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The global transportation sector is transitioning towards renewable energy to combat climate change, with biodiesel playing a critical role. Significant research over the past decades has focused on enhancing biodiesel through novel feedstocks and production methods. The defense community, a major diesel consumer, is particularly interested in biodiesel to support national sustainability goals while also leveraging the benefits of the new technology, including the ability to produce biodiesel locally at the point of need. This paper sets out to review recent advances in biodiesel technology and aligning them with the needs of the defense communities. By doing so, this paper provides insight into the challenges, benefits, and technical feasibility for the two primary consumers of military diesel fuel—naval ships and ground vehicles. For naval applications, algae-based biodiesel shows promise due to its potential for local production near ports. Advances in genetic engineering and cultivation are crucial for increasing lipid content and reducing costs. Innovative methods such as microwave-assisted transesterification and artificial neural networks for optimization could further enhance economic viability. In military ground vehicles, locally produced biodiesel could sustain operations by minimizing supply chain dependencies. Efforts are ongoing to develop mobile production facilities and improve feedstock diversity and methanol-independent transesterification processes. Overall, advancements in biodiesel production from various feedstocks and innovative techniques are poised to significantly benefit the military sector, promoting sustainability and operational efficiency.

Recent advances in all-solid-state battery (ASSB) research have significantly addressed key obstacles hindering their widespread adoption in electric vehicles (EVs). This review highlights major innovations, including ultrathin electrolyte membranes, nanomaterials for enhanced conductivity, and novel manufacturing techniques, all contributing to improved ASSB performance, safety, and scalability. These developments effectively tackle the limitations of traditional lithium-ion batteries, such as safety issues, limited energy density, and a reduced cycle life. Noteworthy achievements include freestanding ceramic electrolyte films like the 25 μm thick Li0.34La0.56TiO3 film, which enhance energy density and power output, and solid polymer electrolytes like the polyvinyl nitrile boroxane electrolyte, which offer improved mechanical robustness and electrochemical performance. Hybrid solid electrolytes combine the best properties of inorganic and polymer materials, providing superior ionic conductivity and mechanical flexibility. The scalable production of ultrathin composite polymer electrolytes shows promise for high-performance, cost-effective ASSBs. However, challenges remain in optimizing manufacturing processes, enhancing electrode-electrolyte interfaces, exploring sustainable materials, and standardizing testing protocols. Continued collaboration among academia, industry, and government is essential for driving innovation, accelerating commercialization, and achieving a sustainable energy future, fully realizing the transformative potential of ASSB technology for EVs and beyond.

In gasoline engines, the combustion process involves a flame’s propagation from the spark plug to the cylinder walls, resulting in the localized heating and pressurization of the cylinder content ahead of the flame, which can lead to the autoignition of the gasoline and air. The energy release from the autoignition event causes the engine cylinder to resonate, causing an unpleasant noise and eventual engine damage. This process is termed as knock. Avoiding knock has resulted in limiting the maximum engine pressures, and hence limiting the maximum efficiencies of the engine. Modern engines employ knock sensors to detect resonances, adjusting the spark plug timing to reduce pressures and temperatures, albeit at the expense of engine performance. This paper sets out to review the different signals that can be measured from an engine to detect the start of knock. These signals traditionally consist of the in-cylinder pressure, the vibrations of the engine block, and acoustic noise. This paper reviews each of these techniques, with a focus on recent advances. A number of novel methods are also presented, including identifying perturbations in the engine speed or exhaust temperature; measuring the ion charge across the spark plug leads; and using artificial intelligence to build models based on engine conditions. Each of these approaches is also reviewed and compared to the more traditional approaches. This review finds that in-cylinder pressure measurements remain as the most accurate for detecting knock in modern engines; however, their usage is limited to research settings. Meanwhile, new sensors and processing techniques for vibration measurements will more accurately detect knock in modern vehicles in the short term. Acoustic measurements and other novel approaches are showing promise in the long term.

The automotive market is currently shifting away from traditional vehicles reliant on internal combustion engines, favoring battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). The widespread acceptance of these vehicles, especially without government subsidies, hinges on market dynamics, particularly customers opting for vehicles with the lowest overall cost of ownership. This paper aims to model the total cost of ownership for various powertrains, encompassing conventional vehicles, HEVs, PHEVs, and BEVs, focusing on both sedans and sports utility vehicles. The modeling uses vehicle dynamics to approximate the fuel and electricity consumption rates for each powertrain. Following this, the analysis estimates the purchase cost and the lifetime operational cost for each vehicle type, factoring in average daily mileage. As drivers consider vehicle replacements, their choice tends to lean towards the most economical option, especially when performance metrics (e.g., range, acceleration, and payload) are comparable across the choices. The analysis seeks to determine the percentage of drivers likely to choose each vehicle type based on their specific driving habits. Advances in battery technology will reduce the battery weight and cost; further, the cost of electricity will decrease as more renewable energy sources will be integrated into the grid. In turn, the total cost of ownership will decrease for the electrified vehicles. By following battery trends, this study is able to model the makeup of the automotive market over time as it transitions from fossil-fuel based vehicles to fully electric vehicles. The model finds until the cost of batteries and electricity is significantly reduced, the composition of the vehicle market is a mixture of all vehicle types.

New York City is the most populous city in North America and the fourth most populous in the world. Due to the high population density and significant commuting population, the city experiences a large number of vehicles operating in a congested environment, leading to a substantial number of traffic accidents. This study examines a dataset compiled by the New York Police Department, which records every major vehicular accident in New York City from 2013 to 2023, exploring aspects such as accident types, severity, causes, and locations. This period includes the COVID-19 pandemic and other external factors like fluctuating gasoline prices, the rise of for-hire vehicle (FHV) services, and vehicles with new safety features. Data from multiple sources are analyzed to understand how these factors impacted accident rates during this timeframe. The analysis shows that the COVID-19 pandemic significantly reduced accidents due to decreased motor vehicle traffic, with post-pandemic accident rates remaining at less than half of pre-pandemic levels. This sustained decline correlates with reduced traffic, increased FHV usage over taxis, and a growing number of new vehicles with advanced safety features. This study uses these datasets to develop a mathematical model to quantify these correlations and to provide insight for urban planners and policymakers seeking to improve road safety and manage traffic flow.

Hybrid electric vehicles (HEVs) are set to play a critical role in the future of the automotive industry. To operate efficiently, HEVs require a robust energy management strategy (EMS) that decides whether the vehicle is powered by the engine or electric motors while managing the battery’s state of charge. The EMS must rapidly adapt to driver demands and optimize energy usage, ideally predicting battery charge rates and fuel consumption to adjust the powertrain in real time, even under unpredictable driving conditions. As HEVs become more prevalent, EMS technologies will advance to improve predictive capabilities. This analysis provides an overview of current EMS systems, including both rule-based and optimization-based approaches. It explores the evolution of EMS development through a technology roadmap, highlighting the integration of advanced algorithms such as reinforcement learning and deep learning. The analysis addresses the technologies that underly this evolution, including machine learning, cloud computing, computer vision, and swarm technology. Key advances and challenges in these technologies are discussed, along with their implications for the next generation of EMS systems for HEVs. The analysis of these technologies indicates that they will play a key role in the evolution of EMS technology, allowing it to better optimize driver needs and fuel economy.

The transportation industry’s transition to carbon neutrality is essential for addressing sustainability concerns. This study details a model for calculating the carbon footprint of the transportation sector as it progresses towards carbon neutrality. The model aims to support policymakers in estimating the potential impact of various decisions regarding transportation technology and infrastructure. It accounts for energy demand, technological advancements, and infrastructure upgrades as they relate to each transportation market: passenger vehicles, commercial vehicles, aircraft, watercraft, and trains. A technology roadmap underlies this model, outlining anticipated advancements in batteries, hydrogen storage, biofuels, renewable grid electricity, and carbon capture and sequestration. By estimating the demand and the technologies that comprise each transportation market, the model estimates carbon emissions. Results indicate that based on the technology roadmap, carbon neutrality can be achieved by 2070 for the transportation sector. Furthermore, the model found that carbon neutrality can still be achieved with slippage in the technology development schedule; however, delays in infrastructure updates will delay carbon neutrality, while resulting in a substantial increase in the cumulative carbon footprint of the transportation sector.

Lithium-ion batteries have enabled electric vehicles to achieve a foothold in the automobile market. Due to an increasing environmental consciousness, electric vehicles are expected to take a larger portion of the market, with the ultimate goal of supplanting traditional vehicles. However, the involved costs, sustainability, and technical limitations of lithium-ion batteries do create substantial obstacles to this goal. Therefore, this article aims at presenting magnesium-ion batteries as a potential replacement for lithium-ion batteries. Though still under development, magnesium-ion batteries show promise in achieving similar volumetric and specific capacities to lithium-ion batteries. Additionally, magnesium is substantially more abundant than lithium, allowing for the batteries to be cheaper and more sustainable. Numerous technical challenges related to cathode and electrolyte selection are yet to be solved for magnesium-ion batteries. This paper discusses the current state-of-the-art of magnesium-ion batteries with a particular emphasis on the material selection. Although, current research indicates that sulfur-based cathodes coupled with a (HMDS) 2 Mg-based electrolyte shows substantial promise, other options could allow for a better performing battery. This paper addresses the challenges (materials and costs) and benefits associated with developing these batteries. When overcoming these challenges, magnesium-ion batteries are posed to be a groundbreaking technology potentially revolutionizing the vehicle industry.

Purpose: The purpose of the present study was to evaluate the practicability of liquid ionization chamber (LIC) for pretreatment dose verification of the advanced radiotherapy techniques such as volumetric modulated arc therapy (VMAT). Materials and Methods: The dosimetric characteristics of LIC such as repeatability, sensitivity, monitor unit linearity, dose rate dependence, angular dependence, voltage-current response, and output factors were investigated in 6 MV therapeutic X-ray beams. The LIC was cross-calibrated against 0.125-cc air-filled thimble ionization chamber. A dedicated dosimetry insert made up of Perspex to incorporate the LIC at proper location in the intensity-modulated radiation therapy thorax phantom was locally fabricated. The collection efficiency and ion recombination correction factor was determined using the two-dose rate method. Pretreatment dose verification measurement of VMAT treatment plans were carried out using the liquid ionization chamber as well as small volume (0.125 cc) air-filled thimble ionization chamber. The measured dose values by the two dosimeters and TPS calculated dose at a given point were compared. Results: The relative percentage differences between the TPS calculated and measured doses were within ± 1.57% for LIC and ± 2.21% for 0.125 cc ionization chamber, respectively. Conclusions: The measured dose values by the two dosimeters and TPS calculated dose at a given point were found comparable suggesting that the LIC could be a good choice of dosimeter for pretreatment dose verification in VMAT.

Objective: The objective of this study was to validate the performance characteristics of in-house developed four-dimensional (4D) dynamic phantom (FDDP). Materials and Methods: There are three target inserts of 1.0, 1.5 and 2.0 cm diameter. The targets were driven in sinusoidal pattern in the longitudinal direction, using the combinations of amplitudes of 0.5, 1.0, and 1.5 cm with frequencies of 0.2 and 0.25 Hz. The amplitude and frequency of motion were measured manually, and by using Real-Time Position Management (RPM) system also. The static, free-breathing, and 4D computed tomography (CT) scans of the phantom were acquired with 1.0 mm slice thickness. The 4DCT scans were sorted into 0%–90% phase, and the maximum intensity projection (MIP) images were also generated. The static, free-breathing, and 4DCT data sets and MIP images were contoured to get VStatic, VFB, V00......V90, and internal target volume ITVMIP, respectively. The individual phase volumes were summed to obtain V4D. The length of the target in the motion was measured using MIP image and compared with theoretical length (TL). The variation of 3D displacement vector of individual phase volume with respect to V00with the phase of motion was studied at amplitude and frequency of 1.0 cm and 0.25 Hz, respectively. The degree of similarity between VFBand V4Dand VFBand ITVMIPwas also studied for all the target sizes at amplitude and frequency of 1.0 cm and 0.2 Hz and 1.0 cm and 0.25 Hz, respectively. Results: The amplitude and frequency of motion agreed within the limits of uncertainty with the manually and RPM measured values. The length of target in the motion matched within 1.0 mm with TL. The 3D displacement of individual phase volume showed no target size dependence, and the degree of similarity between VFBand V4Dand VFBand ITVMIPdecreases with increase in the displacement between the two volumes. Conclusions: The mechanical and imaging performances of FDDP were found within the acceptable limits. Therefore, this phantom can be used for quality assurance of 4D imaging process in radiotherapy.