Browse Topic: Vehicles and equipment

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Development of a model-based data-driven control algorithm for a Drive-By-Wire System for a Level 4 Automated Vehicle2026-01-0043To be published on 04/07/2026
Precision control in Level 4 Automated Vehicles is essential for enhancing operational efficiency, accuracy, and safety, including improvements in time and fuel consumption. This research, conducted as part of ARPA-E’s NEXTCAR program, focuses on developing a robust control algorithm for a 2021 Honda Clarity Plug-In Hybrid Electric Vehicle (PHEV) to enable drive-by-wire functionality. A model-based control algorithm was created for a 2021 Honda Clarity Plug-In Hybrid Electric Vehicle (PHEV) to enable drive-by-wire functionality for longitudinal and lateral motion controls. This vehicle was equipped with a set of sensors and an onboard processing unit to enable Level 4 automation. An algorithm was designed to command steering torque to the electronic power steering module, ensuring the vehicle could attain the desired steering angle position at varying speeds. The system leveraged feedforward and feedback mechanisms to command steering torque to the electronic power steering module
Adsule, Kartik, Bhagdikar, Piyush, Drallmeier, Joseph, Alden, Joshua, Gankov, Stanislav
Navigation Integrated End-to-end Path Planning Development and Vehicle Testing2026-01-0032To be published on 04/07/2026
In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes and lighting conditions, and it shows improved route adherence in vehicle testing. The proposed model is validated in a comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test
Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin
Techno-economic assessment of battery recycling and cascade use for legal and illegal recyclers in China2026-01-0456To be published on 04/07/2026
With the strong momentum of electric vehicles (EVs), the battery recycling industry is undergoing rapid growth. While the Chinese government has implemented a white-list mechanism under which only approved recyclers are allowed to process retired batteries, small-scale illegal battery recycling vendors have posed a serious challenge. This study compares the techno-economic performance of battery recycling between legal and illegal recyclers in China, and makes recommendations to eliminate illegal operations. Our research covers two battery chemistries: lithium nickel-manganese-cobalt oxide (NMC) and lithium iron phosphate (LFP), as well as two technological pathways: resource recycling and cascade utilization. For the general case, the costs of illegal vendors are 35-46% lower than that of legal companies. Although legal companies achieve high resource utilization, their overall economic performance lags behind due to their high costs associated with equipment, environmental protection
Du, Shilong, Li, Haoyang, Dou, Hao, Hao, Han
Enabling Sustainable E-Mobility: An Edge–Cloud Collaborative Framework for Battery Lifecycle Health Management in Electric Vehicles2026-01-0460To be published on 04/07/2026
The rapid adoption of electric vehicles (EVs) is a cornerstone of the transition to sustainable transportation. However, uncertainty regarding battery degradation remains a significant obstacle, hindering vehicle energy efficiency, operational safety, and the recovery of end-of-life value. Accurate estimation of the battery state of health (SOH) and prediction of the remaining useful life (RUL) are therefore critical for sustainable vehicle lifecycle management. This study proposes an edge–cloud collaborative intelligent framework for in-vehicle deployment that leverages a Transformer-based architecture to jointly model SOH and RUL. The cloud-side model retains the full configuration to capture long-term degradation trajectories for high-accuracy RUL prediction. A lightweight edge-side model, engineered via pruning and knowledge distillation, delivers millisecond-level inference for real-time SOH estimation onboard the vehicle. To ensure efficiency, only four core health indicators are
Gao, Weimin, Lv, Zhilong, Ou, Shiqi(Shawn)
Trajectory Prediction Model for Autonomous Vehicles in Highway Scenarios Based on Spatiotemporal Interaction Feature Extraction2026-01-0027To be published on 04/07/2026
Accurately predicting the future trajectories of surrounding vehicles is one of the core tasks in autonomous driving, and its precision is directly related to the safety and reliability of decision-making, path planning, and control execution. However, challenges such as the complexity of traffic participants’ behaviors, the variability of interactions, and the highly dynamic nature of traffic environments make it difficult for existing methods to effectively model spatiotemporal dependencies and achieve accurate long-term prediction in dynamic scenarios, thus limiting their applicability in real-world settings. In this paper, we propose a Transformer-based trajectory prediction model with a spatiotemporal attention mechanism to extract and effectively model vehicle motion and spatial interactions. Specifically, the temporal attention module captures the motion patterns of the target vehicle across the time dimension, while the spatial attention module constructs vehicle interactions
Zhang, Lijun, Hu, Xingyu, Meng, Dejian, Zhu, Zhehui
Performance Analysis and Optimization of a Drizzling-Based Evaporative Cooling for Enhanced Thermal Management of FCEV Stack2026-01-0115To be published on 04/07/2026
In response to increasingly stringent global emissions regulations, including Euro 7, the automotive industry is intensifying its focus on eco-friendly propulsion systems. Among them, hydrogen fuel cell electric vehicles (FCEVs) have emerged as a promising alternative in the commercial transport sector, offering high energy density, rapid refueling, long driving range, and minimal payload limitations. However, the operational efficiency and durability of fuel cell stacks heavily depend on effective thermal management, and insufficient cooling can lead to power degradation and reduced lifespan. This study proposes a novel thermal management strategy that enhances stack cooling performance by directly applying fuel cell discharge water onto the radiator surface, utilizing latent heat of evaporation. To overcome the structural complexity and control challenges of high-pressure spray systems, a gravity-driven low-pressure drizzling method was adopted, achieving both system simplification
Lee, Dongkeon, Seo, Jungmin, Lee, Seogjun, Son, Dongwook, Lee, Hoseong, Shin, Yoonhyuk, Kang, Hyunsung
THE IN-VEHICLE CONNECTIVITY FOUNDATION POWERING AUTONOMOUS DRIVING2026-01-0009To be published on 04/07/2026
Achieving full vehicle autonomy is not just about adding sensors or compute-it requires a fundamental shift in how vehicles are architected. Autonomous systems rely on higher-resolution sensors, massive processing power, and the ability to fuse data from multiple sources in real time. Centralized in-vehicle architectures, which consolidate computing and enable sensor fusion, place unprecedented demands on connectivity. Precise time synchronization across all systems becomes critical, as does advanced control to ensure safe and reliable operation. Any delay or data loss can impact decision-making, making robust, resilient communication links essential. High-performance connectivity is the backbone of this evolution. It must deliver the highest bandwidth to handle massive streams of sensor data, support long-reach connections across the vehicle, and maintain error-free performance even in the most challenging electromagnetic environments. This combination of speed, reach, and reliability
Shwartzberg, Daniel
Collaborative Development Framework for electric-based Software-Defined Vehicles – The European Research project CODE4EV2026-01-0113To be published on 04/07/2026
The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, CCAM, C-ITS). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of SW development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. In the context of the Industrial Action Plan for the European automotive sector , different funding mechanisms have been designed to accelerate development and uptake of SDV. It is the target of this paper to introduce different European programs on SDV - focusing on their main objectives, expected benefits for the R&D&I community, and complementarity of these initiatives. Especially, this paper will introduce Chips-JU programs targeting the creation of a vivid ecosystem for European Software Defined Vehicle of the Future (SDVoF) Initiative
Armengaud, Eric, Permann, Robert, Joergler, Sabrina, Barcelona, Miguel Angel, García, Laura, Rodriguez, José Manuel, Ivanov, Valentin, Li, Zhenqian, Nguyen Quoc, Trieu, Rodrigues, Sandy, Kowalczyk, Bogdan, Avdić Čaušević, Amra
ENERGY AND OPERATIONAL EFFICIENCY OF SHARED AUTONOMOUS FLEET2026-01-0464To be published on 04/07/2026
Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The test aims to verify the
Tang, Kang, Abdulsattar, Harith, Yang, Hao, Wang, Jinghui
EV Propulsion Drive Converter SiC Power Device Over-Temperature Protection under Low Motor Speed Operation2026-01-0436To be published on 04/07/2026
Monitoring power device temperature in an electric vehicle propulsion drive converter is extremely important to achieve full power delivery within the maximum power capability envelope. Usually, on-die temperature sensors are installed on Si-IGBT power devices in electric vehicle propulsion drive converters to enable monitoring device temperature and achieve over-temperature protection. Currently, SiC MOSFET is a promising power device in power converters of electric drives because of its lower loss, higher switching speed, higher voltage capability, and higher junction temperature limit in comparison with widely used Si-IGBT. However, SiC MOSFET is a more expensive device, so the present technology trend is trying to reduce its chip size. Installation of an on-die temperature sensor on SiC MOSFET will significantly increase its cost and complexity. So presently, there is no junction temperature sensor installed in SiC MOSFET due to which there is great difficulty protecting SiC MOSFET
Thongam, Jogendra Singh, Ge, BAOMING, Bradford, Steven, Kulkarni, Milind
Advancing Sustainable Electric Vehicles: A Simulation-Driven Study on Rare Earth-Free Motors and Battery Technologies2026-01-0242To be published on 04/07/2026
As electric vehicles (EVs) become more popular, the growing dependence on rare earth materials in their motors and batteries has raised concerns about supply risks, environmental impact, and rising costs. Finding alternatives to these materials is essential to making EVs more sustainable and affordable for everyone. This study aims to explore new motor designs and battery chemistries that reduce or eliminate the use of rare earth elements. We will focus on promising options like induction and wound rotor motors, as well as ferrite and iron-nitride magnets that can replace traditional rare earth magnets in EVs. On the battery side, we will look at lithium-iron-phosphate (LFP) and sodium-ion batteries, which offer a safer and more cost-effective alternative while maintaining good performance. A big part of this work involves using advanced simulations to predict how these new materials and designs will perform. This lets us optimize the components before building physical prototypes
Saraswat, Shubham, Vishe, Prashant
Development of the Honda S+ Shift Utilizing e:HEV System2026-01-0418To be published on 04/07/2026
Honda is promoting mobility electrification to realize a carbon-neutral society by 2050. Hybrid vehicles will remain advantageous over electric vehicles in terms of manufacturing cost and driving range until renewable energy usage increases, charging infrastructure is sufficiently developed, and battery costs are reduced. In response to this situation, Honda has developed a new control system, “Honda S+ Shift”, which further enhances the “emotional value of driving pleasure” inherent to the e:HEV system and creates new value for hybrid vehicles. Honda S+ Shift synchronizes the engine and vehicle speed and selects a virtual gear position according to the driver's operation such as acceleration, cornering, and deceleration. Subsequently, the system achieves the required system output in cooperation with a dedicated energy management system. It also works with each vehicle system, such as drive force control, sound control, and meter cluster, to stimulate all five senses of the driver
Murata, Naoya, Narimoto, Ryosuke, Saito, Masatoshi, Ishida, Daichi, GUNJI, HIROKI, UKAI, YOHEI, Kurachi, Shinobu, SHIKI, KAZUKI, Nagakura, Akari, Maeda, Sadaharu, Mitogawa, Terumasa
Predictive Maintenance of EV Drive Unit Oil Pumps via Machine Learning on Fleet Telemetry2026-01-0161To be published on 04/07/2026
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. However, direct physical sensors for real-time pump health monitoring are absent in current architectures, making early fault detection particularly challenging. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. The core of the approach is an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage Error (MAPE) between predicted and actual currents, providing a continuous and interpretable measure of
Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne
Impact of Hydrogen on Methane and Pollutant Emissions over Three-Way Catalysts with Natural Gas–Hydrogen Blends2026-01-0357To be published on 04/07/2026
Natural gas (NG) is a domestically abundant, low-cost fuel with the potential to displace gasoline and diesel fuel in commercial and off-road vehicles. NG engines typically produce lower engine-out pollutant emissions, such as NOx, CO and particulate matter, which could simplify emission control systems. Blending NG with hydrogen (H₂) can improve engine performance and increase combustion efficiency to further reduce engine-out emissions. Additionally, the presence of H2 in the exhaust stream may also facilitate the catalytic conversion of methane and other pollutants, resulting in cleaner tailpipe emissions. This study evaluates the impact of H2 on the conversion of methane, CO, and NOx emissions on a commercial three-way catalyst (TWC) in a flow reactor using synthetic gas mixtures that simulate stoichiometric engine exhausts with NG or NG+H₂ combustion. The work examines whether, and how, the additional amount of H₂ in the exhaust stream affects the conversion efficiency of methane
Prikhodko, Vitaly, Wang, Min, Park, Yeonshil, Chen, Hai-Ying, Pihl, Josh
Proactive Faulty Detection in EV Batteries Via Hybrid method: Cell-to-Cell Variation Pattern And Physics-Based Model insights.2026-01-0391To be published on 04/07/2026
This study presents a distinct methodology for the early detection of faulty cells in electric vehicle (EV) battery systems, leveraging temporal voltage deviation patterns under real-world charging scenarios alongside outputs from a physics-based model. A comparative longitudinal analysis was conducted on a fleet of ten EVs—five exhibiting stable performance and five demonstrating early-stage anomalies characterized by intermittent transitions from drive to neutral mode. These behavioral cues were investigated as precursors to deeper battery degradation. The analysis focused on cell-level voltage dispersion during the mid-to-high state-of-charge (SoC) range (approx. 20–30% to full charge). Vehicles in healthy condition consistently displayed minimal voltage differentials among cells, whereas those with latent faults showed markedly higher variance, particularly between the highest and model-expected voltages. Notably, this voltage divergence was often accompanied by a modest yet
Jawle, Bharat Sanjay, Selvakumar, Ashwin, Puttoji Rao, Nagaraj Kumar
Experimental Evaluation of After-treatment Solutions for Heavy-Duty Natural Gas Vehicles to meet future CNVII Regulations2026-01-0376To be published on 04/07/2026
The heavy-duty truck market in China has seen a significant increase in the adoption of natural gas-powered engines over the past two years. Simultaneously, the anticipated release of the China VII emissions regulation proposal by the end of 2025 is expected to impose stricter emissions limits on all heavy-duty engines, including new particulate number (PN10) thresholds analogous to those in the Euro 7 regulation. While tailpipe NOx and methane (CH4) emissions from natural gas engines can be mitigated through tighter lambda control and adjustments to catalyst volume and precious metal (PGM) loading, addressing particulate number (PN) emissions necessitate more advanced after-treatment solutions. Although natural gas combustion is virtually soot-free, the entrainment of lubricating oil into the combustion chamber, especially during cold-start conditions, poses a challenge, leading to potential exceedance of the proposed future China VII limits. Additionally, PN emissions from natural
Jiahui, Gao, Marc C, Besch, Ding, Ning, He, Suhao, zhao, Yuxin, Yixiao, Li, Shen, Ye
Assessing Energy Use, Cost, and Emissions of Small Regional Rail Vehicles: Methodology and Case Study on a German Track2026-01-0419To be published on 04/07/2026
The decarbonization of regional rail remains a pressing challenge in the European Union, where nearly half of the 200,000 km network remains unelectrified. Lightweight, single-car railbuses historically provided efficient service on such lines, but modern replacements are scarce. This work evaluates a standardized 30-ton, 16 m railbus platform 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 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 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
Ahrling, Christoffer, Tuner, Martin, Gainey, Brian, Torkiharchegani, Amir, Scharmach, Marcel, Hertel, Benedikt, ALAKÜLA, Mats
Low-Cost Packaging of SiC Power Modules for EV Traction Inverters2026-01-0119To be published on 04/07/2026
The increasing demand for efficient and cost-effective power electronics in electric vehicle (EV) traction inverters necessitates innovative power module designs. This paper presents a novel SiC power module packaging focused on low-cost fabrication and assembly.. The proposed design features a simplified sandwich structure consisting of a lead frame, the innovative epoxy composite deposited on a high-performance cold plate. The novel epoxy composite eliminates the critical solder connection typically found between the DBC/AMB and the cold plate. This significantly streamlines the thermal path by reducing the number of material layers and interfaces between the SiC chip and the coolant. As a result, the integrated stack-up achieves significantly low junction-to-coolant thermal resistance (Rth) while delivering substantial cost savings related to power module module packaging. This work provides the details on the power module prototype fabrication steps and functionality including the
Chen, Yu, Mena-Garcia, Javier, Chen, Hao, Xiao, Kewei, Gupta, Man Prakash, Degner, Michael
Physics-Based Simulation for Sustainable Fuels Part 2.1: Inter-Ring Pressure Measurements and 2D/3D Ring Dynamics Analysis Across Multiple Engine Platforms – Light Vehicle2026-01-0281To be published on 04/07/2026
The growing demand for sustainable mobility and transportation is accelerating the adoption of alternative fuels, particularly hydrogen, in internal combustion engines. The first part of this publication series highlights the significance of 2D simulation as a crucial and computationally efficient tool for the precise development of hydrogen Power Cylinder Units. This approach demonstrates predictive capability proofed through engine tests, achieving a reduction in lube oil consumption by 5–7 g/h during high-load operations, alongside a 28% decrease in blow-by and an 11% reduction in hydrogen flow to the crankcase. To provide deeper insights into the complexities identified in Part 1, this study employs inter-ring pressure measurements across various engine types and configurations, including light vehicles, heavy-duty trucks, and large-bore applications, covering a broad range of engine displacements from 2 to almost 100 liters. This investigation in Part 2.1 focuses on understanding
Moreira, Rui, Rösch, Hannes, Ehnis, Holger, Köser, Philipp
BEV incremental CO2 emissions in a ranked order electric grid2026-01-0424To be published on 04/07/2026
Battery Electric Vehicles (BEV) have been promoted and required by many governments towards the need to reduce transportations CO2 emissions. BEV are not “Zero Emissions Vehicles” because they run on grid electricity, but could be ‘effectively ZEV’ if the incremental CO2 is ‘very small’. To answer this question at the national level, three metrics are needed and estimated: (1) ICEV fuel consumption, approximated by the total US gasoline consumption divided by the total fleet miles driven, 25 mpg or 350 g CO2/mi. For strong HEV it is about one third less, 240 g CO2/mi. (2) BEV energy consumption, using data from systematic on-road testing of a wide range of vehicles, estimated at 40 kWh/100 mi for a US sales mix. (3) Electric grid marginal CO2, with ranked order sourcing: zero-CO2 sources are prioritized and supplemented by fossil sources. IEA hourly data show that the US 48 contiguous states are nearly self-contained, with zero-CO2 sources providing about a third of total demand. The
Phlips, Patrick
Beyond PFAS: Unlocking the Potential of R290 and R744 for EV Efficiency2026-01-0127To be published on 04/07/2026
The anticipated PFAS ban in the US by 2029 has created a need to evaluate alternative refrigerant solutions for automotive thermal management systems. This work compares three candidates—Propane (R290), Carbon Dioxide (R744), and R1234yf—through system-level testing and demonstration projects. R1234yf remains the current industry baseline. Test results show that Propane (R290) delivers comparable efficiency while offering a significantly lower global warming potential. However, its flammability presents integration challenges, not present with R1234yf or R744. CO₂ (R744) demonstrated promising performance as well. In AVL’s Refrigerant Cooling Project, direct CO₂ cooling battery plate simplified the thermal system architecture and provided adequate cooling capacity during high charging loads. To address safety concerns with Propane, AVL developed mitigation measures including rapid leak detection, robust containment strategies, and optimized circuit layouts designed to reduce ignition
bires, Michael, Possegger, Jonathan
Electrochemical-Thermal Simulation for Evaluating Indirect and Immersion Cooling in High-Power EV Charging2026-01-0403To be published on 04/07/2026
With the rapid advancement of electric vehicle (EV) fast-charging technology, battery thermal management faces increasingly critical challenges due to elevated heat generation and stringent safety requirements. Immersion cooling has emerged as a promising solution because of its superior heat dissipation and uniform temperature distribution compared to conventional indirect cooling methods. This study develops an electro-thermal coupled simulation framework for evaluating indirect and immersion cooling performance under high-power charging conditions. A pseudo-two-dimensional (P2D) electrochemical battery model of an electric vehicle was developed in GT-Suite and validated against vehicle on-board charging data. In parallel, an immersion cooling system model was developed and validated using battery bench test results. This model was subsequently integrated into the vehicle model to allow comparisons with a conventional indirect cooling system under high-power DC fast-charging scenario
Guo, Yuyang, Rockstroh, Toby, Haenel, Patrick, Oezdag, Erdal, Bodemann, Basil, Toghyani PhD, Somayeh
Optimizing EV Battery Models for Full Vehicle Simulations2026-01-0389To be published on 04/07/2026
The increasing adoption of electric vehicles (EVs) demands accurate yet computationally efficient battery models that can be integrated into full vehicle simulations. At the cell level, mechanical battery models often employ fine-scale elements to capture localized deformation and failure phenomena. While such detailed discretization enables high-fidelity predictions, it also imposes significant computational costs that become prohibitive when scaling up to pack-level or full-vehicle crash and durability simulations. This research addresses the challenge by systematically simplifying cell-level mechanical models to reduce computational burden while preserving predictive accuracy. We propose an approach in which larger elements and reduced complexity representations are introduced without compromising the model’s ability to replicate experimentally observed behaviors. The methodology emphasizes model validation against targeted loading conditions, ensuring that the essential mechanics
Sahraei, Elham, Brar, Bavneet, Parmar, Dhruv, Muralidharan, Umachandran
A Novel Development Method for a Control Model of TWC in Hybrid Electric Vehicles Based on Mechanism Simplification and Temperature Update2026-01-0371To be published on 04/07/2026
Under increasingly stringent emission regulations, hybrid electric vehicles face heightened requirements for tailpipe emission control. Their complex operational profiles and frequent transitions between power modes pose significant challenges to experimental efforts in emission measurement and aftertreatment control, leading to substantially increased economic and temporal costs that severely constrain emission optimization. To address these challenges, this study employs the development and calibration of a high-accuracy virtual model to conduct simulation experiments that accurately replicate real-world driving conditions, significantly reducing development costs while maintaining predictive accuracy. Focusing on hybrid electric vehicles, we developed an aftertreatment model integrating catalytic reaction mechanisms and energy conservation equations, incorporating coupled temperature and concentration calculations. This model enables real-time monitoring of dynamic oxygen storage
Lian, Xuetong, Wang, Zhiao, Liu, Weiqiang, Chen, Tao
Propulsion System Design Considerations for HEV versus EV Applications2026-01-0641To be published on 04/07/2026
The design and development of EVs and HEVs has become a growing issue recently due to concerns about pollution and dependence on non-renewable fossil fuels. Accordingly, General Motors (GM) has an evolving vehicle electrification plan over the past several decades and into the future to deliver low-cost and efficient EVs and HEVs. Propulsion system requirements for the applications of EV and HEVs are quite different and therefore, the design principles and directions are also distinct between these cases. From micro-hybrid and full plug-in hybrid applications to full EV applications, design requirements, strategies and outcomes can widely vary. Continuous and peak duty are substantially different depending on the application of the vehicle. Motor operational duty is significantly higher for EV compared to the electric motor of a hybrid electric vehicle. Motor torque, power and efficiency requirements are also higher for EV motors, which greatly influences the choice of motor type and
Momen, Faizul, Jensen, William, Das, Shuvajit, Chowdhury, Mazharul, Alam, Khorshed, Anwar, Mohammad, Reinhart, Timothy
Assessing Plug-in Electric Vehicle Adoption: Methodologies, Policy Effects, and Diverging Market Pathways in China, the U.S., and Europe2026-01-0455To be published on 04/07/2026
Accurate projection of Plug-in Electric Vehicle (PEV) market sales share is vital for evidence-based policymaking, yet existing studies employ diverse and often fragmented methodologies, creating a need for a systematic review to clarify their analytical foundations and comparative strengths. This study classifies mainstream approaches to market projections into theory-driven and data-driven categories and reviews the merits, limitations, and future directions of five representative models. Analysis reveals that leading approaches increasingly employ cross-scale model coupling, theory-data fusion, and modular design to harness complementary strengths, improving model robustness and predictive accuracy. Furthermore, the study compares PEV policies and market outlooks in China, the United States, and Europe—the world's three largest automotive markets. The findings indicate a strong linkage between projection convergence and policy stability. China demonstrates the highest policy
Luo, Wei, Ou, Shiqi(Shawn), Zhou, Pan, Wang, Tianpeng, Qian, Xiaodong
Behavioral Determinants of Electric Vehicle Battery Degradation: Evidence from Large-Scale Real-world Operations2026-01-0458To be published on 04/07/2026
Electric vehicles (EVs) are central to sustainable transport, yet battery service life remains a limiting factor for cost and adoption. Distinct from traditional laboratory-based simulations that often fail to capture the complexity of field conditions, this study investigates how EV user behavior—including driving style and charging demands—influences capacity using large-scale, real-world operational data from daily EV usage. A data-driven framework is developed to quantify driving and charging behaviors through multidimensional feature extraction at the vehicle level and estimate battery State-of-Health (SOH) trajectories, enabling direct linkage between individual behavior patterns and degradation outcomes. Results reveal substantial heterogeneity in aging rates explicitly driven by diverse user behaviors: under identical urban conditions, vehicles with a radical driving style exhibit approximately 81% faster SOH decline per 20,000 km than those with a moderate style ; regarding
Liu, Tianyi, Jing, Hao, Zhu, Jiankuan, Chen, Yongjian, Ou, Shiqi, Qian, Xiaodong
A Large Language Model-Based Database for Analyzing the Electric Vehicle Battery Supply Chain2026-01-0471To be published on 04/07/2026
Global geopolitical volatility poses a critical threat to the resilience of the electric vehicle battery supply chain. Static, manually updated databases fail to capture the sector's rapid dynamics, creating significant information gaps for strategic planning. This study proposes an Artificial Intelligence-driven methodology to construct a comprehensive and dynamic knowledge base. An automated pipeline is implemented, beginning with specialized web crawlers that harvest real-time textual data from curated news and industry sources. This unstructured data is preprocessed through deduplication and cleansing to ensure quality. The core innovation involves applying fine-tuned Large Language Models (LLMs) to perform deep semantic parsing and extract structured information. These models precisely identify key entities—corporations, facilities, production capacities—and delineate the complex multi-tier relationships from raw material extraction to final distribution. The output is an
Zhu, Juntong, Luo, Wei, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi(Shawn), He, Xin
Physics-Based Simulation for Sustainable Fuels Part 2.2: Inter-Ring Pressure Measurements and 2D/3D Ring Dynamics Analysis Across Multiple Engine Platforms – Heavy Duty and Large Bore2026-01-0280To be published on 04/07/2026
The growing demand for sustainable mobility and transportation is accelerating the adoption of alternative fuels, particularly hydrogen, in internal combustion engines. The first part of this publication series highlights the significance of 2D simulation as a crucial and computationally efficient tool for the precise development of hydrogen Power Cylinder Units. This approach demonstrates predictive capability proofed through engine tests, achieving a reduction in lube oil consumption by 5–7 g/h during high-load operations, alongside a 28% decrease in blow-by and an 11% reduction in hydrogen flow to the crankcase. To provide deeper insights into the complexities identified in Part 1, this study employs inter-ring pressure measurements across various engine types and configurations, including light vehicles, heavy-duty trucks, and large-bore applications, covering a broad range of engine displacements from 2 to almost 100 liters. Part 2.1 focuses on understanding the cyclic variations
Köser, Philipp, Moreira, Rui
The Impact of Mid-level Ethanol Blends on Reliability, Performance and Emissions in Gasoline engine2026-01-0302To be published on 04/07/2026
To reduce CO₂ emissions from automobiles, it is essential to enhance system efficiency through the electrification of vehicles equipped with internal combustion engines (ICEs), such as hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs), as well as through improvements in ICE thermal efficiency. Furthermore, biofuels and synthetic fuels are gaining attention as promising options for reducing CO₂ emissions, including from existing vehicles. Among these alternative fuels, ethanol - a bio-derived fuel - is already utilized in varying concentrations across many countries, and its further application is anticipated. While expanding the fleet of flex-fuel vehicles (FFVs) capable of operating on high ethanol blends is one approach, increasing the ethanol content in conventional gasoline fuels, which are more widely used, is considered to have a greater impact on CO₂ reduction. A key issue is the impact and adaptability of existing vehicles when ethanol concentrations
Matsubara, Naoyoshi, Sugata, Kenji, Koyama, Takashi, Ochi, Yuta, Aoki, Mizuki, Hashima, Takashi, Kodama, Kohei, Tomoda, Keiju, Kojima, Masakiyo
Cradle-to-grave assessment of Austrian passenger car traffic based on actual vehicle movements and derivation of future forecasts for minimising greenhouse gas emissions2026-01-0457To be published on 04/07/2026
As part of the decarbonisation process for passenger car fleet in Austria, battery electric cars in particular have been subsidised in recent years, as these vehicles are considered to be largely emission-free during use and are expected to reduce emissions in future. However, in order to sustainably reduce the global greenhouse gas emissions of Austrian passenger car traffic, taking into account all types of propulsion systems, it is necessary to apply a cradle-to-gate approach, as is commonly done in comparable analyses in the literature, which evaluates the emissions of the entire vehicle life cycle. The most important phase in the life cycle assessment remains the well-to-wheel phase, which includes emissions from energy supply and vehicle use. Due to the large number of influencing factors, highly simplified models are usually used for this phase in the literature. As part of this work, a methodology was developed that, allows an in-depth analysis of entire vehicle fleets by
Lischka, Gregor, Tober, Werner
New Inverter Switching Control Method to Enhance Sound Quality for Electric Vehicles2026-01-0437To be published on 04/07/2026
Conventional inverter control uses a fixed switching frequency, which leads to a high-pitched switching noise in electric vehicles (EVs) that does not vary with vehicle speed. Although EVs are generally much quieter than traditional internal combustion engine (ICE) vehicles, some EV owners complain about the lack of dynamic driving sound feedback. A new patented technology has been developed to enhance EV sound quality by dynamically controlling the inverter switching frequencies. This technology generates dynamic propulsion sound with new "switching order" features at multiple harmonics, with the pitch proportional to vehicle speed. A fixed pulse ratio between the switching frequency and the electric motor RPM is implemented to control the switching order. This reduces switching losses during low-speed operation and provides boosted acoustic feedback to the driver during acceleration, which enhances driving experience during sports driving. Furthermore, a special "EV shifting" sound
He, Song, Gagas, Brent, Welchko, Brian, Ball, Kerrie, Gong, Cheng
Optimal Vehicle Sampling for Fleet-Wide Emissions Monitoring using Submodular Maximization2026-01-0283To be published on 04/07/2026
This article addresses the problem of optimal vehicle sampling for fleet-wide in-use emissions monitoring, a necessity driven by the absence of direct emissions sensors in modern production vehicles and the variable impact of in-use changes and operational factors (mileage, time-in-service, workload) on emissions performance across a fleet. Recognizing that comprehensive fleet testing is impractical due to significant downtime and cost, we propose a novel approach to identify a small, yet optimally informative subset of vehicles for sampling. The proposed approach leverages submodular function maximization, a technique rooted in optimal experimental design, specifically D-optimal design, to maximize the determinant of the information matrix (e.g., of $X^TX$, where $X$ is the regressor/design matrix in the case of a linear in parameters model). This approach ensures that the collected data yields maximum information for refining and building accurate models for emissions characteristic
ZHANG, JIADI, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato
From Propane to Pi: Affordable AI-Driven Solutions for Sustainable Bus Heating.2026-01-0164To be published on 04/07/2026
The design of an affordably luxurious school bus means improving passenger comfort while greatly decreasing energy usage and emissions. The key consideration is to replace traditional propane heating with an electric heated-seat system and supported by green electricity/energy efficient smart power management. The system is designed from a 24V/48V battery system; to a Smart Energy Cube - a portable dock in power module requiring solar, battery and grid connection. Seat heating is only applied to seats that are occupied. The on-board image processing models (YOLOv5/YOLOv8, OpenCV, TensorFlow Lite) are run on affordable/portable microcomputers capable of detecting the number of occupied and available seats e.g. Raspberry Pi. There is one bottle neck when using the Raspberry Pi in the experiments to validate concept, when using low/medium power frame rates of ~ 1 fps for Raspberry Pi 3; to 7-8 fps with a Raspberry Pi 5. As a baseline, there is still potential for improvement and
Chikkala, Daney Bhargav, Zadeh, Mehrdad, TAN, Teik-Khoon, Ponnam, Jitin, BATTE, JAI RATHAN
In-Use Life Digital Twin and Swappable Use Case for Battery Durability2026-01-0397To be published on 04/07/2026
In-Use Life Digital Twin and Swappable Use Case for Battery Durability By: Patricia Laskowsky, Josue Albarran, Justin Bunnell, and Andrew Zettel Abstract The UN GTR 22 is required as part of the Environmental Protection Agency’s legislative rulemaking for multi-pollutants emission standards for model years 2027 and later light-duty and medium duty vehicles. The EPA claims it has the right to govern requirements on ZEVs and BEVs. ZEVs, in their viewpoint, are “emission related” because it enables the vehicle to produce zero emissions. With the adoption by the EPA under its Tier 4 regulations of the UN GTR 22, comes several new tracking DIDs, Data Identifiers. These DIDs are tied battery durability and energy tracking They track conditions and usages of electric vehicles that may accelerate the loss of electric range and/or loss of efficiency of the propulsion energy storage and delivery. More specifically, tying these trackers to EPA emission related discussion, the loss of electric
Laskowsky, Patricia, Bunnell, Justin, Zettel, Andrew, Albarran, Josue
Comparison of Ammonia Emission Characteristics Between Different Gasoline Vehicles Equipped with Three-Way Catalysts2026-01-0374To be published on 04/07/2026
In recent years, the tightening of vehicle emission regulations has led to a decreasing trend in regulated pollutants such as NOₓ and CO. However, the emission of ammonia (NH₃), which is unintentionally generated during the purification process in three-way catalyst of gasoline vehicles, has become a growing concern. NH₃ emissions from vehicles can serve as a precursor to PM2.5 and have been reported to cause local roadside pollution. Therefore, there is a growing need for on-road testing to identify conditions under which NH₃ is likely to be emitted. Furthermore, since engine control strategies vary among vehicle types, it is desirable to consider differences in emission behavior across different models. In this study, on-road NH₃ emissions were measured for multiple vehicle models with different powertrains, and the effects of engine behaviors and engine operating duration across vehicles on NH₃ emissions were investigated. To analyze differences in NH₃ emission behavior among
Ashizawa, Keigo, Fukunaga, Chisato, Gao, Tianyi, Sato, Susumu
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-0097To be published on 04/07/2026
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
Rana, Vishva, Balchanos, Michael, Mavris, Dimitri, Valenzuela Del Rio, Jose
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis2026-01-0170To be published on 04/07/2026
Vision-language models (VLMs) offer a promising end-to-end approach for autonomous driving, but their robustness to physical adversarial attacks remains an unaddressed gap for safety-critical deployment. This paper introduces a systematic framework for evaluating these attacks using the Dolphins VLM and CARLA simulator. We employ Natural Evolution Strategies (NES), a black-box optimization method, to generate adversarial patches without internal model access. Our approach uses semantic similarity loss and Expectation over Transformation (EoT) to ensure physical realizability under varying viewing conditions. Patches are strategically placed on realistic advertising infrastructure (bus shelter panels and highway billboards), reflecting a plausible threat model. We establish two complementary scenarios: crosswalk pedestrian detection suppression and highway steering manipulation. Our evaluation reveals critical vulnerabilities. Adversarial patches achieved a 76.0% overall attack success
Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.
CrashML Analyzer: A Data-Driven Approach for Investigation and Fault Assignment in Autonomous Driving Collisions2026-01-0160To be published on 04/07/2026
This study investigates factors contributing to autonomous vehicle (AV) accidents and proposes an automated fault determination framework. A total of 563 accident reports from the State of California Department of Motor Vehicles spanning from 2019 to 2024 were analyzed by converting unstructured standardized reports into structured data using custom extraction tools. Analysis of these reports reveal that AVs were not at fault in 69.4% of cases, and were fully at fault for 22.6% of the cases. The proposed method uses these reports to provide an early indicator of fault likelihood and potentially replaces tedious manual review. Machine Learning (ML) and Natural Language Processing techniques were used to replicate the reported faults, achieving 96% average accuracy across three models: Gradient Boosting, Linear Regression, and Random Forest. Through feature engineering techniques in semantic feature extraction from narrative accident descriptions, quantifiable variables were obtained and
Rwejuna, Florida Perfect, Majid, Nishatul, Goutham, Mithun, Loukili, Alae
Model-Based Design and dSPACE/MathWorks Environment Enable Innovative Engineering Education2026-01-0073To be published on 04/07/2026
The exponentially growing complexity of engineering systems, such as a robotic system, an autonomous vehicle, and an unmanned aerial vehicle, requires sophisticated control strategies that can efficiently coordinate the system’s operation in various environments. The traditional control design approaches present significant challenges for control engineers to keep up with the increasing complexity and changing requirements. To advance embedded control system design, a paradigm shift from traditional development approaches toward more structured, systematic methodologies that can manage the multi-domain nature of control systems is critically needed. Model-based design approach is emerging as a solution for this demand. Model-based design approach uses a system model for control system development, from requirements capture to control system design, implementation, and testing. It provides an integrated environment for design, implementation, automatic code generation, and validation
Repaka, Sindhura, Chen, Bo
Ultra-Low Latency Video Pipeline Optimization for Perception-Centric Autonomous Systems2026-01-0022To be published on 04/07/2026
Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages-sensor acquisition, video encoding, data transmission, decoding, and display-undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software
Indrakanti, Rama Kiran Kumar
Climate Heat Recovery Energy Study in Electric Vehicles2026-01-0129To be published on 04/07/2026
Climate control systems in Battery Electric Vehicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Extended Range Electric Vehicles (EREVs) rely on electrical energy to provide cabin heating. In winter conditions, the absence of waste heat from internal combustion engines necessitates increased energy consumption for thermal comfort, which directly impacts vehicle range. Conventional HVAC systems typically operate with a mixture of cold ambient air and recirculated cabin air. However, the proportion of recirculated air is limited due to windshield fogging risks, constraining energy-saving potential. To address this, MAHLE has developed modular heat recovery technologies that utilize the thermal energy of exhaust cabin air, that would normally leave the passenger compartment through vehicle body vents, to precondition incoming fresh air, thereby reducing the heating load. These solutions are engineered for scalable integration into existing HVAC architectures, allowing
Wolfe, Edward, Wochele, Kerstin, Reid, Bailey
Data-driven feedforward compensation-based trajectory tracking control2026-01-0028To be published on 04/07/2026
Advanced autonomous driving is a critical component in the intelligent development of new-generation electric vehicles. Research on reliable chassis control algorithms ensures the safety and stability of autonomous vehicles during operation. To enhance the control performance of autonomous vehicles and improve the accuracy of trajectory tracking, this paper proposes a data-driven feedforward compensation trajectory tracking control approach. By optimizing the design of the feedforward compensation loop, systematic errors and latency in the vehicle’s steering system are mitigated, thereby enhancing the precision and robustness of the control algorithm. Initially, the paper analyzes the control errors present when the vehicle responds to controller commands. Subsequently, the paper focuses on the steering angle errors in trajectory tracking, identifying and analyzing the most relevant factors. A time-delay neural network (TDNN) based on data-driven principles is designed to model and
Yang, Yijin, Yuan, Yin, Wang, Zhenfeng, Su, Ailin, Zhang, Zhijie, Lu, Yukun
High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety2026-01-0034To be published on 04/07/2026
Ensuring the safety of vulnerable road users is a critical challenge in the development of advanced autonomous driving systems. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and manageable. Compared to pedestrians, vehicles often travel at significantly higher relative speeds when interacting with bicyclists, yet bicyclist motion is generally more predictable and governed by clear traffic rules, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. By using this approach, CLFs enforce system stability, ensuring the vehicle follows a desired trajectory, while CBFs define safety boundaries, preventing collisions between the ego-vehicle and other road users. The optimal control input is then obtained by solving a
Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin
Modeling Non-Uniform Temperature Distribution in Lithium-Ion Batteries2026-01-0120To be published on 04/07/2026
Effective thermal management is critical to the performance, lifespan, and safety of lithium-ion batteries in electric vehicles (EV). Transient and non-uniform temperature distributions within cells accelerate degradation, cause pack imbalance, and reduce efficiency. Localized hotspots, in particular, promote lithium plating, solid electrolyte interface (SEI) growth, and other failure mechanisms. Persistent non-uniformities increase the likelihood of thermal runaway under high-stress operation. To address these challenges, this work develops a one-dimensional thermal model that captures heat generation and dissipation under dynamic operating conditions, incorporating both conduction within the cell and active convective cooling. Using a DFSS-based approach, we evaluate design and cooling strategies that minimize temperature differentials while maintaining acceptable operating ranges. The findings provide practical guidance for battery thermal management system design, emphasizing
El-Sharkawy, Alaa, Asar, Mona, Serpento, Stan, Sheta, Mai
Low-voltage electric vehicle with increased electrical safety and distributed charging system2026-01-0396To be published on 04/07/2026
Abstract - Reducing the high-voltage BEV to a household level of 120-240 volts is considered in the article as an effective means of solving the problems of electrical safety, maintenance and minor repairs of an electric vehicle in household conditions, distributed power supply of BEV with walking distance for the driver. The analysis of the low-voltage electric drive is performed under the assumption that the battery has a nominal voltage of 200 volts. The issues of transforming a high-voltage machine (400 volts) into a low-voltage one (200 volts) by switching the stator phase sections from serial to parallel connection without changing the overall and energy characteristics are considered. It is shown that a two-motor unit with induction machines with a capacity of 50 kilowatts can provide 100 kilowatts in long-term and up to 200 kilowatts in peak modes. The article considers the issues of implementing a low-voltage inverter and modern trends in distributed power supply for BEVs
Smolin PhD, Victor, Sobolevskiy, Anatoliy, Volovich, Georgy
Advancing Safe Battery Design through Experimental Cell Characterization of Thermal, Electrical and Mechanical Properties2026-01-0408To be published on 04/07/2026
Lithium-ion batteries are critical to electric vehicles and grid-scale energy storage. Safe design of battery systems relies on accurate simulation of battery thermal runaway under electrical, thermal, and mechanical abuse. A predictive battery simulation requires the characterization of electrical, thermal, and mechanical properties of the battery at the full cell and cell-component levels. In this study, a commercial cell from an electric vehicle was disassembled, and testing was conducted to support both homogenized and detailed computational models. At the cell level, electrical properties were characterized using Hybrid Pulse Power Characterization (HPPC) testing to assess the cell power capability. Cell punch tests were conducted to characterize mechanical behavior under deformation. These tests were used to develop a multi-physics homogenized cell model. On the other hand, detailed cell modeling that includes different component layers could help users understand localized cell
Challa, Vidyu, Rostami-Angas, Masoud, kong, Kevin, Wang, Leyu, Reichert, Rudolf, Kan, Cing-Dao
Multi-Body Dynamics Control Integration Tool Application in Hybrid Vehicle System Identification2026-01-0417To be published on 04/07/2026
The multi-body dynamics (MBD) model and the MATLAB Simulink model can be integrated to create a control-integration model. Using a high-fidelity MBD model to represent the vehicle as the plant, this integrated model can be used to analyze vehicle system physics and develop control strategies. For hybrid vehicles, this process is more complex because the powertrain and other vehicle systems are often built as separate MBD models. This paper describes a method for integrating a powertrain model developed in AMESIM, a vehicle model developed in SIMPACK, and a control model developed in MATLAB Simulink. The resulting integrated model was then used to perform frequency sweep analysis to identify driveline system properties. In particular, the driveline frequency and the amplitude of the transfer function between motor speed and motor torque are critical parameters. By applying active damping control to the driveline system, the peak amplitude and driveline vibrations can be reduced. The
Xing, Xing, Mathew, Vino
Transformer-Based Deep Learning Framework for Efficient Prediction of Automotive Wind Noise2026-01-0222To be published on 04/07/2026
Aerodynamic wind noise is a critical challenge in modern automotive development, particularly with the rise of vehicle electrification and intelligent mobility, where cabin acoustic comfort is a key quality metric. While reliable, traditional methods like wind tunnel experiment and CFD simulations are both costly and time-consuming. To address this, we propose a novel transformer-based framework for rapid and accurate wind noise prediction. Several model improvements, including the physical attention, geometry wave number embedding, hybrid FPS-random down sampling method and frequency separation output heads are properly employed to reduce the GPU mermory cost and improve the prediction accuracy. This framework is pre-trained on a large-scale transient flow field dataset of 1,000 diverse vehicles generated using Delayed Detached Eddy Simulation (DES). From a vehicle's point-cloud coordinates, the model directly predicts the surface pressure spectrum on the driver-side window and the
TANG, Weishao, Liu, Mengxin, QIN, Ling, Duan, Menghua, wang, chengjun
Novel Steel Tubular Side Sill Reinforcement Design and Validation for Enhanced Crash Performance in EVs2026-01-0478To be published on 04/07/2026
Increased use of Electrical vehicles (EVs) brings in unique structural design challenges particularly in Side Impact scenarios when the battery pack is near the sill region therefore elevating the possibility of Electrical Module penetration and triggering thermal runway event resulting from the crash. Thus, Electric Vehicles present unique structural design challenges. This study intends to evaluate a novel tubular structure design for side sill reinforcement and its performance benefits over a traditional aluminum side sill reinforcement currently commonly utilized for battery protection in EV vehicles body components. The proposed Steel side sill design mainly focuses on structural rigidity and improved load transfer during side pole impact crash cases and address manufacturing complexity and cost reduction opportunity. Multiple iterations for Steel side sill reinforcement design were carried out using finite element analysis with software like LSDYNA Hyper works to propose optimal
Kusnoorkar, Harsha, Khutorsky, Alex, Penumetsa, Vivek, Koraddi, Basavaraj
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