Browse Topic: Vehicle acceleration

Items (2,490)
To address the high failure rate of rollers in coal mine belt conveyors, the inefficiency of manual replacement, and the operational disruptions caused by maintenance shutdowns, this study proposes a robotic arm system capable of replacing rollers without halting conveyor operations. The research focuses on the kinematic performance and path planning strategy of the robotic arm. A kinematic model is established using the Denavit–Hartenberg (DH) parameters, and the workspace distribution is analyzed via the Monte Carlo method. The results show that the horizontal reach exceeds 2020 mm and the vertical reach extends up to 2000 mm, which fully satisfies the spatial requirements for roller replacement across the entire conveyor system. In the path planning phase, an obstacle expansion model is constructed, and an improved Informed RRT* algorithm is implemented to generate collision-free trajectories, ensuring effective obstacle avoidance. To improve trajectory smoothness, path pruning and cubic B-spline interpolation are applied to refine the initial paths. For trajectory planning in joint space, quintic polynomial interpolation is employed, with boundary conditions set to ensure zero velocity and zero acceleration at both the start and end points, thereby guaranteeing smooth and stable motion of the robotic arm. Simulation results indicate that joint angles, angular velocities, and angular accelerations vary smoothly throughout the roller grasping process, without abrupt changes, and converge to zero at the beginning and end of the trajectory. End-effector trajectory tracking error analysis reveals positioning errors within 0.8 mm for side rollers and 3 mm for central rollers, well within acceptable engineering accuracy thresholds. This work provides a theoretical foundation and a practical implementation framework for advancing automation and intelligent operation in roller replacement tasks within coal mine belt conveyor systems.
Pu, CongyuanQian, Ke
To ensure that NURBS curve interpolation meets motion constraints during machining while maintaining low velocity fluctuations, this paper proposes a nested look-ahead velocity planning algorithm. Traditional methods require identifying feedrate-sensitive points and segmenting the curve, which may lead to local velocity exceeding the limits and can involve significant computational effort. The proposed method does not require sensitive-point detection and instead constructs the velocity profile in a globally consistent manner. The algorithm combines a backtracking S-curve acceleration/deceleration strategy to ensure compliance with motion constraints with the Gear prediction–correction method for parameter interpolation, achieving low velocity fluctuation. Through nested iterative refinement, the planned feedrate is continuously corrected until all segments satisfy the imposed constraints. Simulation results show that the method effectively prevents local velocity overshoot, significantly reduces velocity fluctuations compared with conventional second-order Taylor expansion methods, and generates feedrate profiles with continuous acceleration that minimize dynamic shocks during motion. Therefore, the proposed approach provides an effective solution for NURBS-based machining, fully meeting motion constraints while maintaining low velocity fluctuation.
Hu, Jinpeng
During the cold rolling process, when the rolling speed enters the acceleration stage, the rolling force often exhibits a linear decline accompanied by fluctuations. This leads to a decrease in the uniformity of steel strip thickness distribution, resulting in the failure of the outgoing strip to meet quality requirements in terms of shape and thickness. In this paper, a three-dimensional model of a six-high rolling mill is established using Abaqus, and the influence of gap control during the acceleration stage on strip shape is systematically investigated. By analyzing the relationship between rolling force and roll gap variations, a gap compensation strategy based on a dynamic stiffness model is proposed. Simulation results demonstrate that implementing gap compensation during the acceleration stage effectively improves the consistency of strip thickness, with significant reductions in both thickness range and standard deviation.
Tang, YingxinYan, ZhuwenCao, WenjunWu, Jiawei
This study looks at how the human head reacts and gets injured during high-G landing impacts in spacecraft return capsules. We used a vertical drop tower system for the experiments. A standard crash test dummy, called the Hybrid III 50th, was used to imitate how astronauts sit during landing. We applied two common safety standards—the Head Injury Criterion (HIC) and the 3 ms cumulative acceleration rule—to measure head response under high-G impacts. The results show several things. First, head acceleration increases linearly as seat acceleration increases. Second, the peak total acceleration of the head is much higher than the seat acceleration. In particular, acceleration in the X and Z directions is much stronger than in the Y direction. Third, when seat acceleration went over 47.71 g, HIC exceeded the safe limit of 700, and the 3 ms head acceleration also passed the 80 g limit. This suggests that 40 g should be considered a safe upper limit for seat acceleration. This work provides experimental support for improving landing systems to protect astronauts’ heads during high-G impacts.
An, HaoWang, YafengGuo, Yazhou
Based on the theory of vehicle dynamics, this paper first constructs a dynamic model of the cab air suspension system, laying a core theoretical framework for subsequent optimization research. At the level of performance evaluation indicators, the root mean square (RMS) values of the cab’s vertical acceleration, roll acceleration, and pitch acceleration are selected as key parameters. On this basis, an objective function for the damping matching of the cab air suspension system is established, clarifying the optimization direction. Building on this objective function, the paper further takes into account the constraint conditions in the actual operation of the system, using the probability of the cab air suspension system hitting the limit stop as a constraint. Finally, a complete mathematical model for the damping matching of the cab air suspension system is formed, and a genetic algorithm is used to solve this model, ensuring the scientificity and feasibility of the optimization results. To verify the effectiveness of the established model and optimization method, this paper conducts verification based on the aforementioned dynamic simulation model of the cab air suspension system: the frame displacement signals collected under actual random road conditions are used as the model input, and the established mathematical method for damping matching is applied to carry out the optimal matching design of the damping parameters of the cab air suspension system. The simulation optimization results show that the performance of the optimized system is significantly improved: the RMS value of vertical acceleration is reduced by 5% compared with that before optimization, the RMS value of roll angular acceleration is reduced by 11.2%, and the RMS value of pitch angular acceleration is reduced by 4.7%. In conclusion, the method constructed in this paper can effectively improve a practical and feasible reference for the damping optimization design of the cab suspension system.
Li, SaisaiYang, ChangGuo, RuilingZhang, ZhongyuanLiang, DongWu, Shiyu
To explore the impact of guiding and warning visual combination factors at the entrance sections of highway tunnels on drivers’ visual characteristics and driving behavior, this study recruited 16 drivers to conduct on-road vehicle experiments at the entrance sections of the Yunling Tunnel’s left bore (with visual combination factors) and right bore (without visual combination factors). Seven visual characteristics and driving behavior indicators, including pupil diameter and vehicle speed, were collected and statistically analyzed. Representative indicators such as pupil diameter, standard deviation of fixation point position, and vehicle speed were selected to establish a trend surface model of visual characteristics and driving behavior. The results indicate that when driving at the entrance section of the left bore, drivers’ pupil diameter and fixation duration were significantly lower than those at the entrance section of the right bore. With the increase in the sweeping view angle, there was a more dispersed distribution of fixation points. Additionally, there were significant differences in the acceleration and lateral deviation of the driving vehicle, with the range of variation narrowing by 52.5% and 35.7%, respectively. The trend surface model results show that under the influence of visual combination factors, the reduction in drivers’ vehicle speed was smaller, and the impact of pupil diameter and standard deviation of fixation point position on vehicle speed was less pronounced. Overall, under the influence of visual combination factors, drivers’ visual characteristics showed significant changes, with improved speed control and manipulation levels, leading to more stable vehicle operation.
Ma, YanpengHuang, HeHuang, YongYuan, Chen
As automation advances and occupants transition from active drivers to passive passengers, understanding how automated driving behavior is evaluated becomes increasingly important. While longitudinal and lateral vehicle dynamics are known to influence perceived comfort and safety, it remains unclear to what extent motion–perception relationships remain stable across urban traffic contexts. This study compares two real-world investigations of automated driving: a left-turn maneuver at a signalized intersection on a test track and a roundabout maneuver with a shuttle in public traffic. Both datasets include high-resolution vehicle dynamics and structured subjective ratings. A consistent objectification approach was applied to examine the transferability of motion–perception relationships across contexts. However, differences in vehicle platform, automation level, trajectory characteristics, and study design limit direct comparability and require cautious interpretation. Despite partially overlapping ranges in selected peak-based dynamic parameters, such as longitudinal acceleration, subjective comfort and safety ratings were consistently higher in the roundabout scenario. Furthermore, strong associations were observed between motion parameters and subjective evaluations in the intersection context (adj. R2 up to 0.891), whereas objective parameters showed only limited explanatory power in the roundabout scenario (adj. R2 ≤ 0.06). The results indicate that motion–perception relationships derived within a specific context may not be directly transferable across different traffic scenarios. The findings highlight limitations of globally derived motion-based evaluation models and underline the importance of validating objectification approaches across diverse operational environments.
Panzer, AnnaStrenge, EmmaIatropoulos, JannesHenze, Roman
Monitoring inputs and states of a structural dynamic system is often challenging, as direct measurements are costly or even infeasible. A virtual sensing methodology is presented for jointly estimating the input and state of a structure when subjected to multi-directional base excitations. The approach uses a tuned Kalman Filter combined with a model-order reduction of the system model to ensure a low computational cost whilst allowing accurate estimation from a limited number of acceleration measurements. This enables real-time virtual health monitoring strategies and reduction in instrumentation during data acquisition without additional information such as location and direction of application about the inputs. The proposed methodology is validated numerically and experimentally using a notched aluminum beam excited on a multi-directional shaker table, driven simultaneously in two in-plane directions. The study demonstrates accurate full-field estimation of multiple responses along the beam as well as a joint-input-estimation. The results highlight the relevance of multi-axis vibration environments and the importance of multiple-input-multiple-output testing for dynamic characterization and structural health monitoring applications.
Salazar Colunga, RodrigoPandiya, NimishDindorf, ChristianNaets, Frank
Part- or component-level tests are commonly performed by Tiers and OEMs to investigate the NVH behavior and loading mechanisms. However, because test bench dynamics differ from those of the actual vehicle environment, correlating measured sound, acceleration and forces between bench and vehicle often proves challenging. Blocked forces offer a way to address this issue, as they provide test bench and vehicle independent load representations. This effectively enables different Tiers to deliver consistent load data, which OEMs can then use to better tune excitation and noise transmission on their vehicles. This paper focuses on 2 test bench compensation techniques, involving pure test and a simulation models of the tire to obtain accurate blocked-forces. The compensation techniques are validated on four testbenches of different companies.
Reichart, Ronde Klerk, Dennis
For analysing flow and acoustic induced structural vibration, a fully run time coupled framework combining a hybrid CFD-CAA approach with a modal response simulation was validated and presented at the ISVNH 2022 (SAE Technical Paper 2022-01-0938). In this paper i We apply this CFD–CAA–modal coupling method to a series-representative bonnet geometry and demonstrate its capability to capture flow and aeroacoustically driven vibration with two-way coupling. ii We analyse the modal properties of the bonnet and show that confined air volumes beneath the bonnet can introduce significant fluid loading effects, which are already embedded in experimentally validated FE modal models and must therefore be treated carefully in two-way coupled simulations. iii We validate the fully coupled aeroelastic simulation against wind-tunnel measurements with undisturbed inflow, show close agreement with the measured vibration response and analyse that the dominant excitation is in this case from below the bonnet due to acoustic pressure fluctuations.
Schwertfirm, FlorianOcker, JoergHartmann, Michael
When developing a vehicle, the overall body stiffness is an important parameter to be estimated for several automotive attributes. As a complement to the traditional experimental and computational static torsional stiffness assessment, an improved method has been developed to evaluate the body stiffness when driving the vehicle on a test track. This method, valid for both test and simulation, is called Opening Distortion Fingerprint (ODF) and uses the so-called Multi Stethoscope (MSS) to measure the dynamic distortion in each body closure opening and cross section. For evaluating the distortion, from both test and Multi Body Dynamics (MBD) simulation data, the Evaluation-line (E-line) method is used. The E-line method is a linear approach. Consequently, it is only valid in the absence of large rigid body rotations of the vehicle body. Therefore, to assess the validity of the ODF method, it is crucial to identify the frequency at which the distortion results become invalid due to rigid body rotations. To identify this frequency range, in an MBD simulation the total distance output parameter can be requested and used. But for a dynamic full vehicle test, it is a major challenge to measure the total distance. Several tests have been performed without success. To calculate this frequency range from test data, this paper presents a new approach. In this methodology two different signal processing methods (E-line and Diagonal) are combined. To check the validity of the new approach, full vehicle test data has been evaluated. In addition, a simplified beam lab experiment is presented, highlighting the difference between test and MBD simulation when measuring the distortion at large rotations.
Olger, EmmaLindkvist, LisaPiiroinen, PetriKarypidis, JohnPena, MiltonBäcklund, JesperAppelgren, PeterMarberg, HenrikUgale, PravinWeber, Jens
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, MadelenMeng, ShiyangPatton, DeclanMcIntosh, Andrew S.Kleiven, Svein
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 EmilyHamilton-Wright, AndrewHassan, MarwanOliver, Michele L.
The suspension system with variable damping and variable stiffness actuators can realize four-quadrant mechanical output, effectively combining the energy efficiency of the semi-active suspension with the performance levels approaching those of active suspensions. However, the practical effectiveness of this system depends heavily on the ability of the control strategy to adapt to different driving conditions. In order to meet this challenge, this research has developed a multi-mode suspension collaborative control strategy to optimize energy efficiency and ride comfort in various operating scenarios. Based on the four-quadrant characteristics of the actuator, a suspension mode switching framework has been established, and the suspension work is divided into passive, semi-active, pseudo-active and active modes. In order to determine the appropriate switching boundary, first calculate the root mean square (RMS) value of the sprung mass acceleration and suspension dynamic deflection under passive conditions. With the existing human comfort sensitivity as a reference, the switching threshold of sprung mass acceleration is 0.527 m/s2, and the switching threshold of suspension dynamic deflection is 8.31×10−3m, and the corresponding conversion rules are formulated. Then, the LQR controller optimized by the genetic algorithm is used to allocate the control force adaptively according to the suspension mode to realize cooperative multi-mode operation. The simulation results on B-D composite road surfaces show that compared with traditional passive suspension, this method can reduce the sprung mass acceleration, suspension dynamic deflection and tire dynamic load by 10.59%, 16.65% and 32.9% respectively. These results confirm that the collaborative control strategy significantly improves the ride comfort, vehicle adaptability and overall performance in complex road conditions.
Li, ZhiyingLi, JeiZhu, AndingBai, XianxuLi, WeihanLi, Rui
End-to-end autonomous driving in urban environments faces three core challenges. First, camera and LiDAR sensor heterogeneity causes cross-modal perception inconsistencies and sensor fusion instability. Second, diffusion models suffer from training instability due to scale variance and distribution changes, which limits generalization. Third, traditional trajectory decoders lack structured interaction with semantic elements, thereby undermining planning rationality. To address these issues, CMFPNet introduces an integrated framework with three key modules. The HGCF-Backbone integrates LiDAR and camera features using channel focus, deformable cross-focus, and state space modeling to enhance semantic alignment. The NST module maps physical trajectories to normalized space, employing truncated diffusion sampling for stable generation in just 2–4 steps. The NDA models trajectory generation as a semantic narrative, utilizing a six-stage semantic attention flow incorporating BEV context, interactive dynamics, and self-states. Experiments on the NAVSIM dataset demonstrate CMFP Net’s superiority over existing baselines, showing outstanding generalization and trajectory stability in challenging scenarios. Notably, the truncated sampling strategy achieves an 8–10× acceleration during inference while maintaining decision accuracy and reducing computational costs. CMFPNet provides a scalable, semantically consistent solution for diffusion-based autonomous driving with significant potential in both research and practical deployment.
Qu, YanweiMo, Hangjie
This study investigates the dynamics and associated vibratory loads of an underactuated swashplate-less rotor and its impact on the flight dynamics of a small-scale helicopter powered by this rotor via a combined experimental and computational approach. Unlike prescribing cyclic pitch using a swashplate, here the pitch is a response to the 1/rev cyclic rotor speed input. This is enabled on the current two-bladed rotor using a skewed lag hinge that utilizes the cyclic speed variation to produce lagging motion and subsequently pitching the blades in a cyclic fashion (ƍ4 coupling) for generating the pitch and roll control moments. One of the key dynamic characteristics that distinguishes this rotor from a conventional swashplate-controlled rotor is that the two blades have dissimilar pitch and flap responses leading to high fixed-frame vibratory loads. Results show that a large 1/rev vertical shear force is transferred to the fuselage resulting in half-peak-to-peak loads of +/−0.67g. The flap responses of the two blades being out of phase caused a net inertial force, which was the dominant contributor to the vibratory vertical shear force. Upon removal of the flap hinge, the vertical shear force was reduced by almost 85% while increasing the control moment authority by 50%. The inertial moment about the rotor axis generated by cyclic lagging nearly balances the moment due to the angular acceleration (Ω ) of the rotor, which significantly reduced the dynamic torque requirement from the motor.
Stewart, Reuben-WayneBenedict, MobleSieckmann, Alexander
Prior work demonstrated that acceleration washout in motion simulators produces decay-rate sensing ambiguity within the vestibular system, forcing pilots to rely on visual cues for control. While Pilot Induced Oscillation Ratings (PIORs) for flight and simulation have been matched using different sensing thresholds, a quantitative basis for the 50% reduction in the visual decay-rate threshold has remained elusive. This paper provides evidence that pilots perceive decay rate proprioceptively through stick force during both flight and simulation, rather than through vestibular or visual channels. The residues of the stick-force sensitivity transfer function reflect the amplification or attenuation of neighboring zeros and poles; when these residues fall outside the human's 30 dB tactile sensory window, the resulting decay rate becomes imperceptible. Modeling reveals that stabilization via the visual channel in simulators produces dominant mode characteristics - decay rates, frequencies, and residues - that diverge significantly from vestibular-stabilized flight. The Virtual Vestibular Cueing (VVC) technique is introduced to tune the visual channel using pseudo-vestibular rate cueing, optimally aligning the simulated dominant mode with flight-validated residues and decay rates. A preliminary fixed-base study demonstrates that VVC synchronizes performance and subjective ratings by restoring this tactile-vestibular harmony. This work establishes that handling qualities are fundamentally an Information Theory problem: Level 1 ratings occur when the residue-to-decay gradient is tuned to map the dominant mode's physical 'elbow' onto the human sensory window. This paper establishes that perceptual fidelity is not determined by the closed-loop vehicle residues, but by the force sensitivity residues. By identifying the stick-force channel as the superior conduit for this mapping, VVC allows designers to restore informational integrity in virtual environments across all axes of aircraft dynamics. VVC ensures that the visual channel supports a control strategy where the tactile feedback remains within the human sensory window, preventing the 'wrong reading' that leads to simulator-to-flight rating mismatches.
Bachelder, Edward
Automated Vehicles (AV) pose new challenges in road safety, multimodal interaction, and urban planning, requiring a holistic approach that prioritizes sustainability and protects all road users. The KASSA.AST project addresses this by deploying and evaluating an automated shuttle in southern Austria on three routes. The study area is a Park & Ride zone near a train station, enabling seamless transfers and higher transit use. To assess the safety impacts of the automated shuttle, four Mobility Observation Boxes (MOBs) were deployed. These AI-based systems detect and classify road users, track their trajectories and geospatial coordinates, and identify safety-critical events via Surrogate Safety Measures (SSMs). Over 10 days, a trajectory dataset captured interactions among vehicles and the shuttle. The resulting real-world dataset is a core contribution. This dataset underpins microscopic behavior modeling. Trajectory pairs yield car-following and interaction metrics (relative distance, relative speed, acceleration) to calibrate custom models for realistic mixed traffic. Simulations generate a structured interaction database with time spans, trajectories, conflict points, and SSMs (such as Time-to Collision—TTC, Post-Encroachment Time—PET, and Deceleration-rate-to-avoid-crash—DRAC). These outputs support detailed analysis of shuttle interactions, including near misses. To reveal patterns, clustering identified three interpretable safety-relevant regimes: (i) a low-demand background regime (n = 96) with low speeds and near-zero deceleration demand, (ii) a fast-and-tight regime (n = 33) with reduced TTC, elevated critical-event speeds, and high DRAC/Modified (M)DRAC demand, and (iii) an AV-regulated regime (n = 10) dominated by the shuttle as adversary, showing short TTC but stable moderate speeds (~4 m/s) and conservative headway policies. Ensemble-tree supervised learning reproduced these regimes with high accuracy and revealed that critical-event speeds and counterpart headway are the strongest discriminators, while AV role metadata contributes marginally. This integrated approach—linking field data, behavior modeling, simulation, and machine learning—provides a robust framework for assessing AV safety in urban contexts.
Losada Arias, ÁngelRosenkranz, PaulHula, AndreasAleksa, MichaelSaleh, PeterErdelean, Isabela
The organizers of the most prominent Formula Student competitions have recently initiated a preliminary feasibility study on the application of hydrogen-based propulsion technologies in future single-seater race vehicles. These include electric powertrains with electrochemically converted hydrogen in fuel cell–powered vehicles, competing within the electric championship league. Based on the initial set of regulations, this study presents a model-based comparison between battery-powered (BEVs) and fuel cell–powered electric vehicles (FCVs) for Formula Student. The analysis is conducted using energy, power, and efficiency metrics from four candidate models of propulsion systems, implemented in an open and publicly available MATLAB script: two BEVs with varying battery capacities, and two FCVs employing different hybridization strategies. The aim of this study is to pinpoint and quantify the advantages and disadvantages of each technology for the Formula Student use case, and to identify the optimal solution combining the different requirements of maximum acceleration and endurance race.
Martoccia, LorenzoBreda, SebastianoFontanesi, Stefanod’Adamo, Alessandro
Design for durability in the automotive industry depends on a clear understanding of how road surfaces and driving characteristics affect structural road loads and fatigue. Traditionally, road surface classification has been subjective (e.g., city, highway, rural), and done through driving instrumented vehicles over a small selection of roads. The variations in driving characteristics that are often consequent to the road surface quality are rarely accounted for in designing vehicle level durability tests. This makes it difficult to establish targets for durability testing that accurately match the wide variations in real-world roads and driving. This paper presents a data-driven approach to objectively classify road surface and driving characteristics using metrics derived from existing road response metrics like Vibration Dose Value (VDV) and statistical estimates of vehicle speed and acceleration. Data collected at the proving grounds on gravel roads, smooth roads, city-like roads, etc., is used to identify classifiers that categorize road-driving combinations into groups correlating with structural fatigue damage. This correlation between fatigue damage and road-driving classification is developed using Wheel Force Transducer (WFT) measurements from instrumented vehicles. This method shows promise to develop structural fatigue estimates directly from telemetry data. The method provides a path to replacing subjective road classification with a vehicle-sensor and signal-based, objective classification for developing durability targets and tests. This method is also scalable in terms of application on vehicle fleet data in uncontrolled environments, to develop an accurate understanding of real-world use of vehicles by customers.
Shaurya, ShubhamRamakrishnan, SankaranDemiri, AlbionKhapane, Prashant
This study presents a torque distribution control strategy for EVs with e4WD powertrain to overcome the trade-off between ensuring vehicle acceleration and deceleration responsiveness and mitigating backlash shock in the driving system. The deterioration of the drivability which occurs from the intrinsic hardware characteristics of the drivetrain is prevented by designing a response-priority drive mode in which neither front or rear motor torque is allowed to change its sign. Instead, in such drive mode, the front motor torque is only allowed to perform regenerative braking while the rear motor torque is only allowed to produce positive acceleration torque. In order to avoid sacrificing the maximum acceleration by applying such strategy, the mode transition function is implemented as well. In addition, in order to prevent backlash impact due to drivetrain compliance, variable offset torque based on drivetrain compliance model is evaluated in real time and applied to each motor command generation strategy. The enhancement of vehicle drivetrain responsiveness directly leads to improved track driving performance, particularly for the neutral-balance phase during harsh cornering. The effectiveness of the suggested driveline torque distribution method is verified using an actual vehicle driven on the race track, and the vehicle responsiveness followed by track driving performance indices are numerically assessed for comparison.
Oh, JIWONLee, Ho Wook
Toyota vehicles equipped with Toyota Safety Sense (TSS) can record detailed information surrounding various driving events. Often, this data is employed in accident reconstruction to better understand the dynamics of a collision. TSS data is comprised of three main categories: Vehicle Control History (VCH), Freeze Frame Data (FFD), and image records. During an event, it is possible that a vehicle undergoes a catastrophic power loss from the damage sustained during the event. In this paper, the effects of sudden power loss on the VCH, FFD, and images are studied. Events are triggered on a TSS 3.0 equipped vehicle by driving toward a stationary target. After system activation, a total power loss is induced, triggered on the instrument cluster “BRAKE” alert message, at various delays after activation. This testing studies various signals recorded across VCH, FFD and image data including vehicle speed and time and date. Results show that there is a minimum time to record after system activation to record any related data. Power losses which occur before this minimum time record no data and losses after the minimum time record incomplete data sets. Studied vehicle signals were all in general agreement with measured values.
Getz, CharlesYeakley, AdamDiSogra, Matthew
Longitudinal lumbar acceleration is often overlooked as a key variable when biomechanically assessing lumbar response in rear-end collisions. The objective of this study is twofold: (1) to conduct a comprehensive literature review of peak longitudinal lumbar acceleration to statistically evaluate differences between three surrogate occupant types: human volunteers, post-mortem human subjects (PMHS), and anthropomorphic test devices (ATDs) and (2) to construct a mathematical predictive model of longitudinal lumbar acceleration using peak longitudinal vehicle or sled change in velocity (delta-V) and vehicle acceleration in rear-end impacts. Peak longitudinal lumbar acceleration was obtained from peer-reviewed literature and the Insurance Institute for Highway Safety database. Tests included belted human volunteers, PMHS, and ATD occupants seated upright in unmodified, conventional driver seats. Compared to human volunteers instrumented at L5-S1, BioRID ATDs instrumented at L1 displayed greater ratios of longitudinal lumbar acceleration to delta-V, but lower ratios when normalized by vehicle acceleration. Accelerometer placement, crash severity, test configuration and pulse duration across surrogate occupant types were found to influence overall lumbar response, relative to vehicle acceleration and delta-V. Regressions for human volunteers indicated positive relationships for longitudinal lumbar acceleration with respect to vehicle acceleration (R2 = 0.93, p<0.001) and all surrogate occupant types for vehicle delta-V (R2 = 0.96, p<0.001). Longitudinal lumbar acceleration was highly correlated to vehicle delta-V in both human volunteers and BioRID ATDs and was sensitive to crash severity and vehicle crash parameter choice (vehicle acceleration vs. delta-V). Close alignment was found between L1 BioRID ATD and L5-S1 human volunteer longitudinal accelerations at vehicle delta-Vs ≤ 14.3 km/h, suggesting that BioRID ATDs at L1 can reasonably replicate human lumbar response at L5-S1 within this crash severity range. This study quantified differences in longitudinal lumbar acceleration across occupant types in rear-end collisions and developed surrogate-specific and cumulative models for prediction of longitudinal lumbar acceleration from delta-V.
Zambare, KeyaOgbu Felix, JordanArana Barcala, EmilyWestrom, ClydeCaraan, JohnAdanty, KevinShimada, Sean
Drivers often interact with partial automation (SAE Level 2) systems, initiating transfer of control (TOC) either by handing control over to the automation or by taking it back. Accurately predicting these interactions may inform the design of future automation systems that adapt proactively to the operating context, enhance comfort, and ultimately may improve safety. We present a context-aware framework that generates a unified driver–vehicle–environment representation by fusing data from in-cabin video of the driver and of the forward roadway with vehicle kinematics, driver glance, and hands-on-wheel behaviors. This representation was encoded in a hierarchical Graph Neural Network that classified driver-initiated TOCs to: (i) Manual-to-automation and (ii) Automation-to-manual transitions and predicted time-to-TOC. Shapley-based explainable AI was used to quantify how the importance of behavioral, contextual, and kinematic cues evolved in the seconds preceding a TOC. Analysis of a naturalistic dataset of 1,565 driver-initiated TOCs from 16 experienced drivers revealed distinct patterns. Manual-to-automation transitions were preceded by lane count increases, acceleration, and spikes in glances to the instrument-cluster. In contrast, Automation-to-manual transitions were associated with lane count reductions, higher surrounding-vehicle density, deceleration, reduction in secondary-task engagement, and higher steering wheel control. Together, these patterns highlight key cues for predicting the TOC type and time-to-TOC. Using environment-only features, the classifier achieved 78% accuracy; adding vehicle kinematics increased accuracy to 84%, and incorporating driver behavior features further improved prediction to 90%. Across prediction horizons, the Manual-to-automation TOC was consistently predicted more accurately than the automation-to-manual TOC. Shapley analyses underscore that driver behavior provided the strongest cues for predicting TOCs, highlighting the value of fusing driving context with information obtained from monitoring the driver behavior to anticipate the type of driver-automation interaction and its timing.
Zhao, ZhouqiaoGershon, Pnina
This study investigates the impact of sensor location on accelerometer-based sensing of combustion phasing for compression-ignition engines. Ten accelerometer locations were studied on a light-duty compression-ignition engine for a set of conditions with variations in engine load, speed, injection timing, and injection strategy. Start of combustion (SOC) was identified from the filtered acceleration signal using a previously developed approach. Each location was assessed using both signal-based metrics, including magnitude squared coherence (MSC) between block surface acceleration and in-cylinder pressure, as well as SOC outcome-based metrics, such as detection success rate. Results demonstrate that the mounting location has a significant impact on the ability to extract combustion phasing information from the accelerometer signal. Sensors mounted on the front face of the engine produced the strongest signals for an individual cylinder. For multi-cylinder sensing, side-mounted locations delivered the most reliable performance, with SOC detection success above 98 percent, defined as correctly identifying the acceleration peak most closely aligned with the corresponding pressure-derived SOC for each cycle. This work outlines a practical framework for selecting and evaluating accelerometer mounting locations, enabling broader use of accelerometers in engine platforms operating on a range of combustion approaches.
Hegge, GraydonHanson, ReedKim, KennethRothamer, David
This paper presents an approach utilizing Nonlinear Model Predictive Control (NMPC) and Unscented Kalman Filter (UKF) to predict system state and control the trajectory of the vehicle with dual trailers in an intersection turn scenario. The UKF estimates vehicle and trailers’ lateral traversal velocity states and the NMPC controls the vehicle acceleration and steering to maintain the vehicle’s desired heading through the turn. The vehicle’s lateral traversal velocity function is formulated using Lyapunov based method which is used as a propagation function in the UKF to improve the estimation accuracy. The lateral traversal velocity is then used as one of the constraints in the NMPC problem. The overall estimation and the control scheme are formulated and assessed in the simulation environment. The simulation results show good tracking and curb avoidance performance.
Malla, Rijan
Electrified powertrains—such as Power Splits, Series Hybrids, and EVs with Disconnect Actuators—enable flexible management of actuator acceleration and torque from shared power sources. In power-limited or high-demand conditions, the Hybrid Supervisor must balance available power to sustain performance and drivability; poor coordination can cause control imbalance, reduced actuator performance, and unintended motion. Conventional methods often favor a single control objective, compromising overall system efficiency. This paper introduces FLAIR (Fuzzy Learning Adaptive Integral Response) Control, a supervisory strategy for actuator speed profiling and driver demand tracking in single-input multi-output (SIMO) systems. FLAIR integrates an integral of tracking error with fuzzy inferencing to dynamically weigh multiple control goals, adapting acceleration limits in real time while preserving driver power demand tracking. It enables bi-directional power-flow decisions—allocating system power between driver and actuator system based on context and error persistence. Simulation and vehicle results demonstrate smoother transitions, reduced overshoot, and improved power balancing compared to conventional strategies.
Banuso, AbdulquadriSha, HangxingShenoy, AayushMadireddy, Krishna Chaitanya
Industries are following a tedious product development cycle for developing their product. In product development major steps includes design ideas, Drawings, CAD, CAE, Testing and design improvement cycle. This is a monotonous process and takes time which impacts on its time to deliver product and cost on development. Now a days industries are fast growing and targeting to reduce development cycle time and cost. AI&ML is impacting almost all areas in the industry and significantly reducing efforts time and cost. To make use of AI&ML in CAE, Altair Physics AI is an effective tool. To ensure the design of product traditional way is to develop a CAD of the product, develop, perform CAE and analyze performance. If we consider CAE procedure it is time consuming process which includes FEA model build, applying boundary conditions, running simulation and analyzing results which could take minutes to hours. By using ML with Physics AI we can make predictions on new design of the product in seconds and significantly save time and cost. To demonstrate the CAE acceleration process with physic AI we have solved two case studies. The first case study is head impact on hood where ML tool will predict deformation contour of the hood, acceleration and displacement curve of the impactor. The second case study is Tube crush analysis where prediction of tube deformation pattern, force and energy curve for different tube length and impact velocity is carried out. For both Case studies we have used TCS inhouse data to train test and prediction of the ML model. For Head impact case study, it gives lower training loss with more than 90 percent prediction accuracy. Similarly for tube crush study it gives good accuracy and predicts comparable behavior patten with CAE results. Physic AI ML tool accelerates the design and development cycle and can be utilized in different product development. Implementation of ML accelerates the CAE process in design and development of products. It saves a lot of time in multiple design iteration study. Similar method can be implemented for different CAE cases.
Dangare, Anand ManoharKulkarni, Mandar
Vehicle pull under acceleration is a phenomenon commonly observed in high-performance vehicles and electric vehicles (EVs), primarily arising asymmetric driveshaft angles, drivetrain architecture, and suspension geometry. In addition to these mechanical factors, tire characteristics, particularly the tire lateral force generated at the contact patch, significantly influence this effect. The lateral force is intricately tied to the dynamics of the contact patch and the geometric design of the tire tread pattern. This study investigates the relationship between tread pattern geometry and vehicle pull under acceleration, emphasizing the role of tire lateral force variations. By employing finite element (FE) simulation, lateral force response variations (dfy/dfx) resulting from tread block deformation were analyzed. Based on these simulation, a robust analytical methodology for tread pattern evaluation and optimization was established. The developed tread pattern characteristic parameter was validated through a thorough comparison between physical testing and FE simulation results, demonstrating high consistency. Vehicle-level testing further confirmed that the application of the optimized tread pattern design significantly reduced vehicle pull under acceleration. Moreover, performance criteria for tire lateral force were defined based on the maximum torque and output requirements of high-performance and electric vehicles. The study concludes that implementing the developed tread pattern characteristic parameter enables the design of tires with enhanced resistance to vehicle pull under acceleration. Such advancements are poised to enhance steering stability, handling performance, and overall safety in vehicles with high torque outputs, especially EVs and high-performance models.
Yoon, YoungsamJang, DongjinKim, HyungjooLee, Jaekil
Detailed kinetics simulations coupled with 3D CFD offer a powerful analysis tool for combustion and emissions. Such methods allow consistent modeling of multi-component fuels from evaporation to combustion and correctly capture the effects of local inhomogeneities created by preferential evaporation on the performance and emissions of modern powertrains. Such computations are extremely computationally demanding, prompting interest in the development of calculation acceleration techniques that can effectively balance the speed and accuracy of the chemical source calculation terms. Chemical kinetics clustering methods are widely used for that effect. However, such techniques must be not only effective but also robust with respect to the engine conditions and fuel composition changes, to reduce the computational demands introduced by the need to calibrate the parameters of the acceleration method itself. In this paper, an extended chemical kinetics clustering approach is proposed. A calibration methodology for the parameters of this acceleration method is then introduced, based on multi-point single time step optimization for a toluene reference fuel (TRF) surrogate with ESTECO modeFRONTIER, utilizing frozen 3D CFD fields obtained with the Realis Simulation VECTIS code. The optimal clustering parameters thus constructed are then fine-tuned through a DoE exercise performed in VECTIS for the combustion event with the TRF surrogate using a coarse computational mesh. The robustness of the optimal parameters is then evaluated through the application of different perturbations to the species fields. Finally, the optimal clustering parameters are applied to full-load simulations of a typical GDI engine with simple TRF and 8-species E10 gasoline surrogates. The results demonstrate that the acceleration parameters determined by this workflow deliver 2.4× to 4.2× acceleration of combustion source calculation and 1.3× to 1.8× acceleration of the overall simulation while preserving solution accuracy and keeping the resolution of NO and soot emissions within 10% and 15%, respectively. The proposed methodology facilitates the broader use of detailed chemistry in internal combustion engine (ICE) applications, supporting modern powertrain development needs.
Hernandez, IgnacioTurquand d Auzay, CharlesShapiro, EvgeniyShala, MehmetBorg, AndersSeidel, LarsMauss, Fabian
High-precision estimation of key vehicle–road state parameters is crucial for ensuring the accurate and safe control of mining trucks (MT), as well as for reliable trajectory tracking. Among these parameters, the vehicle sideslip angle is particularly critical for assessing and predicting lateral stability. However, its direct measurement is challenging, and its estimation typically depends on an accurate characterization of tire cornering stiffness. For MT, large variations in loading conditions (from empty to fully loaded) pose significant challenges to sideslip angle estimation due to the resulting nonlinearity and variability of tire cornering stiffness. To address this issue, a novel joint estimation framework integrating the Moving Horizon Estimation (MHE) and Square-Root Cubature Kalman Filter (SCKF) is proposed to simultaneously achieve high-precision estimation of both tire cornering stiffness for each tire and vehicle sideslip angle. In this framework, the cornering stiffness of the front, middle, and rear axles is identified and updated in real time using MHE through a forgetting-factor least squares method based on yaw rate and lateral acceleration data within a fixed-length time window. The updated stiffness is then incorporated into the SCKF for accurate estimation of the sideslip angle. This sequential process effectively establishes a coupling between the estimation of the two parameters, forming an integrated joint estimation mechanism. The proposed framework is validated on the TruckSim–Simulink co-simulation platform, and the results confirm its superior accuracy and robustness, demonstrating its potential to improve the safety and control performance of MT.
Xia, XueShen, PeihongJiao, LeqiLi, TaoChen, HuiyongZhao, KunJiao, LeqiZhao, Zhiguo
Topology optimization (TO) of dynamic structures has traditionally been constrained to single-body components and simplified harmonic load assumptions. Extending TO to multibody dynamic systems (MBS) remains challenging due to complex coupling between inertia, mass distribution, and joint constraints. This paper presents an inertia-aware topology optimization framework that integrates mass moment of inertia (MMI) constraints within an enhanced Equivalent Static Displacement (ESD) methodology. Building upon the authors’ previously developed ESD framework, the proposed approach — termed Inertia-Augmented Equivalent Static Displacement (IA-ESD) — explicitly incorporates inertial effects arising from accelerations and joint interactions. The approach enables dynamically consistent optimization by coupling design-dependent inertia tensors with equivalent static displacements derived from nonlinear multibody dynamics. Case studies involving an MBB beam and a piston–connecting rod assembly demonstrate that accounting for MMI constraints yields lighter, stiffer, and dynamically balanced multibody topologies. The proposed method establishes a foundation for inertia-aware structural design with applications in aerospace, automotive, and robotics engineering.
Gupta, AakashTovar, Andres
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, AndrewBarba, Daniel
As motorsports evolve with technological advancements, aerodynamics plays a crucial role in race car performance. This review examines the impact of aerodynamics on car design and its evolution, presenting a statistical analysis of existing sports cars. We highlight key performance factors like engine power, top speed, drag, and weight. The key contribution of this review is the critical synthesis of the safety-performance trade-off, especially linking aerodynamic optimizations to the stability and safety of sports cars. Furthermore, we explore mathematical modeling of vehicle aerodynamics to enhance the understanding of performance aspects such as top speed, acceleration, cornering, and braking. This article also provides a review of recent active and passive aerodynamic devices to assist researchers in selecting designs, with an emphasis on the importance of ground effect. We also present recent numerical methods, particularly 3D simulations. The statistical data can help researchers determine optimal design parameters. Lowering drag enhances top speed, reducing weight improves acceleration, and increasing downforce shortens braking distance. Compared to passive devices, active aerodynamic devices offer greater adaptability, providing enhanced downforce and stability.
Eftekhari, HesamAl-Obaidi, Abdulkareem Sh. MahdiEftekhari, Shahrooz
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, RishikesanChougule, SourabhSrinivasan, RangarajanLaurent, Nicolas
The inertial profiler methodology is traditionally employed in RLDA (Road Load Data Acquisition) to measure road profiles and classify test routes into ISO road classes. However, this approach demands significant time and effort during instrumentation. Also, during data acquisition, laser height sensor data is affected especially during adverse conditions such as rainy seasons or on surfaces with improper reflectivity. Additionally, substantial resources are required for data processing to convert raw measurements into road classifications. To address these challenges, an initial attempt was made to establish a relationship between axle acceleration responses and road profiles, enabling axle acceleration measurements during RLDA to predict ISO road classes. However, this approach relied on a simple linear model that considered only axle acceleration responses, rendering the predictions susceptible to inaccuracies due to varying parameters such as vehicle speed. To overcome these limitations, an alternative method is introduced in this study, incorporating additional & generated parameters and employing a multiple linear regression model based on machine learning techniques. This paper outlines the detailed steps of the machine learning process, including feature engineering methods such as feature extraction, transformation, selection, and reduction. It also explores model fine-tuning strategies guided by performance metrics. The proposed methodology significantly improves the accuracy of road class predictions while reducing the time, effort, and challenges associated with instrumentation, data acquisition, and post-processing activities.
P, Praveen KumarP, DayalanSriramulu, Yoganandam
This study presents an integrated vehicle dynamics framework combining a 12-degree-of-freedom full vehicle model with advanced control strategies to enhance both ride comfort and handling stability. Unlike simplified models, it incorporates linear and nonlinear tire characteristics to simulate real-world dynamic behavior with higher accuracy. An active roll control system using rear suspension actuators is developed to mitigate excessive body roll and yaw instability during cornering and maneuvers. A co-simulation environment is established by coupling MATLAB/Simulink-based control algorithms with high-fidelity multibody dynamics modeled in ADAMS Car, enabling precise, real-time interaction between control logic and vehicle response. The model is calibrated and validated against data from an instrumented test vehicle, ensuring practical relevance. Simulation results show significant reductions in roll angle, yaw rate deviation, and lateral acceleration, highlighting the effectiveness of the proposed approach. Overall, the framework offers a scalable and robust foundation for developing adaptive stability control systems in modern four-wheeled vehicles
Duraikannu, DineshDumpala, Gangi Reddi
Modal analysis is performed to determine the natural frequencies and mode shapes of a structure or system. It helps engineers understand how a system vibrates and how external forces, such as mechanical loads, might excite unwanted resonances. To check the stresses due to vibration inputs, certain G levels are assumed, and stresses are scaled to those vibration levels. This gives an understanding of the stresses of components with respect to its EFR limit and design margins are calculated. But, assumed acceleration levels in pre-prototype stage level can over predict or under predict the design margins. A quick modal analysis correlation technique can be used by using test measured accelerations conducted at prototype stage of the program. In this work, a modal analysis correlation technique is used to perform risk assessment of intake manifold. The intake manifold failed due to high vibration levels which were not captured from high cycle fatigue analysis with assumed G-level. In the modal analysis correlation technique, an effort is made to align the mode shape and frequency of the intake system and then with measured accelerations high cycle fatigue design margins are calculated. This gave accurate high stress location where in actual intake manifold was failed. Further design recommendations were suggested based on stress nature and location. This technique can be a quick risk assessment solution as only modal analysis with few peripheral components are required to be modelled in FEA analysis. This paper explores modal analysis correlation techniques, detailing the steps for aligning the mode shape and frequency of a system, while also addressing the limitations of the method.
Bale, Shrikant BhaskarBawache, Krushna
In pursuit of a distinct sporty interior sound character, the present study explores an innovative strategy for designing intake systems in passenger vehicles. While most existing literature primarily emphasizes exhaust system tuning for enhancing vehicle sound quality, the current work shifts the focus toward the intake system’s critical role in shaping the perceived acoustic signature within the vehicle cabin. In this research work, target cascading and settings were derived through a combination of benchmark and structured subjective evaluation study and aligning with literature review. Quantitative targets for intake orifice noise was defined to achieve the desired sporty character inside cabin. Intake orifice targets were engineered based on signature and sound quality parameter required at cabin. Systems were designed by using advanced NVH techniques, Specific identified acoustic orders were enhanced in the intake system to reinforce the required signature in acceleration as well as in cruising mode. A novel decomposition method was developed to identify exact contribution of intake system’s noise from overall in cab noise. Based on advanced NVH analysis and sound diagnosis a precise identification of intake system contributions during both acceleration and cruising conditions was carried out. Furthermore, sound design strategy was developed which targets a dual-mode acoustic profile. The developed design strategy was validated at vehicle level, confirming that the intake system design met both subjective and objective targets. This integrated approach provides a repeatable framework for intake sound design, offering OEMs a robust pathway to differentiate sporty vehicle character through intelligent intake acoustics. This work not only demonstrates the critical role of intake design in vehicle sound signature development but also proposes a systematic methodology for future vehicle sound engineering.
Sadekar, Umesh AudumbarTitave, UttamPatil, JitendraNaidu, Sudhakara
This manuscript introduces a methodology to reduce the DC link capacitor size in pole-phase modulated (PPM) induction motor drives (IMD). Typically, the DC link capacitor (DCLC) occupies around 25 to 30% of the inverter volume and 20% of the inverter material cost. Reducing the DCLC size and cost is essential to lowering the inverter size and cost. This can be accomplished by lowering the DCLC ripple current. The proposed technique suggests adapting phase-shifted triangular carrier waveforms, in all the operating modes of the PPM drive, to significantly reduce the ripple current through DCLC, successively reduces the size and cost of DCLC. Simulations are performed in MATLAB/Simulink on a 9 phase PPM drive to validate the efficacy of the strategy. Though the suggested concept is verified with a 9 phase PPM drive, which is operated in 2 modes, it can be extended to any 3n PPM drive. The results demonstrate a 60% reduction in ripple magnitude, enabling the use of smaller, more reliable, and cost-effective capacitors.
A, Rajeshwari
Vehicle dynamics is a vital area of automotive engineering that focuses on analyzing how a vehicle responds to driver inputs and external factors like road conditions and environmental influences. Achieving optimal performance, safety, and ride comfort requires a detailed understanding of longitudinal, lateral, and vertical dynamic behavior. The objective of this paper is to develop and validate the model of a concept Race car and evaluate its vehicle dynamics behavior using IPG CarMaker, a high-fidelity virtual testing environment widely used in industry. The model incorporates a range of vehicle parameters, including suspension parameters like spring and damper characteristics, mass distribution, tire properties and powertrain parameters. The performance evaluation is done as per standard guidelines, including Constant Radius turn test, Sine Steer test and other standard tests like Acceleration, Braking along with Ride and Comfort classification. The key parameters that are calculated and validated are vehicle accelerations in the principal axes, stopping distance, yaw velocity and yaw velocity gain, vehicle roll characteristics, steering parameters, ride and driver comfort metrics. Validation of simulation outputs is achieved through comparison with empirical data obtained from literature and mathematical calculations based on vehicle dynamics principles. The test results show a close correlation between mathematical and simulated values, therefore accurately predicting vehicle behavior.
Agrewale, Mohammad Rafiq B.Vaish, Ujjwal
This paper briefly introduces the vehicle characteristics of four-wheel steering. Based on the parameters of an electric SUV, a linear two-degree-of-freedom vehicle dynamics model is established, and the transfer function of the rear wheel steering angle is derived to keep the sideslip angle at the center of gravity(CoG) constant at zero and proportional to the front wheel steering angle under steady state. The active rear wheel steering control strategy based on zero sideslip angle is established by MATLAB/Simulink, and a co-simulation model is built with CarSim and the HIL test bench to simulate and analyze the proposed control strategy. Subsequently, through classic handling stability test conditions such as the snake test, steering angle step test, and double lane change test, the influence of active rear wheel steering on vehicle dynamic response indicators such as sideslip angle, lateral acceleration, and yaw rate is studied, and the control effect is compared with that of the feedforward control rear wheel steering strategy. Test results demonstrate that the rear-wheel steering technology based on zero sideslip angle control improves the vehicle's low-speed maneuverability and high-speed stability. Under the double lane change test condition at 80 km/h, the sideslip angle is reduced by approximately 30%, and the yaw rate gain is decreased by about 25% compared to the feedforward control strategy. These enhancements significantly improve the overall dynamic performance of the vehicle.
Xu, XiangfeiQu, YuanLiu, Jiabao
The presence of time-varying loads on shell structures can result in the generation of undesirable noise in the time domain. This paper presents a time-domain noise control method based on piezoelectric smart shell structures. Firstly, a coupled time-domain finite element/boundary element method (TDFEM/BEM) is used to calculate the sound pressure radiated from shell structures subjected to arbitrary time-varying loads. Then a classical time-domain CGVF algorithm is used to control the vibration and to suppress the sound radiation from structures. Finally, numerical examples demonstrate a 44.2% reduction in the displacement response, a 35.8% decrease in acceleration response, a 36.2% decline in sound pressure of the central node, and a 28.5% decrease in average surface sound pressure. The results show that after CGVF control, the vibration and radiation noise of the plate/shell structure under time domain load are effectively reduced, which is of great significance in engineering projects.
Zheng, HaoWang, HongfuLi, JingjingZhou, QiangSun, YongZhou, LingZhang, HongliangWang, BaichuanHuang, JunsongLiu, XiaorangYin, Guochuan
Safety improvements in vehicle crashworthiness remain a primary concern for automotive manufacturers due to the increasing complexity of traffic and the rising number of vehicles on roads globally. Enhancing structural integrity and energy absorption capabilities during collisions is paramount for passenger protection. In this context, longitudinal rails play a critical role in vehicle crashworthiness, particularly in mitigating the effects of rear collisions. This study evaluates the structural performance of a rear longitudinal rail extender, characterized by a U-shaped, asymmetric cross-section, subjected to rear-impact scenarios. Seventy-two finite-element models were systematically developed from a baseline configuration, exploring variations in material yield conditions, sheet thickness, and targeted geometric modifications, including deformation initiators at three distinct positions or maintaining the original geometry. Each model was simulated according to ECE R32 regulation standards, ensuring validity and compliance with relevant safety criteria. Specific energy absorption (SEA), load uniformity, and structural acceleration were used as key measures of crashworthiness. Simulation outcomes indicated that reductions in thickness significantly increased SEA due to enhanced deformability. Thinner configurations demonstrated greater energy absorption and improved load uniformity, whereas thicker components increased structural rigidity, resulting in decreased energy absorption and higher accelerations transmitted to the vehicle’s B-pillar. Material properties had moderate influence, with higher-strength materials elevating accelerations. Geometric modifications, particularly deformation initiators at specific positions, substantially improved SEA, achieving enhancements up to 42% compared to baseline. These findings highlight the potential of strategic adjustments in geometry, material selection, and thickness to significantly enhance vehicle crashworthiness and occupant safety.
Souza Coelho Freitas, Victor dePereira, Romulo FrancoSouza, Daniel Souto de
Tracked Military Vehicles are well known in armed forces, due to their use and importance in conventional combat, playing a crucial role since World War I until current combats. Also, as it happens in different generations, the environment involved in these wars changes and those vehicles are being used not only in open field situations, but inside residential neighborhoods also. However, despite their relevance, analyses and studies aimed at understanding these vehicles are scarce at the undergraduate level, which creates a gap among the recent graduate engineers that want to learn and understand how tracked vehicles perform in different scenarios. This is important because understanding initial concepts helps to bring more ideas and start more detailed studies in the area. Therefore, to bridge this gap, a detailed dynamic analysis of a tracked military vehicle is conducted using MATLAB with a dynamic model to evaluate performance, level transitions, and acceleration. Additionally, simulations are performed under different scenarios: asphalt, sand, and mud, where conditions in a jam situation are tested.
Dalcin, Pedro Henrique KleimRibeiro, Levy PereiraLopes, Elias Dias RossiRodrigues, Gustavo Simão
Automatic driving technology can achieve precise control of the vehicle. Compared with manual driving, it can greatly avoid bad driving behaviors such as rapid acceleration, rapid deceleration, and idle driving, more stable, efficient and safer control of vehicles, thus reducing energy consumption and pollution emissions, has great potential for eco-driving. Previous research on eco-driving car-following strategy is usually based on the current vehicle state. However, the real driving scene is extremely complex and changeable, which makes the existing research easy to fall into the dilemma of local optimal solution when dealing with complex long-term planning tasks, and it is difficult to gain comprehensive insight into the path of global optimal solution. According to the literature, bad driving behaviors such as rapid acceleration and rapid deceleration have a great impact on the energy consumption and emissions of vehicles, in order to realize eco-driving, planning control method should be used to explore the range optimal strategy to approach the global optimal. Therefore, this paper discusses an eco-driving car-following strategy for autonomous vehicles based on the acceleration prediction of the leading vehicle, in this study, a Transformer-based acceleration prediction model for the leading vehicle is constructed, which uses the historical state time series data of the leading vehicle to predict its future state, so as to provide the future trajectory change trend of the leading vehicle for the following vehicle, the current state and the predicted state (through the feature fusion module) are used as decision variables for eco-driving, and the decision is adjusted according to the state change trend to achieve smooth driving, the strategy is suitable for the scenario where an autonomous vehicle follows a human-driven vehicle.
Luo, ShijeZhao, Qi
In order to reduce conflicts between vehicles at intersections and improve safety, an optimization model of traffic sequence allocation is studied and established for the heterogeneous traffic scenario of connected autonomous vehicles and manual vehicles. With the minimum safe traffic time as constraint, the right of way is allocated to vehicles according to the microscopic traffic characteristics of heterogeneous traffic flow fleet movement and the phase of signal lights, and the optimal trajectory planning control of each vehicle and evaluation indicators are established. A jointly simulation running environment is built using VISSIM and MATLAB. The simulation results indicate that at the micro level, collaborative control slows down the waiting time for manually driven vehicles and improves the utilization of green light travel time. At the macro level, as the penetration rate of connected autonomous vehicles increases, the sum of squares of vehicle acceleration gradually decreases, and the minimum following distance and minimum collision time of vehicles increase. It improves the overall comfort and safety level of traffic flow.
Yuan, ShoutongLi, ZhiqiangLiu, TianyuYu, Zhengyang
Although the number of trucks is low, their accident rate is high, and the consequences of accidents are severe. This paper is based on GPS data from 100 trucks, with each trip chain defined by a vehicle’s stay time greater than 20 minutes. The kinematic parameters for each trip chain are then extracted, and the entropy weight method is used to calculate the weights of various parameters. A random forest model is applied to select 11 key indicators, including speed and acceleration. The entropy weight-TOPSIS algorithm is used to assess the risk of each trip chain for the trucks. Different combinations of continuous and discontinuous trip chain scenarios are constructed. Finally, support vector machines (SVM) and decision tree methods are used for risk prediction under different trip chain combinations. The results show that the 11 selected key indicators provide an accuracy of 95.74% for describing the sample. In general, the SVM model shows better prediction accuracy than the decision tree under different trip chain combinations, though the decision tree results fluctuate significantly. As the penalty parameter in SVM and the minimum leaf node in the decision tree increase, the accuracy of the model gradually decreases.
Huang, YunheXiong, ZhihuaLi, Jiayu
With the development of intelligent networking technology and autonomous driving technology, how to efficiently and safely schedule intelligent networked autonomous vehicles at signalless intersections has become a research hotspot in traffic management. Based on this, this article first designs an objective function that considers both intersection traffic efficiency and intersection traffic safety, taking into account constraints such as safe distance, speed, acceleration, etc., and constructs a signal free intersection CAV traffic scheduling model. On this basis, a model solving algorithm based on rolling ant colony algorithm is proposed. Simulation experiments show that compared with typical signal control methods, this method can significantly improve intersection traffic efficiency and reduce the number of conflicts.
Zhao, YingjieLiu, XiaomingMa, ZechaoWang, Yuanrong
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