Browse Topic: Financial management

Items (1,101)
By the early 2020s, more than 4.5 billion people have been living in urban areas worldwide, compared to just 1 billion in 1960. Rising growth in urban populations present challenges to infrastructure and transportation systems. Higher traffic levels and reliance on conventional vehicles have contributed to heightened greenhouse gas (GHG) emissions, rising global temperatures, and irreversible environmental degradation. In response, emerging transportation solutions—including intelligent ridesharing, autonomous vehicles, zero-tailpipe-emission transport, and urban air mobility—offer opportunities for safer and more sustainable transportation ecosystems. However, their widespread adoption depends not only on technological performance and efficiency, but also on integration with current infrastructure, safety, resilience to unexpected disruptions, and economic viability. A dynamic agent-based System-of-Systems (SoS) transportation model is developed to simulate vehicle traffic and human movement for assessing mobility solutions against different demand scenarios and possible disruptions within a well-defined metropolitan area. The analysis adopts the concept of an airport city—a cluster of residential, commercial, and industrial spaces surrounding major airports—as a representative urban context. Using the Atlanta Aerotropolis as a case study, this work introduces an interactive, parametric decision-support methodology for evaluating the impact and benefits of future mobility options, as part of transportation master planning. Given the multi-objective and multi-stakeholder nature of transportation planning (e.g. local government, urban planners, engineers, and technology providers), the proposed approach leverages simulation-enabled digital twins of mobility solution alternatives to analyze traffic performance across multiple criteria, including energy consumption, emissions, affordability, accessibility, and connectivity within the broader urban infrastructure. The study reveals cost-benefit trade-offs among mobility solutions in the context of disruptive scenarios, such as the 2026 FIFA World Cup hosted by Atlanta, GA. The results highlight the importance of deploying a mix of mobility options over the city’s transportation network to maximize sustainability while maintaining resilient operations.
Rana, VishvaBalchanos, MichaelMavris, DimitriValenzuela Del Rio, Jose
The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.
Sun, RuixiaoSujan, VivekGoulet, NathanWang, Qixing
Electric Vehicles (EV) have become a major focus in the automotive industry. This paper introduces a propulsion system design, which supports the Wide Torque Band (WTB) concept to boost the power density of PM (permanent magnet) motors in EV Trucks resulting in performance, efficiency, and cost benefits. A selectable 400V/800V battery system has been developed to support the WTB concept and enhance the power density of permanent-magnet motors in electric vehicles. The RESS comprises two 400V battery packs that can be charged at 400V in parallel or at 800V in series via a DC fast-charging (DCFC) connection. In this study, an 800V driving mode was additionally implemented. A prototype battery management system (BMS) along with existing production voltage, current and temperature measurement block hardware are applied to perform mode switching, safety, and cell balancing. The success of this dual pack hardware enables high voltage dynamometer testing of a new 800V DU (Drive Unit) and inverters for EVs. The flexibility of switching between two voltage levels (400/800V) from the battery packs enables testing of both current 400V and future 800V drive systems in the same test cell. The control concepts developed for managing the dual packs were applied to convert a truck to operate at 800V using the native 24-module battery with modifications. Vehicle tests validated the BMS with this new feature.
Zhu, YongjieLee, ChunhaoGopalakrishnan, SureshNamuduri, Chandra
This work evaluates a standardized 30-ton, 16 m railbus platform optimized for unelectrified regional service, focusing on propulsion system design and trade-offs between range, cost, and emissions. A MATLAB/Simulink drive-cycle model was developed to simulate energy consumption and component performance under realistic operating conditions. The Erfurt–Rennsteig route in Germany (130 km round trip, gradients up to 6 %) was selected as a representative case study. The model incorporates detailed sub-models for traction motors, lithium-ion batteries (LFP and LTO), fuel storage, fuel cells, and ICE gensets across multiple fuel options (diesel, gasoline, methane, ethanol, methanol, HVO, FAME, and hydrogen). Battery lifetime is estimated using a combined cycle- and calendar-aging model using the rainflow algorithm to extract charge cycles, while cost models include capital, fuel, maintenance, track fees, and staffing. Results show that battery-electric configurations achieve 1 kWh/km energy use, while hybrid systems range from 2–4 kWh/km depending on fuel and secondary power unit. Control strategies that enable deeper cycling of the traction battery reduce fuel consumption by 7–18 %, with further savings possible from larger battery or genset capacities. Well-to-wheel greenhouse gas emissions vary widely: from near-zero for renewable fuels and clean electricity mixes to over 1,000 gCO2/kWh for fossil-based options. Lifecycle cost analysis indicates that while fuel may represent up to 25 % of total costs, track and station fees dominate operational expenses. Autonomous operation could eliminate oboard staffing costs, amounting to 25–35 %.
Ahrling, ChristofferTuner, MartinGainey, BrianTorkiharchegani, AmirScharmach, MarcelHertel, BenediktAlaküla, Mats
Precise time synchronization is the backbone of today's connected world, keeping telecom networks, data centers, and financial systems running seamlessly. Without accurate timing, our digital infrastructure would quickly fall out of sync. Septentrio designs and manufactures world recognized Global Navigation Satellite System (GNSS) timing receivers for critical infrastructure and leading industry organizations. The Septentrio mosaic-T timing module delivers nanosecond-level precision for synchronization and is trusted by companies such as Meinberg, VIAVI, and Saab. Built-in AIM+ technology protects against intentional and unintentional GNSS jamming and spoofing, ensuring maximum system uptime even in challenging or hostile conditions.
The regulatory mechanisms to measure emissions from automobiles have evolved drastically over the years. Certification of CO2 emissions is one of them. It is not only critical for environmental protection but can also invite heavy fines to OEMs, if not complied with. In homologation test of a Hybrid Vehicle, it is necessary to correct the measured CO2 to account for deviations in measurement from failed Start-Stop phase and difference between start and end State of Charge (SOC) of battery. The correction methodology is also applicable for vehicle simulation in Software-in-Loop environment and for analyzing vehicle test data for CO2 emissions with programmed digital tools. The focus of this paper is on the correction of CO2 derived from SOC delta in the WLTP homologation drive cycle. The battery energy delta due to difference in SOC between start and end of drive cycle should be converted to corresponding CO2 expended from Internal Combustion Engine. The resulting correction factor is known as the REESS factor. To provide a reasonable correction factor for one type of engine in a particular car/weight class, a minimum of 3 measurements are required. Digitalization of the same will provide a significant cost benefit and a faster prediction of REESS factor with wider boundary condition of SOC balance applied. The current full vehicle simulation model was adopted to have better validation with REESS correction factors from measurement. A detailed analysis of the impact of operating strategy on the REESS correction factor is reviewed in this paper. The simulations are carried out on well validated models of different powertrain types. The aim of this study was to achieve a simulation setup which can predict REESS factor in tolerance range of +-0.03 (gCO2/km)/(Wh/km) in comparison to measurement.
Gopinath, Shravanthi PoorigaliKhatod, Krishna
The global shift to electric vehicles (EVs) is vital for reducing greenhouse gas emissions, but their sustainability hinges on effective battery lifecycle management. This review examines the interplay between Life Cycle Assessment (LCA) and circular economy (CE) principles in EVs, with a focus on both international trends and India-specific challenges. We analyze CE strategies such as extending battery lifespan, second-life applications, and recycling integrated with LCA to evaluate environmental impacts from raw material extraction to disposal. Key areas include battery chemistry, LCA methodologies, policy frameworks, and industrial practices, informed by a synthesis of over 50 peer-reviewed articles, technical papers, and sustainability reports. Challenges include inconsistent LCA baselines, low material recovery in informal recycling, and regulatory gaps, particularly in India. Despite these, innovations like solid-state batteries and advanced recycling techniques offer promise, potentially reducing emissions by 30–40 percent through closed-loop systems. Research gaps remain in areas like the durability of recycled materials, economic viability of CE strategies, and socio-ethical considerations. This review provides a holistic overview, actionable insights, and a roadmap for integrating CE into EV design and policy, especially tailored to India’s evolving automotive ecosystem. By addressing these issues, it aims to guide policymakers, industry stakeholders, and researchers toward a more sustainable, circular future for transportation.
Haregaonkar, Rushikesh SambhajiKumar, OmSankar M, GopiKumar, Rajiv
The transition to TREM V emission norms presents significant challenges for naturally aspirated (NA) off-highway engines. Off-highway applications like construction and agriculture segments require high load variability and extended duty cycles with increased BMEP resulting in high PM emissions, and increased exhaust temperatures with lower lambda levels. Given the cost-competitive nature of the segment, it also requires designing leaner intake and exhaust system. To overcome above mentioned challenges, holistic calibration strategies need to be adapted during development phase. To meet TREM V emission norms, solutions like advanced combustion, high-pressure fuel injection, EGR (exhaust gas recirculation), and optimized calibration had to be explored along with aftertreatment systems like Diesel Particulate Filters and Diesel oxidation catalysts. Implementation of aftertreatment systems for TREM V pre-dominantly with naturally aspirated engines will result in challenges associated to soot accumulation and thermal management. This paper attempts to examine, the key technical challenges coming from the market towards use of large implements and heavy soil with NA engine demanding high BMEP, and challenges associated to aftertreatment system due to low operating lambda, smoke emissions and high exhaust gas temperature under different use cases. The research identifies the strategies, such as optimized air-fuel management, optimal specific soot load adaptation and multistage thermal control, to enhance system safety and reliability. Ultimately, the paper provides a strategic roadmap for industry stakeholders to achieve TREM V emissions while ensuring durability, efficiency, and economic viability in off-highway applications.
Patil, Madhavi M.Ravukutam Sr, AnikethRaghu, M YMadhukar, Prahlad
When Thierry Piéton stepped into the role of executive vice president and chief financial officer of Medtronic earlier this year, he entered one of the largest and most complex organizations in the medical technology industry. Yet, despite that complexity and the company’s recent years of uneven performance, Piéton says he came in with conviction. Medtronic, in his view, is sitting on the edge of a long-awaited inflection point.
The advancement of electric mobility has driven the development of technologies aimed at enabling smart, secure, and interoperable electric vehicle (EV) charging. In this context, this paper presents a technical and market analysis of the Vehicle-to-Grid (V2G) and Plug & Charge (PnC) functionalities, focusing on their architectures, applicable technical standards, communication protocols, levels of commercial maturity, and emerging applications. The discussion begins with a review of the main national and international standards relevant to charging infrastructure, with emphasis on IEC 61851, IEC 62196, and ISO 15118 series, which address the technical requirements of equipment, connectors, and vehicle-to-grid communication. The operation of V2G is then discussed as a technology that enables bidirectional energy flow between the EV and the power grid, with a focus on topological configurations, pilot project applications, and regulatory and economic challenges that currently limit its large-scale adoption. In parallel, Plug & Charge solutions based on ISO 15118 are presented, which enable automatic authentication and billing directly through vehicle-charger communication, eliminating the need for user interaction via cards or apps. Market analysis identifies commercially available V2G- and PnC-compatible EV models, charging stations, backend systems, and roaming platforms such as Hubject and Digital Charging Solutions (DCS). The findings show that while V2G is still in a validation phase with limited deployments, Plug & Charge is already undergoing broader commercial adoption. This study provides technical and strategic insights to support national research and development initiatives, outlining the main technical requirements, standards, and regulatory challenges for the effective implementation of these technologies in the Brazilian electric mobility ecosystem.
Marques, Felipe L. R.Arioli, Vitor T.Bernardo, RodrigoNakandakare, Cleber A.Pizzini, Luiz R.Nicola, Eduardo V.
In recent decades, interest in alternative fuels has grown exponentially. Hydrogen has been researched as total or partial substitutes for gasoline in light vehicles, showing great potential. However, this fuel has unique characteristics and properties that can bring improvements or limitations in engine performance. Therefore, a quick analysis of the pressure and HRR curve can highlight changes in combustion and performance. To this end, the aim of this work is to develop a visual interface generated by MATLAB capable of showing the performance parameters of a spark ignition engine when using hydrogen as fuel, initially. This graphic interface is supported with a zero-dimensional model based on the Wiebe function and Woschni correlation to estimating the pressure and HRR values. The interface is designed to receive operating conditions and geometry of the engine, as well as combustion angles. From the information entered, it is possible to visualize mass fraction burned, heat transfer, fluid properties and estimate thermal efficiency and fuel consumption. This focus helps in the study of combustion and in making decisions about the economic viability of using hydrogen in internal combustion engines.
Rincon, Alvaro Ferney AlgarraAlvarez, Carlos Eduardo CastillaOliveira Notório Ribeiro, Jéssica
This paper analyzes the potential of combining natural fibers with nanomaterials to develop advanced composites for automotive sector applications, providing a sustainable alternative to parts traditionally produced with metallic materials. The metallic alloy in the automotive industry is widely used in vehicle manufacturing, but faces significant challenges, such as high production costs, high weight, susceptibility to corrosion, and rigorous recycling processes. Natural fibers stand out for favorable mechanical properties, low cost, low weight, and eco-friendly material, making promising alternatives to metals and synthetic fibers. The combination of natural fibers and nanomaterials creates composites with improved mechanical and thermal, reducing any limitations inherent to natural fibers. Therefore, composites combined, called hybrid, have a high potential for use in various automotive components, such as in structural and non-structural applications. This study also analyzes the performance of these composites with metals used in the automotive industry. Considering aspects such as mechanical properties, thermal properties, corrosion resistance, collision behavior, weight, economic and sustainable impact, and social impact, hybrid composites presented greater advantages for applications than metal alloys. A finite element analysis (FEA) is conducted to assess the viability of composites, evaluating key mechanical parameters under realistic loading conditions. The methodology of the computational simulation quantitatively evaluates the structural behavior of the new hybrid composites that can be used for applications in vehicles and the automotive industry. The environmental impact and economic viability of replacing metals with natural fiber-reinforced composites are also discussed, highlighting the advantages in terms of sustainability and energy efficiency. Finally, the technical challenges and future perspectives for implementing these composite materials are presented, analyzing the optimization and performance to facilitate large-scale production in the automotive sector.
Corrêa, KarythaCabral, GabrielSantiago, MarceloVeloso, VerônicaChaves, Matheus
This study estimates the automated detection costs for rural road pavement conditions in 32 provinces across China using conventional and lightweight equipment, respectively. Assuming full automated detection coverage, the detection costs for rural roads in Changji Hui Autonomous Prefecture and its subordinate counties are calculated to analyze the development path of automated rural road condition detection. The results show that the average detection cost using lightweight equipment is generally lower than that using conventional equipment. Based on national average detection costs, employing lightweight equipment for automated rural road detection in Changji Prefecture could reduce fiscal expenditure by approximately CNY 770,000. It is recommended that Changji Prefecture, in promoting rural road informatization, enhance the application of lightweight automated road condition detection equipment, expand the sharing and utilization of automated detection data, and strengthen the use of such data to support the establishment of a scientific decision-making mechanism for road maintenance. Excluding Tianjin and Tibet, the average detection cost in most provinces is generally below CNY 600 per kilometer, with higher average detection costs in western regions compared to eastern and central regions. Except for Guangdong, Sichuan, and Xinjiang, the full inspection costs for rural roads in other provinces are generally below CNY 80 million, with eastern regions showing lower full inspection costs than central and western regions. It is proposed to strengthen research and oversight of financial investment in rural road automated detection for provinces with higher average detection and full inspection costs and to adjust policy support accordingly.
Yang, YutingZhang, MengWang, YajieLi, BingXu, Yongji
When manufacturers seek to leverage specialized expertise, advanced processing capabilities, or proprietary technologies without assuming the financial burden of acquiring and maintaining dedicated equipment or facilities, they often turn to toll processing.
The electrification of off-highway vehicles presents a complex landscape of challenges, particularly in the realm of cost engineering for motors. These challenges stem from technological complexities, use of specialty materials and processes, economics of scale, and operational factors, each requiring careful consideration to ensure accurate and efficient cost modeling. The lack of standardized cost data for specialty materials poses a significant barrier to accurate cost engineering. Furthermore, the cost of key materials and components, such as electrical steel and permanent magnets, can fluctuate due to supply chain disruptions, material shortages, introducing uncertainty into cost projections. The economies of scale play a crucial role in cost engineering for off-highway electrification. Many off-highway vehicles are produced in lower volumes compared to on-road vehicles, which can result in higher unit costs for electric motors and other. In this paper, we delve into the primary challenges, as well as probable solutions to accurately estimate and predict the cost of motors using first principal methodology and fact-based analysis.
Chauhan, ShivPadalkar, Bhaskar
The transition from ICE to EV faces various challenges and innovations in vehicle maintenance. The automotive industry, followed by EV technology, addresses the unique components and systems of electric powertrains, high voltage, and electronic control systems. Unlike traditional cars, EVs should require specialized tools; high voltage safety protocols are trained as personnel. This paper also described the key difference between ICE and EV maintenance. Also, it explained the various challenges related to limited expertise, battery diagnosis, battery replacement, cost analysis, and charging solutions. To understand the various factors of this study involved the EV service industry as smoother transitions.
Raja, SelvakumarBrainee, Daniel SolomonR. S., NakandhrakumarNandagopal, SasikumarPalani, LoganathanMuthiya, S Jenoris
Zero emission vehicles are essential for achieving sustainable and clean transportation. Hybrid vehicles such as Fuel Cell Electric Vehicles (FCEVs) use multiple energy sources like batteries and fuel cell stacks to offer extended driving range without emitting greenhouse gases. Optimal performance and extended life of the important components like the high voltage battery and fuel-cell stack go a long way in achieving cost benefits as well as environmental safety. For this, energy management in FCEVs, particularly thermal management, is crucial for maintaining the temperature of these components within their specified range. The fuel cell stack generates a significant amount of waste heat, which needs to be dissipated to maintain optimal performance and prevent degradation, whereas the battery system needs to be operated within an optimal temperature range for its better performance and longevity. Overheating of batteries can lead to reduced efficiency and potential safety hazards, while low temperatures can decrease battery performance and range. The multiple temperature control loops in the thermal system design of the current FCEVs require significant energy for continuous heating and cooling. This is due to the fact that each of them exchanges energy directly with an external source or sink without redistributing energy among themselves. This can lead to energy losses during the heat exchange process. Our goal is to optimize thermal energy usage while maintaining the same performance and efficiency of both battery electric system and the fuel cell stack in a vehicle. In this paper, an analysis of thermal energy utilization of a single system is compared to the exchange of thermal energy across multiple systems, considering various heating and cooling scenarios. We compare our proposed strategy (with redistribution) with the existing strategy (without redistribution) quantitatively with respect to controller effort/ energy spent in achieving thermal target.
BHOWMICK, SAIKATChuri, Chetana
The China Container Freight Index (CCFI) is an important barometer of the global container shipping market. It is very important for participants in the shipping market to understand its composition. This study takes six representative routes as the research objects and conducts a detailed analysis of the composition of CCFI. The freight rate indices of these routes are decomposed and reconstructed by using the Empirical Mode Decomposition (EMD) algorithm, aiming to clarify the economic significance of each route and the fluctuation law of the reconstructed components. The research results show that the freight rate fluctuations of the west Coast, Southeast Asia and Mediterranean routes exhibit a complex nonlinear interdependence, and the simple linear model cannot fully reflect this relationship. On the contrary, the trend components of the European and Mediterranean routes effectively identify and represent the main trends within the original freight rate index. Global major events represented by the financial crisis and the COVID-19 pandemic have had a huge, lasting and profound impact on the freight rate indices of CCFI’s various routes, and have had a significant influence on the fluctuations of the original series. In contrast, short-term market dynamics and unforeseen events have a relatively limited impact on the route index, characterized by short duration and high frequency. These research results have enhanced the understanding of the formation mechanism of CCFI, which is conducive to shipping market participants better understanding market dynamics and formulating effective market strategies.
Yin, Sitian
Delamination of transparent armor (TA) is one of the costliest and most frustrating failures facing the tactical vehicle community. When purchased, all TA appears equally pristine and has identical protective abilities, but some parts delaminate after only a few years while other parts last over a decade. Recent high delamination rates have resulted in large costs – a Warstopper study showed that transparent armor accounted for 20% of the maintenance cost for the HMMWV. One major advance in the last few years has been the Army-led development of an ‘Accelerated Life Test’ which consistently causes field relevant delamination in transparent armor parts. We present the development of a method to correlate test results with field life, thus allowing for life prediction and life cycle cost analysis. We demonstrate how the life prediction tool can be used to drive purchasing strategies, field use decisions, and vehicle design.
Merrill, Marriner H.Magner, Matthew J.Key, Christopher T.Humphrey, Barry A.
Long-haul truck drivers are mandated to take off-duty time of 10 h (a.k.a. hoteling) before driving. During the hotel phase, drivers spend time inside their trucks (sleeper cabs) and idle the internal combustion engine for comfort by utilizing the heating, ventilation, air-conditioning (HVAC), and other onboard appliances. For one 10-h period, the average cost is about $40, which can be a lot when considering a million truck drivers idling overnight. SuperTruck II is a 48 V mild-hybrid heavy-duty truck with auxiliary loads powered by an onboard battery pack. An optimal control algorithm is developed to charge the battery pack during the drive phase up to a certain state-of-charge (SOC) level, sufficient to meet the power demands of the auxiliary load during the hotel phase. This article captures the research done to predict energy consumption in a mild-hybrid heavy-duty sleeper truck during hoteling. Physics-based gray box models are developed to estimate the power consumption of an electronically controlled compressor. For other auxiliary loads, a machine learning algorithm is developed to predict the power as a time series by tracking the user activity. The developed physics and data-driven models are validated with experimental data from heavy-duty trucks to show their efficacy. These validated models generate precise load profiles fed to the developed dynamic programming framework to generate the optimal SOC trajectories. These models help the vehicle battery pack charge only up to the SOC necessary for the hotel phase during the drive time. When the vehicle is out of charge during the hotel phase, these models also help in estimating the amount of idling required to charge the battery enough to support the rest of the hotel period. This saves unnecessary idling. As a result, a cost savings of $40 and CO2 reduction of 175 lb to the environment is achieved for a single heavy-duty truck with a sleeper cab during the hoteling phase.
Khuntia, SatvikHanif, AtharAhmed, QadeerLahti, JohnJorgensen, Iner
This article details the development of a plug-in hybrid electric powertrain system for a wheel loader. The work included both computer modeling and fired engine testing. A methodical approach was utilized, which included identifying system requirements, an architecture study, component sizing, and cost analysis. After the optimal system was designed, the engine and hybrid motor were installed in a powertrain test cell and evaluated over an in-use duty cycle. A bespoke utility factor, relevant for wheel loader operation, was developed to enable realistic fuel economy and emissions weighting between charge depleting and charge sustaining operation. Finally, an exhaust heater was used to ensure rapid warmup of the aftertreatment system. Compared to an internal combustion engine–only baseline, the hybrid powertrain system resulted in a 48% reduction in CO2 and an 84% reduction in NOX emissions when operated over an 8-h shift, with daily recharging.
Bachu, PruthviMichlberger, AlexanderMeruva, PrathikBitsis, Daniel Christopher
Despite a slight drop in the installations of factory robots in 2024, the push to automate automotive and other manufacturing was carrying momentum into fiscal 2025 and could benefit from U.S. federal priorities, said Jane Heffner, vice president of the International Federation of Robotics, at an industry expo in Detroit. Heffner, also the VP of sales for Teradyne Robotics, said the outlook for 2025 is strong, based on orders in Q4 of 2024. “We're seeing a double-digit increase in order intake from Q4 to Q1 of 2025,” she said. She acknowledged the global economic uncertainty but said the “interest in moving forward is still there.” She delivered her remarks at Automate 2025, the Association for Advancing Automation's annual expo, held this year in downtown Detroit.
Clonts, Chris
Computer-aided synthesis and development tools are essential for discovering and optimizing innovative concepts. Evaluating different concepts and making informed decisions relies heavily on accurate assessments of system properties. Estimating these properties in the early stages of vehicle development is challenging due to the depth of modelling required. In order to enable a cost prognosis for driving assistance and automated driving functions including software and hardware properties a cost model was developed at the Institute of Automotive Engineering. The methodology and cost model focuses on multiple combined approaches. This includes a bottom-up approach for the hardware. The costs of the software components are integrated into the model with the help of existing literature data and an exponential regression. For a comprehensive view of the total costs, the model is the model is also supplemented by a top-down approach for estimating the costs of other hardware components. The cost model was utilized for a market forecast and cost analysis. In the coming years, a significant reduction in costs can be seen, particularly for software, due to economies of scale. In contrast, the costs for control units are expected to increase proportionally, making this component the biggest cost driver. The forecast market development suggests that there is significant economic potential of advanced driver assistance systems. The market value of control units, LiDAR sensors and cameras in particular is expected to rise.
Sturm, AxelHichri, BassemRohde García, ÁlvaroHenze, Roman
Computer-aided synthesis and development tools are essential for discovering and optimizing innovative concepts. Evaluating different concepts and making informed decisions relies heavily on accurate assessments of drive system properties. Estimating these properties in the early stages of development is challenging due to the depth of modelling required. In addition, defined requirements play a critical role in drive system sizing. This paper presents a tool chain for the synthesis of new electrified drive concepts, with emphasis on requirements definition and modelling. The requirements definition method combines market analysis with a generalized calculation and estimation approach, providing a novel perspective. In addition, we introduce mass and cost modelling capabilities integrated into the tool chain. The mass model achieves high accuracy, with deviations of only 1.6 % at the vehicle level and 6.1 % at the component level. Finally, the paper examines the mass and cost implications of various dedicated and add-on hybrid concepts. Dedicated hybrid transmissions have the advantage of lower transmission costs, although this is offset by higher electrical component costs.
Sturm, AxelHenze, Roman
This study presents a comprehensive techno-economic assessment (TEA) of an integrated e-methanol production system building upon previously published foundational research utilizing Aspen Plus modeling for e-methanol production from sugar cane and sugar beet biomass. The established integrated system converts biomass into ethanol through fermentation and synthesizes e-methanol using both captured CO2 and syngas derived from biomass residue gasification. This approach maximizes CO2 and biomass utilization, promoting a circular carbon economy. The TEA quantifies capital expenditures (CAPEX), operational expenditures (OPEX), and levelized costs of Methanol (LCOM), providing a detailed economic analysis of the potential for commercializing e-methanol. A sensitivity analysis evaluates the impact of feedstock prices and Technology Readiness Levels (TRL), identifying key leverage points affecting financial viability. The study aims to explore the potential of utilizing existing agricultural infrastructure for sugar cane and sugar beet to minimize setup costs and expedite market readiness. The system’s capacity to reduce carbon emissions significantly aligns with global sustainability goals. This study provides strategic recommendations for scaling e-methanol production and improving its economic viability in the renewable energy sector. The sensitivity analysis particularly aids in developing robust strategies to mitigate risks associated with economic and market fluctuations.
Fernandes, Renston JakeShakeel, Mohammad RaghibNguyen, DucduyIm, Hong G.Turner, James W.G.
The switch to electrified off-highway vehicles can help reduce reliance on hydraulic components that decrease system efficiency via parasitic losses. The off-highway machine industry is embracing new technologies to optimize operations, specifically regarding electric and hybrid off-highway equipment. The electric off-highway equipment market is poised for growth, with an expected 12.5% compound annual growth rate (CAGR) from 2025-2034, reaching over $17 billion, according to Market Research Future. These off-highway vehicles operate on tough terrain and require unprecedented amounts of power for long duty cycles. Diesel engines have always been the conventional application for this kind of work, but now hybrid and electric vehicles are starting to gain traction thanks to new innovations and more investment. While the implications of replacing traditional combustion engines with hybrid or electric counterparts can be intimidating, learning the challenges and opportunities each option holds gives end users the power to determine what's right for their needs.
Liu, Zifan
The emergence of electric Vertical Takeoff and Landing (eVTOL) air vehicles is transforming how people and freight are moved in short distances. This transformation has a profound impact on surrounding infrastructure necessary to provide Aircraft On Ground support for eVTOLs. The hover capabilities of eVTOLs have similar operating characteristics within terminal and uncontrolled airspace. However, the need to conserve battery energy via rapid approaches and departures affects terminal airspace management. To attract eVTOL operators, existing airports, landing zones, and vertiports are modifying their infrastructure to include fixed electric charging stations, additional taxiways, upgraded fire suppression systems, separate hangers, and capable MRO facilities. Augusta Regional Airport (KAGS) is the base airport for the annual Masters Golf Tournament which experiences five times the normal airport traffic and some 40,000 commuting patrons. eVTOLs can offset land traffic issues associated with commuters and supplies. Since KAGS is centroid to 32,000 square miles of territory void of major highways, basing eVTOLs can offer expedited transit services for people and goods which will have a profound impact on the economic viability and quality of life in the area.
Stanzione, KaydonJohnston, Diane
In addition to providing safety advantages, sound and vibration are being utilized to enhance the driver experience in Battery Electric Vehicles (BEVs). There's growing interest and investment in using both interior and exterior sounds for pedestrian safety, driver awareness, and unique brand recognition. Several automakers are also using audio to simulate virtual gear shifting of automatic and manual transmissions in BEVs. According to several automotive industry articles and market research, the audio enhancements alone, without the vibration that drivers are accustomed to when operating combustion engine vehicles, are not sufficient to meet the engagement, excitement, and emotion that driving enthusiasts expect. In this paper, we introduce the use of new automotive, high-force, compact, light-weight circular force generators for providing the vibration element that is lacking in BEVs. The technology was developed originally for vibration reduction/control in aerospace applications, has been recently tested in various vehicles, and demonstrates the effectiveness for providing a real haptic feel across the entire vehicle. Shaking the vehicle globally provides a unique capability for BEVs, including Hybrid Electric Vehicles and for helping to create a smooth transition between Gas and Electric power, for example. The technology can be used to generate and emulate high-performance, high power, combustion engine feel, including idle, engine run-up/acceleration, simulated gear shifts, and Advanced Driving Assistance and Systems (ADAS) haptic indicators. The optional and customizable vibration can also mask road vibration which becomes very noticeable in otherwise smooth BEVs and can provide the perfect supplement to existing audio enhancements and gear shifting features. Additionally, the paper describes how the force generating device can be packaged in a light weight, compact, low-power manner. The technology will be compared to other force generating methods, and discuss its pros and cons.
Norris, Mark A.Orzechowski, JeffreySanderson, BradSwanson, DouglasVantimmeren, Andrew
Fuel cell vehicles (FCVs) offer a promising solution for achieving environmentally friendly transportation and improving fuel economy. The energy management strategy (EMS), as a critical technology for FCVs, faces significant challenges of achieving a balanced coordination among the fuel economy, power battery life, and durability of fuel cell across diverse environments. To address these challenges, a learning-based EMS for fuel cell city buses considering power source degradation is proposed. First, a fuel cell degradation model and a power battery aging model from the literature are presented. Then, based on the deep Q-network (DQN), four factors are incorporated into the reward function, including comprehensive hydrogen consumption, fuel cell performance degradation, power battery life degradation, and battery state of charge deviation. The simulation results show that compared to the dynamic programming–based EMS (DP-EMS), the proposed EMS improves the fuel cell durability while approaching the control effectiveness of DP global optimization. In comparison to the back-propagation-based EMS (BP-EMS), the proposed EMS obtains a 0.37% reduction in the equivalent hydrogen consumption and a 4.72% increase in effective Ah-throughput; the fuel cell performance degradation reduces by 40.09%, balancing the degradation of fuel cell and power battery while ensuring low energy consumption and improving the overall performance of the system. Finally, the adaptability of the proposed strategy to driving conditions is validated in this article.
Song, DafengYan, JinxingZeng, XiaohuaZhang, Yunhe
This study evaluates the performance of alternative powertrains for Class 8 heavy-duty trucks under various real-world driving conditions, cargo loads, and operating ranges. Energy consumption, greenhouse gas emissions, and the Levelized Cost of Driving (LCOD) were assessed for different powertrain technologies in 2024, 2035, and 2050, considering anticipated technological advancements. The analysis employed simulation models that accurately reflect vehicle dynamics, powertrain components, and energy storage systems, leveraging real-world driving data. An integrated simulation workflow was implemented using Argonne National Laboratory's POLARIS, SVTrip, Autonomie, and TechScape software. Additionally, a sensitivity analysis was performed to assess how fluctuations in energy and fuel costs impact the cost-effectiveness of various powertrain options. By 2035, battery electric trucks (BEVs) demonstrate strong cost competitiveness in the 0-250 mile and 250-500 mile ranges, especially when primarily charged at depots. Fuel cell electric vehicles (FCEVs) remain competitive in the 250-500-mile range, particularly under higher diesel prices. For distances over 500 miles, FCEVs become the preferred solution, providing greater range and operational flexibility. By 2050, technological advancements and reduced truck costs further enhance the feasibility of both BEVs and FCEVs. The BEV500 shows improved efficiency and resilience to energy price fluctuations, making it viable for medium-range operations and cost-effective even with high en-route electricity rates. FCEVs are expected to remain competitive in both medium and long-range operations, especially when diesel prices are elevated, positioning them as strong alternatives to conventional powertrains for long-haul routes.
Mansour, CharbelBou Gebrael, JulienKancharla, AmarendraFreyermuth, VincentIslam, Ehsan SabriVijayagopal, RamSahin, OlcayZuniga, NataliaNieto Prada, DanielaAlhajjar, MichelRousseau, AymericBorhan, HoseinaliEl Ganaoui-Mourlan, Ouafae
Automotive industry is growing rapidly with innovations leading to increase in new features and improving the Quality of vehicles. These new components are developed with the available design standards across global OEMs. This Quality research paper aims to address the need of revision of design standards due to environmental factors prevailing in India. With the increase towards autonomous mobility, the number of electronics is also increasing, and this involves hardware & software evaluation. The hardware testing is a point of concern due to increase in the failure rate from the markets. Environment changes are very much evident with the growing economies and OEMs are developing the components with innovation, but if the basic design standards are not revised in parallel with the changing environment, the issues will continue to trouble the end customers. The failed cases data received from across the country was analyzed and observed that the cases are majorly reported from urban localities established near to the city drains. The lab report of failed components shows a chemical reaction with environment gases leading to conductivity issue. Based on the study, Quality research was done around the localities to understand the gaseous concentration. The data shows 50X high gaseous concentration compared to the development standard and the same was then simulated. This data gave us a new perspective to revise the development testing standards & can also enable OEMs to better understand the market problems & take action for environment factors. With the ever-changing environmental conditions & expansion of automotive market, the possibilities of such impacts are limitless & this approach can be used to further study the automotive issues due to other factors like Dust, Water, Temperature, Humidity, Snow, Insects etc.
Marwah, RamnikPyasi, PraveenBindra, RiteshGarg, Vipin
In numerous automotive and industrial applications, efficient heat extraction is crucial to prevent system inefficiencies or catastrophic failures. The design of heat exchangers is inherently complex, involving multiple stages defined by the depth of analysis, number of design variables, and the accuracy of physical models. Designers must navigate the trade-offs between highly accurate yet computationally expensive models and less accurate but computationally cheaper alternatives. Multi-fidelity modeling offers a solution by integrating different fidelity models to deliver precise results at a reduced computational cost. In addition to managing these trade-offs, designers often face multi-objective challenges, where optimizing one aspect may lead to compromises in others. Multi-objective optimization, therefore, becomes essential in balancing these competing objectives to achieve the best overall design. In this context, Gaussian Process-based methods have gained prominence as effective tools for integrating information from models of varying fidelities while simultaneously addressing multiple objectives. A key component of multi-fidelity modeling is understanding the relationships between these fidelity models. This paper explores various Gaussian Process-based multi-fidelity and multi-objective optimization techniques, focusing on the different types of relationships between fidelity models, such as linearity, non-linearity, and variable correlation. Each technique is discussed within a unified design automation framework, with an emphasis on the connections between different methodologies. These approaches are evaluated through plate fin heat exchanger-related engineering problems to determine their respective advantages and limitations relative to specific problem characteristics.
Chaudhari, PrathameshTovar, Andres
In 2022, the U.S. transportation sector was the largest source of greenhouse gas emissions in the country, with the combination of passenger and commercial vehicles contributing 80% of these emissions. As adoption of passenger electric vehicles continues to climb, sights are being set on the electrification of heavy-duty commercial vehicle (HDCV) fleets. The sustainability of these shifts relies in part on the addition of significant renewable energy generation resources to both bolster the grid in the face of increased demand, and to prevent a shift in the source of greenhouse gas (GHG) emissions to the grid, as opposed to a true net reduction. Additionally, it is necessary to quantify the variations in economic viability across the country for these technologies as it pertains to their productive capabilities. Doing so will encourage investment and ensure that the transition to electrified HDCV fleets is commercially viable, as well as sustainable. In an effort to meet these goals, multiple computational frameworks are used to locate suitable land for renewable infrastructure development, and to quantify spatiotemporal variations in the potential energy generation and financial viability of development sites across the Unites States. First, the Oak Ridge Siting Analysis for power Generation Expansion tool (OR-SAGE) is used to assess the suitability of land for potential wind and solar energy development across the contiguous U.S. From there, resource data from the National Solar Radiation Database (NSRDB) and the Wind Integration National Dataset (WIND) are used in concert with the National Renewable Energy Laboratory (NREL) Renewable Energy Potential (ReV) model to calculate the variation in potential generation capacity for each resource. Additionally, the capital and operational expenditures are calculated for an example configuration of each renewable technology. These measures are then used to calculate the levelized cost of energy (LCOE) of potential sites. All of these results are then processed and analyzed to determine where in the U.S. solar and wind energy are most viable. This viability is based on available generation potential, consistency and stability of energy generation over time, and economic viability with respect to LCOE.
Miller, BrandonSun, RuixiaoSujan, Vivek
Vehicle sideslip is a valuable measurement for ground vehicles in both passenger vehicle and racing contexts. At relevant speeds, the total vehicle sideslip, beta, can help drivers and engineers know how close to the limits of yaw stability a vehicle is during the driving maneuver. For production vehicles or racing contexts, this measurement can trigger Electronic Stability Control (ESC). For racing contexts, the method can be used for driver training to compare driver techniques and vehicle cornering performance. In a fleet context with Connected and Autonomous Vehicles (CAVS) any vehicle telemetry reporting large vehicle sideslip can indicate an emergency scenario. Traditionally, sideslip estimation methods involve expensive and complex sensors, often including precise inertial measurement units (IMUs) and dead reckoning, plus complicated sensor fusion techniques. Standard GPS measurements can provide Course Over Ground (COG) with quite high accuracy and, surprisingly, the most challenging measurement is the vehicle orientation. This study presents a low- or moderate-cost method for real-time vehicle sideslip estimation using Real-Time Kinematic (RTK) Global Position System (GPS) receivers. The approach involves a pair of specialized GPS receivers with a moving base and moving rover RTK setup. RTK corrections are provided via an online wireless internet connection. The moving base is positioned at the vehicle's rear axle and the companion rover GPS device is located at the vehicle's center of gravity (CG). This arrangement provides both vehicle orientation and vehicle course over ground at 7Hz. RTK provides direct measurement of both quantities needed to compute vehicle sideslip in real time. The results demonstrate the feasibility of this approach and offers a practical solution for real-world automotive systems. A simple set of driving experiments demonstrate the method’s effectiveness. This approach is a cost-effective solution for sideslip estimation, with applications in ESC, CAVs, driver training and motorsports performance analysis.
Hannah, AndrewCompere, Marc
It is a fool's errand to make timely comments - in print! - about our current political turmoil. Even so, it feels important to place a marker in the sand to note the ongoing political reign of tariff threats, the upheaval potential of a demolished regulatory state affecting road and vehicle safety, and the damage that cuts to electric vehicle support might do to American automakers attempting to keep technological pace with their global automaker peers. It's a lot. The mainstream press is reporting the broad strokes of the industry's reaction to the new president. Ford CEO Jim Farley said Trump's erratic threats and changes are adding “a lot of cost and a lot of chaos” to the automotive industry and that a 25% tariff would “blow a hole in the U.S. industry that we've never seen.” Volvo Cars CEO Jim Rowan said that profitability would suffer under any tariffs, whether those are the general 25% tariffs on Canada and Mexico (now seemingly canceled after Trump backed down), just-announced 10% tariffs on steel and aluminum or some yet-to-be-spoken-aloud new tariff concept. Industry analyst Sam Fiorani told The Independent the obvious truth that “raising the price of what is among the most important components of the vehicle is only going to raise the prices of an already expensive product.”
Blanco, Sebastian
Shared autonomous vehicles systems (SAVS) are regarded as a promising mode of carsharing service with the potential for realization in the near future. However, the uncertainty in user demand complicates the system optimization decisions for SAVS, potentially interfering with the achievement of desired performance or objectives, and may even render decisions derived from deterministic solutions infeasible. Therefore, considering the uncertainty in demand, this study proposes a two-stage robust optimization approach to jointly optimize the fleet sizing and relocation strategies in a one-way SAVS. We use the budget polyhedral uncertainty set to describe the volatility, uncertainty, and correlation characteristics of user demand, and construct a two-stage robust optimization model to identify a compromise between the level of robustness and the economic viability of the solution. In the first stage, tactical decisions are made to determine autonomous vehicle (AV) fleet sizing and the initial vehicle distribution. In the second stage, operational decisions are made under scenarios of fluctuating user demand to optimize vehicle relocation strategies. To enhance the efficiency of model resolution, the original two-stage robust optimization model is decomposed into separable subproblems, which are transformed using duality theory and linearization. An effective solution is achieved through a precise algorithm utilizing column-and-constraint generation (C&CG). Numerical experiments are conducted on a small-scale network to validate the effectiveness of the model and algorithm. Furthermore, adjustments to the demand fluctuation scenarios are made to assess the impact of uncertain budget levels Γ on the total revenue of SAVS. This research provides AV sharing service operators with an optimal relocation scheduling strategy that balances robustness and economic efficiency.
Li, KangjiaoCao, YichiZhou, BojianWang, ShuaiqiYu, Yaofeng
This research explores the use of salt gradient solar ponds (SGSPs) as an environmentally friendly and efficient method for thermal energy storage. The study focuses on the design, construction, and performance evaluation of SGSP systems integrated with reflectors, comparing their effectiveness against conventional SGSP setups without reflectors. Both experimental and numerical methods are employed to thoroughly assess the thermal behavior and energy efficiency of these systems. The findings reveal that the SGSP with reflectors (SGSP-R) achieves significantly higher temperatures across all three zones—Upper Convective Zone (UCZ), Non-Convective Zone (NCZ), and Lower Convective Zone (LCZ)—with recorded temperatures of 40.56°C, 54.2°C, and 63.1°C, respectively. These values represent an increase of 6.33%, 11.12%, and 14.26% over the temperatures observed in the conventional SGSP (SGSP-C). Furthermore, the energy efficiency improvements in the UCZ, NCZ, and LCZ for the SGSP-R are considerable, showing increases of 39.18%, 35.46%, and 39.64%, respectively, when compared to the SGSP-C. The numerical simulations are in strong agreement with the experimental results, exhibiting minimal deviations of less than 5% in both temperature distribution and energy efficiency across all zones. This study underscores the potential of SGSPs with reflectors for enhanced thermal storage performance.
J, Vinoth Kumar
Soft skin coverings and touch sensors have emerged as a promising feature for robots that are both safer and more intuitive for human interaction, but they are expensive and difficult to make. A recent study demonstrates that soft skin pads doubling as sensors made from thermoplastic urethane can be efficiently manufactured using 3D printers.
This paper presents the strategy design, development, and detailed simulation of an Energy Management System (EMS) for a range extender energy storage microgrid project. Initially, a microgrid system model including photovoltaic (PV) and energy storage devices was established. Secondly, the Latin Hypercube Sampling (LHS) method was employed to generate possible operational scenarios, and an improved K-means clustering algorithm was used for scenario classification. Subsequently, a series of constraints were constructed for the economic viability of the microgrid to minimize its annualized comprehensive cost, while satisfying power balance and equipment operation. Finally, the microgrid system was simulated and solved using the GUROBI solver, covering cost analyses of the energy storage system and diesel generators under different configurations, as well as the State of Charge (SOC) variations of the energy storage system. The simulation results indicate that, after considering the one-time investment costs of PV, battery cells, and Power Conversion Systems (PCS), the microgrid under different modes can pay back within a few years, while the diesel storage simulation results provide a detailed cost analysis under various diesel generator configurations. These results provide important reference for the planning, design, and economic assessment of microgrids, proving the potential of energy storage microgrids in improving energy efficiency, reducing costs, and promoting the use of renewable energy.
Hua, YuweiJin, ZhenhuaHuang, HuilongWang, Zihao
This study introduces the Total Cost of Ownership per Unit Operating Time (TCOP) as a novel indicator to assess the economic impact of vehicle durability. A comprehensive analysis is conducted for fuel cell vehicles (FCVs), battery electric vehicles (BEVs), and internal combustion engine vehicles (ICEVs) in light- and heavy-duty scenarios. The results show that in HDVs, the advantages of low prices for hydrogen and electricity are fully demonstrated due to their high durability. In contrast, for LDVs, the purchase cost plays a much larger role, accounting for 68% of the total cost, indicating a significant difference between vehicles. Improving durability can significantly enhance the competitiveness of FCVs. For FCVs, increasing the durability from the current levels of 150,000 km for LDVs and 600,000 km for HDVs to 20,8500 km and 1,122,000 km, respectively, would align their TCOP with that of current ICEVs. A sensitivity analysis shows that for HDVs. The focus should be placed on improving the durability of fuel cell systems in order to reduce fuel costs over the long term, while for LDVs, the key to reducing TCOP is to reduce the manufacturing cost of the whole vehicle. By 2040, assuming that the durability of FCVs is improved to the same level as ICEVs and that the cost of fuel cells continues to fall, FCVs will be more competitive than EVs and ICEVs in terms of long-term operating costs.
Qin, ZhikunYin, YanZhang, FanYao, JunqiGuo, TingWang, Bowen
The transition from internal combustion engine (ICE) industry to electric vehicle (EV) industry has significant financial implications for both the automotive industry, government, and associated partners. The shift to EVs could lead to savings in foreign exchange reserves, the creation of new jobs, and a reduction in greenhouse gas emissions. However, the transition could also result in job losses in the automobile and its associated manufacturing industry. This study aims to analyze the impact of this transition on different stakeholders in India. The study takes into account the different financial aspects that includes production, technology, government policy, skilling, employability, job creation, and other associated aspects on Indian economy. For the projected study different cases were considered with 2030 as the projected year with 30% EVs. A modest attempt is made to analyze the impact on associated partners. The findings of the study suggest that the transition to EVs could lead to reduced imports, job creation in new emerging areas, reduction in emissions, and enhanced technical and innovation capabilities in the EV sector if collaborative efforts are there among three major stakeholders, i.e., government, automotive, and petroleum industry.
Vashist, DevendraMalik, VarunPandey, Sachchidanand
The automotive industry is facing unprecedented pressure to reduce costs without compromising on quality and performance, particularly in the design and manufacturing. This paper provides a technical review of the multifaceted challenges involved in achieving cost efficiency while maintaining financial viability, functional integrity, and market competitiveness. Financial viability stands as a primary obstacle in cost reduction projects. The demand for innovative products needs to be balanced with the need for affordable materials while maintaining structural integrity. Suppliers’ cost structures, raw material fluctuations, and production volumes must be considered on the way to obtain optimal costs. Functional aspects lead to another layer of complexity, once changes in design or materials should not compromise safety, durability, or performance. Rigorous testing and simulation tools are indispensable to validate changes in the manufacturing process. Marketing considerations are also significant to the success of cost reduction strategies. Brand reputation and customer perceptions of quality must be safeguarded when changes are implemented. To that end, communication strategies to convey the benefits of cost reduction without compromising perceived value are a key factor to enhance market acceptance. Another crucial element in the execution of cost reduction projects is operational efficiency. Streamlining production processes, optimizing supply chain logistics, and embracing automation technologies require careful planning and implementation to avoid disruptions in production schedules. In conclusion, addressing the challenges of reducing costs in automotive body exterior parts demands a holistic approach that considers financial, functional, and marketing aspects. Striking the right balance between these elements is essential for the success of cost reduction initiatives, ensuring that the automotive industry remains competitive while meeting the demands of a cost-conscious market. A solid technical background for all the parties involved is imperative, and this text provides an overview of the pivotal topics in that context.
Oliveira Neto, Raimundo ArraisSouza, Camila Gomes PeçanhaBrito, Luis Roberto BonfimGuimarães, Georges Louis Nogueira
North American automakers and EV battery firms have five years to erase China's dominance in technology and manufacturing or they may face the reality of buying batteries from China for the foreseeable future. That was the message from battery-analysis company Voltaiq CEO Tal Sholklapper at a media briefing in Detroit. “We're in the final innings now,” Sholklapper said. “If the industry around batteries and electric vehicles and all the follow-on applications wants to make it, we're going to have to change the way we play.”
Clonts, Chris
The future of wireless technology - from charging devices to boosting communication signals - relies on the antennas that transmit electromagnetic waves becoming increasingly versatile, durable and easy to manufacture. Researchers at Drexel University and the University of British Columbia believe kirigami, the ancient Japanese art of cutting and folding paper to create intricate three-dimensional designs, could provide a model for manufacturing the next generation of antennas. Recently published in the journal Nature Communications, research from the Drexel-UBC team showed how kirigami - a variation of origami - can transform a single sheet of acetate coated with conductive MXene ink into a flexible 3D microwave antenna whose transmission frequency can be adjusted simply by pulling or squeezing to slightly shift its shape.
This study aims to explore the multifaceted influencing factors of market acceptance and consumer behavior of low-altitude flight services through online surveys and advanced neuroscientific methods (such as functional magnetic resonance imaging fMRI, electroencephalography EEG, functional near-infrared spectroscopy fNIRS) combined with artificial intelligence and video advertisement quantitative analysis. We conducted an in-depth study of the current trends in low-altitude flight vehicle development and customer acceptance of low-altitude services, focusing particularly on the survey methods used for market acceptance. To overcome the influence of strong opinion leaders in volunteer group experiments, we designed specialized surveys targeting broader online and social media groups. Utilizing specialized knowledge in aviation psychology, we designed a distinctive questionnaire and, within just 7 days of its launch, gathered a significant number of valid responses. The data was then analyzed using AI to provide original, insightful data on the acceptance of low-altitude services. Furthermore, we addressed the limitations of traditional manual survey methods by designing an advanced system combining EEG and AI analysis to automatically generate surveys by measuring neural and physiological responses while subjects watched video advertisements for low-altitude services. Our research offers a comparison with existing online survey forms and proposes specific predictions to potentially improve the accuracy of online surveys.
Ma, XinDing, ShuitingLi, Yan
Vehicle electrification has gained prominence in various industries and offers sustainability opportunities, especially in the context of heavy-duty vehicles such as school buses. Despite the prevalence of conventional diesel school buses (CDSB), the adoption of electric school bus (ESB) and other eco-friendly alternatives is increasing. In the United States alone, there has been a notable increase in the adoption of ESBs, indicating substantial growth. The electrification of school buses not only promises energy savings, but also offers health benefits to children, reduced greenhouse gas emissions, and environmentally friendly transportation practices, aligned with broader eco-friendly initiatives. This paper investigates the potential for energy savings and reduction in environmental footprint through electrification of school buses in the Columbus, OH area. Analyzing current bus routes and road terrain data allows one to estimate energy demand and environmental impact, accounting for the unique characteristics of school bus operations, such as low-speed travel and frequent stops. The study suggests that electrifying school buses in the region could lead to approximately 61% of energy savings.
Moon, JoonHanif, AtharAhmed, Qadeer
In vehicle Noise Vibration Harshness (NVH) development, vibroacoustic simulations with Finite Element (FE) Models are a common technique. The computational costs for these calculations are steadily rising due to more detailed modelling and higher frequency ranges. At the same time the need for multiple evaluations of the same model with different input parameters – e.g., for uncertainty quantification, optimization, or robustness investigation – is also increasing. Therefore, it is crucial to reduce the computational costs dramatically in these cases. A common technique is to use surrogate models that replace the computationally intensive FE model to perform repeated evaluations with varying parameters. Several different methods in this area are well established, but with the continuous advancements in the field of machine learning, interesting new methods like the Gaussian Process (GP) regression arises as a promising approach. In Gaussian Process regression there are important parameters that strongly influence the prediction accuracy of the GP Model, namely length-scale, variance, and mostly the kernel function. In this contribution these parameters and their influence on the results are evaluated, with a focus on vibroacoustic simulations. For the kernel function, four different types – stationary, nonstationary, spectral and deep learning kernel, respectively – are under investigation. As a result, it can be shown that their performance corelate with the data complexity. Further investigations focus on the frequency as input parameters and the influence of the number of training samples. In these evaluations there is an interesting difference between a simple academic model and a body in white model. The underlying effects, such as damping, system complexity, uncertainty and load case are discussed in detail. Finally, a recommendation using GP as a surrogate model for vibroacoustic simulations is given.
Luegmair, MarinusDantas, RafaellaSchneider, FelixMüller, Gerhard
Dynamic wireless charging (DWC) systems can make up electrified roads (eRoads) on which electricity from the grid is supplied to electric vehicles (EVs) wirelessly while the EVs travel along the roads. Electrification of roads contributes to decarbonizing the transport sector and offers a strong solution to high battery cost, range anxiety, and long charging times of EVs. However, the DWC eRoads infrastructure is costly. This article presents a model to minimize the infrastructure cost so that the deployment of eRoads can be economically more feasible. The investment for eRoad infrastructure consists of the costs of various components including inverters, road-embedded power transmitter devices, controllers, and grid connections. These costs depend on the traffic flow of EVs. The configuration and deployment strategy of the proposed eRoads in Southeastern Canada are designed with optimized charging power and DWC coverage ratio to attain the best cost-effectiveness. Well-designed intermittent or partial DWC systems are shown to be an effective approach to reducing the overall investment. The economic feasibility of the DWC eRoads is assessed using a levelized cost metric. The results show that the DWC technology is economically viable, particularly for long-haul truck transport. In addition, a sensitivity analysis is conducted to evaluate which parameters have a more significant impact on the economic viability of the DWC eRoads.
Qiu, KuanrongRibberink, HajoEntchev, Evgueniy
An SAE white paper on the different engineering approaches taken by traditional automakers and recent arrivals indicates that each category is remarkably aware of the others' strengths and weaknesses. Sven Beiker, a management lecturer at Stanford University, authored the report “Two Approaches to Mobility Engineering.” He gathered commentary from every corner of the vehicle ecosystem, from suppliers to software companies to manufacturers, and summarized the findings in a presentation at WCX 2024 in Detroit. Rather than “old companies,” Beiker likes to refer to traditional automakers as “incumbents.” Here are a few common observations from the report, which will be published this summer: Newer players are better at simplifying complexity, such as Tesla's ability to build vehicles with fewer parts. Older automakers are better at managing complexity, such as integrating disparate systems. Newer companies are constrained by financial resources and a shortage of available talent. Traditional companies are constrained by existing staff and “this is the way we've always done it” thinking and inflexible facilities. Newer companies are good at innovation in the name of customer centricity. Incumbents are very good at creating and maintaining processes.
Clonts, Chris
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