Browse Topic: Weather and climate

Items (3,783)
To investigate the disaster evolution characteristics and associated risks of heavy rainfall and flooding on urban transportation infrastructure, this study takes the extreme rainstorm event in Zhengzhou as a typical case. A multidimensional dynamic risk assessment model is employed to analyze the disaster evolution process and conduct risk evaluation. First, the three-stage evolution process and its characteristics are systematically examined. Then, based on the theory of natural disaster risk elements, a dynamic risk assessment model is constructed. The improved Order of Priority Approach (OPA) is used to determine the weights of multidimensional risk factors, and interval type-1 fuzzy logic is introduced to address the uncertainty of fuzzy indicators. Finally, the overall risk level of the heavy rainfall–flooding disaster chain is calculated and evaluated. The results indicate a high-risk level, which is consistent with the findings of the field investigation report, thereby validating the feasibility of the proposed disaster chain evaluation method combining multiple models. This analysis provides a theoretical basis for future studies on similar urban storm flood risk scenarios.
Zhang, YongchengWang, JianweiWu, ZiyiWang, YanLuo, QingKang, Pingping
Current lithium-ion batteries should generally only be charged above 0 °C, as charging below this temperature can promote lithium plating and irreversible degradation. However, conventional pack-level heating elements increase system mass and design complexity. In addition, heat is transferred from outside into the cell, causing the temperature inside the cell to rise slowly. This study evaluates internal Joule heating of cylindrical Li-ion cells using a zero-mean square-wave current excitation and quantifies the associated aging impact. LG INR21700-M50L cells were tested at 0 °C, −10 °C, and −20 °C with three excitation frequencies (50 Hz, 1 Hz, 10 mHz) at 5 A amplitude. Each cycle consisted of 30 min heating followed by 60 min cooling; reference capacity-based state of health (SOH) was assessed every 50 cycles up to 400 cycles. A maximum surface temperature rise of 14.3 K was achieved, with larger temperature rise at lower ambient temperature and lower excitation frequency. Capacity fade remained below approximately 1% for most conditions; however, at −20 °C and 10 mHz a pronounced SOH decrease to 87% was observed, indicating a critical operating regime. The results provide practical guidance for pulse-heating parameter selection and highlight the need for safeguards and further diagnostics in extreme low-frequency excitation at very low temperatures. This heating approach is particularly suitable for simpler battery-electric applications without thermal management, such as e-bikes or power tools. However, it may also be relevant for applications with existing thermal management systems, as it simplifies battery pack design.
Raiber, StefanAllmendinger, FrankDegler, DavidParschau, Anke
The electrification of heavy-duty vehicles is a critical pathway toward improved energy efficiency in the freight sector. The current battery electric truck technology poses several challenges to commercial vehicle operations, such as limited driving range, sensitivity to climate conditions, and long recharging times. Estimating the energy consumption of heavy-duty electric trucks is crucial to assessing the feasibility of fleet electrification and its impact on the electric grid. This article focuses on developing a model-based simulation approach to predict and analyze the energy consumption of electric trucks by considering the impact of weather and geographical conditions on vehicle road load and auxiliary components power consumption, as well as the impact these factors have on driving range. Specifically, drayage trucks employed in logistics around maritime ports are used as a case study, with consideration of seasonal climate variations and geographical characteristics at different locations. The article includes results for three major container ports within the United States, providing region-specific insights into the energy requirements and driving range of the electric drayage trucks in these regions, which will inform decision-makers in integrating electric trucks into the existing drayage operations and plan investments for electric grid development.
Shiledar, AnkurVillani, ManfrediLucero, Joseph N. E.Sun, RuixiaoSujan, Vivek A.Onori, SimonaRizzoni, Giorgio
Passive fatigue can cause accidents with automated and regular vehicles. A proof-of-concept prototype [made with light-emitting diode (LED) matrices and white LED (WLED)] and a preliminary comparative usability test (N = 7) are used to study whether the active manipulation of simulated weather cues can be a potential countermeasure to passive fatigue. Participants rated system suitability, system impression, and their fatigue level similarly when they viewed a weather windshield heads-up display (HUD) versus a speedometer windshield HUD [no significant differences found and relatively small 95% confidence interval (CI) ranges around 0]. Qualitative analysis of interviews found that participants saw the potential value of the weather display and that display placement, dynamic graphics, and user activation were commonly mentioned themes. These results suggest the concept is theoretically possible, though further work is needed to prove the concept in practice.
Ensafjoo, MohsenLi, Jamy
To minimize noise caused by interior components rubbing against each other, automotive materials are usually tested in advance with the established stick-slip method according to VDA standard 230-206. This procedure is widely used for soft materials, upholstery and plastics. However, it is limited to constant climatic and selected loading conditions. Contrary, in real application, changing climates and dynamic excitations can nevertheless trigger noise issues even in materials rated as suitable in the prior tests. To address this gap, a new test method has been developed that evaluates the stick-slip behavior of material combinations for a wide range of loading and climatic conditions. Conducted in a climate chamber with a standard stick-slip test bench, the procedure applies sinusoidal excitations, dynamic climatic shifts and advanced data analysis. In addition to the usual results the new method also evaluates realistic scenarios such as starting a vehicle in different seasons or sudden jolting movements with high excitation speeds. The result is a detailed map of stick-slip behavior as a function of excitation speed and climate. While requiring a similar level of effort as the traditional test, this approach delivers far greater insight. It enables a more reliable optimization of materials and facilitates targeted material selection for specific applications. In this manner, it can not only contribute to improve product quality but also to achieve quiet interiors and customer satisfaction.
Fritz, SusanneStrangfeld, Martin
The longevity of proton-exchange membrane fuel cells is governed by degradation processes whose rates depend on local operating conditions such as temperature, humidity, liquid-water saturation, and reactant availability. Along-the-channel gradients imposed by the flow field can therefore be relevant when interpreting operating behavior and when formulating models intended to support control and system studies. The AlphaPEM framework provides a dynamic through-plane description of electrochemical and water-management states, but in its baseline form does not resolve how these states vary along the gas channels. This paper presents a pseudo-2D (1+1D) extension of AlphaPEM that couples a discretized along-the-channel gas-channel model to a segment-wise MEA submodel. For each axial segment, the MEA equations are evaluated with local boundary conditions obtained from the channel (e.g., reactant and vapor concentrations), while retaining the key dynamic states of the original formulation, including cathode over-potential and membrane/catalyst-layer water variables. Electrical coupling between segments is treated explicitly. In addition to a uniform-current closure, an equipotential bipolar-plate closure is implemented, in which a common cell voltage is determined such that the sum of segment currents matches a prescribed operating point. The same structure enables frequency-domain analysis and interpretation in terms of segment-resolved apparent impedances. The contribution focuses on model formulation and coupling strategy and illustrates how axial gradients can be represented within an efficient, control-relevant PEM fuel cell model.
Ringeisen, BjörnGünthner, MichaelKargl, Pascal
Neural Network Enabled Synthetic Air Data System: Development and Validation2026-26-07206/1/2026
Synthetic Air Data System (SADS) provides a smart solution that can be used to predict critical air data parameters in the absence of conventional air data sensors. Traditional air data sensors, such as pitot-static tubes and vanes, are generally expensive, require regular maintenance, and can fail in harsh weather conditions. In addition, these sensors, along with their processor and computers, add weight to the aircraft. To address these issues, a synthetic air data system is proposed using a Recurrent Neural Network (RNN). Several flight variables were checked for Pearson correlation coefficient with respect to the angle-of-attack and angle-of-sideslip, and thereafter, input features were selected based on the thresholding technique. The proposed neural network has two hidden layers and regularization technique was implemented by adding two dropout layers to each hidden layer to prevent overfitting of the model. The neural network was trained using actual flight test data, supplemented with simulated data wherever gaps were observed in the entire flight envelope. The RNN model is trained to predict the aerodynamic flow angles, viz., angle-of-attack and angle-of-sideslip. The proposed model was found to be able to predict the aerodynamic angles with a degree of accuracy. The accuracy was also checked with several complementary actual flight data to check the fidelity of the trained neural network model.
Sahu, SanjuC, PoornimaKaliyari, DushyantTK, Khadeeja NusrathHebbar, Archana
Sustainability needs to be practical. That was a point Peter Voorhoeve, president of Volvo Trucks North America, made clear at CONEXPO 2026 in Las Vegas. “We're running a business, so we are focusing a lot on efficiency and uptime,” he said, referencing the up-to-10% improvement in fuel efficiency with the new VNL. “That helps our customers to run their operations at a better pace and a lower cost, but at the same time we have a very positive impact on the climate.” Voorhoeve also teased the launch of a new vocational truck. “We are strong in long haul. We are a leading sleeper manufacturer, very strong in regional haul, and we now have renewed focus on vocational,” he said. “In August we will launch a new truck specifically for the vocational segment that's built on the same platform as the VNL and VNR.” (See page 22 for our feature story on the new VNR.)
Gehm, Ryan
This study aims to summarize the influence of air pollution on clouds and precipitation over the ocean and land. This paper summarizes global aerosol observation networks, including GAW and AERONET, as well as aerosol observation networks from various countries. Six typical regions, including North America, North Africa, South Africa, India, China, and the Indian Ocean, demonstrate aerosols’ seasonal and compositional variation patterns. This study also summarizes the impact of aerosols on the microphysical characteristics of stratiform clouds and precipitation mechanisms. The effect of aerosols on clouds varies across regions over land and ocean, and the impact of aerosols on the cloud water path differs significantly. Air pollution significantly affects precipitation by altering the microphysical properties of clouds, and this study is of great importance for understanding and predicting weather changes.
Wang, Mingxin
In the context of the global active response to climate change and the strong advocacy of green development, China’s energy industry is demonstrating a steadfast commitment to low-carbon transformation. In this process, green power trading has gained significant development by virtue of its unique advantages and potential. In this process, green power trading has gained significant development by virtue of its unique advantages and potential. The core objective of the Pinglu Canal Project, a pivotal initiative promoting green and low-carbon development in the region, is to establish a “net-zero carbon” initiative by facilitating the supply of green energy throughout its entire life cycle. This initiative is designed to promote a green and low-carbon transition. This paper conducts an in-depth study on the green power supply path during the construction period of the Pinglu Canal project, and proposes four practicable options. In order to scientifically and objectively determine the optimal path, this paper constructs a comprehensive evaluation index system and a TOPSIS evaluation method based on comprehensive weights. The system encompasses the four dimensions of feasibility, economy, technology, and demonstration, enabling a comprehensive and precise evaluation of the advantages and disadvantages of each path. The findings of the empirical analysis demonstrate that the combined scores of Path 1 (participation in green power trading), Path 2 (purchase of thermal power with green certificates), Path 3 (rooftop distributed photovoltaic system and purchase of new energy power), and Path 4 (rooftop distributed PV system and purchase of thermal power with green certificates) are 0.8166, 0.7486, 0.2197, and 0.2885, respectively. The comparative analysis reveals that participation in green power trading is the optimal strategy for the project’s construction period.
Huang, ZeyiWei, YuchenLi, XiayangWang, Cuixian
Causal discovery within time series is crucial for revealing the actual causal mechanisms in dynamic systems, and it has major impacts in various fields like economics, healthcare, and climate science. Even though it’s important, accurately figuring out causal relationships from observational temporal data is still quite a difficult task. Traditional Granger causality based methods are often limited by noise sensitivity, large amount of data, and the inability to distinguish between real causality and false correlation caused by hidden factors. In order to solve these problems, this paper presents CausalAugVeri, which is a new algorithm that cleverly mixes data augmentation with causal verification to make causal discovery more solid and precise. This work has three main points: First, we carefully check that using convolutional data augmentation techniques can greatly improve how well time series predictions work, giving a steadier base for detecting Granger causality. Second, the suggested method rebuilds cause variables using specific intervention ways and adds a causal verification part that strictly removes wrong findings, keeping only real causal connections. Third, we do thorough experiments on both made-up and real-world time series datasets, showing that CausalAugVeri always does better than current top methods, particularly when there’s little data and lots of noise. The results prove that our way gives a dependable and expandable answer for causal discovery in complicated time-related situations, connecting the gap between augmentation based on deep learning and traditional causality analysis. This study not only gives a methodologically strong structure but also offers practical tools for real-world uses that need sturdy causal understanding.
Yang, JingChen, XiaotaoQin, XuanliXu, XianjunHu, Zhangxiang
This paper evaluates the feasibility of Restricted Icing operations for light to medium helicopters, which typically lack Full Ice Protection Systems (FIPS). Current regulations normally prohibit these aircraft from flying in known icing conditions, leading to frequent mission cancellations for HEMS and SAR operators. To address this, Airbus conducted flight test campaigns in Norway (2023, 2025) to characterize a safe icing envelope for "cold blade" operations. Results demonstrate that the H145 was able to sustain continuous flight in icing conditions between 0°C and -3°C and perform time-limited operations (5–10 minutes) down to -6°C without compromising safety, handling, or structural integrity. Safe Restricted Icing operations require an operational framework that ensures proper planning, safe routing, briefing, in-flight decision making, and specialized crew training. The study concludes that a Restricted Icing Clearance could significantly enhance winter flight safety. By providing an IFR alternative to VFR flights in marginal weather within a clear operational framework, the introduction of a Restricted Icing Clearance could ensure the availability of critical life-saving missions in typical winter weather.
Ockier, CarlNormann, ErikDezitter, Fabien
Maintaining optimal in-cabin humidity levels is part of occupant comfort, air quality, and the effective operation of climate control systems, particularly for functions like windshield defogging. This paper introduces a novel sensor fusion methodology for predicting in-cabin humidity distribution without dedicated humidity sensor. The proposed approach leverages readily available vehicle data, integrating information from ambient temperature sensors, in-cabin temperature sensors, occupant detection systems, window status, and climate control settings. By intelligently fusing these diverse data streams, a predictive model is developed to infer the dynamic humidity conditions within the vehicle cabin. We discuss the complex interactions between these parameters, such as the moisture contribution from occupants, the influence of external air ingress through open windows, and the dehumidifying or humidifying effects of the Heating, Ventilation, and Air Conditioning system. The paper details the development and validation of the predictive algorithm, highlighting its capability to estimate humidity levels under various operational scenarios. Challenges in modeling the transient and non-linear relationships between inputs and humidity, as well as the evaluation of the model's accuracy against ground truth data, are presented. Alos, initial results demonstrate the feasibility and robustness of this sensor fusion approach, offering an integrated solution for intelligent services and cabin climate conditioning are summarized.
Ghannam, MahmoudSchroeter, RobertShaik, Faizan
With rapid growth of Electric Vehicles (EVs) in the market, challenges such as driving range, charging infrastructure, and reducing charging time needs to be addressed. Unlike traditional Internal combustion vehicles, EVs have limited heating sources and primarily uses electricity from the running battery, which reduces driving range. Additionally, during winter operation, it is necessary to prevent window fogging to ensure better visibility, which requires introducing cold outside air into the cabin. This significantly increases the energy consumption for heating and the driving range can be reduced to half of the normal range. This study introduces the Ceramic Humidity Regulator (CHR), a compact and energy-efficient device developed to address driving range improvement. The CHR uses a desiccant system to dehumidify the cabin, which can prevent window fogging without introducing cold outside air, thereby reducing heating energy consumption. CHR is based on desiccant dehumidification technology. Unlike conventional desiccant rotors, it features an integrated structure that combines the desiccant material with a honeycomb-type Positive Temperature Coefficient (PTC) heater. This enables highly efficient direct heating regeneration and a compact design optimized for EVs installation. Previously, the heating power reduction achieved by CHR was measured, and the extended driving range was estimated based on those results. In contrast, this study conducted a complete driving test from full to empty battery charge in a cold laboratory environment. The test was performed using the CLTC (China Light-Duty Vehicle Test Cycle) driving mode. Using an EV equipped with a CHR prototype, tests were conducted with CHR turned ON and OFF respectively. A 13% improvement in winter driving range was actually observed, confirming the real-world benefits of the concept. In conclusion, this study demonstrates that CHR is a promising solution for extending EVs driving range under winter conditions while improving energy efficiency and passenger comfort.
Sakai, NaokiTakahiko, NakataniShinoda, NarimasaIhara, YukioWakida, NorihiroKato, KyoheiAnoop, Reghunathan-Nair
The performance of a full battery pack with its effective thermal management system (BTMS) depends on coolant flow and heat transfer characteristics inside the pack. To develop a full BTMS using model-based design (MBD), the model must capture the coolant pressure drop ∆?? and heat-exchange performance from the cell to ambient air via the coolant, cooling flow channels, air gaps, and pack cases. Predicting battery pack responses (i.e., voltage, SOC, temperature) under all weather conditions is a challenge, as a complete pack contains several hundred to thousands of cells, coolant lines, coolant line bends, and coolant channels. This work presents a detailed approach to identifying heat transfer and ∆P correlations that can capture the real-time thermal-electrical performance of a mass-produced LIB pack under constant speed (in winter) and transient driving (in summer). A vehicle test is conducted using a Tesla Model Y, 2-motor model equipped with a 75-kWh LIB pack. The LIB pack's thermal and electrical performance is recorded at 60 km/h under cold conditions and during transient driving in summer. The pack is based on the 2RC equivalent circuit model, reduced from the P2D-based NCA/Gr-SiOx Li-ion cell, to accelerate simulation times at the pack and vehicle levels. The approach to identifying ∆P and heat transfer correlations are discussed, with pack model validations under coolant temperatures ranging from 0 to 40 °C and coolant flow rates of 4 to 14 L/min. The thermal and electrical performances (voltage, SOC, ∆P, and temperatures of the coolant, bricks, and modules) of the high-fidelity battery pack model are validated against vehicle test data at 60 km/h driving (ambient temperature Ta = -10 °C) and repeated FTP+HWFET cycle (Ta = 30°C). The whole pack model achieves an average accuracy of 90%, and this work can serve as a guideline for designing battery packs with their BTMS using MBD.
Sok, RatnakKusaka, Jin
LiDAR (Light Detection and Ranging) systems are essential for autonomous driving (AD) and advanced driver-assistance systems (ADAS), providing accurate 3D perception of the surrounding environment. However, their performance significantly deteriorates under adverse weather conditions such as fog, where laser pulses are scattered by airborne particles, resulting in substantial noise and reduced ranging accuracy. This scattering effect makes it difficult to detect objects within or behind particulate matter, posing a serious challenge for reliable perception in real-world driving scenarios. To address this issue, we propose an algorithm that combines adaptive multi-echo signal processing with a feature-integrated, rule-based denoising framework to enhance LiDAR performance in noisy environments. The multi-echo approach selectively utilizes meaningful signal returns by evaluating both intensity and relative echo positions. Based on predefined rules, the algorithm identifies the echo most likely to represent a real object. The rule-based denoising algorithm dynamically adjusts thresholds by integrating multiple features, including point clouds density, intensity, and echo width. These features are evaluated in conjunction with measured distance to adaptively suppress fog noise and improve signal reliability. This synergistic method enables robust detection of real objects even in low-visibility conditions. Experimental evaluations demonstrate that the proposed algorithm significantly improves effective ranging distance under adverse conditions compared to conventional methods. Furthermore, it eliminates up to approximately 99% of noise induced by airborne particles in foggy scenarios. These results highlight the potential of our approach to enhance LiDAR reliability and safety in real-world automotive applications, contributing to the advancement of autonomous driving technologies under all-weather conditions.
Kaito, SeiyaZheng, ShengchaoFujioka, IbukiBeppu, Taro
Reliable environmental perception under adverse and contaminated conditions is a critical requirement for autonomous driving systems. Although LiDAR sensors play a central role in such perception, their performance is significantly degraded by surface contamination caused by environmental factors such as rain, snow, dust, anti-icing materials, and bug splatter impacts. However, most existing public datasets and prior studies rely on simulated or laboratory-generated contamination scenarios, which limit their applicability to real-world autonomous driving. To address this gap, we construct a large-scale real-world dataset collected from approximately 22,000 km of on-road driving across diverse regions of the United States, covering a wide range of naturally occurring environmental contamination conditions. The dataset was acquired using a multimodal sensing platform integrating LiDAR, perception RGB cameras, infrared camera sensors, and external monitoring systems, enabling comprehensive observation of sensor behavior under realistic operating environments. Based on this dataset, we propose a scalable contaminant classification framework that focuses on LiDAR surface contamination. A key contribution of this study is the introduction and exploitation of near-field point cloud features, which capture backscattered laser signals caused by surface contamination and exhibit a strong correlation with contamination severity and type. Using raw LiDAR signals, we utilize sixteen feature functions and train supervised learning models to classify seven distinct contaminant categories. Experimental results demonstrate that the proposed approach achieves classification accuracy exceeding 95% under real-world driving conditions, significantly outperforming prior laboratory-based studies. Furthermore, the framework is designed for practical deployment and can be extended to additional contaminant types and geographic regions through incremental data collection and learning. The proposed methodology enables real-time identification of LiDAR contamination sources, providing a critical foundation for adaptive sensor-cleaning strategies. By supporting contamination-aware sensor maintenance, this work contributes to cost- and weight-efficient sensor system design and represents an essential step toward achieving reliable Level 4 autonomous driving.
Kim, Hunjae
This study presents a simulation method for reproducing slush accumulation on underbody components, with a particular focus on the floor undercover, during vehicle operation on slush-covered roads. As electrified vehicles become increasingly important in the pursuit of carbon neutrality, the adoption of aerodynamic undercovers to improve driving range has accelerated. However, these components are exposed to various environmental stresses, including water, chipping, and especially snow and slush, which can lead to damage and performance degradation. While previous research has addressed water and chipping stresses through simulation, studies on slush-induced stress have been limited. To address this gap, the Moving Particle Semi-implicit (MPS) method was applied, incorporating a power-law model to represent the non-Newtonian flow characteristics of slush. Parameter identification was conducted through steel ball drop tests and tire scattering tests, ensuring both qualitative and quantitative agreement between experimental and simulation results. The simulation’s accuracy was further validated by comparing the scattering direction and accumulation locations with those observed in actual vehicle tests. The method was also applied to different floor undercover specifications and multiple vehicle models, demonstrating its versatility and independence from vehicle type. Quantitative evaluation of slush accumulation was achieved, and the simulation results showed excellent agreement with experimental data across all tested conditions. This Computer-Aided Engineering (CAE) approach enables efficient and highly accurate assessment of underbody component stress during slush road driving, supporting both aerodynamic performance and environmental durability in the development of electrified vehicles. Remaining challenges include the variability of slush properties under real-world conditions, the limitations of the power-law model, and computational costs associated with the MPS method. Further research is required to enhance the method’s accuracy and applicability.
Matsuura, TadashiAnnen, TeruyukiHarada, TakeyukiUeno, ShigekiAsai, MikioWatanabe, Haruyuki
This paper presents research and digital twin modeling results to support work on a methodology to properly account for the energy consumed by the thermal system of a BEV, for use within both existing Petroleum-Equivalent Fuel Economy (PEFE) calculations, and the proposed addition of hot and cold weather range values to the consumer-facing Monroney label [1]. Properly accounting for thermal system impacts would incentivize minimizing energy consumption of these systems, since 1) BEV PEFE is a direct input to an OEMs overall CAFE performance, and 2) the values on the Monroney label has some impact on consumer vehicle choice. The impetus for this work was Final Rules issued by the EPA and NHTSA in early 2024 eliminating A/C Efficiency Credits for BEVs from the 2027 MY, thus eliminating regulatory incentives to minimize energy consumption of these systems. Higher energy consumption will produce a number of negative secondary effects, including higher real-world greenhouse gas emissions, reduced vehicle range, greater strain on the nation’s electrical grid, and higher vehicle mass leading to reduced vehicle safety - should OEMs opt to merely install larger batteries to address cold and hot weather range impacts instead of implementing lower energy-consuming technology. The results from the analysis, which ideally would be confirmed with follow-up vehicle tests, show that for a baseline, PTC-heat based system, thermal system energy consumption represents 19.2% of the total energy consumed by a BEV on an annual basis, using an ambient-VMT weighted approach. It seems to be the technical equivalent of “straining at a gnat while swallowing a camel” to focus so much time and energy on identifying incremental improvements in energy consumption from the propulsion-portion of a BEV, while by comparison ignoring the system that according to this analysis can account for nearly 20% of the total on an annual basis.
Taylor, Dwayne
The increasing demand for electrified transportation is leading to accelerated development of highly efficient hybrid and battery electric vehicles. A major concern for customers adapting to battery electric vehicles (BEV) is range anxiety due to low charging speeds, charging infrastructure not matching expectations and unreliable range estimations shown to the customers by their vehicles. Estimating the range more accurately has been difficult due to the sensitivity of vehicle’s energy consumption to real-world environmental and driving conditions. This paper aims to find out the effect of true wind in the road load experienced by BEVs in the real-world driving scenarios and how using a highly accurate wind speed measurement improves the energy consumption estimation better. On-road tests were conducted on public roads and in controlled test-track environments to collect reliable wind speed measurements using a dynamic multi-hole pressure probe. Additional coastdown tests were also conducted to find appropriate road load coefficients which provided a slightly better alternative to EPA coefficients to be used in our estimation models. A high-fidelity energy model was developed to estimate energy consumption with greater accuracy than simplified energy models, which are commonly used in the remaining range calculations shown in the information displays in vehicles. Finally, this paper also explores the need for a machine learning correction model which predicts the gap between the high-fidelity energy model estimations and actual energy consumption, thus compensating for dynamic losses which are hard to estimate using physical models. This hybrid approach of a physics-based model complemented by a data-driven residual correction model provides a unique way to increase the accuracy of traditional modeling techniques and also helps to understand the gaps in those techniques better. Results are used as a baseline benchmark for developing fast executing, lower-fidelity models that can be used in production level applications.
Raghupathy, Vishnu PrasaadKim, ShinhoonEvans, NicNiimi, KeisukeMochihara, Takahiro
Climate change and the depletion of fossil fuels have increased the need for renewable energy sources such as biodiesel. Biodiesel is an environmentally friendly fuel derived from various vegetable oils through a process known as transesterification. In this study, a new graphite-based heterogeneous catalyst was developed by modifying it Na2CO3, K2CO3, Al2O3 and was used for biodiesel production from linseed, cottonseed, sunflower, olive oils. Catalyst activity gradually decreased from 90.0 to 76.7% for cottonseed oil, from 93.0 to 76.0% for olive oil, from 95.0 to 77.0% for sunflower oil, and from 89.0 to 69.0% for linseed oil after the fourth operation. The fuel properties of the obtained biodiesel samples were investigated and the most favorable characteristics of cottonseed oil–based biodiesel were found to be d 4 20 = 0.8448, ν 40 = 3.3820, flash point of 93°C. Based on the X-ray broad peaks at 22.8° and 26.4°, we can note that after the four-time reaction cycle, the structure of the catalyst was destroyed to expanded and pure graphite with the loss of catalytic activity. Additionally, the influence of the amount of oleic, linoleic, linolenic, and saturated acyl groups in oil samples on exploitation properties was investigated by NMR spectroscopy.
Mamedov, IbrahimMamedova, GulbenMamedova, Yegana
The growing global adoption of electric vehicles (EVs) has resulted in a spike in the number of EV charging stations. As EVs have become more and more popular worldwide, a large number of EV charging stations are opening up to accommodate their demands. During grid failures, an EV charging station can also serve as a flexible load connected to the grid to balance out voltage fluctuations. An EV charging station when powered using a separate source, such as solar or wind, can function as a powerhouse, bringing electricity to the grid when it's needed. Therefore, instead of installing more equipment to sustain voltage, the current EV charging station can be efficiently used to meet the grid's needs during failures. These stations have the potential to be dynamic, grid-connected assets for sustainable cities and communities in addition to their core function of vehicle charging (SDG 11). Because of their dual purpose, they can serve as adaptable loads that reduce voltage variations during grid outages, making it easier for people to obtain dependable electricity (SDG 7). By making use of the current EV infrastructure, a low-carbon energy transition is promoted, and resource efficiency (SDG 13- Climate Action) is supported, while lowering the demand for additional grid-support devices.
R, UthraRangarajan, RaviD, SuchitraD, Anitha
The recent discovery of glacier remains in Noctis Labyrinthus, the "Maze of the Night" near Mars' equator sheds new light on the history of water on Mars, the evolution of the planet’s climate and geology, and the possibility of life. It also opens the possibility for massive amounts of clean glacier ice to be accessed by astronauts at low latitudes on Mars, alleviating the need to operate in more frigid higher latitudes. Further reconnaissance of the site requires a robotic vehicle capable of traversing rough, salt-crusted glacier surfaces and leaping across crevasse fields. To address this need, we propose a conceptual hybrid aerial/ground vehicle, LILI (Long-term Ice-field Levitating Investigator). LILI combines episodic rotary-wing flight with ground mobility as a propeller-driven sled through an arrangement of skis/runners, wheels, and tilting proprotors. A high-level look at the Noctis Labyrinthus "relict glacier" site is presented, along with a notional LILI mission traverse concept designed to ensure critical scientific measurements are captured. The NASA Design and Analysis of Rotorcraft (NDARC) software is utilized to ensure that mission requirements and sizing constraints are met. Furthermore, future work considers guidance, navigation, and control requirements to satisfy mission objectives, and an initial construction for a simplified LILI small-scale prototype.
Schatzman, NatashaYoung, LarryDominguez, MichelleLee, PascalNagami, KeikoCaudle, DavidPichay, Isabelle
This Aerospace Recommended Practice (ARP) outlines the causes and impacts of moisture and/or condensation in avionics equipment and provides recommendations for corrective and preventative action.
AC-9 Aircraft Environmental Systems Committee
In recent years, the automotive industry has been looking into alternatives for conventional vehicles to promote a sustainable transportation future having a lesser carbon footprint. Electric Vehicles (EV) are a promising choice as they produce zero tail pipe emissions. However, even with the demand for EVs increasing, the charging infrastructure is still a concern, which leads to range anxiety. This necessitates the judicious use of battery charge and reduce the energy wastage occurring at any point. In EVs, regenerative braking is an additional option which helps in recuperating the battery energy during vehicle deceleration. The amount of energy recuperated mainly depends on the current State of Charge (SoC) of the battery and the battery temperature. Typically, the amount of recuperable energy reduces as the current SoC moves closer to 100%. Once this limit is reached, the excess energy available for recuperation is discharged through the brake resistor/pads. This paper proposes a method to minimize the energy wastage due to the SoC constraints by predicting an optimal start SoC. The optimal SoC is calculated in such a way that it maximizes energy recovery during regeneration while taking the route attributes, weather conditions, and charger availability into account. On a hilly route, it was noticed that the recuperated energy was 5 times more while using the optimal SoC, compared to the 100% start SoC. This reduction in SoC prevents overcharging and contributes to lesser charging time. Consequently, this approach would positively impact overall battery health, energy efficiency, and contribute to promoting sustainability.
Barik, MadhusmitaS, SethuramanAruljothi, Sathishkumar
The automotive industry is encountering difficulties in balancing occupant thermal comfort with HVAC system energy efficiency, particularly under the hot Indian conditions, to meet user expectations and address range anxiety in electric vehicles. Front-loaded comfort-based approach simulations during the development stages have the potential to increase energy savings compared to the stages required at the end of product design. The focus of the current research targets HVAC energy consumers, such as blower flow rates, temperatures, and Cabin heaters, and investigates how these factors influence occupant overall comfort. Additionally, design elements like glass properties and the impact of solar radiation on human comfort are studied at the early concept stages to adopt an energy-based approach for comfort optimization. Simulations are conducted using GT-SUITE and GT-TAITherm software, integrated with CFD field maps platforms to obtain exact flow field predictions. The simulation results are validated with test results obtained from climatic wind tunnel experiments. Key parameters, such as relative humidity (RH), are analyzed to understand their effect on the comfort index and control strategies to maintain vent temperatures that meet comfort requirements with minimal energy consumption. The impact of solar glass properties on comfort indices is studied. To evaluate thermal comfort comprehensively, the Berkeley model provides localized insights into physiological comfort by accounting for variations in temperature and airflow, while the Fanger model assesses overall comfort parameters using predictive indices. We identified the optimal RH levels that can reduce HVAC load while focusing on localized comfort indices for occupants. This helps to go deeper into occupant comfort under multiple scenarios, including extreme temperatures, and evaluates their physiological aspects. This exercise has helped find possible areas for front-loading comfort-based vehicle development processes and pinpoint opportunities for reducing energy consumption. Furthermore, this study reduces reliance on costly physical prototype testing and accelerates the design and development of sustainable automotive solutions, addressing critical challenges in the transition to sustainable mobility.
Bavrisetti, Sai Sampath KumarChothave, AbhijeetGummadi, GopakishoreKhan, ParvejThiyagarajan, RajeshRaju, KumarA Sr, Mahesh
The invention tackles the main drawback of traditional electric vehicle charge ports which use Vehicle Control Unit (VCU) communication intensively and tend to have separate actuators to fulfill the locking function and requirements. These existing systems do not only limit autonomous operation of the charging lid in ignition-off condition but they also add mechanical complexity and packaging space, as well. To overcome these limitations, this research work introduces a Smart Charge Port Housing (CPH), which combines a rotary actuator with an onboard microcontroller and single shaft self-locking device, which allows intelligent and autonomous control of the flaps without relying on vehicle wide control networks. The actuator can remember the last position that the charging lid was in so it can be operated even while the VCU is in the inactive state. The integrated self-locking functionality is achieved by using a specially designed hinge shaft that allows a certain free play for rotation of the shaft at a specific angular range allowing the lock-unlocking functionality to be performed without any extra actuators. Various use cases supported by the system are manual close, auto-close with anti-pinch detection and LIN-based communication (only during the ignition on and VCU active states), IP69 sealing, laser-welded joints. Improved waterproofing features allow the charging bowl to handle the unfavorable environmental conditions. This solution provides a considerable upgrade in user experience, security, and design integration of modern EV cars because of the blend of mechanical reliability and intelligence incorporated in the control architecture. The architecture is scalable, space-efficient and can be used on next generation of electric vehicle platforms requiring both a functional and aesthetic step change in the user accessible charging systems.
Mohunta, SanjayKhadake, Sagar
A significant contributor to particle mass (PM) emissions originating from road transport are particles emitted from brakes, which in Europe are considered in the upcoming Euro 7 emission legislation. UN-GTR (United Nations Global Technical Regulation) no. 24 describes the methodology for measuring brake particle emissions in a test cell setting with a dynamometer, both in terms of PM and PN (particle number). A regulation-compliant test fulfills various quality criteria for different control parameters, which can often be met by applying different control strategies. In this study, we evaluate the effects of implementing different control strategies for torque applied to the brake by the dynamometer, as well as for sampling flow. Additionally, we discuss the cost-saving potential of increasing the automation degree of testing, as well as modifying existing testbeds to accommodate brake emission testing. The torque control strategies applied in this study did not influence PN or PM emissions. For mass-based sampling flow control, adjusting the flow according to momentary readings of pressure and temperature will lead to variation in isokinetic ratio. Conversely, setting constant values of pressure and temperature will lead to variation in volume flow through the cyclone. For realizing cost-saving potential, we present two new technical solutions: AVL PM Sampler xChange for automating the PM measurement, and AVL Brake Emission 3rd Party Integration platform for integrating AVL brake emission measurement instruments into already existing testbed infrastructures, that are only missing the instrumentation (e.g., a converted engine dynamometer).
Martikainen, SampsaWeidinger, ChristophHuber, Michael Peter
As atmospheric CO₂ concentrations continue to rise at unprecedented rates, the urgent need for breakthrough technologies that can efficiently capture carbon directly from the air and convert it into sustainable synthetic fuels has never been clearer. While numerous capture and conversion methods have been propose, many remain at an early stage of development, facing significant challenges such as low energy efficiency, limited scalability, and high operational costs. This lack of technological maturity underscores a vast, largely untapped potential for innovation and transformative advancement. In response to this gap, the present study compiles and critically examines a wide spectrum of emerging capture and conversion technologies. Through a detailed exploration of their functionalities, potentials, advantages, and challenges, the paper accumulates a comprehensive and well-informed dataset. This holistic understanding not only reveals key bottlenecks but also identifies promising pathways to overcome them, offering a valuable foundation for future research and practical implementation. At its core, the study explores how strategic integration and optimization of capture and conversion systems can significantly enhance overall energy efficiency potentially more than doubling current benchmarks. Through this hypothesis-driven approach, it uncovers new possibilities for elevating technology readiness and achieving commercially viable solutions. Serving as a vital resource for researchers, industry stakeholders, and policymakers, this work advances scientific understanding and offers a clear roadmap to accelerate innovation and investment. The insights presented hold the promise to revolutionize sustainable fuel production, facilitate the global reduction of carbon emissions, and catalyze the transition toward a resilient, circular carbon economy that benefits both society and the environment.
Jain, GauravPremlal, PPathak, RahulGore, Pandurang
As the transportation industry pivots towards safer and more sustainable mobility solutions, the role of advanced surface technologies is becoming increasingly critical. This paper presents a novel application of electroluminescent (EL) coating systems in heavy-duty trucks, exploring their potential to enhance vehicular safety and reduce environmental impact through lightweight, energy-efficient lighting integration. Electroluminescent coatings, capable of emitting light uniformly across painted surfaces when electrically activated, offer a transformative alternative to conventional external lighting and reflective materials. In the context of heavy-duty trucks, these systems can significantly improve visibility under low-light and adverse weather conditions, thereby reducing the risk of road accidents. Furthermore, the uniform illumination achieved without bulky fixtures contributes to aerodynamic efficiency, supporting fuel economy and reducing carbon emissions. use of this coating system, can optimize tooled up plastic part and sub-assemblies specially to those parts we use for indication, marking, highlight & lighting assisting during dark This paper identifies and evaluates specific use cases where EL coatings can deliver substantial benefits: for example, Exterior Lighting systems, Perimeter Lighting for Night Operations, Ingress/degrees illumination with Integrated Safety Features, Emergency and Breakdown Visibility & Trucking illumination accessories. Accentuate brand specific, Technology & design features over a truck
Harel, Samarth DattatrayaBorse, ManojL, Kavya
Electric buses (e-buses) are essential to sustainable public transport, but their real-world efficiency and range are heavily affected by auxiliary systems, particularly the Heating, Ventilation, and Air Conditioning (HVAC) system. This study investigates how ambient temperature variations and HVAC loads influence energy consumption, range, and efficiency in e-buses operating under diverse climatic conditions. The methodology combines field data collection from urban e-buses across seasons—including extreme summer and winter—with controlled laboratory testing. Field measurements included ambient temperature, HVAC demand, vehicle speed, state of charge (SOC) variation, and energy consumption. These inputs were used to develop real-world duty cycles, replicating actual thermal loads, passenger profiles, idling periods, and driving patterns. In the laboratory, these cycles were simulated using a chassis dynamometer and environmental chamber, with HVAC systems tested at controlled ambient temperatures (−5 °C to 45 °C). Energy split analysis quantified the proportion of energy used for propulsion versus HVAC, revealing the range impact under extreme conditions. Key results show a 20–40 % range reduction during peak HVAC operation, with variability tied to cabin insulation, HVAC control strategies, and route dynamics. The study compares climate control, thermal pre-conditioning, and dynamic thermal management to optimize efficiency. By bridging real-world data with laboratory validation, this research delivers actionable insights for original equipment manufacturers (OEMs), fleet operators, and policymakers to mitigate HVAC-related energy losses and ensure reliable e-bus deployment across climates.
Vishe, PrashantDalela, SaurabhSaraswat, ShubhamJoshi, Madhusudan
The automotive industry is advancing rapidly with the integration of cutting-edge technology, aesthetics, and performance. One area that has remained relatively underexplored in the pursuit of sleek, minimalistic interiors is the packaging of Sunshade in door trim system. Traditional sunshade design, often bulky and increasingly incompatible with the trend towards compact design and packaging. The car sunshade is a shield that is placed on a car side window and used for regulating the amount of light entering from the car window and helps improve the passenger comfort inside the cabin. Car Interior components, specifically plastic and seats are based on thermal stress properties. When we expose these parts to direct contact with sunlight, humidity and ambient temperature above threshold limit, the interior plastic parts can start to soften and melt. Due to this, they start emitting harmful chemicals which cause anemia and poor immune systems. So, the Sunshade, in addition to protecting passengers’ comfort inside car, it also protects passenger from harmful radiation and enhances overall visual appeal of the vehicle. The main objective of this paper is to address the following: An innovative approach to the design of sunshade for Door trim Meeting shoulder room target Focusing on enhancing aesthetics, Low weight impact, robust design, and assembly, Managing sunshade quality as per regular standard.
Palyal, NikitaD, GowthamBhaskararao, PathivadaBornare, HarshadRitesh, Kakade
Accidents during lane changes are increasingly becoming a problem due to various human based and environment-based factors. Reckless driving, fatigue, bad weather are just some of these factors. This research introduces an innovative algorithm for estimating crash risk during lane changes, including the Extended Lane Change Risk Index (ELCRI). Unlike existing studies and algorithms that mainly address rear-end collisions, this algorithm incorporates exposure time risk and anticipated crash severity risk using fault tree analysis (FTA). The risks are merged to find the ELCRI and used in real time applications for lane change assist to predict if lane change is safe or not. The algorithm defines zones of interest within the current and target lanes, monitored by sensors attached to the vehicle. These sensors dynamically detect relevant objects based on their trajectories, continuously and dynamically calculating the ELCRI to assess collision risk during lane changes. Additionally, adherence to R79 regulations and usage of safety distances enhance the algorithms handling uncertainties in the system and environment. Additionally, separate thresholds for ELCRI in each zone allow modular lane change assessments. The inclusion of the above additions to the algorithm serves as an extension to already existing similar risk index concepts, therefore the term “Extended” LCRI has been used. The algorithm has been tested in simulated scenarios and compared with real-world data to evaluate its strengths and limitations. While very high relative velocities between the object and self-vehicle can affect ELCRI accuracy, the algorithm has proven effective in improving lane change safety under typical traffic conditions.
Dharmadhikari, MithilS, MrudulaNair, NikhilMalagi, GangadharPaun, CristinBrown, LowellKorsness, Thomas
Rainfall, as a common trigger condition in the Safety of the Intended Functionality (SOTIF) framework, can impair autonomous driving perception systems, leading to unexpected functional failures. However, studies focusing on sensor performance degradation under natural rainfall conditions are limited, primarily due to the lack of datasets with detailed rainfall information. To address this gap, this study present RainSense, a multi-sensor autonomous driving dataset collected under natural rainfall conditions, featuring fine-grained rainfall intensity annotations. RainSense was recorded at nine representative intersection scenarios in the campus, where a single dummy target was placed at various distances as a detection target. A laser-optical disdrometer was deployed to continuously measure rainfall intensity (mm/h), while camera images, lidar point clouds, and 4D radar data were synchronously collected under different rainfall levels. In total, the dataset comprises 728 cases, including 145 with clear condition, 214 with light rain, 204 with moderate rain, 98 with heavy rain, and 67 with torrential rain. Each case is segmented into 10-second windows and includes 2D and 3D bounding box labels of the dummy target. To investigate how rainfall affects different perception modalities, perception metrics were applied to each sensor type. Results reveal that under heavy and torrential rain, camera images suffer from blur, while lidar experiences sparse and weakened point returns, both leading to substantial perception degradation. In contrast, radar shows minimal variation across all rain levels, maintaining stable signal characteristics and demonstrating strong resilience to adverse weather conditions. The dataset and benchmark suite will be released open-source at: https://github.com/IVtest-Lab/RainSense.git.
Xia, TianYang, XingboChen, TianruiZhang, LonggaoYe, ShaolingfenChen, Junyi
Currently, we face the challenge that ensuring ADS safety remains the primary bottleneck to large-scale commercial deployment—while benchmarks such as the CARLA Leaderboard have spurred progress, their coarse evaluation granularity, inability to quantify procedural risks, and lack of differentiation among algorithms in complex scenarios make in-depth diagnostics and functional safety validation exceedingly difficult. To address these challenges, we propose EvalDrive, a framework that seems to offer a more comprehensive approach to multi-scenario performance evaluation for modular autonomous driving systems. Within this broader analytical framework, EvalDrive appears to provide what seems to be three key contributions. (1) It constructs what appears to represent a structured and extensible scenario library, comprising a majority of 44 interactive scenarios, 23 weather conditions, and 12 town environments, which are then systematically expanded through parameterized variations. (2) Our paper presents a multi-dimensional evaluation approach that shifts the emphasis from outcome-based safety to process-oriented safety, enabling the quantification of near-collision behaviors. Moreover, context-aware metrics—such as the Index of Driving Efficiency (IDE)—are employed to characterize distinct driving styles. (3) The framework further implements a highly integrated closed-loop co-simulation platform. By tightly coupling the CARLA simulator with the Apollo ADS, it establishes a high-fidelity, reproducible software-in-the-loop (SIL) environment. What this pattern seems to suggest, therefore, is that EvalDrive provides what appears to be a more comprehensive paradigm—from scenario construction and multi-dimensional evaluation to closed-loop validation—seeming to offer more robust diagnostic support for the iterative optimization and safe deployment of autonomous driving systems.
Jia, ChunyuKong, YanMa, YaoPei, Xiaofei
Based on field investigations of loess slopes along highways in the Lüliang region, a numerical infiltration model of highway loess slopes was established using the ABAQUS finite element software. The study examined the time to plastic zone coalescence and variations in infiltration range under two intense rainfall scenarios for slopes of different heights. Furthermore, a landslide numerical model of the loess slope was constructed using the FEM-SPH method, and a predictive formula for landslide runout distance of highway loess slopes was derived through data fitting.The results indicate that under the same slope height, increased rainfall intensity leads to a certain degree of reduction in landslide runout distance. Conversely, under the same rainfall condition, greater slope height significantly increases the runout distance. This study provides a theoretical foundation and methodological support for stability evaluation and runout distance prediction of loess slopes under intense rainfall conditions.
Liu, ManfengLi, Hong
Automotive air conditioning systems are essential for ensuring thermal comfort for passengers. However, these systems require the elimination of refrigerants with high Global Warming Potential (GWP) and a transition toward more environmentally friendly alternatives. For many years, R134a has been the industry standard in automotive applications, following the phase-out of chlorofluorocarbons (CFCs) such as R12. This study evaluates the energy efficiency and environmental impact of several refrigerants in automotive air conditioning systems in tropical climates. A comprehensive literature review is conducted to select the refrigerants to be compared with R134a. The following is chosen: R1234yf, R744 (CO2), R290, R600a and R152a. Then a mathematical model is prepared and validated. The deviation between the results presented by the mathematical model and those in the literature varies from -1.21% to 8.33%. The simulation results suggest that the Coefficient of Performance (COP) of R152a and R600a is approximately 3% higher than that of R134a. However, these fluids are flammable, requiring additional safety investments. While R1234yf shows a slight loss in efficiency, at almost 4% lower than R134a, it offers a degree of compatibility due to its similar overall properties. The performance of R290 is slightly worse, while that of R744 is significantly lower. Finally, the environmental analysis based on TEWI indicates that R152a and R600a result in a slightly lower impact compared to R134a. In contrast, R290 and R1234yf exhibit a slightly higher environmental impact than R134a.
Oliveira Dias, Vinícius José deBarbieri, Paulo Eduardo LopesMoreira, Thiago Augusto AraújoSantos, Alex HenriqueFreitas Paulino, Tiago de
The road transport mode is predominant in Brazil, representing more than 50% of greenhouse gas (GHG) emissions from energy sector [1]. Currently, trucks use internal compression combustion engine (ICCE) with fuel Diesel as propulsion, considering the reference for technical and economic studies for alternative propulsions such as: electrification or hydrogen (H2) as fuel. Both technologies are extremely important to achieve the goals defined by Brazilian nationally determined contribution (NDC) (commitment to Paris agreement target) to avoid climate changes catastrophic issues due climate temperature risk to exceed 2°C. In addition, several companies have announced sustainability compromises to contribute with reduction of GHG emissions in scopes 1,2 and 3, focusing on Environmental, Social and governance (ESG), where road transportation has a larger contribution to achieving the target. Contran Resolution (CR) n° 882/2021 defines the maximum weights and dimensions of vehicles to be authorized to circulate in Brazilian roads. A major challenge is the eligibility of the system to be installed, as well as the layout arrangement in the vehicle. In the context, during the concept phase, it is necessary to evaluate the load distribution on the axles, maximum weights and maximum dimensions of the vehicle complying with the legal requirements. Legal requirements modifying has been started in some countries, for example Chile where recently had public a resolution n° 181/2025 allowing to increase 350 kilograms (kg) in a single front axle, probably part of new policies to make feasible alternatives propulsions to reduce GHG emissions. The proposal of this work will evaluate the impact of load distribution through the assessment of possible layouts for purely electric propulsion or hydrogen fuel propulsion using software as tool, searching for greater agility in concept evaluation. The challenge is to create a model where it is possible to modify the gravity of center (CoG) along the vehicle considering curb weight, implementation, gross weight and payload, checking if it possible to follow the same premises of ICCE and current CR without miss customer by criteria. The results show the impact of reduced payload by 15-34% due to mass added in vehicle for zero emission vehicle (ZEV) using alternative propulsion (electric and hydrogen) in all scenarios simulated, considering the same dimensions of ICCE complying with CR. As conclusion, has been observe challenges for truck decarbonization due to payload reduction, generating direct impacts in customers due the possible total cost operation (TCO) increase. In additional this work can contribute to new decarbonization mobile polices discussion in the future (technical or compensation rules), where the tool used can contribute to build Fastly many different scenarios for decision. As recommendation, the CR updated n°1015/24 does not comply all decarbonization truck scenarios and will need be discussed how reduce the impact for ZEV concepts, resulting in CR updates to make the plan feasible for the truck decarbonization,
Ferreira, Bruno FranciscoOliveira Da Silva, Laura de
The control of rainfall runoff drainage in large airports presents significant challenges, particularly in terms of real-time coupling with meteorological warnings. This paper proposes an optimization method for the layout of sponge-like drainage ditches in large airports under BIM-3DGIS coupling. A BIM water supply and drainage model is constructed, with detailed inspections conducted on the functions and connections of the pipeline system in Revit software. The flow velocity and equivalent water supply pressure within the pipelines are analyzed, and collision detection is performed on the components. Based on 3DGIS technology, an optimization model for the layout of sponge-like drainage ditches is established, taking into comprehensive consideration various factors such as airport topography, rainfall characteristics, and surrounding environment. By calculating the water level changes within the infiltration and drainage ditches under different design rainfall scenarios, the storage ranges, water levels, and waterlogging duration curves of various facilities during rainfall events with different return periods are simulated. Case studies demonstrate that this method can effectively improve airport drainage efficiency, reduce peak drainage flow, mitigate the risk of waterlogging, and decrease the number of collision points in drainage pipelines. It provides a scientific basis and technical support for the planning and design of sponge-like drainage ditches in large airports.
Geng, LiangsuiZhao, ZhenyuHu, Jing
Automatic emergency braking (AEB) systems are crucial for road safety but often face performance challenges in complex road and climatic conditions. This study aims to enhance AEB effectiveness by developing a novel adaptive algorithm that dynamically adjusts braking parameters. The core of the contribution is a refined mathematical model that incorporates vehicle-specific correction coefficients and a real-time prediction of the road–tire friction coefficient. Furthermore, the algorithm features a unique driver-style adaptation module to optimize warning times. The developed system was functionally tested on a vehicle prototype in scenarios including dry, wet, and snow-covered surfaces. Results demonstrate that the adaptive algorithm significantly improves collision avoidance performance compared to a non-adaptive baseline, particularly on low-friction surfaces, without introducing excessive false interventions. The study concludes that the proposed adaptive approach is a vital step toward all-weather capable AEB systems.
Petin, ViktorKeller, AndreyShadrin, SergeyMakarova, DariaAntonyan, AkopFurletov, Yury
With air resistance being one of the two major energy losses in on-road vehicles (the other one being tire losses) and therefore heavily contributing to the range of battery electric and fuel cell electric vehicles, it is necessary to account for realistic air resistance in a priori assessments like vehicle range estimations, component dimensioning, and system simulations. However, lack of input data tempts analysts to instead assume unrealistic “nominal conditions” throughout—a simplification which usually underestimates the amount of energy actually required to overcome air resistance and completely ignores the fact that varying environmental conditions will lead to significant variances in energy consumption and therefore vehicle range. Using “nominal conditions,” it is thus impossible to assess the robustness of these measures and, therefore, difficult to design robust systems and to perform meaningful trade-off studies. In this study, we show how publicly available data from weather observations can be used to assess the long-term variation of air resistance of a truck with a semitrailer. Realistic distributions of energy losses due to air resistance, covering multiple years, are derived—showing not only average values but the complete envelope in which the energy losses vary. This, in turn, enables to follow up with probabilistic calculations of vehicle performance in order to assess robustness and trade-offs on various system levels of interest. As a consequence, consumption and range predictions of EVs and ICE vehicles can be performed with higher accuracy and confidence.
Filla, Reno
Perception radar company Arbe was at IAA Mobility in Munich this year to press the case that customers can and should trust automated vehicles. One reason is the global trend of stricter regulations from the NHTSA, Euro NCAP, and in China, which now require automated vehicles to safely meet demanding use cases that are not covered by current sensors, according to Arbe co-founder and CTO Noam Arkind. Arkind told SAE Media that one such category is detecting vulnerable road users (VRU) in poor weather and lighting conditions. “We know from recent tests that a lot of Chinese cars, for example, failed VRU detections in the dark,” he said. “Camera alone doesn't really have reliable pedestrian detection in a dark situation. Radar is a great sensor. It's very sensitive. It's not dependent on weather conditions or lighting conditions, but it's noisy, it's low resolution, and it's hard to use.”
Blanco, Sebastian
Researchers have created a simulation model to analyze how coastal management activities meant to protect barrier islands from sea-level rise can disrupt the natural processes that are keeping barrier islands above water.
Whether it’s the meeting room of an office building, the exhibition room of a museum or the waiting area of a government office, many people gather in such places, and quickly the air becomes thick. This is partly due to the increased humidity. Ventilation systems are commonly used in office and administrative buildings to dehumidify rooms and ensure a comfortable atmosphere. Mechanical dehumidification works reliably, but it costs energy and — depending on the electricity used — has a negative climate impact.
To provide needs of food, clothing and infrastructure for growing population of the world, off-highway vehicles such as those in construction, agriculture and commercial landscaping are moving towards electrification for enhanced precision, productivity, efficiency and sustainability. It has also paved way to adopt autonomy of these vehicles to address challenges like skilled labour shortage for timely and efficient execution. There are many challenges and opportunities of electrification in off-highway domain, be it through completely replacing engine in vehicles or efficiency improvements using hybrid architecture for powertrain and auxiliary power demands, electrification being key enabler precision and speed of the complex operations, automation of complex operation. This paper explains the need of electrification in electric off-highway vehicles and shows how the electrification solves the current challenges faced by off-highway heroes like farmers, construction site owners and workers, commercial lawn and golf turf owners and workers, etc. It first discusses the challenges faced by this industry in terms of scarcity of skilled labour, changing weather conditions, operator fatigue and ever-increasing pressure of productivity, uptime and cost. Then paper presents why electrification is key to solve these issues and how increasing adoption of such technologies becomes relevant.It further explains some architecture/application case studies in farm equipment to cater needs of complex operations like crop care and harvesting, manoeuvring through different soils, lands as well as doing repeated and complex operations at construction sites and other type of off-highway applications like oil fracking industry, trucks, mining, etc.. This case studies describe how electrification has directly enabled more productivity through speed and precision, less operator fatigue, fuel efficiency, farm input efficiency and has become enabler for autonomy.
Deshpande, Chinmay VasudevMujumdar, ChaitanyaBachhav, Kiran
Off Highway vehicles recreation has rapidly expanded across the globe hence it is important to consider the safety of off-highway vehicles which is significantly influenced by various environmental factors, which can pose unique challenges and risks. it is important to make sure that the entire vehicle operates safely and reliably even in the toughest conditions. This paper investigates the impact of environmental conditions on the safety and performance of off-highway vehicles, such as construction equipment, agricultural machinery, and mining vehicles. By examining factors such as terrain, weather conditions, visibility, and natural obstacles, the study aims to identify key hazards and propose strategies to mitigate them. The paper explores how advanced technologies, including digital twins and predictive analytics, can be leveraged to enhance safety measures and improve vehicle resilience in diverse environmental settings. Through comprehensive case studies and empirical data, we demonstrate the critical role of environmental factors in shaping safety protocols and maintenance practices for off-highway vehicles. The findings underscore the importance of proactive safety management and the adoption of innovative technologies to ensure the reliable and safe operation of off-highway vehicles in challenging environments.
Mogal, MasthanvaliChennamalla, Chandra Shekar
In the agricultural industry, the logistics of transporting and storing bales, used as cattle feed, pose significant challenges for large scale farms. Traditional storage of bales in barns is labor-intensive, high in capital expenditure and requires multiple trips of transport vehicle on and off the field. Improper handling during this transition can lead to substantial losses in time, resources and loss of hay. This development aims to eliminate the last-mile transportation step, by enabling year-round storage of bales directly in the field. A patented wrapping material, along with strategic orientation of wrapped bales, enhances their resistance to weather conditions. Field experiments demonstrated that this innovative material not only protects the bales from adverse environmental factors but also effectively retains their nutrient and moisture content. A critical aspect of this solution is ensuring the correct orientation of the wrap seams, as the bales are continuously rotated within the baler machine. Correct orientation of the wrapped bale is achieved through implementation of position-tracking algorithm, which provides real-time feedback to the operator via audio-visual alerts, facilitating timely adjustments for optimal bale placement. By integrating a single sensor hardware modification, with software algorithms changes, this solution allows for backward compatibility with existing machinery, significantly enhancing deployment efficiency. This advancement ultimately reduces operator workload and increases overall operational productivity. This paper leverages principles of systems engineering to present a forward-looking solution that addresses multiple industry challenges, thereby contributing to enhanced efficiency and sustainability in agricultural operations. The outcomes of this project are expected to yield significant improvements in productivity and resource management, marking a notable advancement in agricultural technology.
Kadam, Pankaj
Electricity is a fundamental necessity for individuals worldwide, serving as a force driving technological progress hitherto unimaginable. Electricity generation uses diverse methodologies based on available natural resources in a given geographic region. Conventional methods like thermal power from coal and natural gas, water-based hydropower, solar power from the sun, wind power, and nuclear power are used extensively, the former two being the dominant sources. The generation of nearly 70% of the world's electricity is estimated to be from thermal power plants; however, these operations lead to widespread environmental destruction, greenhouse emissions, and the occurrence of acid rain. Conventional thermal power plants run on the Rankine cycle principle of a boiler, a turbine, a condenser, and a pump. A similar method may be used in the Organic Rankine Cycle (ORC) with the use of solar energy, where heat is transferred to the working fluid in the boiler using a heat pipe, a passive heat transfer device. A closed system makes use of Liquefied Petroleum Gas (LPG) as the working fluid in the Organic Rankine Cycle, while acetone serves as the working fluid when used inside the heat pipe. The boiler is constructed to function within the pressure range of 4-7 bar, while the turbine is constructed to function at temperature levels of 150-200°C when optimized for maximum thermal efficiency. In this current research, a refrigerant boiler has been designed incorporating thermal management strategies to optimize efficiency. The rate of heat transfer from the solar collectors was analyzed under various conditions, and it was found that the evacuated tube collectors had temperature efficiencies ranging from 40-60% at various irradiation levels. Technical parameters unique to the solar collectors are an average flux of 500 W/m2 and a collector efficiency of 65% at the peak of sunlight intensity. The system can also sustain a boiler temperature of 250°C to allow for maximum system working fluid vaporization and pressure generation. The performance of the system was also subjected to different weather conditions, with particular emphasis on temperature variation and the effect on system efficiency. This research offers an insight into the development of solar-powered ORC systems with emphasis on their capability to generate clean and renewable energy. The research can also be applied to enhance the heat management of refrigerant boilers to allow for efficient temperature control and increased overall system efficiency in solar electric energy conversion.
Deepan Kumar, SadhasivamKumar, VDhayaneethi, SivajiMahendran, MSaminathan, SathiskumarR, KarthickA, Vikasraj
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