Browse Topic: Weather and climate

Items (3,803)
To enhance China’s disaster and accident emergency response capabilities and strengthen the digital battlefield system for emergency rescue, an integrated multi-payload unmanned aerial surveillance and communication support system has been developed for extreme weather conditions and ‘triple-disconnection’ disaster scenarios. This paper sets out to address the limitations of traditional emergency drones, including poor environmental adaptability, weak payload capacity, and operational inconvenience. The system’s resistance to wind and rain has been significantly enhanced through the optimization of its airframe design. The innovative design incorporates dual-station symmetric conjugate antennas with planar blind-spot coverage systems, integrating public and self-organizing network base stations to achieve three-dimensional signal coverage and heterogeneous network integration. This enhances ground cellular network resilience. Multi-functional reconnaissance payloads are integrated and compatible with day/night and smoke/rain scenarios, thus overcoming the limitations of single-source visual information perception. The system employs zero-length deployment and parachute recovery methods, thereby facilitating rapid deployment and terrain-independent take-off and landing capabilities. The simulation results obtained demonstrate excellent aerodynamic performance, thus permitting safe operation in wind conditions up to Force 8. The antenna system under discussion is innovative in nature and has been developed to achieve 360° three-dimensional signal coverage. The primary function of this system is to ensure sustained communication link integrity. The field trials further corroborate the aircraft’s stable low-altitude cruising capability in Force 8 winds, thereby averting congestion in constrained rescue airspace. The dual-base station design, incorporating symmetric conjugate antennas and blind-spot compensation antennas, has been demonstrated to reliably restore public ground network signals within a 6.7-kilometre radius. The development of this unmanned aerial patrol system addresses a significant gap in low-altitude rescue capabilities for intelligent unmanned equipment in harsh environments. It underpins the integrated emergency command and operations system for intelligence, command, and execution, as well as the integrated emergency communication support system spanning the air, land, and sea domains. This advancement has been demonstrated to enhance disaster response efficiency and auxiliary decision-making effectiveness under extreme conditions.
Bian, LuFang, YudongYang, JixingZhang, ChenHu, BinZhang, Mingyue
Extreme winter weather often leads to ice accretion on transmission lines. Manual removal is inefficient, costly, and poses safety risks. To address this issue, this paper presents the design of a de-icing robot to replace manual operations for transmission line de-icing. The main content focuses on the detailed structural design of the robot, including the mobile platform, de-icing mechanism, and adaptive adjustment module. Finite element simulations are conducted on key components to verify the structural rationality and the correctness of material selection. The proposed de-icing robot enhances the safety of the de-icing process, improves operational efficiency, and provides a valuable reference for transmission line de-icing methods, demonstrating significant practical value.
Chang, HaoZhen, ChenHan, FengmeiLi, Cheng
Engineers at the University of Massachusetts Amherst have recently shown that nearly any material can be turned into a device that continuously harvests electricity from humidity in the air, work that was published in the journal Advanced Materials. The secret is being able to pepper the material with nanopores less than 100 nanometers in diameter.
In the race to combat climate change, scientists are developing technologies to turn carbon dioxide (CO2) into valuable fuels and chemicals. These innovations help curb greenhouse gases while providing a low-carbon fuel to power our future. As the U.S. shifts to a low-carbon economy, we need environmentally friendly fuels to power vehicles that are hard to electrify, like planes, ships and trains. Scientists are developing the technology to convert CO2 to fuel. However, this conversion requires a lot of energy and water.
Ship propulsion shaft systems are subjected to ice load excitation when ships are navigating in polar ice regions. Compared to conventional hydrodynamic effects, the ice load imposes higher requirements on the shaft system’s ability to withstand torsional stresses. To improve the power of the ship propulsion system when sailing in ice areas and reduce the power loss of the shaft system, while considering the vibration performance. In this paper, a multi-objective optimal design of the shaft system is carried out using the Non-dominated Sorted Whale Optimization Algorithm (NSWOA) to reduce stresses on both the motor shaft and the intermediate shaft. The coupled system model of motor-shaft system-propeller structural components is established, and the system dynamics response model is obtained by solving using the Newmark-β method. Based on the response model, a multi-objective whale optimization algorithm is used to optimize the power and vibration performance of the shaft system simultaneously. The optimized results show that the shaft system transfer efficiency is improved by 0.27%, and the stress at the shaft end is reduced by 11.8% and 12.3% respectively.
Hu, ChuanxiLi, YaoPan, ShuxianLiu, ZhiyongXie, YutengYe, JunZhou, Ruiping
Cyclone abrasive pigging technology, with advantages like environmental friendliness, easy construction, and low destructiveness, has broad application prospects. Studying how the process parameters affect the erosion-wear characteristics of gathering pipelines is crucial for improving pigging efficiency and effectiveness. This study adopted numerical simulations based on gas-solid two-phase flow erosion theory to explore such effects and verified the simulations via a self-designed experimental platform. Results showed that within the given parameter range, erosion rate rose significantly with velocity, especially at 20-30 m/s, peaking at 60 m/s; 0.6 mm abrasives and 0.25 kg/s mass flow rate led to higher erosion rates. Experimental data matched simulations with <10% error, confirming accuracy. Thus, cyclone abrasive process parameters significantly influence pigging performance, and the findings can guide practical operations within the studied range.
Wang, HaoranZhou, XianjunLi, LongSong, HuifangZhang, JinJv, Xiaolong
Desulfurization equipment in electric power industry is in a multi-field coupled corrosion environment with high temperature, high humidity, strong acid and solid-containing slurry. The annual direct economic loss of corrosion exceeds 5 billion yuan, and the equipment replacement cycle is only 1.5-2 years. Traditional protective coatings are difficult to meet the needs. The concept of “bionic barrier-intelligent response-in-situ purification” is proposed to construct multifunctional protective coatings: The Langmuir-Blodgett technique was used to alternately assemble MXene nanosheets and polysilazane. Ti-O-Si covalent bonds enhanced the interface bonding, resulting in a coating hardness of 4H and an elongation of 200%. After 1500 hours of extreme environment test, the coating has low weight loss rate, high self-repair and antibacterial rate, and its service life is extended by 8 times. The engineering application makes the maintenance period of desulfurization tower of a 660MW unit extended from 8 months to 6 years, saving 1.2 million yuan annually, and increasing 200,000 yuan annually by recovering H ˇ SO 2. It provides a cross-scale scheme for electric power corrosion protection.
Nie, PengfeiGao, JiangyuChen, Wei
Solar greenhouses in winter or mountainous areas can be at risk of roof snow accumulation, leading to collapse, poor lighting, and sudden drops in temperature. The snow removal technologies presently employed on these greenhouses have the disadvantages of being cumbersome to adjust, being intricately structured, having a high cost, having high energy consumption, and being poorly adaptable to the curvature of the plastic. An intelligent snow removal device for removing snow on a northern solar greenhouse roof, and an automatic alarm safety system were designed to solve the problems. The device consists of a snow-clearing mechanism, a traversing mechanism, and detection-alarm modules. The mechanism for snow removal consists of a crank-slider with a curved guide rail. The snow removal rod is driven by the gear motor, which goes back and forth on the arched top. A bevel gear transmission system drives the gear motor mechanism. Due to this, the transverse mechanism moves with an interrupting jump-action on transverse rails around many different zones. The system for monitoring snow pressure has a distributed sensor that is programmed as a shield using an Arduino software system. The sensors detect the pressure of the snow in real-time. When the snow pressure hits the threshold, it activates the mechanism for coordinated functioning. This mechanism triggers snow clearing when the pressure threshold is achieved to avoid energy consumed through “premature clearing”. It also fits well on the curved surfaces of the greenhouse without any jamming. The snow removal machine’s various components and operations would accomplish full span snow removal and make it possible to overcome high labour intensity, slow manual response, energy waste, and others. The technology can enhance the safety of winter production of northern greenhouse crops and improve the disaster-resistant capacity of modern agriculture facilities. This technology has been granted a patent for invention.
Fu, ChengguoWei, ShanxiangZhang, RongxianDing, XuefengGao, Yulan
The extreme cold environment has a significant impact on the mechanical properties of welded hollow ball nodes, which are crucial components in large-span steel structures. In this paper, based on the comprehensive test data of drum-shaped welded hollow sphere nodes from Beijing Daxing International Airport, a sophisticated finite element model incorporating welding residual stress is established. Through detailed static loading analysis and systematic hysteresis performance studies, the research thoroughly explores the influence mechanisms of low temperature on node bearing capacity, deformation capability, and energy dissipation performance. The investigation reveals that while the bearing capacity of the nodes increases significantly in low-temperature environments, both their plastic deformation capacity and energy consumption performance are notably reduced. These findings provide valuable theoretical references for the design and optimization of large-span mesh frame structures in cold regions, enabling engineers to better account for temperature effects in structural calculations and safety assessments. The results have important implications for improving the reliability and durability of steel structures in extreme cold environments.
Luo, YanzhiJin, Changming
This study prepares high-performance PI/VIP composite thermal insulation materials for buildings by integrating polyimide (PI) composite membranes and vacuum insulation panels (VIPs), and uses EnergyPlus to explore their impacts on building energy conservation, operating costs, and carbon emissions under different climates. Experimental results show the materials have low thermal conductivity, long service life, and excellent thermal insulation and flame-retardant properties due to their internal vacuum structure inhibiting heat transfer. Simulations in Jinan (tropical monsoon), Heilongjiang (cold temperate), and Shenzhen (subtropical humid) climates indicate that compared with traditional XPS and rock wool boards, buildings using PI/VIP composites achieve 21.3%, 34.7%, and 18.9% higher annual energy-saving efficiency respectively, with 27%-41% lower carbon emissions; the most significant effects in Heilongjiang highlight the material’s great promotion potential in severe cold areas.
Bian, ChenqianChen, Zhaofeng
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
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
Just as the world needs the next generation of nuclear reactors to meet climate goals, it needs new options for safely and securely storing radioactive materials. As of 2022, there were approximately 400,000 metric tons of spent fuel being stored globally, with roughly 90,000 tons in the U.S. alone.
PlanetIQ Golden, CO
Climate change was poised to create an interesting catch-22 for electric vehicles. Electrifying transportation can go a long way to reducing carbon emissions that are driving up global temperatures. But warmer temperatures also accelerate the degradation of batteries, whose performance can be a make-or-break factor for people considering an EV purchase.
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
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
Wherever hydrogen is present, safety sensors are required to detect leaks and prevent the formation of flammable oxyhydrogen gas when hydrogen is mixed with air. It is therefore a challenge that today’s sensors do not work optimally in humid environments — because where there is hydrogen, there is very often humidity. Now, researchers at Chalmers University of Technology, Sweden, are presenting a new sensor that is well suited to humid environments — and actually performs better the more humid it gets.
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
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
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
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
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
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
Even in arid parts of the world, there is usually moisture in the air. This moisture could provide much-needed water for drinking and irrigation, but extracting water out of air is difficult. A new technology developed by KAUST researchers can consistently extract liters of water out of thin air each day without needing regular manual maintenance.
A team at MIT is hoping to fortify coastlines with “architected” reefs — sustainable, offshore structures engineered to mimic the wave-buffering effects of natural reefs while also providing pockets for fish and other marine life.
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
Window glass is a component of the side door assembly of cars. It provides a clear vision for passengers and outsiders. It functions as a temporary opening and ventilation system for the car. It is a part of a car’s aesthetics; it adds stiffness to the door and protects the occupants from different weather conditions. The objectives of this study were to understand the effect of fully and partially opened or closed window glass on the dynamic behaviors of door assemblies and to develop a process to assess these dynamic behaviors. An assessment methodology was developed to determine the effects of various window glass positions on the dynamic behavior of the door assembly. An authenticated finite element (FE) model was used to complete this investigation. The finite element model of the door assembly was validated by correlating the modal frequencies with their corresponding mode shapes. The correlated FE model with the window glass fully closed was called the baseline (W0), and eight other models with the window glass partially and fully opened were analyzed and assessed. The baseline and eight models were assessed in terms of modal results and total vibrations. The investigation was based on modal analysis, frequency response, and total vibrations, which were calculated based on the area under the curve using the trapezoidal rule. The total vibrations were evaluated at all 15 critical locations. The discussion and interpretation led to a few conclusions. There is a minor effect on the natural frequency, mode shape, and number of modes between 0 Hz and 100 Hz. There was a significant increase in vibration around the window glass and a negligible effect on locations away from the window glass. Finally, the work was concluded with suggestions for door designers and a few recommendations for car drivers.
Jadhav, Pandurang MarutiWaghulde, Kishor B.Bhortake, Rupesh V.
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
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
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
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
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
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
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
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