Browse Topic: Vehicle performance

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A Comprehensive Analysis of a Truck–Semi-Trailer Performance Based on Hierarchical Controller Design to Enhance Driver Comfort and Dynamic Stability02-20-01-00028/18/2026
This study presents an integrated suspension system to improve the ride quality and stability of semi-trailer truck vehicles. The system consists of both an Air/MR-controlled suspension on the driver’s seat and on the truck cab, and an active air main suspension for the truck to improve vehicle stability. All components are connected to the truck and semi-trailer via a series of coupled connections. An analysis of dynamic stability reveals how the truck’s motions affect slosh forces within the semi-trailer during operation. An experimental validation of the new Air/MR suspension damper, developed using ANSYS 2023 R1 Computational Fluid Dynamics (CFD) software, yielded results consistent with prior experimental evidence. The control strategy uses a hierarchical architecture in which an inverse LSTM network controls the MR damper, accurately following the damping forces generated by a higher-level RNN controller implemented in MATLAB. The RNN system adaptively adjusts the stiffness of the cab and driver seat suspension, isolating the cab from vibrations caused by uneven roads. The truck and trailer’s main air suspension uses LQR control, using state inputs to counteract vertical motions caused by road roughness and sloshing. An integrated nonlinear co-simulation model of the truck and semi-trailer—including the truck, sloshing trailer, nonlinear air springs, and CFD-based MR damper demonstrates the control system’s effectiveness. Simulation results compared with a passive suspension system show that the proposed integrated controlled suspension system significantly reduces the truck chassis bending moment, improves ride comfort (49.98%), reduces cab body displacement (63.60%), and increases dynamic stability (57.78%). Maximizing dynamic tire load due to slosh dynamics in both half-loaded and full-loaded tanks does not affect the effective dynamic stability of tire hopping at high frequencies because the stiffness coefficient is adaptively adjusted, thereby synchronizing disturbance rejection and long-term riding comfort in frequency-based control under different road conditions.
Gad, Ahmed Shehata
Research on Parameter Matching of Electric Vehicle Powertrain System2026-99-09058/7/2026
This paper focuses on the parameter matching of key components and the improvement of overall vehicle performance for a certain front-wheel drive pure electric vehicle. Firstly, based on the target performance of the vehicle, the rated/peak power, speed, and torque of the permanent magnet synchronous drive motor, as well as the capacity, voltage, and series-parallel scheme of the LiFePO4 power battery, are systematically calculated. Meanwhile, the gear ratio of the transmission system is determined based on the dual constraints of the maximum speed and the maximum gradeability. Subsequently, the vehicle model is built using AVL Cruise, and the maximum speed, 0-100 Km/h acceleration time, maximum gradeability, and NEDC range are simulated and verified under steady-state and transient conditions. The results show that the maximum speed of the prototype vehicle reaches 139 Km/h, the 0-100 Km/h acceleration is 7.98 s, the maximum gradeability is 33.2%, the power consumption per 100 Km is 12.12 KWh, and the range is 485 Km, all of which are superior to the design indicators. The research verifies the rationality of the proposed parameter matching scheme and can provide a theoretical basis and engineering reference for the forward development of the power system of pure electric vehicles of the same level.
He, Yuefan, Zhang, Baoping, Tang, Shujian, Chen, Hanbang, Jin, Biao
Advanced Damping Force Modeling Using Machine Learning for Next-Generation Electric Vehicle Suspensions2026-01-50537/31/2026
The rapid evolution of electric vehicles (EVs) has led to the development of innovative approaches to optimize ride comfort, handling, and the overall suspension performance. EVs introduce unique challenges due to their distinct weight distribution, powertrain dynamics, and noise characteristics, unlike their conventional internal combustion engine (ICE) counterparts. This paper outlines an advanced damping force modeling methodology using machine learning (ML) techniques to enhance the suspension design process for next-generation EVs. The analysis is based on data-driven ML algorithms, i.e., Gradient Boosting, Random Forest, and Neural Networks, to simulate the nonlinear and frequency-dependent phenomenon of dampers in different operating conditions. A comprehensive dataset, generated through simulation and experimental testing, captures the effects of road profiles, vehicle dynamics, and damping settings. Additionally, this research evaluates the impact of machine-learned damping force predictions on critical ride and handling metrics, including ride comfort, road-holding ability, and energy efficiency. The results demonstrate that the ML models can enhance the iterative design process considerably and help to create the adaptive suspension systems that will address the particular requirements of EVs. This paper contributes to advancing the state-of-the-art of the suspension modeling, incorporating the ML-based insights in the development cycle. It highlights the possibility of artificial intelligence to transform suspension design, paving the way for superior ride quality and vehicle performance in electric mobility.
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Aircraft Performance Prediction Model Based on Deep Learning2026-99-18577/17/2026
Aiming at the problems of traditional physical model methods in aircraft endurance prediction, an end-to-end prediction model based on depth deterministic policy gradient (DDPG) is proposed. The model realizes continuous mapping from flight parameters to range index through Actor-Critic dual network architecture, and combines experience playback mechanism and soft update strategy of target network to effectively suppress training oscillation and improve convergence stability. UAV Delivery Aircraft Versus hybrid dataset was used to verify model performance in test samples. The results show that the MAE of the model is 9.2 km, which is 42.1% lower than that of DQN; the prediction accuracy of the model is the best (MAE 7.3 km) in cruise phase, which is due to the dynamic compensation of time series difference error to wind speed disturbance; in environmental disturbance test, the error increment (50.0%) is significantly lower than that of DQN (78.0%) at low temperature (-5 ° C), which highlights its robustness to battery voltage sag. The model provides real-time and reliable decision support for aircraft endurance management in high-dynamic airspace.
Bai, Rongqiang, Chen, Li
Research on Calculation Methods for Climb Performance of Civil Aircraft2026-99-18317/17/2026
The climb gradient along the takeoff trajectory at each point during takeoff reflects the aircraft’s ability to clear obstacles and reach a safe altitude, ensuring the safety of civil flights. Airworthiness regulations specify certain requirements for the single-engine-out climb gradient. Given that the data used in conventional calculation methods are significantly influenced by the flight status during the process, this paper explores two new climb performance calculation methods based on the existing ones. A set of data was calculated, and the resulting errors were all no more than 10%, indicating that both new calculation methods are effective and reliable. Therefore, they provide a certain reference value for the climb gradient calculation of transport category aircraft.
Jiang, Tianjun, Liu, Tao
Multi-Scale Modelling of Localized Battery Degradation in Heavy-Duty Series Hybrids: From Electrochemical Impedance Spectroscopy to Powertrain and Battery Pack Performance2026-01-07477/1/2026
Hybrid electric vehicles rely heavily on battery pack power capability, which is often compromised by non-uniform aging and thermal gradients. Conventional battery models typically use bulk state-of-health metrics, failing to capture localized degradation that leads to current imbalances and reduced pack utility. This paper presents a multi-scale modelling framework that integrates Electrochemical Impedance Spectroscopy data into a fractional-order equivalent circuit model to simulate localized degradation in Lithium Iron Phosphate cells. Results show that the terminal voltage of LFP cells can be accurately modelled using the proposed fractional-order equivalent circuit with a discrete transfer-function implementation, maintaining root-mean-square errors below 20 mV across most state-of-health and state-of-charge conditions. The validated cell model is then extended to a degradation-aware battery pack representation. The battery pack in this work utilizes a 200-kWh, 800 V architecture consisting of five modules connected in parallel, each module composed of 13 parallel strings of 250 series cells, evaluated under multiple degradation scenarios. By integrating this pack model into a Class-8 series hybrid powertrain simulation, this study quantifies how cell-to-cell heterogeneity impacts vehicle performance under the VECTO regional delivery drive cycle. At the vehicle level, these battery constraints influence engine duty cycles and battery pack stress metrics. When localized degradation reaches up to 40% in one module while the remaining modules degrade up to 20% to 30%, such inhomogeneous degradation reduces the minimum pack terminal voltage by approximately 27% and increases peak discharge current by more than 30%, resulting in more rapid degradation. These battery-level limitations translate into higher fuel consumption by up to 6% in a charge-sustaining scenario.
Safavi, Seyed Reza, Homayouni, Hooman, Shoa, Tina, Wang, Jason, McTaggart-Cowan, Gordon
Passive Thermal Management of Battery Disconnect Units for Fast-Charging Electric Vehicles: A Comparative Study of Phase Change Material Heat Transfer Configurations2026-01-07597/1/2026
The widespread adoption of electric vehicles is currently hindered by long charging durations and limited infrastructure. While fast-charging technologies address these issues, they impose significant thermal loads on high-voltage components. Within this architecture, the Battery Disconnect Unit plays a critical role as it monitors and controls the connection between the battery, powertrain, and charging system. However, the high currents required for fast-charging often drive these units' temperatures beyond safe operating limits, necessitating advanced thermal solutions that do not require extensive redesigns of the vehicle's electrical layout. To address this challenge, this study proposes a passive thermal management solution using Phase Change Material heat transfer devices to enhance the thermal robustness of the component. The methodology employs a dual approach involving initial experimental testing to pinpoint specific thermal hotspots under high-power conditions, followed by detailed numerical simulations using GT-Power software to predict system behavior. Furthermore, the paper provides a comparative analysis of various configurations, assessing their impact on temperature reduction, response time, and thermal uniformity. The results demonstrate that appropriately designed passive solutions significantly improve thermal performance, effectively enabling higher charging power capabilities while minimizing system complexity and integration effort. This innovation provides a scalable and efficient path for improving overall vehicle performance and safety during rapid energy transfer events.
Salameh, Georges, Goumy, Guillaume, Frecinaux, Anthony, Ratajczack, Christelle, Palluel, Marlène, Noiseau, Pascal, Lardeux, Sébastien
Development of a Load Responsive Mechanical Shifting Mechanism and its Benefits for Battery Electric Vehicles2026-37-00266/9/2026
Many high-end electric vehicles use an automatic two-speed transmission. The ability of the drivetrain to switch between two gear ratios improves vehicle performance and increases driving range. The aim of the presented research work is to transfer these advantages to small and lightweight battery-electric vehicles, which face significant cost and weight constraints and therefore cannot rely on highly sophisticated electric motors. Direct-drive systems are widely used in this vehicle class due to their simplicity and high baseline efficiency. However, they offer limited flexibility in adapting the operating point of the electric motor under varying load conditions. A two-speed transmission can overcome this limitation by enabling load point shifting, allowing the motor to operate closer to its optimal efficiency region during both urban and extra-urban driving. This results in improved energy consumption without adding substantial system complexity. Currently, only actuated transmissions are offered on the market, with automation adding a high degree of complexity and representing a major cost driver. Therefore, the focus during the concept development phase was on designing a fully mechanical, self-shifting system to meet the cost pressures of the targeted vehicle classes. Hence, the team at ITnA developed and patented a solution that enables automatic gear changes solely based on output torque, which reflects the motor load and the current driving situation. In the present work, both the operating principle of the technology and the advantages regarding the performance and efficiency of electric vehicles are described. Owing to its simple architecture and the absence of electronics, the transmission is inherently robust and durable, making it a significant contribution to the development of sustainable and affordable e-mobility for the mass market.
Napetschnig, Christof, Tromayer, Juergen, Stückler, David
Top Speed Test Procedure for Electric MotorcyclesJ3007_202605 (Current)5/27/2026
This SAE Recommended Practice incorporates a track-based test procedure that produces a representative value for vehicle top speed when operating on a level paved road with a fully charged battery.
Motorcycle Technical Steering Committee
Effects of Partial Boundaries and Variable Wind Conditions on a Free-Flying Multirotor PlatformF-0082-2026-00915/5/2026
The effects of hover operations near a partial boundary structure were assessed for a free-flying quadrotor platform under both wind-off and wind-on conditions. The partial boundary structure was selected to replicate a building facade or urban vertiport environment, providing a realistic operational context for these free-flight tests. Test points were chosen to investigate operations near the partial boundary wall and edge, and across a range of partial ground effect conditions to capture the progressive onset of ground effect characteristics. Regions of degraded vehicle performance, quantified primarily by rotor thrust coefficient (CT ) and power requirements, emerged near the partial boundary edge. These performance trends were attributed to localized changes in rotor inflow profile, characterized by near-field rotor pressure measurements. Partial ground effect was found to not resemble full ground effect until much of the vehicle had traversed over the partial boundary, with the vehicle airframe and fuselage serving as the primary factor driving the onset and development of ground effect characteristics. Under wind-on conditions, the interaction between the wind freestream and the partial boundary structure significantly shaped vehicle performance and handling qualities. The most adverse handling qualities coincided with regions of peak flow vorticity, highly unsteady flow, and large velocity gradients at the shear layer above the partial boundary.
Herz, Sage, Taylor, Julia, Clar, Lauren, McCrink, Matthew
TOC99-08-02-0TOC4/13/2026
TOC
Tobolski, Sue
Thermal Management of Electric Microtruck Motors2026-01-50264/9/2026
This study investigates the gradeability performance of an L7e-class electric micro truck from both vehicle dynamics and thermal perspectives. A 1D simulation model (Amesim) was developed and validated with multiple test results. Using inputs such as motor characteristics, drivetrain configuration, and vehicle mass, the model analyzed vehicle performance on a 20% gradient, calculating the required torque, achievable motor speed, and corresponding vehicle speed. Furthermore, gradeability limits were evaluated, and the effects of gear ratio and airflow rate around the air-cooled motor on both gradeability and thermal behavior were examined. The findings provide practical insights for improving the powertrain and cooling system design of lightweight electric vehicles. The results showed that selecting an appropriate gear ratio can enable the motor to operate more efficiently under demanding driving conditions. A 20% increase in the gear ratio was found to delay motor heating by up to 10%. However, its effect was observed to be negative under driving conditions such as WLTP, which require variable RPM demand.
Turan, Azim, Kantaroğlu, Hasan Hüseyin, Akbaba, Mahir, Kasım, Recep Faruk, Yarar, Göktuğ
Data-Driven Study on Chassis Suspension Performance Degradation2026-01-05824/7/2026
The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Implementing real-time health monitoring for chassis systems contributes to preventing severe consequences such as increased body roll or loss of handling stability caused by shock absorber softening or spring stiffness degradation under deteriorating operating conditions, while circumventing the substantial costs associated with professional facility-based chassis inspections. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve online monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a half-car model incorporating both vertical and pitch motions was established to simulate bumpy road conditions, with the aim of constructing a dataset that includes key vehicle suspension parameters and vehicle states related to their degradation characteristics. Subsequently, a DNN model comprising three hidden layers is developed to assess suspension performance degradation. To optimize model performance, the effects of different numbers of neurons and hidden layers on model accuracy are explored. Experimental results show that the maximum absolute percentage errors of the DNN model in predicting suspension stiffness and damping coefficients are less than 0.13% and 0.17%, respectively, with average absolute percentage errors below 0.046% and 0.06%. The coefficients of determination (R2) exceed 0.999. The proposed method accurately predicts the trend of key suspension parameters, providing robust data support for health management and maintenance decision-making. This is expected to reduce safety risks and maintenance costs while enhancing overall vehicle performance and reliability.
Liao, Yinsheng, Lei, Yisong, Su, Ailin, Wang, Zhenfeng, Shi, Shuai, Zhang, Lei, Zhang, Junzhi, Ma, Changye
On-Road Vehicle Testing and Characterization2026-01-01954/7/2026
Vehicle testing for fuel economy and emissions is typically performed indoors over standard dynamometer drive schedules to minimize variability and maximize repeatability of the results. In contrast, during on-road operation, operational parameters such as vehicle speed and acceleration and environmental factors such as temperature and wind will change unpredictably. These factors influence vehicle fuel economy and emissions, making on-road operation much more variable than dynamometer results. However, even though on-road conditions may be unpredictable, the on-road operational data can still be used to characterize vehicle performance. This paper describes the development of an on-road vehicle test methodology, with a focus on accounting for on-road factors with a high degree of accuracy while requiring only an achievable and reasonable amount of data. To develop this methodology, a 2016 Honda Civic was instrumented and driven multiple times over a route covering urban, rural, and freeway segments. Vehicle operational data, environmental conditions, fuel consumption, and emissions were recorded. The route was divided into segments and drive cycle parameters were calculated for each segment. Simple empirical equations were developed for this vehicle correlating fuel consumption with drive cycle parameters and the environmental conditions. The empirical models were compared to fuel consumption data from dynamometer tests with good results. Criteria pollutants (CO, NOx, and THC) were also measured and compared to dynamometer data. Finally, the amount of testing and data required to adequately characterize vehicle performance is discussed.
Moskalik, Andrew, Barba, Daniel
Lap Time Performance Sensitivity to Tire Model Parameters: An Optimal Control Framework for Model Selection and Calibration2026-01-02144/7/2026
The push for vehicle development through virtual prototyping and testing in motorsports highlights the critical challenge of tire model selection and calibration, especially when vehicle dynamics must be accurately captured. The calibration process for tire models such as the Pacejka Magic Formula (MF) relies on parameter identification and experimental data fitting. While optimization algorithms have been implemented to calibrate tire models, few studies explore the effects of parameter selection on overall vehicle performance, complicating prioritization for the vehicle’s modeling and simulation strategy. To bridge this gap, this paper leverages optimal control methods to quantify how the variability of MF tire model parameters propagates to the overall vehicle model and impacts lap time prediction accuracy. To achieve this, a subset of parameters critical to combined slip of the MF tire model are varied through a Design of Experiments (DOE). These variations are executed on a flat oval track to simplify the dynamics yet exhibit combined slip characteristics using a fixed vehicle configuration. The minimum lap time problem is solved using collocation methods via Dymos, an optimal control library for multidisciplinary systems. A neural network surrogate model enables an interactive profiler to visualize lap time sensitivity to tire model parameters. The primary contribution of this work is a framework that parametrically connects high-level, vehicle-wide metrics such as lap time to the calibration process and selection of tire models. The parametric and interactive nature of the framework allows high-level insights across the whole design space of tire model parameters. Insights derived from this framework provide a basis to develop a strategy for prioritizing testing and calibration efforts driven by vehicle level impacts of model parameter uncertainties.
Zarate Villazon, Angel M., Brown, Ian, Balchanos, Michael, Mavris, Dimitri
Thermal Insulation Materials Used in Battery Pack Assembly: A Review of Materials, Test Methods, and Key Considerations2026-01-04064/7/2026
Electric vehicle (EV) battery packs have undergone substantial advancements in recent years, driven by engineering design improvements, material innovations, and increasingly stringent regulatory enforcement. These developments have enabled battery packs to become more energy-dense, which is essential for extending driving range and improving overall vehicle performance. However, with increased energy density comes a higher severity of thermal events, such as thermal runaway, which continues to raise concerns regarding vehicle safety, reliability, and long-term durability. This review highlights the critical role that thermal insulation materials play in mitigating the impact of such thermal events within EV battery systems. It presents an overview of commonly used thermal insulation materials, emphasizing their chemical composition, thermal resistance, and mechanical integrity under extreme conditions such as high temperatures and physical stress. The ability of these materials to maintain performance during thermal abuse is essential for protecting both the battery and surrounding vehicle components. In addition to material properties, the review compares the methodology and performance metrics of common methods for evaluating flammability, including flame retardance test such as UL 94 and torch and grit flammability tests such as one described in UL 2596. These comparisons are crucial for identifying insulation materials that can withstand severe thermal conditions without compromising safety. Beyond flammability, high temperature, smoke, and other important considerations such as thermal properties, environmental durability, corrosion resistance, and dielectric strength will be discussed with examples. These factors contribute to the overall effectiveness and reliability of insulation materials in EV applications. By understanding and optimizing these properties, engineers can better design battery packs that are not only high-performing but also safe and durable under demanding operating conditions.
Ng, Sze-Sze, Dhyani, Abhishek, Gorin, Craig, Jeon, Junho, Nuguri, Sravya, Repollet Pedrosa, Milton, Rylski, Adrian, Shete, Abhishek, Steinbrecher, Jacob, Thomas, Ryan
Multi-Body Dynamics Control Integration Tool Application in Hybrid Vehicle System Identification2026-01-04174/7/2026
The multi-body dynamics (MBD) model and the MATLAB Simulink model can be integrated to create a control-integration model. Using a high-fidelity MBD model to represent the vehicle as the plant, this integrated model can be used to analyze vehicle system physics and develop control strategies. For hybrid vehicles, this process is more complex because the powertrain and other vehicle systems are often built as separate MBD models. This paper describes a method for integrating a powertrain model developed in AMESIM, a vehicle model developed in SIMPACK, and a control model developed in MATLAB Simulink. The resulting integrated model was then used to perform frequency sweep analysis to identify driveline system properties. In particular, the driveline frequency and the amplitude of the transfer function between motor speed and motor torque are critical parameters. By applying active damping control to the driveline system, the peak amplitude and driveline vibrations can be reduced. The hybrid vehicle studied includes a transmission system with ten different gears. When the vehicle operates at different gear level, the system behaves differently. The analysis results can assist the driveline control team in developing appropriate strategies to improve overall vehicle performance.
Xing, Xing, Mathew, Vino
Research on Driving Performance of Vehicle Body Control Using Vehicle Height Adjustment System2026-01-06264/7/2026
In recent years, premium vehicles have increasingly incorporated suspension systems capable of adjusting ride height. The primary function of these systems is to enable the vehicle to traverse uneven terrain by elevating the chassis, thereby preventing contact between the underbody and the road surface. Notably, air spring-based mechanisms enhance ride comfort by modulating the wheel rate. The system proposed in this study achieves ride height adjustment through vertical displacement of the spring’s lower seat. By constructing a detailed mechanical topology model using a dynamic simulation tool, this research aims to evaluate the feasibility of improving driving performance not only through height regulation but also by actively controlling the vehicle’s posture during motion.
Park, Jaeyong, Sang Hoon, Lee, Jong Min, Kim, Choi, Jang Han
Adaptive Torque Vector Control for Distributed Drive Intelligent Electric Vehicles Using Model and Data Driven Approaches2026-01-06284/7/2026
To effectively improve the performance of chassis control of distributed drive intelligent electric vehicles (EVs) under difference road conditions, especially in combing road information and chassis control for improving road handling and ride comfort, is a challenging task for the distributed drive intelligent EVs. Simultaneously, inaccurate chassis control and uncertainty with system input, are always existing, e.g., varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis control strategy of distributed drive intelligent EVs become a hot topic in both academia and industry. To issue the above mentioned, an adaptive torque vector hierarchical controller based on road level and adhesion is proposed, which optimizes the comprehensive. First, combined with the characteristic of the unbalance dynamic force caused by the air gap between the stator and the rotor of the in-wheel motor, a nonlinear vehicle model based on motor unbalanced electromagnetic force is developed. Then, using the deep neural network, an algorithm for road level and adhesion recognition based on system response data is designed. Meanwhile, an adaptive torque vector controller based on road information is designed to improve the driving safety and handling stability of chassis. Finally, the proposed algorithm is validated on the full-car test rig platform, results show that the proposed algorithm can improve chassis performance under double lane-change test. The research achievements develop a reasonable algorithm to apply to the improving road handling and ride comfort performance for distributed drive intelligent EVs.
Wang, Zhenfeng, Zhao, Gaoming, Zhang, Zhijie, Zhou, Zitao, Huang, Taishuo, Ma, Changye
Automotive Aerodynamics Surrogate Modeling using NVIDIA PhysicsNeMo on Mid-Sized SUV GM Dataset2026-01-06004/7/2026
Aerodynamic simulations are crucial in vehicle design and performance evaluation. Traditionally, these simulations utilize Computational Fluid Dynamics (CFD) techniques to compute flow quantities such as velocity, pressure, and wall-shear stresses. Accurate prediction of these quantities is vital for estimating drag and lift forces, which directly impact fuel efficiency, stability, and acoustics. This study focuses on developing an AI surrogate for aerodynamic design of production mideo-size SUVs using NVIDIA’s PhysicsNeMo framework. Firstly, high-fidelity 3D CFD data are generated using first-principles solvers on 102 different geometry variants at a uniform inlet velocity of 38.89 m/s and a fixed set of boundary conditions. The DoMINO (Decomposable Multiscale Iterative Neural Operator) AI model, part of the PhysicsNeMo framework, is then used to train on this dataset, accurately predicting surface pressure and flow fields around vehicles for rapid estimation of critical aerodynamic metrics such as drag and lift. DoMINO is a neural operator that learns local geometry representations from point cloud data and predicts PDE solutions on discrete points using dynamically constructed computational stencils in local regions. By leveraging both short- and long-range geometric features, the DoMINO model predicts solution fields on the vehicle’s surface and in the surrounding flow domain—capabilities essential for informed design and engineering decisions in industrial applications. In this study, the DoMINO model is evaluated on a realistic production mid-size SUV vehicle designed by General Motors. Comprehensive hyperparameter tuning is conducted to optimize model performance, along with an analysis of input grid sensitivity. The findings highlight DoMINO's effectiveness as a robust and accurate tool for aerodynamic analysis in the automotive sector, enabling accelerated design cycles and enhanced vehicle performance.
Keum, Seunghwan, Raul, Vishal, Grover, Ronald, Parrish, Scott, Ranade, Rishikesh, Ghasemi, Abouzar, Kamenev, Alexey, Tadepalli, Srinivas
The Development of an Experimental Methodology for the Measurement of Aeroelastic Components2026-01-06484/7/2026
In high-end motorsport engineering, aerodynamic devices such as front and rear wings are prone to aeroelastic deformations under certain conditions, which can be exploited for vehicle performance gains. Considering the complex interactions between the aerodynamics and structures, experimental evaluation can prove to be a time-effective approach for design, optimisation, research and development regarding aeroelastic bodies. This study presents the development and experimental validation of a deformation tracking system using depth-sensing LiDAR (Light Detection and Ranging) camera technology. The system is based on the use of reflective markers mounted on a given model of interest; this project, a front wing model with a flexible, 3D printed flap element was used as a benchmark. Surface deformation is captured by post-processing point cloud data to extract three-dimensional displacement vectors. A series of controlled measurement tests were first conducted to assess accuracy and repeatability under known displacements. A full wind tunnel test campaign was then carried out to record surface deformation under aerodynamic loading, with flow speeds ranging from 10 to 35 m/s. Accuracy tests using a rigid marker setup showed a root mean square error (RMSE) range of 1 to 2 mm across a two-camera configuration with a combined error of sub-mm accuracy. The system was able to resolve consistent displacement trends as flow speed increased, with larger deformations observed near the centre-span of the flap element. Measured displacements exceeded 30 mm in the most flexible regions, and results were repeatable across test runs. The method demonstrated stable tracking performance and provided a practical alternative to more complex setups for characterising flexible aerodynamic components in controlled environments.
Altinbas, Koray, Soares, Renan F.
High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety2026-01-00344/7/2026
Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting with bicyclists as compared to their interactions with pedestrians which makes collision avoidance system design for bicyclist safety more challenging. Yet, bicyclist movements are generally more predictable and governed by clear traffic rules as compared to the sudden and sometimes erratic pedestrian motion, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes and develops a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. Through this framework, CLFs constraints guarantee system stability so that the vehicle can track its reference trajectory, whereas CBFs constraints ensure system safety by letting vehicle avoiding potential collisions region with surrounding obstacles. Then by solving a QP problem, an optimal control command that simultaneously satisfies stability and safety requirements can be calculated. Three key bicyclist crash scenarios recorded in the Fatality Analysis Reporting System (FARS) are recreated and used to comprehensively evaluate the proposed autonomous driving bicyclist safety control strategy in a simulation study. Simulation results demonstrate that the HOCLF-HOCBF-QP controller can help the vehicle perform robust, and collision-free maneuvers, highlighting its potential for improving bicyclist safety in complex traffic environments.
Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin
Look-Ahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks2026-01-00394/7/2026
Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the efficiency of the traction electric machine (EM), gear losses, and battery dynamics, which are essential for optimizing overall energy consumption and improving vehicle performance. This research paper shows the impact of powertrain efficiencies on the optimal speed profile generation with a predictive cruise controller (PCC). The PCC optimizes electric truck operation by focusing on three primary factors in its cost function: 1) battery energy consumption, 2) total trip time, and 3) battery state of charge (SOC). To achieve the optimal speed profile, Sequential Quadratic Programming (SQP) is used. A comparison was made with a conventional cruise controller, which simplifies the vehicle model by minimizing the required energy at wheels and ignores powertrain losses in its energy calculations. The results show that the proposed PCC offers a 12.85% improvement in battery SOC and 10.83 % improvement in energy consumption as compared to baseline.
Safder, Ahmad Hussain, Villani, Manfredi, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Predicting the Impact of FIA 2026 Power Unit Regulations on the Performance of Formula 1 Car2026-01-02684/7/2026
This work investigates the impact of the forthcoming 2026 FIA Formula 1 power unit regulations on vehicle track performance. This new regulation introduces a rebalanced power distribution between the internal combustion engine and the Motor Generator Unit-Kinetic (MGU-K), with each unit contributing up to 350 kW. This transition nearly triples the previous 120 kW output of the MGU-K while constraining the internal combustion engine through newly imposed fuel energy limits. A full vehicle powertrain model was developed in GT-Suite following the 2026 FIA technical directives. Particular attention was given to Articles 5.4.7 to 5.4.10, which define key constraints on hybrid operation: a maximum variation of 4 MJ in battery state of charge, up to 9 MJ of recoverable energy per lap, and a peak MGU-K electrical power output of 350 kW. The model includes updated architecture specifications, active aerodynamic modules, energy deployment logic, and component-level constraints. Telemetry data from the 2024-2025 qualifying sessions was employed for model development, validation, and benchmarking, enabling performance comparisons under realistic track conditions. Analysis results reveal notable deviations in powertrain response and vehicle performance across a range of circuits, establishing a correlation between the powertrain performance and track layouts. The model enables the decomposition of overall system effects into discrete contributions from specific regulatory requirements and individual component limitations. This paper will propose a justification and scheme for developing circuit-specific strategies based on maximum energy constraint and vehicle performance.
Giaquinta Aitala, Lucio, Samuel, Stephen
Development of a Virtual Test Rig to Evaluate Thermal Management of a Battery-Electric Vehicle under Battery Heating-Cooling Modes2026-01-04164/7/2026
A battery-electric vehicle (BEV) has multiple powertrain components (battery, inverter, e-motor), a thermal management system (compressor, heat exchanger, cabin heating, ventilation, and air-conditioning), and a vehicle body, among others. Vehicle testing is time-consuming, and changing powertrain components during the testing and design process is costly. Simulation models (aka virtual or simulation test rig) have been widely used for efficient vehicle design. This work presents a systematic approach to developing a virtual test rig to evaluate the thermal performance of battery-electric vehicles. A Tesla Model Y is tested in a chassis dynamometer, and the measured vehicle performance data are used as boundary conditions for the complete vehicle model. The detailed lithium-ion battery (LIB) pack model, including its cooling system, was developed and calibrated using various transient driving cycle data. The HVAC model uses a simplified controller to maintain the cabin temperature at 25 °C in both battery heating and cooling modes. The predicted thermal and electrical performance of the BEV is well validated by test data. Then, the complete vehicle model is used to compare the thermal performances of the BEV under cabin heating and cooling modes for various transient driving cycles. The simulated results show that using an external cabin air circulation model can reduce the battery energy consumption and dissipated heat by 9.9% and 2.4%, respectively. This calibrated virtual test rig can be used to evaluate a new HVAC system.
Sok, Ratnak, Kusaka, Jin
Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets2026-01-04544/7/2026
The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.
Sun, Ruixiao, Sujan, Vivek, Goulet, Nathan, Wang, Qixing
Energy Efficiency Analysis of U-Haul Truck and Trailer Systems: Impact of Tongue Weight and Load Variations2026-01-04664/7/2026
The demand for improved energy efficiency in real-world vehicle operations continues to grow with technology enhancement. When transporting large cargo loads with passenger pickup trucks and rental trailers, the interaction between vehicle payload, towing configuration, and fuel consumption becomes a key factor in overall system efficiency. Understanding how towing configurations and trailer loading influence fuel consumption and vehicle performance is critical for both consumer guidance and vehicle system design. This study investigates the energy efficiency of U-Haul truck and trailer systems, with a particular focus on the influence of trailer tongue weight. U-Haul truck and trailer simulation models were developed using AVL Vehicle Simulation Model (VSM) software, with an F-350 engine brake-specific fuel consumption (BSFC) map integrated to represent realistic engine performance. Two configurations with equal payload were evaluated: (1) a U-Haul truck alone, and (2) a U-Haul truck towing a trailer. Within these configurations, multiple scenarios were analyzed, including variations in payload levels and tongue weight distributions. Driving cycles were selected to capture common moving conditions such as urban stop-and-go traffic and extended highway operation. Simulation outputs quantified the interactions among vehicle dynamics, powertrain load, and fuel consumption. Results show clear differences in energy consumption between standalone and towing configurations, with tongue weight distribution exerting a significant influence on both efficiency and stability. The findings provide practical insights into the energy trade-offs between independent vehicle operation and towing scenarios. Moreover, the study highlights the importance of load distribution and driving cycle considerations in optimizing fuel consumption, offering a framework that can be extended to rental, commercial, and consumer towing applications where energy efficiency and vehicle performance are important.
Wang, Gang, Kathadi, Mohammad, Yang, William, Chen, Yan
Modal Analysis Approach on Vehicle Transient Behavior Considering Roll and Pitch Dynamics2026-01-02104/7/2026
When a vehicle performs planar motion, the tire side force induces a jacking-up effect determined by the suspension roll center height governed by suspension geometry. These jacking forces also excite pitching motion. In this study, the pitching degree of freedom, along with roll degree of freedom, was incorporated in the bicycle model of the vehicle motion, hence it becomes four-degree-of-freedom model, and a new analytical method that applies modal analysis method to the model decomposes the motion of the sprung mass of the vehicle into mutually independent vibration modes. Since the superposition of these vibration modes can reproduce vehicle motion, these vibration modes are the fundamental factors governing sprung-mass behavior. Therefore, understanding how these vibration modes respond to design parameters provides a theoretical foundation to design desired vehicle dynamics from the early stage of car development. This report presents, by conducting modal analysis of the four-degree-of-freedom model, that the pitching dominant mode and the mode associated with planar motion and roll, which constitute a three-degree-of-freedom system, are mutually independent dynamically. Furthermore, the suspension design method that controls the pitch-dominant mode can ameliorate the initial turn-in response of the sprung mass in the desirable direction. The insight presented in this report can offer a systematic understanding of the essential characteristics of sprung mass dynamics and can provide new theoretical framework for vehicle dynamics performance design.
Kusaka, Kaoru, Yuhara, Takahiro, Koakutsu, Shingo
An Integrated Model Approach for Predicting Vehicle Dynamics Performance in HiL Validation2026-28-00442/12/2026
In the automotive industry, increasing noise regulations are influencing product sales and passenger comfort, creating a need for more effective noise testing methods. Hardware-in-Loop (HiL) based virtual acoustic testing serves as a critical step before Driver-in-Loop testing, allowing for the assessment of vehicle performance and noise levels inside and outside the vehicle under various conditions before physical prototype testing is performed. The Hardware-in-the-Loop (HiL) simulator setup is equipped with joystick control that requires a physical representation of the vehicle dynamics model provided as a Functional Mock-up Unit (FMU) in real-time format. In contrast, the vehicle control logic is implemented in C++ code. The simulator incorporates both lateral and longitudinal dynamics. Additional interfaces are integrated to support joystick input and virtual road visualization enabling realistic vehicle maneuvering and dynamic performance evaluation. However, performing all test protocols directly on the HiL setup can be time-consuming and costly. To address this limitation of full HiL testing, in this study, an offline Software-in-the-Loop (SiL) Co-simulation framework was developed as an alternative. This method replicates the HiL environment within MATLAB/Simulink, where joystick actions are simulated according to predefined driving protocols. The dynamic behavior of the vehicle during a reverse driving protocol, involving a 540° constant steering angle and 0–100% acceleration pedal input, was analyzed and compared between Offline SiL and HiL environments. Results demonstrated that 85% of key parameters exhibited strong correlation (R2 > 0.9), confirming that the offline SiL-based approach effectively replicates HiL performance. The remaining parameters also showed acceptable consistency. These findings indicate that the proposed Offline Co-simulation method is a promising, cost-effective, and scalable alternative for accurately predicting vehicle dynamic behavior, aligning well with current automotive industry needs for early-stage validation and optimization.
Visuvamithiran, Rishikesan, Chougule, Sourabh, Srinivasan, Rangarajan, Laurent, Nicolas
Measurement of Light Truck Tire Operating Deflection Shapes Using Scanning Doppler Laser Vibrometry: Issues and Insights10-10-02-00102/10/2026
As internal combustion engines are replaced by quieter electric motors in ground vehicles, noise and vibration sources aside from the powertrain have become relatively more important. This is especially true of tires. Measurement of the dynamic vibratory characteristics of tires is critical to understanding their influence on the noise and vibration performance of vehicles, both outside the vehicle body and inside of it. In this work, the normal modes and operating deflection shapes of a Yokohama Geolander A/T light truck tire are measured using traditional modal analysis techniques as well as a non-contact Scanning Laser Doppler Vibrometry (SLDV) approach. Boundary conditions including free, fixed, loaded, and rotating are implemented to the tire and investigated. Rotating conditions are accomplished in a physical chassis dynamometer environment, with the measured tire mounted on the front axle of a Chevrolet Silverado 1500 pickup truck. Modes of vibration and associated natural frequencies that are measured in all four boundary conditions, including steady-state rotation, are reported and illustrated. Results of the study show that operating deflection shapes of a rotating light truck tire can be measured on a chassis dynamometer using SLDV, assuming the tire is undergoing steady-state rotation, but certain disadvantages in the dynamometer environment make the measurement procedure challenging. Specific concerns such as tire rotating speed consistency and sufficient spatial and frequency resolution of the measurements are delineated in this work. Moreover, practical recommendations for measurement of rotating tire operating deflection shapes using a SLDV are included, and a comparison with the Digital Image Correlation (DIC) method of measurement is presented.
Bastiaan, Jennifer M., Chauda, Gaurav, Baqersad, Javad, Gupta, Arjun, Dhami, Kevalya
Benchmark of Conventional and By-Wire Brake System Layouts for Electric Vehicle Applications by Numerical Simulations2026-01-50082/3/2026
The recently increasing global concern about sustainability and greenhouse gas emission reduction has boosted the diffusion of electric vehicles. Research on this topic mainly focuses on either re-designing or adapting most conventional vehicle subsystems, especially the propulsion motor and the braking components. In this context, the present work aims to model, analyze, and compare three-braking system layouts design alternatives focusing on their contribution to vehicle performance and efficiency: a commercial vacuum-boosted hydraulic braking system, a commercial integrated electrohydraulic braking system, and a concept distributed electrohydraulic brake system. Braking systems performance are evaluated by simulating key maneuvers adopting a full model of a battery electric vehicle (BEV), which includes all relevant components like tires, and powertrain dynamics, which is validated against real-world data. Implementation and integration of the first two systems are discussed, followed by the design and detailed modeling of the third, which includes a control strategy for pressure modulation, including antilock braking system (ABS) and electronic stability control (ESC) functionalities. Once the simulation environment is set, simulations are performed and KPIs are defined to compare the three braking systems from both the performance and the energy consumption point of view. The results show that the distributed electrohydraulic system reduces the time to lock by 30.8%, the stopping distance by 5.89%, and the energy consumption by more than 50% in specific test cases compared to the analyzed vacuum-boosted system due to its distributed hardware and control architecture and power-on-demand operation.
Savi, Lorenzo, Garosio, Damiano, Floros, Dimosthenis, Vignati, Michele, Travagliati, Alessandro, Braghin, Francesco
2026 Nissan Sentra review: putting the pieces together26AUTP02_162/1/2026
The Nissan Sentra has provided straightforward behavior and performance for sedan shoppers in the U.S. for over forty years. For 2026, Nissan took the solid 2025 model and made enough mechanical tweaks and visual changes to call it an all-new vehicle. This might sound like a bit of a stretch, but given how the advancements add up to an improved drive experience in a better-looking vehicle, we'll let it slide. Available in four grades - S, SV, SR and SL that range from $22,400 to $27,990, before destination fees and packages - the 2026 Sentra puts on airs like it's a simple vehicle, hiding some of its advanced technology to keep the interior clean and clear, from the driver's screen to the steering wheel buttons. Wireless device charging and wireless Apple CarPlay/Android Auto minimize wire clutter. The standard 12.3-in NissanConnect infotainment touchscreen hides its options in a selection of tiles, and it has a single round volume button that makes it easy to turn down quickly.
Blanco, Sebastian
A Comparison of 2, 3, and 4 Bladed eVTOL Lift Rotor Performance and Vibratory LoadsSM-2026-VLADA-51651/27/2026
This study investigates the performance and vibration characteristics of representative lift rotors for a notional lift+cruise electric vertical takeoff and landing (eVTOL) configuration. As new eVTOL concepts continue to be developed and others progress towards FAA certification, it is crucial to understand the performance and vibratory considerations associated with different lift rotor design choices, including the number of blades and the method of thrust control (e.g., variable blade pitch/fixed RPM vs. fixed blade pitch/variable RPM). The NASA Revolutionary Vertical Lift Technology (RVLT) Lift+Cruise configuration was chosen as the baseline vehicle for this analysis (Ref 1). The investigation includes the evaluation of multiple lift rotors with 2-, 3-, and 4-bladed configurations as well as variable- vs. fixed-pitch designs. Both isolated rotor and full vehicle simulations were assessed to demonstrate some of the design variables applicable to the full vehicle performance and vibratory content. Industry-standard rotorcraft comprehensive analysis software, the Rotorcraft Comprehensive Analysis System (RCAS) (Ref 3), was used to evaluate and compare each configuration for performance and vibratory loads at key points in the rotors and in the fuselage
Saberi, Sayna, Hasbun, Matthew, Saberi, Hossein
Digital Twin for Motor & MCU Incorporating Deep Learning AI-ML Algorithms for Intelligent Prognostics for Electric Vehicles2026-26-04791/16/2026
As electric vehicles (EVs) become more advanced, so ensuring the reliability of critical components like the motor and Motor Control Unit (MCU) is essential. This paper presents a digital twin model designed to predict failures in motor and MCU components using machine learning. The approach focuses on detecting early signs of failure through real-world data and advanced analytics. We collected thermal and performance data from field vehicles, capturing both normal (healthy) and abnormal (faulty) operating conditions. Using this dataset, we developed and trained an Auto Encoder-based machine learning model that learns what “normal” looks like and flags deviations as potential issues. One key outcome of this study is the successful early prediction of Insulated Gate Bipolar Transistor (IGBT) degradation, where the system identified subtle behavioral changes long before any visible failure symptoms appeared. This digital twin acts as a virtual replica of the physical components, continuously monitoring and comparing real-time data to the learned normal behavior. It serves as a powerful tool for predictive maintenance, helping to reduce downtime, avoid unexpected failures, and optimize vehicle performance. The strength of this work lies in combining actual component-level data with a robust machine learning pipeline to create a scalable and practical failure prediction system. We are currently expanding this model to cover a wider range of failure scenarios for both motors and MCUs. This study offers a significant step toward smarter, more reliable electric vehicles by enabling early detection of potential failures through digital twins and AI.
Joshi, Pawan, Pandey, Suchit, KONDHARE, Manish, Upadhyay, Abhay, JaganMoahanarao, Vana, Tank, Prabhu
E-Drive System Selection Criterion for Hybrid Electric Heavy Commercial Vehicles Segments2026-26-04671/16/2026
The automotive industry has been expediting progress toward electrification since climate change driven by global warming represents a significant environmental challenge with far-reaching implications. While electric vehicles offer considerable potential for mitigating CO₂ emissions, their elevated upfront costs pose a notable challenge to large-scale market penetration. Hybrid electric vehicles can serve as an effective intermediary solution, bridging the gap between conventional internal combustion engine vehicles and fully electric vehicles, owing to their comparatively lower initial costs. Hybrid electric vehicle component selection is a complex process that must fulfill multiple requirements: fuel economy, performance, drivability, packaging, total cost of ownership and comfort. Additionally, the selection of hybrid configuration also plays a vital role in determining the cost of the hybrid electric vehicle. Hence, it is a great challenge to select the right powertrain configuration, including architecture selection (P1, P2, P3 and P4), motor and overall gear ratio to achieve better overall performance compared to conventional vehicle constraints and targets. The present study investigates the principles guiding the selection of an electric drive system for hybrid electric vehicles in the heavy commercial vehicle segment. The analysis encompasses motor sizing, transmission configuration, and overall gear ratio optimization to meet defined vehicle-level performance targets. The automotive market in India is analyzed and the Heavy commercial vehicle segment selected to specify hybrid powertrain configuration. The principle is based on vehicle performance, design, cost and efficiency requirements. The simulation is done to estimate motor torque/power requirements & overall gear ratio meeting the vehicle target and constraints. Based on the analysis, a comparative matrix consisting of various parameters for heavy commercial vehicle (HCV) application is established. The analyzed results and comparative assessment indicate that
Shendge, Raman, Jadhav, Vaibhav, Wani, Kalpesh, Warule, Prasad
Tailgate Stiffness Mitigation and Engineering Strategy for Full Width Tailgate-Mounted Tail Lamp Integration2026-26-04941/16/2026
The tailgate, as the rearmost vehicle opening, plays a pivotal role in defining the rear aesthetic theme while ensuring structural durability and maximizing luggage space. Contemporary automotive design trends highlight an increasing demand for Full width tailgate-mounted tail lamp configurations, which deliver a bold and dynamic visual appeal. Enhanced by animated lighting features, these designs cater to the preferences of Gen Z customers, becoming a decisive factor in purchasing decisions. However, integrating these complex tail lamp structures introduces significant engineering challenges, including increased X-dimension lamp volume, thereby providing reduced design space, and intricate mounting schemes constrained by panel stamping limitations. These factors necessitate the development of innovative joinery strategies and structural definitions to maintain durability targets, including achieving 25,000–30,000 slam cycles without failure, while preserving luggage space. This paper presents a comprehensive design and engineering approach aimed at enhancing the modal performance of automotive tailgate systems, with a particular focus on configurations incorporating full width taillamps. The study addresses key structural challenges associated with maintaining stiffness and durability while accommodating complex styling and packaging constraints. By optimizing outer panel joinery, refining mounting strategies, and redefining inner structural reinforcements, the proposed methodology achieves significant improvements in dynamic stiffness characteristics. Experimental and simulation-based evaluations demonstrate a 12% increase in modal stiffness for conventional tailgate architectures and a 45% improvement in coupe-type liftgate configurations. The findings offer valuable insights into the co-development of structural and styling elements in modern tailgate systems, contributing to improved vehicle performance, NVH behavior, and customer satisfaction.
Beryl, Joshua, Mohanty, Abhinab, Unadkat, Siddharth, Selvan, Veera
Methodology for Optimizing the Bench Testing of Steering I-Shaft Using Vehicle Level Data to Reduce Overall Product Development Time and Cost2026-26-05601/16/2026
Steering I-shaft with rubber coupling (or hardy disc) is an important part of complete steering system mainly in body on frame (BOF) vehicles. Hardy discs are used to dampen the vibrations that transmit to steering wheel through frame, steering gear and I-shaft. They also support to accommodate the variation between frame and BIW (Body in white) of body on frame vehicles. They are made up of rubber or other polymer composites, which have less torsional stiffness as compared to metals. The overall torsional stiffness of steering system reduces since the hardy disc is used in series in steering system, that impacts on the overall performance of steering system. So, during development of I shafts with different design, stiffness of hardy discs are used to optimize the steering and NVH performance of vehicle. Considering the development time and cost, each design of I-shaft cannot be validated at vehicle level. The torsional and axial force or displacement of hardy disc is measured at vehicle level on different test tracks and block cycles are made that consists of different displacement / force along with frequencies. These block cycles are then used at bench level testing of I-shafts. This paper summarizes the methodologies to measure the force or displacement of I- shaft, converting raw data to useful block cycles and test set up for bench testing of steering I-shaft.
Kabdal, Amit
Enhancing Range Prediction Accuracy for the BEV: Bridging the Gap between Simulation and Real-World Testing2026-26-05211/16/2026
Accurate range estimation in battery electric vehicles (BEVs) is essential for optimizing performance, energy efficiency, and customer expectations. This study investigates the discrepancies between physical test data and simulation predictions for the BEV model. A detailed range delta analysis identifies key contributors to the observed deviations, including regenerative braking inefficiencies, increased propulsion demand, auxiliary loads, and estimated drivetrain losses within the Electric Drive Module (EDM) during traction and regen. Results indicate that the test vehicle exhibits lower regenerative braking efficiency, higher traction forces and lower regen energy than predicted by simulations, primarily due to EDM inefficiencies and friction brake usage during regeneration. The study underscores the importance of refining simulation methodologies by integrating real-world, test based EDM loss maps to improve accuracy and better align predictive models with actual vehicle performance. Future work will focus on enhancing simulation fidelity and minimizing range estimation deviations to support BEV development and validation.
Mahajan, Prasad, Kesarkar, Sidhesh, Ali, Shoaib
Evaluating Passenger Car Tyre Performance: Lateral and Longitudinal Dynamics in Varied Testing Conditions2026-26-05781/16/2026
The lateral and longitudinal dynamics of passenger car tyres are critical to overall vehicle safety, handling, and stability. These characteristics directly influence braking, acceleration, and cornering performance. This study investigates the impact of key input parameters, namely inflation pressure, vertical load, and inclination angle, on tyre behaviour using a dual approach: Indoor testing with a Flat-Trac CT+ (FTCT+) and Outdoor evaluation using a skid trailer. Lateral dynamics are evaluated at slip angles to analyze lateral force and aligning moment characteristics. The influence of inclination angle, pressure, and load is quantified through cornering stiffness and aligning stiffness. The tests are conducted in both sweep and steady-state modes. To maintain data consistency, all tests use tyres of a single specification sourced from the same production batch. Longitudinal behaviour of a tyre is characterized by various parameters such as peak friction coefficient, sliding friction coefficient, and longitudinal slip stiffness. Comparisons between indoor and outdoor environments offer insight into the variability and consistency of test results under controlled versus real-world conditions. The study compares tyre performance using FTCT+ indoor testing and Skid Trailer outdoor evaluations, including an analysis of steady vs transient behaviour on FTCT+. Steady-state tests showed consistently higher cornering and aligning stiffness, by up to 9.1% and 24.1%, respectively, across different camber angles and inflation pressures. Similarly, FTCT+ yielded higher brake Mu peak (10-16%) and longitudinal slip stiffness values (30-50%), against outdoor results. The key trends identified in these variations provide insights on how various input parameter and test environments influence the tyre performance and offer input for the advancements of test methodologies.
Sethumadhavan, Arjun, Duryodhana, Dasari, Tomer, Avinash, Ghosh, Prasenjit, Mukhopadhyay, Rabindra
Integrated Powertrain Domain Controller Using Wide Band Gap Devices for EV Applications2026-26-00551/16/2026
The rapid evolution of electric vehicles (EVs) has amplified the demand for highly integrated, efficient, and intelligent powertrain architectures. In the current automotive landscape, EV powertrain systems are often composed of discrete ECUs such as the OBC, MCU, DC-DC Converter, PDU, and VCU, each operating in isolation. This fragmented approach adds wiring harness complexity, control latency, system inefficiency, and inflates costs making it harder for OEMs to scale operations, lower expenses, and accelerate time-to-market. The technical gap lies in the absence of a centralized intelligence capable of seamlessly managing and synchronizing the five key powertrain aggregates: OBC, MCU, DC-DC, PDU, and VCU under a unified software and hardware platform. This fragmentation leads to redundancy in computation, increased BOM cost, and challenges in system diagnostics, leading to sub-optimal vehicle performance. This paper addresses the core issue of fragmented control architectures in EV powertrains by proposing a domain controller based integrated solution for EV powertrain referred as Integrated Powertrain Domain Controller (IPDC).
Kumar, Mayank, Deosarkar, Pankaj, Inamdar, Sumer, Tayade, Nikhil
Optimizing Powertrain Performance a Customer Usage-Centric Approach2026-26-00671/16/2026
The US trucking industry heavily relies on the diesel powertrain, and the transition towards zero-emission vehicles, such as battery electric vehicles (BEV) and fuel cell electric vehicles (FCEV), is happening at a slow pace. This makes it difficult for truck manufacturers to meet the Phase 3 Greenhouse Gas standards, which mandate substantial emissions reductions across commercial vehicle classes beginning of 2027. This challenging situation compels manufacturers to further optimize the powertrain to meet stringent emissions requirements, which might not account for customer application specifics may not translate to a better total cost of ownership (TCO) for the customer. This study uses a simulation-based approach to connect customer applications and regulatory categories across various sectors. The goal is to develop a methodology that helps identify the overlap between optimizing for customer applications vs optimizing to meet regulations. To use a data-driven approach, a real-world customer usage pattern analysis was conducted to identify key performance metrics required to optimize driveline components. Additionally, the impact of certification requirements on vehicle performance is examined to ensure compliance while maximizing the benefits of the proposed optimization strategies. The findings of this research will provide valuable insights for manufacturers, enabling the development of trucks that are not only efficient and high-performing but also compliant with environmental standards, ultimately leading to a more sustainable future in the trucking industry.
Mohan, Vignesh, Darzi, Mahdi
Multi Speed – Multi Motor Torque Vectoring Control for a Commercial Electric Truck to Enhance Drivability and Performance2026-26-00581/16/2026
This paper explores the requirement of multi speed – multi motor torque vectoring in a battery electric commercial truck. The area of focus was to compare the vehicle performance and range of a BEV truck with conventional central drive single motor configuration with the same vehicle consisting of a multi speed – multi motor torque vectoring control strategy. Through this exercise, we have analysed the motor power and torque requirements to meet the vehicle performance along with the required reduction ratios. A MATLAB based vehicle model is used to simulate the effect of multi motor operation on the vehicle range. Also simulated the effect of torque vectoring control algorithm on the vehicle performance like steady state cornering(SSC), Double Lane change (DLC), Off road dive Cycle, vehicle stability and turning circle diameter(TCD).
Pethkar, Shivanand, S, Srivatsa, Ghosh, Sandeep, Londhe, Santkumar
Development of a New Methodology for Measuring the Dimensions of Lighting Components in Construction Equipment Vehicles (CEVs) as per IS/ISO 12509:2004 using 3-Dimensional Planar Laser2026-26-01451/16/2026
The light and light signaling devices installation test as per as per IS/ ISO 12509:2004 & IS/ISO 12509:2023 for Earth Moving Machinery / Construction Equipment Vehicles is a mandatory test to ensure the safety and comfort of both road users and operators. Considering the shape and size of construction equipment vehicles, accurate measurement of lighting installation requirements is crucial for ensuring safety and regulatory compliance. The international standard IS/ISO 12509:2004 & IS/ISO 12509:2023 outlines specific criteria for these installation requirements of lighting components, including the precise measurement of various dimensions to ensure optimal visibility and safety. Among these dimensional requirements, the dimension 'E' i.e., the “distance between the outer edges of the machine and the illuminating surface of the lighting device” plays a critical role in the performance of vehicle lighting systems. Traditional methods of measuring this dimension, such as using a measuring tape and long straight rod, in another method Using Rope, Plumb and Measuring tape have limitations in terms of precision and consistency due to machine size and shape. This paper presents a method development approach utilizing a 3-Dimensional planar laser for measuring dimension 'E' in Construction Equipment Vehicles (CEVs). Measurement through the planar laser method is found to offer significantly higher accuracy compared to conventional measuring techniques, particularly when applied to the complex shapes and sizes of CEV’s such as Motor Graders, Wheel Loaders and Backhoe Loaders. This approach not only enhances the measurement accuracy but also improves the efficiency of the testing process. The paper discusses the methodologies, results, comparison of 3 measuring methods and potential applications of Planar laser in the context of IS/ISO 12509:2004 & IS/ISO 12509:2023, offering a promising alternative method for future testing and certification of Construction Equipment Vehicle’s lighting systems.
Ghodke, Dhananjay Sunil, Belavadi Venkataramaiah, Shamsundara, Tambolkar, Sonali Ameya
System-Level Simulation to Design a Hydrogen Powered Refuse Collection Vehicle Based on Real-World Fleet Operational Data2026-26-03991/16/2026
This study addresses one of the challenges in the energy transition of heavy-duty vehicles by converting a diesel Refuse Collection Vehicle (RCV) into a hydrogen-powered prototype. The research is part of the VeH2Dem project funded by NextGenerationEU and focuses on dimensioning the complete hydrogen propulsion system for a RCV, including the energy storage capacity, without compromising payload or operational functionality. The development of the propulsion system is based on a comprehensive analysis of operational data extracted from fleet management systems, complemented by detailed instrumental monitoring of various collection routes. This methodology ensured that the prototype inherits performance equivalent to the original internal combustion engine vehicle across all evaluated scenarios. The vehicle performance objectives were established following a comparative analysis with solutions currently available in the RCV market, incorporating statistical analyses to ensure continuous operation capability across multiple work shifts.
Cano, Pablo, Barrio, Roberto, Roche, Marina, de-Lima, Daniela, Batista, Sara, Bertolí, Xavier
Simulation Research on Torque Vectoring, Braking, Chassis Control Strategies for Electric & Hybrid Vehicles using System Simulation2026-26-04231/16/2026
Electric Vehicles and Plug-in Hybrids alleviate the energy crisis but pose a unique challenge for vehicle dynamics. Though significant developments in motor control strategy and energy density management are evolving, we face significant challenges in torque management, with several ADAS features being an integral part of the EVs/xHEVs. It demands high-fidelity physical and control model exchanges between electric chassis, ride-handling, tire modelling, steering assist, powertrain, and validation using a 0D–1D platform. This paper explicates a unified strategy for improving overall vehicle performance by intelligently distributing and coordinating drive torque to enhance traction, stability, and drivability across diverse operating conditions through co-simulation. The co-simulation platform includes physical models in AMESIM, and control strategies integrated in MATLAB/Simulink. The platform features comprehensive representations of digital vehicles that require detailed modelling of the electric motor, transmission, differential, suspension, wheel dynamics, resistive forces (aerodynamic drag, rolling resistance), and road gradient effects, enabling accurate emulation of real-world vehicle behavior. Correlation of test vs. simulation validates the functionality and robustness of the interaction between physical, basic, and application software control strategy. Digital vehicle validation includes traction-based torque limitation (Correlation: >90%), distributing proportionate hydraulic and regenerative braking to improve braking performance, one-pedal driving and stoppage (Correlation: > 85%), SOC influence on regenerative braking, and smooth torque vectoring during dynamic behavior. Evaluation of diverse driving scenarios like, Gradient profiles (Uphill/Downhill/Curvilinear banking), Gradient-μ surfaces for real-world road profiles extracted from GPX/OSM data. Outcome of correlation details reduction of torque fluctuation, vehicle jerk during mode switching & stoppage, Anti-rollback, Aggressive Acceleration, Failure mode mimicking inverter failure in E-powertrain to construct a dynamic target to avoid lateral deviation.
Eruva, Patrickxavier, Sarapalli Ramachandran, Raghuveeran, Chougule, Sourabh, Natanamani-Pillai, Siva Subramanian, Scheider, Clement, Leclerc, Cedric, Natarajasundaram, Balasubramanian
The Selection of a Two-Speed Gearbox for Small Commercial Electric Vehicles Aims to Optimize Powertrain Efficiency2026-26-04171/16/2026
In response to the significant environmental challenges posed by climate change driven by global warming, the automotive industry is accelerating the transition toward electrification. While electric vehicles offer considerable potential for mitigating CO₂ emissions, their elevated upfront costs pose a notable challenge to large-scale market penetration. Energy efficiency improvement of electric vehicles is emerging field of research to reduce total cost of ownership. Electric vehicle powertrain component selection in small commercial vehicles including three and four wheelers is a complex process which has to fulfil multiple requirements which includes range, performance, drivability, packaging, total cost of ownership of vehicle and comfort. In addition, powertrain configuration including battery, motor and transmission ratio selection plays a fundamental role in cost of electric vehicle. Hence, The task of selecting the right powertrain configuration, encompassing architecture, motor selection, and gear ratio, is a critical challenge in optimizing overall vehicle performance while addressing the constraints and objectives of conventional vehicles. This study focuses on benefits of two speed transmission ratio for small commercial electric vehicle segment. The automotive market in India is analysed and the small commercial vehicle segment selected to define powertrain configuration. "The principle is grounded in the requirements for vehicle performance, design, cost, and efficiency, each of which must be carefully balanced to achieve optimal outcomes. The simulation are done to estimate motor torque/power requirements & overall gear ratios meeting the vehicle target and constraints. Based on the analysis, a comparative matrix consisting of various parameters for small commercial vehicle (SCV) application is established. The analysed results and established comparative matrix conclude that: 1 Small commercial vehicles necessitate an E-Drive system with high torque capability at low speeds, requiring deeper gear ratios, a lower maximum system speed, and overall reduced costs. For equivalent power ratings, motors with higher peak torque tend to be more expensive than those used in passenger vehicles, which typically employ motors with lower peak torque. 2 Two speed transmission, first gear increases gradeability performance by more than 40% with same motor and second gear allows motor to work in higher efficiency zone results up to 5% EV Range improvement 3 This approach allows to reduce the peak motor power/torque by 20-30% due to 2 speed gearbox, this is huge cost reduction solution with modularity. 4 The two-speed gearbox helps the motor operate more efficiently, which slightly reduces the electrical power needed from the battery. This leads to lower average battery current and less heating, which can improve battery life and reliability. As a result, it may reduce the need for extra warranty coverage on the battery, which is one of the most expensive parts of an electric vehicle. Considering the significant volume of the electric three-wheeler (e3W) market, it would be prudent for OEMs to explore the adoption of a two-speed automatic gearbox as a standard offering for light commercial vehicles (LCVs), especially if they have previously overlooked this rapidly growing sector.
Wani, Kalpesh, Jadhav, Vaibhav, Shendge, Raman, Warule, Prasad
Fatigue Life Prediction Methodology for Vehicle Suspension Systems and Correlation with Physical Test2026-26-04591/16/2026
Fatigue analysis is a vital aspect of suspension design, especially for load bearing components such as the Rear Twist Beam, where durability under cyclic loading is essential for long-term vehicle performance. Among the various durability tests, the roll fatigue test is a key procedure for validating suspension strength and reliability. However, conducting physical roll fatigue tests can be both expensive and time consuming, particularly when multiple design iterations are required. This not only increases cost but also extends the development timeline. This study presents a virtual simulation methodology that replicates roll fatigue test conditions within a finite element analysis environment, enabling early fatigue assessment and design optimization. Developed to support the early design phase, the roll fatigue test simulation process ensures robust designs that meet targeted fatigue life requirements. The approach begins with a detailed understanding of the physical roll fatigue test setup, which is then reproduced through Finite Element Analysis to closely match real test conditions. Enforced displacement applied at wheel center according to duty cycles, and boundary conditions are defined to match those of the physical test. Cumulative damage is then calculated in fatigue solvers to predict the fatigue strength of the components. The methodology is validated by correlating results with physical test data. This approach enables early detection of potential fatigue-critical areas, supports design optimization, and reduces both development cycle time and cost by minimizing physical testing. As a result, development cycles are shortened, costs are lowered, and durable, manufacturable designs can be achieved earlier in the product development process.
Kokare, Sanjay, Nagapurkar, Tejas, Iqbal, Shoaib
Driving Behavior Modelling with Graph Attention Networks2026-26-06611/16/2026
In automotive engineering, understanding driving behavior is crucial for decision on specifications of future system designs. This study introduces an innovative approach to modeling driving behavior using Graph Attention Networks (GATs). By leveraging spatial relationships encoded in H3 indices, a graph-based model constructed, which captures dependencies between various vehicle operational parameters and their operational regions using H3 indices. The model utilizes CAN signal features such as speed, fuel efficiency, engine temperature, and categorical identifiers of vehicle type and sub-type. Additionally, regional indices are incorporated to enrich the contextual information. The GAT model processes these heterogeneous features, learning to identify patterns indicative of driving behavior. This approach offers several significant advantages. Firstly, it enhances the accuracy of driving behavior modeling by effectively capturing the complex spatial and operational dependencies inherent in vehicle data. The use of GATs allows for the dynamic weighting of different features, ensuring that the most relevant information is prioritized in the analysis. Secondly, the integration of regional indices provides a deeper contextual understanding, enabling the model to discern region-specific driving patterns that might otherwise be overlooked. Furthermore, this method facilitates the identification of abnormal behavioral trends, offering valuable insights for design engineers. By understanding region-based driving behavior, engineers can modify vehicle systems to better meet the needs of specific areas, leading to improved performance and user satisfaction. The combination of graph-based methods with attention mechanisms represents a significant advancement in vehicle performance monitoring, paving the way for a more comprehensive understanding of driving behavior across different regions.
Salunke, Omkar
Drive Cycle Type Identification Using Data Driven Methodologies2026-26-06491/16/2026
Identifying the type of drive cycle is crucial for analyzing customer usage, optimizing vehicle performance and emission control. Methods that rely on geographical location for drive cycle identification are limited by varying driving conditions at the same location (e.g. heavy traffic during peak hours vs. free-flowing traffic at night). This paper proposes a methodology to identify the type of drive cycle (city, interurban, highway or hybrid) using drive characteristics derived from vehicle data rather than geographical location. Real-world vehicle data from testing trucks is taken, whose drive profiles are already known. Initially, multiple characteristic features of the drive cycle are identified from literature surveys and domain experience. These features, which can be extracted from basic signal data, include gear shifts, time spent in different driving modes (acceleration, cruise, standstill), velocity distributions, and an 'aggressiveness factor' representing overall driving style. Using ML based feature selection techniques, the most important features are selected for this cause. With these finalized parameters, a data-driven classification model is developed. This model is trained, validated, and tested using the identified real-world vehicle data. It classifies drive cycles into four major types: city, interurban, highway, and hybrid with a high degree of accuracy. This classification enables accurate identification of drive cycles, addressing the limitations of location-based methods. The developed model is employed to determine the type of drive cycle driven by customers, thereby aiding in the analysis of the influence of drive cycles on vehicle performance and emissions.
Reddy, Mallangi Prashanth, Gorain, Raju, Ganguly, Gourav
Representative Drive Cycle Velocity Profiles Prediction Using ML Algorithms for Energy Efficiency2026-26-06381/16/2026
Maximizing vehicle energy efficiency and its performance is a high priority for automotive industries as customers’ expectations rise. Engineers constantly face the challenge of balancing the conflicting goals of achieving superior performance and maximizing energy efficiency, all while meeting increasingly tight development timelines. Leveraging digital methods can potentially enable considerable reduction in development timelines. Driving cycles function as standardized measurement procedures for certifying vehicle fuel efficiency and driving range. Representative velocity profiles condense numerous real-life driving cycles to enable quicker energy analysis and driver feedback evaluations. This paper introduces a novel methodology for generating synthetic drive cycles, such as average velocity cycles and ideal consumption velocity cycles, based on real-life driving scenarios. In this study, the importance of creating representative drive cycles to enhance vehicle performance and energy consumption is highlighted. A detailed analysis of city cycle drives is provided, focusing on the prediction of average and ideal consumption velocity profiles using advanced machine learning, deep learning, and reinforcement learning techniques available in the field of data science. By leveraging data from numerous drives conducted at various ambient temperatures throughout the year, these velocity profiles were forecasted. The validation of these models is discussed in the paper, along with how effective and usable this method can be to save time in the overall vehicle development process. The merits and demerits of each ML model used to create synthetic drive cycles are discussed in the paper. This research offers valuable insights for automotive testing and development, significantly improving driver feedback analysis and providing essential insights to enhance driving habits and overall vehicle efficiency.
Kanakannavar, Rohit, Kelkar, Kshitij, Sadalge, Anand
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