in benefiting the environment relies on the current electricity generation mix. In accordance with national energy goals, this study evaluates the environmental effects of EV adoption in Greece until 2035, utilizing a scenario-based approach grounded in the forecasts of the Greek National Energy and Climate Plan. Three different electrification pathways are examined to explore how varying levels of electric vehicle adoption and progress in decarbonizing the power sector could reduce air pollution, particularly in cities. By comparing the projected CO₂, CO, NO_x, PM₁₀, and SO₂ pollutant output from BEVs and PHEVs with those of internal combustion engine vehicles, the study highlights the significance of integrating renewable energy sources and assesses the potential for EVs to reduce emissions within Greece’s changing energy mix. Full article

The increasing trend towards electrification in transportation highlights the potential for electric ships and the demand for safe, rapid charging systems. Underwater capacitive power transfer (UCPT) is considered a suitable solution due to its high power density potential. This article presents research on and optimization of UCPT for shore-to-ship charging in freshwater environments. It proposes a design for an insulation coupler and explores the influence of parasitic capacitance in the environment. This study demonstrates how the ship and shore impact the coupler’s coupling coefficient and mutual capacitance, thereby affecting the power and efficiency. Through theoretical calculations and finite element analysis, a kW-level UCPT coupler is designed. Experimental verification under different cases confirms the efficiency and constant current output characteristics, with superior performance observed when the coupler is positioned further away from the ship or shore. This study showcases the potential of UCPT to provide reliable and efficient power supply for electric ships, while emphasizing the importance of considering environmental factors and parasitic effects in system design and operation. These findings contribute to advancing UCPT technology and offer insights for further optimization to enhance practical applicability. Full article

Real-time monitoring of lubricants is crucial to the development of transport vehicles. Accidental and fatal failures of components in vehicles occur every day, which threaten the service life of equipment. Inspired by the work of solid–liquid triboelectric nanogenerators (S-L-TENG), we propose a method to retrofit a self-powered sensor for real-time monitoring of lubricating oil leakage. The previous work does not have a systematic study on the influence of various modification methods on the electrification signal of oil-solid contact. This study identifies an optimal modification method with the highest electrification performance by comparing the energizing signals of different modification methods, which provides a new approach for the real-time monitoring of lubricating oil leakage and the detection of lubricating oil impurities. Full article

The design of battery modules for Electric Vehicles (EVs) and stationary Energy Storage Systems (ESSs) plays a pivotal role in advancing sustainable energy technologies. This paper presents a comprehensive overview of the critical considerations in battery module design, including system requirements, cell selection, mechanical integration, thermal management, and safety components such as the Battery Disconnect Unit (BDU) and Battery Management System (BMS). We discuss the distinct demands of EV and ESS applications, highlighting trade-offs in cell chemistry, form factor, and architectural configurations to optimize performance, safety, and cost. Integrating advanced cooling strategies and robust electrical connections ensures thermal stability and operational reliability. Additionally, the paper describes a prototype battery module, a BDU, and the hardware and software architectures of a prototype BMS designed for a Hardware/Model-in-the-Loop framework for the real-time monitoring, protection, and control of battery packs. This work aims to provide a detailed framework and practical insights to support the development of high-performance, safe, and scalable battery systems essential for transportation electrification and grid energy storage. Full article

European energy and climate policies have enabled reductions in greenhouse gas emissions across many sectors, with transport standing out as an exception. In this area, one of the most promising solutions is the electrification of vehicles. In urban contexts, the shift towards electrifying transport—particularly local public transport (LPT)—can yield significant benefits, especially when paired with an increasingly decarbonized electricity mix, effectively reducing tailpipe emissions of both greenhouse gases and other pollutants. Nevertheless, it is essential to assess whether eliminating tailpipe emissions simply shifts environmental impacts to other stages of a vehicle’s life cycle. The Life Cycle Assessment (LCA), employing a comprehensive cradle-to-grave approach, serves as the principal tool for such evaluations. In this framework, this study focuses on the Italian situation by using a dynamic LCA for the electricity mix. Results show that the electric bus reduces the impact on climate change (28.5 gCO₂eq/pkm vs. 66.7 gCO₂eq/pkm for Diesel, −57%), acidification, photochemical ozone formation, particulate matter, and the use of fossil resources. However, it presents higher impacts in terms of human toxicity (both carcinogenic and non-carcinogenic) and the use of mineral and metal resources, mainly due to battery production and the use of metals such gold, silver, and copper. Full article

The maximum demand payment component (MDPC) of the electricity bill, which reflects the highest level of power demand during a billing period, is a well-recognized barrier to the feasibility of electric vehicle fast-charging facilities (EVFCFs). While several studies have explored control strategies to mitigate demand peaks, they primarily focus on slow-charging facilities and fail to account for maximum demand prices. On the other hand, the few existing EVFCF-particular strategies overlook the diminished user-desired quality of service caused by the additional charging time needed for demand management. Moreover, their implementations under real-world conditions also remain unexplored. To address these issues, this work proposes a managed charging solution that explicitly considers the impact of maximum demand prices while maintaining user-desired quality of service, and implements it under real-world conditions in three different metropolitan areas in the United States. Simulation results indicate that the proposed solution can increase an EVFCF’s operational profits by 5–26% compared with conventional charging methods. The findings also highlight that the outcomes of the proposed solution are significantly influenced by the EVFCF utilization rate, the time between consecutive EV arrivals, the incumbent electric utility-specified maximum demand prices, and the user preferences of selecting the various “alternative fuel-grade options” offered at an EVFCF. These findings could pave the way for a more informed deployment of managed charging solutions at EVFCFs, thereby accelerating equitable transition to transportation electrification. Full article

Vehicle electrification has been proposed as a strategy for decarbonizing the transport sector. However, companies operating fleets of light-duty internal combustion engine vehicles (ICEVs) for personnel and freight transportation still lack the data and decision-making tools necessary to evaluate the transition to electric vehicles (EVs). This study proposes a novel methodology that combines the use of web applications with longitudinal vehicle dynamics to determine energy consumption and regenerative braking potential. In addition, it incorporates energy consumption data, taxes, subsidies and vehicle discounts to conduct a comparative analysis of the total cost of ownership of EVs versus IECVs. The proposed methodology was applied to evaluate the feasibility of an energy transition in a fleet of vans and pickup trucks used for transporting personnel and materials. The results show that the model can estimate energy consumption with an average error of 7.6% compared to monitored data. Replacing 10 ICEVs with 5 electric vans and 5 electric pickup trucks could reduce energy consumption by up to 62%. The operating cost of the electric van is 8.5% lower than its ICEV counterpart, while the electric pickup achieves a 13.8% reduction in operating costs compared to the combustion model. The technical findings and the methodology of this study are expected to provide a solid basis for companies to evaluate the energy and economic feasibility of electrifying their fleets. Full article

Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions. Full article