Journal Articles - SAE Mobilus

SAE journals provide rigorously peer-reviewed, archival research by subject matter experts--basic and applied research that is valuable to both academia and industry.

Items (11,270)

Experimental Study on the Combined Effects of Swirl and Inlet Offset on the Flow through an Automotive Catalyst Monolith Substrate

03-19-04-0019

8/7/2026

Maldistributed flow within an automotive catalyst can cause reduced conversion efficiency, high pressure loss, and premature deactivation. However, packaging constraints often result in uneven flow distribution between the monolith channels, thus compromising design and, inevitably, performance of the device. Flow uniformity may be improved by the introduction of swirl upstream of the catalyst assembly, and in turbocharged applications the residual swirl from the turbine can serve that purpose. Indeed, low swirl has been shown to provide favorable flow uniformity in the monolith substrate in an axisymmetric flow setup. However, the automotive exhaust aftertreatment setups are seldom axisymmetric, and the combined effects of inlet swirl and offset on the flow profile through a monolith substrate are unknown. To address this gap, this study provides the first systematic experimental characterization of the coupled influence of inlet swirl and packaging-relevant inlet offset on flow development and uniformity in a sudden expansion catalyst assembly. Particle image velocimetry (PIV), wall pressure measurements, and hot-wire anemometry (HWA) are combined to link the upstream separation and recirculation structures to the velocity distribution downstream of the monolith. The results reveal a previously unreported swirl-dependent sensitivity to geometric asymmetry: under no-swirl and moderate-swirl conditions, flow uniformity is robust to inlet offset, varying by no more than 1.4%, whereas at low swirl the offset reduces uniformity by up to 8% at high mass flow rate. Increasing mass flow rate reduces uniformity by up to 15%, while swirl improves uniformity by up to 19% relative to axial flow. These findings demonstrate that improvements observed for swirl in axisymmetric assemblies cannot be assumed to transfer directly to offset geometries. Swirl intensity and inlet alignment must instead be considered as coupled design variables. The measurements also provide a benchmark dataset for validating computational fluid dynamics simulations before their application to production-type systems.

Rusli, Ijhar, Aleksandrova, Svetlana, Medina, Humberto, Benjamin, Stephen F.

Delay-Compensated Active Anti-Roll Bar of Tractor-Trailers Using Two-Dimensional Piston Pump Electro-Hydrostatic Actuators

02-19-04-0027

8/5/2026

As tractor-trailers are essential to global logistics, their roll stability during emergency maneuvers is a critical safety concern. This paper presents a novel delay-compensated active roll control strategy for tractor-trailers using a two-dimensional piston pump electro-hydrostatic actuator (EHA). Unlike existing advanced strategies that assume ideal actuator behavior, this approach specifically targets the inherent response delay in high-tonnage applications. A detailed EHA model, including pump flow characteristics and hydraulic mechanics, was developed and validated through step response experiments. A seven-degree-of-freedom vehicle dynamics model and a model predictive controller were also constructed to compute the required anti-roll moment under emergency driving conditions. In order to address the EHA actuator’s response delay, a delay feedforward controller (DFC) was designed, integrating acceleration feedforward, feedback regulation, and delay disturbance estimation. TruckSim–Simulink co-simulations under double lane-change (DLC) maneuvers at 40 km/h, 60 km/h, and 80 km/h show that DFC improves displacement tracking and reduces peak trailer roll angle by up to 15% compared to a velocity-feedforward proportional-integral-derivative (VFPID) controller. It also enhances control efficiency, as evidenced by lower average motor speeds and pressure response of EHA. The system demonstrates high power-to-weight ratio and efficient tracking capabilities under dynamic conditions. Although active control provides limited benefit at low speeds, the proposed strategy effectively improves roll stability and driving safety under dynamic conditions.

Chen, Lijie, Yin, Yuming, Zeng, Yuhang, Ruan, Jian, Li, Hangqi, Sun, Peng

Machine Learning Approach for Bidirectional Modeling and Optimization of Inconel 625 Deposition in Wire Arc Additive Manufacturing

05-19-04-0030

7/30/2026

Machine learning (ML) techniques are increasingly being applied to establish correlations between input parameters and key process responses in the wire arc additive manufacturing (WAAM) process. Despite their potential, there remains limited understanding of how to develop an integrated ML framework that simultaneously considers both the dataset characteristics and the modeling approach to ensure accurate and reliable predictions. The present study addresses this gap by developing an integrated ML framework to predict the deposition behavior of Inconel 625 in WAAM. To capture nonlinear system behavior, three ML methods, namely artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS), were developed and systematically evaluated for predictive modeling and process optimization, considering deposited geometry, area, and efficiency as the key output characteristics. The input parameters, i.e., voltage, wire feed rate, torch travel speed, and shielding gas flow rate, were identified as critical factors influencing the deposition process. The datasets were preprocessed to remove noise and analyzed to extract relevant features that captured the intrinsic physical behavior of the process. Performances of the ML models were evaluated using a separate test dataset, and predictions were assessed through mean absolute percentage deviation (MAPD). Results demonstrated that integrated ML framework could accurately represent intricate interdependencies among process parameters on deposition outcomes, providing a robust method of predictive modeling and parametric process optimization for Inconel 625 deposition by WAAM process. The ANN model demonstrated satisfactory performance for forward modeling with MAPD values of 12.24, 14.87, and 11.91 for deposition geometry, deposition area, and deposition efficiency, respectively. For inverse modeling, the ANN accurately predicted key inputs from outputs, with MAPD values of 1.39, 18.91, 12.25, and 19.36 for voltage, wire feed rate, torch speed, and shielding gas flow rate, respectively. Bidirectional predictive modeling keeps to set operating conditions to achieve desired depositions and process automations.

Samanta, Avishek, Maji, Kuntal

Co-optimization of Energy Efficiency and Drivability for Dual-Motor Electric Commercial Vehicles: A Topology-Aware Hierarchical Approach

02-19-04-0026

7/29/2026

Dual-motor architectures provide additional operating degrees of freedom for electric commercial vehicles (ECVs), but the integration of automated manual transmissions (AMTs) introduces torque discontinuities during gear-related mode transitions. Existing energy management strategies usually focus on steady-state efficiency optimization, while the mechanical feasibility of mode transitions is often considered separately or neglected. To address this issue, this study proposes a topology-aware hierarchical control framework for dual-motor ECVs. The framework combines an offline global efficiency map with an online transition-feasibility arbitration mechanism. In the offline layer, the energy-oriented operating mode and torque split are extracted over the vehicle-speed and wheel-torque domain. In the online layer, a topology-based transition matrix is used to identify mechanically singular mode transitions, and potentially torque-interrupting commands are re-routed through feasible bridge modes. The proposed method embeds powertrain topology constraints into the real-time implementation of an offline optimal map, thereby complementing conventional global optimization methods with transition-feasibility arbitration. Simulation results under the CHTC driving cycle show that the proposed strategy improves torque continuity during mode transitions while retaining most of the energy-saving benefit of the unconstrained efficiency-oriented strategy. Compared with the rule-based strategy, the proposed method reduces SOC-equivalent energy consumption by 10.7%, and recovers 65.5% of the DP-achievable energy-saving potential. Hardware-in-the-Loop (HIL) results further demonstrate that the proposed online arbitration logic can be executed within the controller sampling period.

Song, Dafeng, Chen, Lexin, Zeng, Xiaohua, Ni, Lixin

Simulation and Experimental Evaluation of a Vehicle–Road Cooperative Positioning Method in Global Navigation Satellite Systems (GNSS)-Denied Environments

12-09-04-0029

7/29/2026

In complex urban environments, vehicle positioning based on Global Navigation Satellite Systems (GNSS) is prone to failure or accuracy degradation due to signal blockage and multipath effects. To address this issue, this article proposes a vehicle–road cooperative positioning method based on factor graph optimization for GNSS-denied environments and evaluates its performance through both simulation and real-vehicle experiments. In the proposed approach, road codes deployed on the road surface serve as absolute position references on the road surface, and high-precision vehicle position estimates are obtained in real time by fusing roadside positioning information with onboard sensor measurements using factor graph optimization. Furthermore, to reduce the experimental cost and development cycle of the vehicle–road cooperative positioning method, a performance simulation platform is developed based on the CARLA simulator, RoadRunner, and the CARLA-ROS (Robot Operating System) bridge for real-time communication between simulation and positioning modules. The platform supports road code generation and deployment, customized scenario construction, and positioning performance simulation, and a multi-objective optimization approach is employed to obtain an optimal deployment scheme for road code spacing. Finally, the effectiveness of the proposed vehicle–road cooperative positioning method is validated through both simulation and real-vehicle experiments. Real-vehicle experiments show that the proposed method reduces the average RMSE (Root Mean Square Error) by 26.6% compared with the ESKF (Error State Kalman Filter) baseline, achieving an RMSE of 0.25 m and a maximum error of 0.95 m at a vehicle speed of 60 km/h and a 10 m code spacing, thereby confirming decimeter-level continuous accuracy in GNSS-denied environments.

Shen, Chuanfu, Zhao, Zhiguo, Yan, Danshu, Ling, Yubin

Systematic Assessment of State-of-the-Art Approaches Available for Variable Camber Morphing

01-19-01-0005

7/29/2026

Aerodynamicists around the globe are developing mechanisms and structures inspired by nature that enable variable camber morphing (VCM) for aerodynamic surfaces. The implementation of the VCM mechanism in an airplane wing enhances the performance and stability during various flight segments. The present review article is focused mainly on the up-to-date VCM methods in a qualitative as well as quantitative approach that are specific to Aircraft/unmanned aerial vehicle (UAV) wing configurations. Initial literature discussions are confined to the conventional mechanisms that enable VCM in different aircraft configurations and the added aerodynamic advantages such as lift enhancement, drag reduction, boundary layer separation, and flow control. However, those designs need either external shape optimization or internal structural refinements to ensure the factor of safety (FoS). The modern aviation industry is also focused on bioinspired technology because of the adaptive flying capabilities and stall-delay characteristics. Therefore, a review of bioinspired VCM methods that are assessed based on the aerodynamic potentials is sequentially organized in the article. Additionally, considerations are motivated by the application of various compliant structural patterns for VCM in the aircraft industry. The discussion indicates the prospective benefits of morphing toward the future of the Green Aviation industry.

Manjunath, S. V., Jini Raj, R.

Comparison of Female and Male Post Mortem Human Subject Kinematic Responses and Injuries in Rear-Facing Seat Configurations in High-Speed Frontal Impacts

2026-22-0007

7/28/2026

This study investigates female post-mortem human subject (PMHS) responses and injuries, comparing them to previously published data from male PMHS tested at a change in velocity (delta-V) of 56 kph in high-speed rear-facing frontal- impact (HSRFFI) scenarios. Twelve small female PMHS were subjected to the same HSRFFI pulse. The subjects were positioned in reinforced production seats, identical to those from the previous male PMHS studies, and set to recline angles of either 25 or 45 degrees. Instrumentation was used to measure kinematics of the head, spine, pelvis, and ribs. Whole-body kinematics were recorded using motion capture. Female PMHS consistently showed lower head restraint, seatback, and lap belt loads compared to males across all test conditions (Bio Rank System [BRS] scores >1.0), with BRS scores for head restraint forces as high as 4.0. While head and T1 kinematics were consistent with males in all-belt-to-seat (ABTS) conditions (BRS < 1.0), significant differences were found in other body regions (BRS>1.0). Female PMHS had larger head forward rotation and smaller pelvis Z-axis displacement (less ramping) than males in the fixed D-ring (FDR) conditions, leading to major discrepancies (BRS>2.0). In addition, female chest deflection was smaller in one FDR condition (BRS=1.98), and tibia acceleration onset was earlier. Female PMHS sustained severe to critical rib fractures (Abbreviated Injury Scale [AIS]3-5) similar to males. However, five females in the FDR conditions and one in the ABTS condition sustained sacral fractures, an injury not seen in males. Females also had a higher frequency of lower extremity fractures (7 of 12) and vertebral body fractures (7 of 12) compared to males. These findings suggest that existing male PMHS data may not adequately predict responses and injury risks for female PMHS, emphasizing the need for female-specific biomechanical data to enhance safety tools and models in HSRFFI scenarios.

Kang, Yun-Seok, Baker, Gretchen H., Ramachandra, Rakshit, Marcallini, Angelo, Kwon, Hyunjung, Foster, Craig D., Moorhouse, Kevin, Agnew, Amanda M., Bolte, John H.

Model-Based Optimization and Evaluation of Hybrid Powertrains for Commercial Heavy-Duty Class 8 Truck Applications

02-19-04-0025

7/27/2026

In an ever-evolving landscape of emission regulations, charging infrastructure, customer demands, fuel/energy costs and decarbonization goals, heavy-duty on-road vehicle manufacturers continue to evaluate alternative powertrain technologies. While most heavy-duty vehicle manufacturers now have battery electric vehicles (BEVs) in their portfolio, significant challenges of charging infrastructure, range anxiety, payload capacity reduction and upfront costs have contributed to their lower adoption rates. Plug-in hybrid electric vehicles (PHEVs) have significant potential of leveraging upcoming BEV infrastructure and component supply chains to reduce operating costs while still maintaining longer range and payload capacity benefits of conventional ICE powertrains. This article applies a model-based approach to evaluate multiple Class 7–8 heavy-duty powertrain configurations. A system-level (1D) model of the conventional diesel ICE-based truck was developed in GT-Suite and validated against on-road test data. Using the diesel ICE model as a baseline, system-level models for different hybrid configurations were adapted, and their powertrain architecture was optimized at the system level. Additionally, an equivalent consumption minimization strategy (ECMS) for energy management was also optimized for each of the hybrid powertrain configurations to maximize fuel efficiency and emission benefits. All the hybrid configurations were then compared against the conventional diesel ICE Class 8 truck in terms of performance (acceleration, top speed, gradeability and startability), fuel economy (real-world cycles and certification cycles), emissions, and range for long-haul applications. Unique to this approach is the simultaneous co-optimization of powertrain component sizing and supervisory control logic by utilizing a Genetic Algorithm–based optimization approach. Results indicate that all parallel hybrid architectures (P2, P2–P3, and P4) achieve performance (acceleration, top speed, gradeability, and startability) parity or improvement compared to baseline diesel architecture. P2-based architectures demonstrated a 12–14% improvement in fuel economy on representative real-world cycles when operating in a blended charge-depleting–charge-sustaining mode of operation, and a 4–6% improvement in fuel economy when operating in charge-sustaining mode alone. By quantifying these results across diverse topologies, this work addresses a significant research gap in the holistic evaluation of Class 8 hybrids, specifically, the trade-off between multi-speed electric drives, system-level mass increases, and real-world fuel economy, that remains underexplored in current literature.

Baburaj, Adithya, Paul, Sumit, Dhanraj, Fnu, Joshi, Satyum, Franke, Michael

Efficiency Modeling, Experimental Validation, and Machine Learning-Assisted Model Calibration of a Two-Speed E-Drive Transmission for Construction Equipment

02-19-03-0019

7/27/2026

This study details the development and experimental validation of a high-fidelity one-dimensional (1D) simulation model for a two-speed transmission designed for off-road vehicles, such as tractors and backhoe loaders used in agricultural and civil engineering applications. The model, implemented in the AMESim platform from Siemens, integrates physics-based loss sub-models for all major components, including gears, bearings, seals, and fluid drag (churning) losses. After development, the model was rigorously validated against test bench data, with efficiency measurements taken across various speed, torque, and oil level combinations, demonstrating a strong correlation with experimental results. A detailed analysis enabled the quantification of the contribution of each loss mechanism, identifying the countershaft gears and input shaft bearings as the primary contributors. Furthermore, a Machine Learning (ML)–based calibration framework, employing Bayesian Optimization, was implemented to reduce discrepancies between simulation and experiment and to generate a synthetic dataset for the creation of fast-executing surrogate models. The study concludes that the proposed methodology constitutes an effective tool for efficiency analysis and optimization during early design stages, establishing a foundation for future integration with ML techniques and the development of digital twins.

Ferreira, Tiago Simao, Fallahi, Farzad, Kedziora, Slawomir, Hichri, Bassem, Kiefer, Jean-Daniel

3D Computational Fluid Dynamics Analysis of the Performance and Emissions of an Off-Highway Port Fuel Injection Hydrogen Engine

03-19-04-0018

7/27/2026

To fulfil the global aspiration of achieving net-zero emissions, hydrogen as a fuel seems to be one of the promising candidates. High energy density per unit mass and zero carbonaceous emissions are the two salient advantages that hydrogen offers. In the present study, a set of detailed chemistry-based 3D CFD combustion simulation has been carried on a 3-cylinder turbocharged, water-cooled port fuel injection SI Hydrogen engine to understand its optimum air–fuel ratio, compression ratio, spark timing and combustion chamber geometry. The simulations have been conducted at the full load of the rated power and maximum torque engine rpms. During simulation, the λ zone for study is restricted between 2.1 and 2.7. Two different bowl geometries (spherical and cylindrical), with two compression ratio options (12 and 14) are explored in the simulations. While the spherical bowl seems to accommodate flame front better than the cylindrical bowl, the compression ratio of 12 is a safer choice to control the maximum rate of pressure rise (dp/dθ). At full load and rated speed, the indicated thermal efficiency drops by 7.7% as the λ swings from 2.1 to 2.7, whereas the indicated specific NOx and dp/dθ drop by 99% and 81%, respectively. Similarly, at full load and maximum torque RPM, the indicated thermal efficiency drops by 6.4% with λ swing from 2.1 to 2.7, whereas the indicated specific NOx and dp/dθ drop by 99% and 91%, respectively. Beyond λ = 2.4 NOx reaches almost to zero, however, at a compromise of the thermal efficiency. The dp/dθ remains well within the acceptable limit under this scenario. To account this trade-off between the performance and emission parameters, optimum λ zone has been found out to be between 2.3 and 2.5.

Satre, Santosh Dadasaheb, Mukherjee, Nalini Kanta, Kumar, Sanjeev, Nene, Devendra

Geometric Deep Learning–Based AI Framework for Nonlinear Crash Behavior of Extruded Rails

15-19-03-0012

7/27/2026

Extruded Rails are critical energy-absorbing components in automotive structures designed to mitigate impact loads during the frontal collisions. Traditional crashworthiness design relies heavily on computationally expensive finite element simulations and iterative design exploration. This work proposes a machine learning–driven framework for rapid front extruded rails design using a trained geometric deep surrogate model. A design-of-experiments (DoE) was conducted by varying geometric parameters including width, height, and wall thickness of a thin-walled extruded rail structure. For each design variant, LS-DYNA simulations were performed to obtain performance metrics such as mean crush force and peak crush force. These simulation results were used to train an AI surrogate model capable of predicting crash responses directly from geometric parameters. The proposed approach significantly reduces computational cost by replacing repeated high-fidelity crash simulations with machine learning surrogate predictions. By enabling fast and accurate evaluation of crash response metrics, the workflow shortens design cycles and supports sustainability-driven crashworthiness assessment by reducing simulation resource usage. The framework establishes a scalable, simulation-driven engineering pathway across vehicle platforms and provides a foundation for future closed-loop, AI-assisted crash design workflows.

Kumar, Manik, Srinivasan, Sriram

An Updated Data-Driven Greenhouse Gas Emission Rate Analysis for Vehicle Comparisons Applied to Europe

14-15-03-0016

7/25/2026

This study presents a data-driven lifecycle assessment (LCA) framework for evaluating greenhouse gas (GHG) emissions from passenger vehicles across European electricity systems. The analysis compares battery electric vehicles (BEVs), full hybrid electric vehicles (FHEVs), and internal combustion engine vehicles (ICEVs) using both conventional average electricity emissions factors and time-resolved marginal emissions, referred to as real charging emissions (RCE). Hourly generation and interconnector/cross-border flow data for 2023 from 29 European countries are processed to estimate consumption-based marginal emissions rates that account for grid dispatch behavior and cross-border electricity flows. The approach is applied to two vehicles where multiple powertrains are available on the same platform, the 2024 Hyundai Kona (available as a BEV, FHEV, and ICEV) and Peugeot 2008 (available as a BEV and ICEV), to isolate drivetrain-related lifecycle differences. Results show substantial divergence between average and marginal emissions estimates, with a mean absolute difference in BEV–FHEV lifecycle emissions of 31–36 g CO2 eq/km across Europe. In several countries with carbon-intensive marginal generation, including Poland and Cyprus, BEVs may exhibit higher lifecycle emissions than comparable hybrids, while low-carbon grids such as Norway, Sweden, and France provide large BEV advantages. Sensitivity analyses demonstrate the importance of transmission losses, temperature effects, electricity imports, and charging timing. These findings highlight the limitations of average grid emissions factors in vehicle LCAs and underscore the importance of geographically and temporally resolved data-driven electricity emissions when assessing electrified vehicle climate impacts.

Drew, Alfred, Burton, Tristan, Senecal, Kelly, Davy, Martin, Leach, Felix

Coupled Lateral–Longitudinal Trajectory Tracking Control for Autonomous Vehicles Based on Multi-Agent Reinforcement Learning

12-09-03-0024

7/23/2026

Trajectory tracking control serves as the core operational component of autonomous vehicles, directly determining driving safety and passenger comfort by ensuring control precision and stability. To enhance the tracking accuracy and stability for autonomous vehicles, this study proposes a coupled lateral–longitudinal trajectory tracking controller based on multi-agent reinforcement learning. The framework first establishes a Model predictive controller (MPC) derived from vehicle dynamics, formulating the lateral control process as a Markov decision process. A reward function incorporating lateral error, heading error, and steering angle is designed, followed by the construction of a Deep Q-Network (DQN) Agent to optimize the prediction horizon of the MPC. Subsequently, a position–velocity dual-loop PID controller is developed for longitudinal control, with its parameter optimization strategy learned through a Deep Deterministic Policy Gradient (DDPG) Agent. The Extended State Observer (ESO) is incorporated to perform steering angle compensation for internal modeling errors and external disturbances. Co-simulation experiments are conducted in CarSim and MATLAB/Simulink, and the results demonstrate that the coupled controller achieves superior tracking accuracy and stability in both overtaking and lane-changing scenarios compared with the decoupled controller.

Kun, Feng, Jinxiang, Zhai, Li, Wenli

A Review of Physical and Digital Infrastructure Standards and Guidelines to Support Deployment of Automated Vehicles in Rural Environments

12-09-04-0030

7/23/2026

Connected and Automated Vehicles (CAVs) represent a transformative innovation poised to revolutionize roadway transportation by leveraging automated driving systems equipped with advanced sensors, high-performance computing, and communication technologies. While urban areas are the primary focus of current CAV developments, rural transportation systems risk being left behind despite the significant benefits that CAVs can bring to these regions. This article, therefore, explores the physical and digital infrastructure requirements for the safe deployment of CAVs in rural areas, drawing insights from standards, recommendations, and guidelines developed by leading standard organizations. The study highlights the specific design of physical infrastructure, including traffic signs, traffic signals, and pavement markings, and digital infrastructure, including communication, sensing, and mapping, to ensure rural communities are effectively prepared to benefit from the potential of CAVs. As its primary contribution, this article provides a comprehensive review of existing standards and guidelines relevant to rural CAV deployment. By synthesizing guidance across multiple standard-setting organizations, this review delivers a structured analytical assessment of existing standards, revealing their limitations and misalignment with rural transportation contexts while highlighting emerging good practices. The article clarifies the applicability of current guidance to rural infrastructure, identifies systemic infrastructure-related failure modes, and informs context-aware planning considerations for efficient and scalable CAV deployment in rural areas.

Zakaria, Mohammed, Getahun, Tesfamichael, Tavasoli, Mahsa, Pandey, Venktesh, Sarrafzadeh, Abdolhossein, Karimoddini, Ali

Effect of Aftermarket Suspension Modifications on ADAS Functionality of Five Light Vehicles

12-09-04-0031

7/15/2026

Advanced Driver Assistance Systems (ADAS) are increasingly prevalent in light vehicles, both in the United States and worldwide. Moreover, ADAS are steadily being incorporated into regulatory requirements globally. Like ADAS, the automotive aftermarket is also increasing in size and significance. As both ADAS and the aftermarket industry are growing, the effect of aftermarket modifications on ADAS functionality should be examined. However, there is very little information available in the public domain about the effect of aftermarket modifications on original equipment ADAS. This work is centered on a considerable research project that was conducted to address the knowledge gap at the intersection of ADAS and the aftermarket. The project investigates five light vehicles that are important to the aftermarket, including four pickup trucks and one sport-utility vehicle. It focuses solely on the effect of popular aftermarket suspension modifications, and it does not evaluate aftermarket ADAS equipment. Typical suspension modifications were applied to the test vehicles in five modification categories, including stock, lower kits, level kits, 3–4 in. lift kits, and 6 in. lift kits. Six ADAS test procedures were performed for the test vehicles, comprised of blind spot detection, crash imminent braking, lane departure warning, pedestrian automatic emergency braking, rear cross traffic alert, and traffic jam assist. The physical tests were developed based on National Highway Traffic Safety Administration (NHTSA) New Car Assessment Program (NCAP) written experimental procedures. Statistical hypothesis testing was performed for the purpose of determining if average measured dynamic responses varied in the modified vehicles compared to stock. The results show that vehicles modified with typical aftermarket modifications will likely retain their ADAS functionality, given the limitations of the small sample size of five vehicles. Vehicles with 6 in. lift kits are expected to exhibit greater variability in their dynamic responses compared to stock. Plans for future work and unanswered research questions are outlined, with the goal of advancing aftermarket ADAS integration and ensuring the safety and performance of modified vehicles.

Bastiaan, Jennifer M., Morales, Luis, Muller, Mike

ERRATUM: Ergonomic Assessment of Glove-Box Design in Passenger Vehicles for the Indian Scenario

15-19-02-0009.1

7/15/2026

ERRATUM

Jujjavarapu, Sreeram, Rajakumaran, Sriram, Kota, Srinivas, Kotkunde, Nitin, Jasti, Naga Vamsi Krishna

Numerical Investigation of Inverted Airfoils in Ground Effect for Motorsport Applications

01-19-01-0004

7/15/2026

Ground effect plays a critical role in enhancing the aerodynamic performance of race cars by increasing downforce without a proportional rise in drag. Despite its importance, the influence of airfoil geometry on inverted airfoils operating in ground proximity remains underexplored in open literature. This study addresses this gap through a detailed numerical investigation of chord-dominated ground effect using two-dimensional Reynolds-Averaged Navier–Stokes (RANS) simulations. A range of NACA four-digit airfoils is systematically analyzed to isolate the effects of camber, thickness, and camber location on aerodynamic performance in ground proximity. Results show that increased camber enhances downforce and efficiency both in and out of ground effect; thinner airfoils yield higher downforce and efficiency in ground effect; and forward camber locations outperform rearward ones in maximizing downforce contrary to out-of-ground-effect trends. Detailed pressure distribution and flow separation analyses explain the underlying mechanisms, offering actionable guidelines for optimizing ground effect airfoil design in motorsport.

Chowdhury, Rohan, Shukla, Dhwanil

On Disturbance-Aware Minimum-Time Trajectory Planning: Evidence from Tests on a Dynamic Driving Simulator

10-10-03-0026

7/13/2026

This work aims to investigate how disturbance-aware, robustness-embedding reference trajectories translate into actual driving performance when executed by professional drivers in a dynamic driving simulator. The study compares three planned reference trajectories against a free-driving baseline (NO-REF) to assess the trade-offs between lap time (LT) performance and steering effort: NOM, the nominal time-optimal trajectory; TLC, a track-limit-robust, time-optimal trajectory obtained by tightening margins to the track edges; and FLC, a friction-limit-robust, time-optimal trajectory obtained by tightening against axle/tire saturation. All reference trajectories share the same minimum LT objective with a small steering-smoothness regularizer, and are evaluated with two professional drivers driving a high-performance car on a virtual track. The reference trajectories stem from a disturbance-aware minimum-LT framework recently proposed by some of the authors, where worst-case disturbance growth is propagated over a finite horizon and used to tighten tire-friction and track-limit constraints, preserving performance while delivering probabilistic safety margins. LT and steering energy (SE) are evaluated as indicators of driving performance and steering effort, respectively, while RMS values of lateral deviation, speed error, and drift angle are used to characterize driving style. The results reveal a Pareto-like trade-off between LT and SE: NOM achieves the shortest LT, but with the highest SE, TLC minimizes SE at the expense of longer LT, while FLC lies near the efficient frontier, markedly reducing SE relative to NOM with only a minor LT increase. Removing reference trajectories (NO-REF) leads to both higher SE and longer LT, confirming that trajectory guidance improves pace and control efficiency. Overall, the findings highlight reference-based and disturbance-aware planning, particularly the FLC variant, as effective tools for training and for achieving fast yet stable trajectories.

Masoni, Matteo, Palermo, Vincenzo, Gabiccini, Marco, Gulisano, Martino, Previati, Giorgio, Gobbi, Massimiliano, Comolli, Francesco, Mastinu, Gianpiero, Guiggiani, Massimo

Fuel Savings from Predictive Charge Control Based on Short-Trip Duration in Series Hybrid Electric Vehicles

14-15-03-0015

7/10/2026

Series hybrid electric vehicles (HEVs) employ an electric motor for propulsion, while the internal combustion engine operates solely as a generator under energy-efficient speed and load conditions. Owing to this architecture, series HEVs can achieve high fuel efficiency with a relatively simple control structure. However, conventional energy management systems (EMSs) often prioritize battery state-of-charge (SOC) stabilization, which can lead to frequent engine start–stop operations and unnecessary fuel consumption, particularly in short-trip driving. This study aims to enhance energy management performance in series HEVs by optimizing engine power generation timing based on predicted short-trip duration. A computationally efficient, rule-based prediction model is developed using real-world driving data, in which short-trip duration is estimated from vehicle speed and acceleration. Due to its low computational load, the proposed model is suitable for implementation in an onboard electronic control unit (ECU). The proposed control strategy initiates engine power generation when the battery SOC is low and the predicted trip duration is long, and suppresses generation when the SOC is sufficiently high or the predicted trip is short. A detailed vehicle model incorporating an engine, generator, electric motor, inverter, and battery is developed in Modelica to evaluate the proposed strategy. Simulation results demonstrate that the proposed EMS significantly reduces the frequency of engine start–stop events, leading to fuel economy improvements of 3.6% under the WLTC (excluding the extra-high phase) and 13.4% in a real-world urban–rural driving cycle, compared with a commercialized baseline vehicle. These results confirm the effectiveness and practical applicability of the proposed EMS for passenger vehicle applications.

Mizushima, Norifumi, Sato, Akira, Kuboyama, Tatsuya, Moriyoshi, Yasuo

Steering System Fault Diagnosis and Fault-Tolerant Control of Active Wheel-Corner Electric Vehicles

10-10-04-0036

7/9/2026

The Active Wheel-Corner (AWC) integrates driving, braking, steering, and suspension systems into the wheel end, forming a fully drive-by-wire, four-wheel independent steering and four-wheel independent driving (4WIS&4WID) vehicle platform. While improving vehicle control performance, the full by-wire architecture also places higher demands on system reliability and fault tolerance. The steer-by-wire system has electrical, communication, and software failure risks, which may cause the vehicle to lose steering capability and trigger severe traffic accidents. This article proposes a hierarchical active fault-tolerant control strategy based on fault information reconstruction (FAST-FTC), enabling fault diagnosis and active fault-tolerant control when the steer-by-wire system fails, effectively ensuring the steering maneuverability and lateral stability of the vehicle under fault conditions. First, the strategy designs an adaptive observer combined with the Dugoff tire model to estimate nonlinear tire forces, while introducing a fault factor to achieve quantitative grading of steering system faults. Second, a hierarchical controller is designed for steering system faults. The upper-level controller, based on Adaptive Super-Twisting Sliding Mode Control (AST-SMC), determines the generalized forces required to track the desired trajectory under different fault conditions. The lower-level controller, based on the Fault-Aware Model Predictive Control (FA-MPC) strategy, dynamically adjusts weight matrices according to the fault factor and tire reconstructed stiffness, coordinating the allocation of four-wheel driving and the steering of healthy wheels to ensure lateral stability. Finally, the effectiveness of the proposed active fault-tolerant control strategy is validated through Hardware-in-the-Loop (HIL) simulation and real-vehicle tests.

Xiao, Feng, Jiang, Yueyong, Cheng, Rui, Xu, Changhe, Tang, Xiangjiao, Gao, Fengling, Li, Jianhua

Crashworthiness of WC19 Wheelchairs in Vertical Dynamic Aircraft Seating Tests

09-14-02-0004

7/9/2026

Currently, people who use wheelchairs are not permitted to use their own wheelchairs as seats on commercial aircraft. To advance equitable aircraft travel for these passengers, we need to determine whether wheelchairs would be safe seating for their occupants and not pose a safety hazard for other passengers in case of emergency landing. We hypothesized that wheelchairs meeting the voluntary standards for vehicle crashworthiness (Rehabilitation Engineering Society of North America [RESNA] Section 19 Wheelchairs Used as Seats in Motor Vehicles [WC19]) would be able to pass the Federal Aviation Administration (FAA) vertical crashworthiness standards for aircraft seating. Wheelchairs were secured using surrogate 4-point strap tiedowns using the geometry specified by WC19. The FAA Hybrid III anthropomorphic test device (FH3 ATD) was restrained by both the wheelchair-attached lap belt and a vehicle-mounted lap belt identified as necessary to pass FAA dynamic horizontal test requirements. For the dynamic vertical testing with the wheelchairs oriented 60 degrees relative to horizontal, modeling demonstrated the suitability of using the trapezoidal pulse achieved with the UMTRI sled produced rather than the typical triangular shaped FAA pulse. Of the five manual and three power wheelchairs tested, four had broken components that would not impede emergency exit, four did not have visible damage, and the FH3 remained within the seat in all tests. The three power wheelchairs did not meet lumbar compression requirements. Based on these results, it may be feasible for people to use their own WC19-compliant wheelchairs on aircraft when secured to the aircraft with 4-point strap tiedown systems, supplemented by an occupant lap belt anchored to the aircraft, notwithstanding the lumbar force requirement.

Manary, Miriam A., Orton, Nichole Ritchie, Vallier, Tyler, Boyle, Kyle J., Klinich, Kathleen DeSantis

A Framework for Precautionary Safety for Automated Driving Systems—Toward Tactical Decisions Fulfilling Quantitative Risk Acceptance Criteria

12-09-04-0028

7/8/2026

Safety of Automated Driving Systems (ADSs) is arguably one of the main remaining barriers before widespread market deployment. While there exists a plethora of methods for planning a trajectory that fulfils certain constraints, what those constraints should look like, to enable effective planning of safe trajectories, is still being discussed. In this article, we generalize the concept of Precautionary Safety (PCS) and present a framework providing constraints on the tactical and operational decisions of the ADS. Such constraints consider the ADS’ capabilities, the external conditions, knowledge of statistically relevant events and behaviors of other traffic actors, as well as the controllability of these events. The proposed framework enables assessment of the statistical fulfilment of quantitative risk acceptance criteria (QRACs), including requirements on accident, injury, and fatality rates. The framework further provides a means to dynamically adapt the constraints used for trajectory planning, i.e., to adapt the driving to the situation at hand. A case study, considering a possible collision scenario with a jaywalking pedestrian and a rear-end collision with a trailing vehicle, is provided to showcase the applicability and usefulness of the presented framework. The simulation-based case study displays the safety benefits from considering QRACs with multiple injury risk levels and further shows how the proposed PCS framework can be applied in practice.

Gyllenhammar, Magnus, de Campos, Gabriel Rodrigues, Sandblom, Fredrik, Törngren, Martin, Fredriksson, Jonas

Average-Differential-Coordinates-Based Variable Slip Ratio Control of Electrified Vehicles with Torque Vectoring Differential

10-10-04-0035

7/7/2026

These days, the vehicle dynamics control of electric vehicles (EVs) with multi-actuated architectures has been widely investigated. Such EVs have a torque vectoring differential (TVD), which can generate a torque difference between the left and right wheels. As one of TVDs, a two-motor-torque difference amplification mechanism (TDA-TVD), has been proposed. The TDA-TVD can generate a greater torque difference compared to an individual-wheel-drive (IWD) system. However, it has controllability difficulties due to its two resonance modes. Previous studies first proposed a frequency response model of the TDA-TVD and anti-vibration feedforward torque controllers based on an average-differential coordinates (ADC) transformation. Subsequently, wheel speed control (WSC) and slip ratio control (SRC) based in the ADC were presented. However, only the WSC was designed with frequency domain analysis, and the SRC was designed with manual tuning. In this study, the closed loop of the SRC of the TDA-TVD is modeled in the frequency domain, and a parameter determination method based on Nyquist plot and sensitivity function analysis of the SRC, which is the outer loop of the WSC, is suggested. Next, several SRC strategies are proposed, depending on the driver’s preference. Lastly, experimental results using a real vehicle with the TDA-TVD on slippery surfaces are shown. Newly proposed and conventional SRCs are compared. The effectiveness of the proposed strategies is analyzed and presented.

Fuse, Hiroyuki, Fujimoto, Hiroshi, Sawase, Kaoru, Takahashi, Naoki, Takahashi, Ryota, Hayashi, Takayuki

A Methodology for the Conversion from Physics-Based to Ultrafast-Running Data-Driven Powertrain Models for Real-Time Applications

03-19-04-0017

7/3/2026

The virtualization of powertrain systems is a key enabler for modern powertrain development. While physics-based 0D/1D simulation models provide accuracy and interpretability, these models are typically computationally demanding, prolonging the development process and usage throughout the V-cycle. Moreover, achieving real-time-capable simulation models through model simplifications remains challenging, as it often leads to significant losses in accuracy. In contrast, data-driven approaches can achieve high computational efficiency without significantly compromising model accuracy. This opens the possibility for not only online control applications, such as model predictive control or reinforcement learning, but also for computational expensive offline control prototyping using ultrafast-running data-driven digital twins. This work focuses on the elaboration of a scalable methodology for the development of ultrafast-running powertrain models for stationary and transient engine operation. This includes the efficient generation of training data with great variance, data analysis, and preparation, an optimized partitioning method using the Jensen–Shannon distance, feature engineering, model training of a multilayer perceptron (MLP), a long short-term memory (LSTM), and gated recurrent unit (GRU) network, followed by the model evaluation using test data and the concluding model deployment. In order to demonstrate the concept, a calibrated 0D/1D model of a dual-fuel marine main engine provided by WinGD Ltd. for a pure car and truck carrier is utilized as the reference physics-based model. The case study provides a comprehensive examination of the development of ultrafast-running data-driven fuel consumption models in both stationary and transient engine operation. The results show that the proposed methodology yields robust results and minimizes the loss of accuracy to 1.80%–2.14% for the MLP predicting the steady-state fuel consumption and to 0.67%–0.96% (GRU) and 1.52%–1.68% (LSTM) for predicting the transient fuel consumption, while achieving a multiple 104-fold reduction of the real-time factor (RTF) on an identical CPU.

Weller, Louis, Zanelli, Alessandro, Yang, Qirui, Brutsche, Martin, Grill, Michael, Kulzer, André Casal

A Multiphysics Approach to Mitigate Pulse-Width-Modulation-Induced Acoustic Noise in Permanent Magnet Synchronous Motor Drives for Electric Vehicles

14-15-03-0014

7/3/2026

This article investigates high-frequency noise in permanent magnet synchronous motors (PMSMs) for electric vehicles, originating from pulse width modulation (PWM). A theoretical model is developed to formulate the phase voltage under space vector PWM (SVPWM), explicitly accounting for the additional harmonic components generated by the discrete-time voltage update in digital control systems. This derived voltage waveform serves as the excitation source in an electromagnetic finite-element model, from which the PWM current harmonics and their resulting high-frequency electromagnetic forces are computed. Critical components of the electromagnetic force are then extracted through two-dimensional Fourier transform. A structural model of the motor, incorporating practical assembly constraints, is established and validated by experimental modal tests on a fully assembled motor unit. To enable rapid noise prediction over the wide speed range, vibro-acoustic transfer functions are introduced. The predicted noise shows good agreement with experimental data. Leveraging this multiphysics model, the influence of switching frequency on noise characteristics is analyzed. The study identifies that avoiding excitation of the motor’s zero-order mode is critical for noise suppression. Accordingly, an optimal frequency-hopping strategy is proposed. Experimental validation confirms the strategy’s effectiveness in reducing noise over the wide speed range.

Lin, Fu, Chen, Yihui

The Influence of Spatial and Aspatial Factors on the Safety Driving Behavior of Motorcycle Couriers in the Gig-Economy Era: A Case Study of East Jakarta, Indonesia

09-14-02-0005

7/3/2026

This study aims to analyze the impact of spatial and aspatial factors on the safety driving behavior of motorcycle couriers in East Jakarta within the context of the gig economy. Both factors are integrated to clarify how spatial conditions and individual characteristics jointly shape couriers’ safety driving behavior. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method was employed to examine the relationship between spatial and aspatial factors on safety driving behavior. Data were collected through questionnaires from 253 motorcycle couriers operating in three subdistricts in East Jakarta, namely Cakung, Pasar Rebo, and Pulo Gadung. The results show that safety driving behavior is significantly influenced by aspatial factors, particularly socioeconomic characteristics and personality traits. In contrast, spatial factors such as road conditions and daily activity patterns do not directly influence safety driving behavior, but exert indirect effects through the couriers’ personality traits.

Wahyuddin, Yasser, Sitorus, Paldibo Alfriramson, Putri, Kharunia, Maharani, Garnierita

A Transition Curve-Based Design Method for the Recirculation Channel in Internal Recirculating Ball Screws

15-19-03-0011

7/1/2026

Internal recirculating ball screws are widely used as linear motion components in automotive active safety systems, owing to their simple structure and compact size. The recirculation (or deflection) channel is a key feature that distinguishes this type from other ball screw designs. The objective of this article is to investigate this key feature that has been rarely addressed in existing research on internal ball screw. The conventional design method for the recirculation channel involves sweeping the cross-section along the center curve. The center curve is typically defined by various classical equations. These equations are applied in different application scenarios. In automotive braking systems, high loads and strict size constraints place critical demands on both the recirculation channel and its center curve. As a representative best-practice example, the machined channel in the screw is typically employed in this application. This article compares several classical center curve equations and proposes a new general approach based on a family of transition curves. The mechanical analysis identifies the inherent structural characteristics of recirculation channel and develops corresponding design guideline. Furthermore, parameter optimization is performed using MSC ADAMS Multibody Dynamics (MBD) software.

Xia, Xinan, Xia, Yanzhe, Zhao, Tina

Assessing Geographical and Seasonal Influences on Energy Consumption and Driving Range for Electric Drayage Trucks

14-15-03-0013

6/29/2026

The electrification of heavy-duty vehicles is a critical pathway toward improved energy efficiency in the freight sector. The current battery electric truck technology poses several challenges to commercial vehicle operations, such as limited driving range, sensitivity to climate conditions, and long recharging times. Estimating the energy consumption of heavy-duty electric trucks is crucial to assessing the feasibility of fleet electrification and its impact on the electric grid. This article focuses on developing a model-based simulation approach to predict and analyze the energy consumption of electric trucks by considering the impact of weather and geographical conditions on vehicle road load and auxiliary components power consumption, as well as the impact these factors have on driving range. Specifically, drayage trucks employed in logistics around maritime ports are used as a case study, with consideration of seasonal climate variations and geographical characteristics at different locations. The article includes results for three major container ports within the United States, providing region-specific insights into the energy requirements and driving range of the electric drayage trucks in these regions, which will inform decision-makers in integrating electric trucks into the existing drayage operations and plan investments for electric grid development.

Shiledar, Ankur, Villani, Manfredi, Lucero, Joseph N. E., Sun, Ruixiao, Sujan, Vivek A., Onori, Simona, Rizzoni, Giorgio

Model-Guided Scenario Expansion for Data-Driven Autonomous-Vehicle Safety Testing: A Feasibility Demonstration

12-09-04-0032

6/29/2026

This article presents a data-driven pipeline for autonomous-vehicle (AV) safety testing. The pipeline integrates real-world traffic observations with model-guided scenario expansion and safety-metric evaluation to enable an end-to-end AV safety testing framework, demonstrated on a canonical highway scenario. The framework enhances test diversity, realism, and coverage by generating statistically informed variants of observed driving behaviors. Key parameters such as vehicle speed, trajectories, and headways are extracted from naturalistic data and used to train a probabilistic model of traffic dynamics. Scenario variants are sampled from this model and encoded as behavior trees (BTs) for modular, simulation-ready execution. Each scenario is simulated using a consistent AV control configuration, and safety metrics such as minimum safe distance violation, minimum safe distance factor, time to collision, and aggressive driving are applied to evaluate safety outcomes independently of system-specific tuning. A case study based on the highD dataset (110,000+ trajectories) demonstrates the framework’s ability to generate realistic and safety-relevant scenarios, providing an initial demonstration of pipeline feasibility and metric-based evaluation. This initial study is intentionally scoped to a single scenario class and a simplified parametric model to isolate and validate the end-to-end integration of the pipeline.

Elshenawy, Mohamed, Aboudina, Aya, Abdelmotaleb, Anhar, Amr, Mariam, El-darieby, Mohamed

Experimental Investigation of Fuel Cutout Strategies in a Heavy-Duty Gasoline Compression Ignition Engine

03-19-03-0015

6/26/2026

This study investigates Gasoline Compression Ignition (GCI), a family of advanced combustion strategies that can be used to achieve low engine-out criteria pollutant emissions in the heavy-duty transportation sector. In particular, high fuel stratification GCI (HFS-GCI) has been shown to have high thermal efficiencies while maintaining a highly controllable and responsive mixing-controlled combustion event. However, stable combustion at low loads has been shown to be the principal challenge to the implementation of HFS-GCI in production applications. It has also been observed that several strategies that achieve stable combustion at low loads result either in increased emissions or efficiency penalties. While the achievement and maintenance of high enough exhaust temperatures for efficient aftertreatment operation is a significant challenge at low loads even for traditional diesel engine operation, this challenge is exacerbated by the low reactivity and colder flame temperature of gasoline. In recent single-cylinder and 1D simulation studies, fuel cutout strategies have been proposed as an enabling strategy to simultaneously improve combustion stability at low loads and increase exhaust temperatures. In this study, fuel cutout strategies are studied in a prototype multicylinder heavy-duty GCI engine based on a Cummins ISX15 diesel engine. Steady-state engine studies are conducted at warm and cold idle conditions to identify combinations of cylinders that provide the most benefit. NOx and soot limits are set and the performance of cutout strategies are compared to a pre-optimized baseline. The most optimal strategies from steady-state testing are then implemented under transient test cycle conditions similar to those required under United States regulatory testing. The strategies were found to offer simultaneous improvements in stability, fuel consumption, criteria pollutants, and turbine outlet temperature. The choice of cylinders whose fuel supply was cut was seen to be important in realizing the observed benefits. The use of fuel cutout strategies offered optimal performance at all the conditions considered, offering an additional lever to improve the performance of HFS-GCI and highlighting a promising pathway to the use of gasoline-like fuels as alternatives to diesel in heavy-duty engines.

Viswanathan, Aravindh Babu, Zhang, Yu, Merritt, Brock

Automosphere: Exploring Vehicle Heads-Up Display with Weather-Related Windshield Cues for Passive Fatigue Management

12-09-03-0025

6/26/2026

Passive fatigue can cause accidents with automated and regular vehicles. A proof-of-concept prototype [made with light-emitting diode (LED) matrices and white LED (WLED)] and a preliminary comparative usability test (N = 7) are used to study whether the active manipulation of simulated weather cues can be a potential countermeasure to passive fatigue. Participants rated system suitability, system impression, and their fatigue level similarly when they viewed a weather windshield heads-up display (HUD) versus a speedometer windshield HUD [no significant differences found and relatively small 95% confidence interval (CI) ranges around 0]. Qualitative analysis of interviews found that participants saw the potential value of the weather display and that display placement, dynamic graphics, and user activation were commonly mentioned themes. These results suggest the concept is theoretically possible, though further work is needed to prove the concept in practice.

Ensafjoo, Mohsen, Li, Jamy

Spray–Wall Interaction at Thermal Swing Coatings in SI Engines

03-19-03-0014

6/25/2026

As a contribution to the reduction of greenhouse gas emissions in the transportation sector, the indicated efficiency of SI engines can be increased via thermal swing coatings. Thereby, a decrease in greenhouse gas emissions can be achieved, although not at all operating conditions. Here, the often-observed increased hydrocarbon emission partially overcompensates the reduced wall heat losses. The main root cause is always attributed to the increased surface roughness and porosity, leading to an increased crevice volume. Further investigations were performed at a single-cylinder engine equipped with a FTIR for species analysis of hydrocarbon emissions. A comparison of direct injection and port fuel injection were performed for RON95 E10 and methanol to assess the influence of mixture preparation. 3D CFD was used to additionally investigate the in-cylinder processes. The comparison of port fuel injection and direct injection showed a significant influence on the fuel hydrocarbon emissions for the direct injection when the thermal swing coating was applied. The effect is more pronounced for methanol. For port fuel injection nearly the same or reduced fuel hydrocarbon emissions can be observed. This is mainly attributed to an increased wall film agglomeration at the piston for the thermal swing coating in case of direct injection, which can be observed in 3D CFD. Due to the low thermal effusivity of the coating, the droplet impingement leads to a notable decrease in the surface temperature. This results in lower evaporation of the fuel and a longer droplet lifetime. Consequently, a fuel wall film is still present at top dead center after ignition leading to additional hydrocarbon emissions.

Fischer, Marcus, Pischinger, Stefan

Impact of Network-Induced Delays on Autonomous Vehicle Steering Stability

10-10-03-0024

6/25/2026

This article presents a cross-layer framework that integrates realistic vehicle-to-network-to-vehicle (V2N2V) delay characterization with a rigorous stability analysis of automated vehicle steering control. Both constant and network-induced time-varying delays modeled via deterministic bounds are addressed. For constant delays, delay-independent stability regions within the controller gain space are analytically derived. For time-varying delays with stochastic network origins, modeled using deterministic bounds, a refined Lyapunov–Krasovskii functional (LKF) incorporating augmented single- and double-integral terms is constructed. To establish delay-dependent linear matrix inequality (LMI) conditions, a reciprocally convex combination approach is employed to handle the delay interval partitioning, and the second-order Bessel–Legendre inequality is applied to tighten the integral quadratic bounds. The resulting LMI conditions explicitly capture the coupled effects of delay magnitude, delay variation rate, and control gains on closed-loop stability. Simulations of a lane-keeping scenario confirm that the predicted stability boundaries accurately match the closed-loop system behavior. Notably, incorporating a realistic time-varying V2N2V delay profile into the controller design reduces the lateral-state root-mean-square error (RMSE) by over 54% and decreases the settling time by a factor of 10 compared to designs relying on an average-delay assumption. However, high packet loss rates are shown to still induce residual oscillations due to information scarcity. Ultimately, these results elucidate delay-induced instability mechanisms and provide practical guidelines for designing delay-robust steering controllers for connected and automated vehicles.

Li, Jialin, Lu, Jianwei, Wei, Heng, Ao, Di

BO-NRPPO: A Bayesian Optimization–Tuned Proximal Policy Optimization Framework for Semi-Active Suspension Control

10-10-03-0025

6/25/2026

Semi-active suspension systems enhance ride comfort and handling performance by adaptively modulating damping characteristics. However, conventional model-based controllers often fail to maintain optimal performance under uncertain and time-varying vehicle conditions. This article proposes Bayesian Optimization–Tuned Proximal Policy Optimization with Non-Parametric Rewards (BO-NRPPO), a novel reinforcement learning (RL) framework that integrates Bayesian Optimization (BO) with Proximal Policy Optimization (PPO) and a non-parametric reward function (NRF). The proposed approach enables adaptive self-tuning, data-driven reward shaping, and uncertainty-aware policy learning. Moreover, a Trapezoidal Simple Moving Average (TSMA)–based reward normalization scheme is introduced to accelerate convergence and stabilize training. Simulation results across diverse driving scenarios demonstrate that BO-NRPPO outperforms the passive suspension, the classical Linear Quadratic Regulator (LQR), and PPO with parametric rewards. Specifically, compared to the passive suspension and the LQR baseline, BO-NRPPO achieves up to 6.63% and 5.14% improvements in handling stability, respectively. Concurrently, it delivers maximum enhancements of 46.96% and 42.55% in ride comfort over these two baselines. For real-world vehicle applications, this adaptive self-tuning capability significantly reduces the time-consuming manual calibration efforts typically required in chassis development. Furthermore, Hardware-in-the-loop (HiL) validation confirms its real-time applicability and robustness under uncertain driving conditions, highlighting its immense potential as a scalable intelligent suspension control solution.

Chen, Guoying, Wang, Xinyu, Wang, Jiaqi, Zhan, Xinwang, Bi, Chenxiao, Cong, Shiqi, Hua, Min, Sun, Tianjun, Gao, Zhenhai

Female PMHS Thoracic Biomechanical Response Corridors with Preliminary Consideration of the Influence of Breast Tissue in Frontal Impacts

2026-22-0006

6/24/2026

Thoracic injuries are common for belted occupants in frontal motor vehicle crashes. However, there remains a lack of female post-mortem human subject (PMHS) data in the literature to generate female-specific biomechanical response corridors and evaluate engineering tools such as anthropomorphic test devices (ATDs) and computational human body models (HBMs). Additionally, the effect of breast tissue on thoracic response has not been directly investigated despite female ATDs and HBMs having features representing breasts. As such, this study sought to utilize simplified frontal hub impacts to (1) generate female PMHS thoracic response corridors both with breasts positioned with a bra and without breasts (no bra) and (2) preliminarily explore the influence of breasts on the thoracic responses of female PMHS. Twelve female PMHS (9 small and 3 midsize) were subjected to frontal impacts at mid-sternum with a 14.0 kg circular impactor at 4.3 m/s in conditions with and without breasts. Force versus deflection (FD) response corridors were generated, and comparisons were made between groups and to scaled FD corridors representing female response. Overall, female PMHS with and without breasts displayed differences in FD response compared to scaled corridors in terms of the shape of the initial response and peak force and deflection. Additionally, female PMHS with breasts produced lower peak force and greater peak deflection compared to those without breasts. These results suggest the importance of collection and evaluation of female biomechanical data that can be used for continued evaluation of female-specific safety tools as well as the further reduction of injury risk for all occupants during motor vehicle crashes.

Baker, Gretchen H., Kang, Yun-Seok, Marcallini, Angelo, Lang, Ryan, Hutter, Erin, Moorhouse, Kevin, Agnew, Amanda M.

Evaluating the Impact of Fuel Additives on Stochastic Pre-ignition Using Center for Quality Assurance (CQA) Approved Methodology

03-19-03-0016

6/22/2026

Stochastic preignition (SPI) or low-speed preignition (LSPI) is an abnormal combustion phenomenon observed in downsized turbocharged direct-injection spark-ignition engines at highly boosted conditions. SPI results from the ignition of the air-fuel mixture from a fuel or oil droplet or a detached deposit before the spark discharge, and its occurrence can lead to extremely high peak pressures and severe knock, which can cause physical damage to the engine. This phenomenon limits the downsizing and boosting potential of direct-injection spark-ignition engines, thereby constraining the efficiency benefits that can be achieved. The propensity for SPI to occur is impacted by engine operating conditions as well as the properties of the fuel, fuel additives, lubricant, and lubricant additives. To mitigate its occurrence, it is important to understand the factors that impact the frequency of SPI events. As this abnormal combustion phenomenon is relatively recent, there was a lack of a standard procedure to detect the impact of a parameter on SPI frequency. This study details the development and validation of an engine dynamometer test procedure—the TOP TIER™ Standardized Dynamometer Test Method to Evaluate Additized Detergent Gasoline for SPI—approved by the Center for Quality Assurance (CQA), to evaluate gasoline additives for their impact on SPI. In this project, the newly validated SPI test protocol was used to compare the relative SPI tendencies of four TOP TIER™ fuel additives at maximum retail concentration against unadditized SPI test fuel, which served as the baseline. All four fuel additives were tested three times in randomized order. The results revealed that none of the TOP TIER™ additives tested had a statistically significant impact on the SPI rate.

Gopujkar, Siddharth, Davis, Richard, Worm, Jeremy, Tuma, Nic, Shukla, Prajwal, Reilly, Veronica, Chapman, Elana, Ciaravino, Joseph, Seyfried, Philipp

Study on the Influence of Structural Parameters of Ejectors for High-Power Fuel Cells

13-07-01-0005

6/22/2026

This study focuses on a hydrogen ejector for a proton exchange membrane fuel cell (PEMFC) with a maximum power of 150 kW. Experimental tests were conducted to obtain the operating parameters of the stack under 100 kW and 150 kW conditions, which were used as simulation boundary conditions. A three-dimensional numerical model of the ejector was established and validated. Based on this model, the effects of key structural parameters—including nozzle throat radius (Rnt ), nozzle position (NXP), mixing chamber radius (Rm ), diffuser outlet radius (Rde ), secondary flow inlet radius (Rs ), suction chamber radius (Rf ), and constant-pressure mixing chamber length (Lpm )—on ejector performance were systematically analyzed. The results indicate that Rnt and Rf are negatively correlated with ejector performance, while Rs and Lpm are positively correlated. In contrast, NXP, Rm , and Rde exhibit an optimal range, leading to a single-peak characteristic in ejector performance. This research provides a theoretical basis and design reference for the structural optimization of high-power fuel cell ejectors.

Liu, Guoqing, Tai, Shupeng, Xi, Fuqiang, Li, Zongji, Ji, Shaobo, Wang, Xiuyu, Wei, Hui

Study of Hot Air Recirculation in Engine Compartment of a Newly Developed Sport Utility Vehicle

03-19-03-0013

6/17/2026

During idling tests of a newly developed sport utility vehicle (SUV) under tropical high-temperature conditions, the condenser surface temperature exceeded the allowable range, degrading the air-conditioning system’s cooling performance. In this study, a three-dimensional computational fluid dynamics (CFD) model of the engine compartment flow field was established using STAR-CCM+. The results reveal that under idling conditions, the kinetic energy of hot air passing through the cooling module was insufficient to overcome the pressure difference between the front and rear sections, thus inducing hot air recirculation (HAR) and increasing the overall compartment temperature. To address the unfavorable flow field characteristics, four structural improvements were proposed and simulated for both flow and temperature fields. Through comparative analysis, the optimal scheme was determined: installing a flow guide baffle above the engine. Simulation results show that the airflow velocity above and below the engine increased by 54% and 71%, respectively, and HAR was effectively suppressed. The optimal scheme was further validated under real-vehicle idle conditions, and the temperature deviation between simulation and measurement was within 2%, confirming the reliability of the numerical model. In addition, the optimized scheme was verified under three typical harsh driving conditions, including hill climbing, high-speed climbing, and high-speed driving. Both simulation and test results indicate that the scheme significantly enhances airflow velocity in the engine compartment, with temperature errors maintained within 5%. The present study effectively mitigates the compartment temperature rise caused by HAR, and the proposed baffle scheme provides a feasible solution for the thermal management design of new SUVs under both idle and severe driving conditions.

Shi, Huojie, Rao, R.H., Chen, J., Zheng, Z.L.

A Simplified Layered Model for Large-Scale Impact Simulations of Li-Ion Prismatic Cells

14-15-02-0012

6/16/2026

This article presents a novel finite element modeling approach to predict the mechanical response of jellyrolls in large-scale explicit crash simulations up to the experimental occurrence of internal short-circuit. The proposed simplified layered model embeds membrane elements within a solid element mesh to improve the prediction in load cases dominated by the buckling and sliding of the jellyroll’s layered structure. The model was validated against experimental results from in-plane, out-of-plane, and bending tests on jellyroll samples extracted from prismatic lithium-ion cells. The experimental results confirmed the jellyroll’s high compressibility under out-of-plane loads and its behavior as a collection of unconnected layers under in-plane and bending loading. Compared to the widely used crushable foam model, the simplified layered model offered additional flexibility, especially for in-plane and bending load cases. Additionally, it meets critical time increment requirements for explicit analysis and requires a limited number of calibration tests. These results highlight the model’s potential to improve the prediction of the jellyroll’s mechanical behavior in large-scale simulations.

Cioni, Daniele, Morin, David, Strating, Arjan, Kizio, Stephan, Costas, Miguel

Comparison of Concussion Risk Functions Generated from Two Different Datasets

2026-22-0004

6/11/2026

To estimate risk of concussion, risk functions based on injuries occurring in sports are often used. A range of datasets have been used to develop injury risk functions for concussion based on either global kinematics or tissue-level predictors. Two such datasets are one from American football, and another one from Australian football and rugby. These two datasets constitute the largest published collections of video-verified concussive cases in sports with known kinematics suitable for constructing risk functions. The objective of this study was to analyze the differences between two datasets of concussion for injury predictions to better understand the influence on injury risk functions. The kinematics were applied to the KTH head model and risk functions for different kinematic- and tissue-based predictors were developed and compared. The accuracy, sensitivity, specificity, and AUC were also compared. The two datasets evaluated in this study generated different risk curves. The datasets had some similarities such as having no significant difference in resultant linear acceleration, but also some differences, for example having a significant difference in resultant angular velocity. The Australian cases had relatively equally distributed major x-, y-, and z-components for angular velocity while the majority (59%) of the NFL cases had a major x-component (coronal plane rotation) representing more than 50% of the resultant. The y-component of the linear acceleration (lateral direction) was the major component in 64% of the Australian cases and 72% of the NFL cases. The two datasets, from Australian football/rugby, and American football, generated different injury risk curves with a lower 50% risk of concussion for the Australian dataset. This indicates that the choice of data as input for the development of injury risk functions is important. Therefore, it is necessary to improve methodology with focus on sampling methods and reliable/valid data collection.

Fahlstedt, Madelen, Meng, Shiyang, Patton, Declan, McIntosh, Andrew S., Kleiven, Svein

Influence of Joint Structure in Multibody Model on Behavior Analysis of Crash Test Dummy

2026-22-0005

6/11/2026

This study developed a new multibody model that accurately represents the collision behavior of crash test dummies using PC-Crash. The model replicates the shape and weight of an actual dummy. To investigate the influence of joint structures on collision behavior, an additional multibody model was developed to reproduce the joint structure of the actual dummy. These models were applied to analyze occupant behavior in a full-frontal rigid barrier and pedestrian behavior in a vehicle-to-pedestrian impact experiments. A comparison of the multibody model simulations with actual dummy impact experiments revealed that the behavior of the multibody model, which simulates the joint structure of the dummy, closely matched that of the actual dummy. The results indicate that joint structure significantly influences collision behavior, and accurately recreating it improves the precision of crash test dummy collision behavior analysis using PC-Crash.

Usui, Masatoshi, Matsui, Yasuhiro, Hosokawa, Naruyuki, Tanaka, Yoshinori

Effect of Static Fin on the Stability and Aerothermodynamic Characteristics of the Re-entry Vehicle

01-19-01-0003

6/11/2026

Investigating high-speed aerodynamics and aerothermodynamics presents a significant challenge for manned re-entry missions. The thermal effects on the surface of the re-entry vehicle and atmospheric stresses are primarily influenced by re-entry type and flight trajectory. This study investigates the monostability characteristics and aerothermodynamics of the Orion re-entry vehicle by incorporating static fins onto the aft fuselage of the vehicle, ensuring the lift-to-drag ratio remains unaffected throughout the numerical simulations. The study evaluated two different Mach numbers of 7 and 9 at various altitudes. The models were analyzed at different angles of attack from 0° to 90° in increments of 15°. The model with static fins exhibits a displacement in the monostable trim point, a reduction in the heat-shield pressure coefficient, and enhanced heat transfer throughout the re-entry vehicle.

Sabapathy, Santhosh

Heavy-Duty Nonroad Material Handler Electrification Part 1: Real-World Drive Cycle Development

02-19-03-0015

6/10/2026

Knowing a detailed operating cycle is critical for developing and testing equipment. Operating cycles can be separated by two clear distinctions: (1) regulatory or non-regulatory and (2) application at the engine-only or full machine level. The Environmental Protection Agency’s (EPA) Nonroad Transient Cycle (NRTC) may be a good representation of engine use in many types of equipment, but there is a gap in standardized and validated drive cycles specifically for nonroad material handlers. Lacking a standardized drive cycle makes it difficult to accurately benchmark machine performance and validate new powertrain technologies. The objective of this investigation is to illustrate the development of a custom drive cycle augmented with real-world customer use data that serves multiple purposes: (1) understand the range of operation and utilization that formulated inputs for electrified architecture analysis and (2) develop a repetitive and consistent maneuver to establish baseline energy consumption enabling equivalent comparison to future electrified prototype builds. This article presents a solution specifically for a 23-ton nonroad material handler in which material handling, machine transport, and extended idle were homologated to form representative short cycles defined by machine velocity and hydraulic cylinder position. The most intensive material handling short cycles had a load factor of 40% and an average fuel rate of 16 L/h. Combined with a visual aid, the short cycles exhibited low variability, having less than 5% root mean square (RMS) error in lift and reach position with respect to the average. The machine’s performance on these short cycles at the Advanced Power Systems Research Center (APSRC) was compared to results from two real-world customer locations operating the instrumented test machine in a cyclical manner, and for similar ground conditions were found to be comparable in fuel consumption.

Czarnecki, Alexander, Goodenough, Bryant, Worm, Jeremy, Robinette, Darrell, LaTendresse, Phil, Westman, John

Challenges in Predicting Fatigue Damage during Agricultural Operations: Insights from Machine Learning Models

02-19-03-0017

6/10/2026

Agricultural vehicles operating in rough environments experience increased fatigue damage accumulation, which may decrease machine safety and reliability. Autonomous agricultural machines offer an opportunity to incorporate fatigue damage considerations into path planning. This work investigates whether machine learning can predict fatigue damage to a tractor chassis using light detection and ranging (LiDAR)-based terrain features, vehicle speed, and rotational vehicle state data (e.g., triaxial angle, angular velocity, and angular acceleration). Fatigue damage was estimated using the Rupp filter and the Durability Transfer Concept. Following poor predictive performance of the machine learning models, an exploratory analysis of damage histograms, dominant frequency, and acceleration magnitude was performed. Results indicated that most estimated fatigue damage occurred in the 0–2 Hz band, which coincides with the frequency range of terrain-induced acceleration. On-road driving led to the greatest fatigue damage, potentially due to the harder driving surface and increased vehicle speed. Differences between root mean square (RMS) acceleration magnitude and fatigue damage indicate that isolated high-magnitude events may have contributed to increased estimated fatigue damage. Several suggestions for future development were identified. Identification of the endurance limit of the tractor chassis will permit the removal of nondamaging events, improving label accuracy. Furthermore, the presence of a front-loader implement may have impacted chassis acceleration. Thus, a comprehensive dataset with multiple implement configurations is needed to determine the influence of implement configuration on dynamics and resultant damage.

Govers, Megan Emily, Hamilton-Wright, Andrew, Hassan, Marwan, Oliver, Michele L.

Ergonomic Assessment of Glove-Box Design in Passenger Vehicles for the Indian Scenario

15-19-02-0009

6/10/2026

Passenger vehicles experience severe packaging constraints around the instrument panel, rendering glove-box operation a critical yet ergonomically underexplored interaction. Although glove-box interaction occurs frequently during routine vehicle use, its potential implications for ergonomic risk remain largely unexamined in existing automotive research. To isolate the influence of driver-side packaging constraints from component-level design effects, this study adopts a comparative evaluation of driver and co-driver glove-box interaction as a built-in control condition. This study introduces a discomfort-based evaluation framework that integrates Digital Human Modeling with India-specific anthropometric datasets. A composite loss-function scoring model is developed to quantify functional usability differences across four glove-box configurations, defined by variations in latch placement (center or side) and storage-bin mechanisms (fixed or rotating). Indians are utilized to assess reachability and visibility during glove-box interaction. Ergonomic performance is analyzed through reach and visibility metrics for both latch actuation and storage-access tasks. For the co-driver, all configurations exhibit 0% loss, confirming that usability remains unaffected. In contrast, the driver assessment reveals pronounced limitations. Center-mounted latches prove inaccessible from a neutral seated posture, reflecting an approximate loss function of 55%. Among the side-latch alternatives, the rotating-bin configuration achieves the lowest discomfort score (41%), supported by more favorable access posture and smoother hand-entry alignment. The findings specify that ergonomic limitations stem primarily from driver-side packaging constraints rather than inherent flaws in the glove box unit. Based on the reach and visibility loss values obtained through the developed framework, the Side-Latch + Rotating-Bin configuration emerges as the most suitable design option for passenger-vehicle layout. The proposed methodology offers a practical decision-support tool for early stage ergonomic evaluation of glove-box configurations in passenger vehicles.

Jujjavarapu, Sreeram, Kota, Srinivas, Kotkunde, Nitin, Jasti, Naga Vamsi Krishna

A Review of Welding Techniques in Metal Additive Manufacturing: Advancements, Challenges, and Future Directions

05-19-04-0029

6/10/2026

The present review evaluates recent advances in the development of Welding-Based Additive Manufacturing (WBAM) technologies using arc, high-energy density, solid-state, and hybrid welding systems by providing an interdisciplinary assessment of technological aspects, sensing, process optimization, and multi-process strategies. It is concluded that, in spite of considerable progress in process optimization and control, there exist numerous paradoxes associated with relationships among process conditions, structure, and properties, especially those related to heat input effects on material microstructure and performance. An important finding is the fragmentation of predictive modeling approaches, where physics-based and data-driven methods remain inadequately integrated, limiting generalizability and accuracy. Another important conclusion is related to the dominance of the effect of thermal history and multi-physical phenomena on the mechanical performance of the material produced by WBAM technologies. Besides, the complexity and contradiction in defect generation mechanisms, monitoring, and evaluation methodologies restrict the development of process standardization and certification. New directions in intelligent fabrication based on artificial intelligence and digital twins are identified.

Santhana Babu, A.V., John Rajan, A., Mishra, Aishwary, Chakravarthy, P., Jayabalakrishnan, D.

A Comprehensive Review of System Risk in Automotive Crash Safety

15-19-02-0010

6/9/2026

Occupant protection has been at the forefront of risk evaluation regarding vehicle crashworthiness design. However, the vehicle is a member of a larger transportation system with varied stakeholders. This article identifies an opportunity for assessing risk in a crash event through emerging safety science paradigms. Conventional Safety I and Safety II frameworks handle well-defined hazards but falter with uncertainty, variability, and emergent behaviors in real crashes. A comprehensive literature review was performed on peer-reviewed research to situate automotive crash safety risk within the Safety III paradigms. The review addresses two questions: (1) How is “risk” defined across the crash safety literature and adjacent safety science domains? and (2) What limitations arise from these definitions in practice? Findings show a dominant probabilistic framing alongside a minority of system-oriented interpretations. Current crash safety practice lacks a coherent, system-level definition of risk that integrates uncertainty and knowledge strength, leading to fragmented methods and limited alignment with modern safety science. Based on this synthesis, the article proposes guiding principles for Safety III-aligned guidelines and recommendations that integrate consequences, uncertainty, and knowledge strength to improve transparency, traceability, and adaptability in crash safety decision-making.

Rye, Patrick J.

Measurement Framework for Automated Vehicle Systems Interaction

12-09-04-0027

6/9/2026

1Systems level and integration testing are an integral part of the design and development of Automated Vehicles (AVs). Measurement science plays a pivotal role in testing to ensure the safe and efficient operation of AVs. This science establishes a common understanding of the units of measurement, crucial in linking human activities. This article describes the significance of measurement in studying interactions between key system technologies in AVs, including AI for perception, sensing, communications, and cybersecurity. To address the complexities of these interactions, a novel, adaptable, and interactive framework called the System Technology Interaction Model (STIM) is introduced. STIM considers both designed and emergent interactions between these system technologies, allowing AV developers to explore tailored experiments with the flexibility of filtering for focused testing. The framework currently models system interactions statically, not in real-time, to define potential relationships and influences during the design phase. The novelty of this framework comes from providing a holistic evaluation that captures testing of interactions between modules in addition to component-level testing, while other frameworks focus on testing individual component behaviors. It also assesses the equality of two interactions, meaning it ensures that two interactions behave the same way for consistent results. Moreover, the framework serves as a valuable tool for AV designers and safety regulators to aid in establishing robust design and assessment approaches. This work highlights the need for a common framework to thoroughly test AVs and gain a holistic understanding of system interactions. Finally, the framework aims to understand how to mitigate potential influences leading to AV malfunctions to advance the development and deployment of safe and reliable Automated Vehicles. The work focuses on level 1 and level 4 automated driving features to simplify the work, although it can be from level 1 to level 5. Although framework performance is inherently difficult to quantify, this framework’s performance can be reflected through its ability to accurately capture system interactions for improved AV design and support a broader usability among AV stakeholders. In the future, the framework can be expanded to include additional elements, such as infrastructure or other vehicles, to analyze information provided to AVs, allowing experts from various domains to collaborate, create similar models, integrate them when feasible, and model the interactions in real-time.

Griffor, Edward R., Arora, Mahima, Kootbally, Zeid, Nguyen, Vinh

A Novel Looped-Freezing Mean Detached Eddy Simulation (DES) Based Approach to Evaluate Underhood Cooling Performance of Commercial Vehicles

02-19-03-0018

6/9/2026

A novel looped-freezing mean approach based on Detached Eddy Simulation (DES) approach is developed in context of assessing underhood cooling performance in heavy-duty vehicles. The method involves computing a temporally averaged flow field from DES simulations, which is then frozen and used by the energy solver to predict temperature distributions. This process is iteratively repeated until a statistically steady-state temperature field is achieved. It is demonstrated that traditional DES approach demonstrates superior accuracy in capturing forced convection heat transfer compared to the Reynolds-Averaged Navier–Stokes (RANS) method. The validation against experimental data for flow over a heated sphere at a Reynolds number of 105 shows that DES yields Nusselt numbers with better correlation than RANS. However, it is observed that DES approach captures unsteady flow features that introduce temporal fluctuations in heat transfer. In the context of underhood cooling evaluations where properties of the fluid are strong functions of temperature and coupled with iterative processes such as dual-stream heat-exchanger modeling, these instabilities can frequently lead to numerical divergence of the simulation. The novel looped-freezing mean DES method is then applied to a reduced underhood model, including the heat exchanger and fan assembly, bounded by walls representing adjacent vehicle components. The study show that the novel looped-freezing mean DES approach provides stable and converged thermal predictions for the reduced underhood model. This approach is particularly beneficial for simulations involving highly transient flow fields coupled with thermal phenomena, enabling accurate and reportable temperature evaluations in critical regions.

Holay, Sarang, Sankar, Hari, Dixit, Pritish, Singh, Ramanand

Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving

13-07-02-0006

6/9/2026

Large language models (LLMs) have shown remarkable capabilities for perceiving driving environments and making interpretable, logical decisions for autonomous driving. However, their potential for more comprehensive driving strategies, especially concerning energy efficiency, remains underexplored. Most existing studies primarily focus on driving safety, which may inadvertently increase energy consumption. To address this issue, this study explores the use of LLMs as high-level controllers to jointly optimize driving safety and energy efficiency. A textual prompt is designed for the LLM, incorporating few-shot examples that describe scenarios, states, and actions. The LLM processes the scenario and state prompts describing the surrounding traffic environment. It generates a high-level control signal, which is then translated into low-level vehicle motion commands in a high-fidelity traffic simulator with realistic physics, vehicle dynamics, road slopes, and network topology. Experiments in campus-scale digital twin car-following scenarios demonstrate that the proposed LLM-based framework achieves an average reduction of 4.16% in energy consumption compared to the reinforcement learning paradigm, while maintaining driving safety and providing interpretable high-level decision-making. This study highlights the potential of LLMs for longitudinal eco-driving applications under the evaluated simulation settings, extending previous LLM-based autonomous driving research that primarily focused on safety to also consider energy efficiency.

Wang, Haoyu, Li, Zhenning, Wang, Siying, Zhou, Zijing, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi (Shawn), Qi, Hao

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