Journal Articles - SAE Mobilus

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

Items (11,328)

Detecting Fuel Adulteration in Otto-Cycle Vehicles Using On-Board Diagnostics Data and Machine Learning–Based Anomaly Detection

04-19-03-0015

10/15/2026

Fuel adulteration increases operating costs, reduces vehicle efficiency, and contributes to air pollution. Fuel-quality verification typically requires laboratory infrastructure or dedicated sensors that are not available to most end users. In our previous work, we demonstrated that a software-only approach based on standard on-board diagnostics (OBD) signals and supervised machine learning can detect water adulteration in hydrated ethanol under real-world driving conditions with high accuracy. However, supervised classifiers remain challenging because fuel-trim dynamics vary across vehicles due to powertrain calibration, component aging, and operating environments, which can limit model generalization. This work addresses this limitation by introducing a detection framework that learns a vehicle-specific baseline of normal operation using gradient boosting regression (GBR) and flags anomalous events through residual-based cumulative sum (CUSUM) monitoring. The baseline model is trained using compliant-fuel data, and the CUSUM decision threshold is calibrated from temporally ordered normal-operation data using an expanding-window procedure with an initial warm-up segment. Across two vehicles, the proposed framework achieved recall values of 99.77% and 84.38%, with F1 scores of 99.88% and 91.53%, respectively, while maintaining zero false positives under compliant-fuel operation.

Marchezan, André Ricardo, Giesbrecht, Mateus

A Practical Crash-Outcome Occupant Screening Index for Passenger Vehicles with Data-Limited Crash Data

09-15-01-0002

10/14/2026

This article introduces the Crash-Outcome Occupant Screening Index (COSI), a methodology for comparing police-reported harm outcomes among passenger-vehicle occupants involved in reported crashes. COSI estimates the proportion of crash-involved occupants of a given vehicle model who were recorded by police as injured or killed. The index is designed for data-limited crash datasets where detailed injury severity coding, exposure measures, and crash reconstruction variables are unavailable or inconsistent. COSI was applied to five years of police-reported crash data from Iran to examine high-volume passenger-car models in a market with relatively limited model diversity and long production cycles. The results are presented with 95% confidence intervals and supported by year-to-year repeatability and platform-consistency checks. The index is intended as a descriptive screening and prioritization tool, not as a causal measure of vehicle crashworthiness, registration-based risk, or exposure-adjusted injury risk.

Sadeghipour, Emad

Development and Validation of Quasi-Dimensional Multi-Zone Combustion Model for Homogeneous Reactivity-Controlled Compression Ignition with Renewable Fuels—Part 2: Complete Model and Validation

03-19-05-0024

10/14/2026

As a part of the project, the homogeneous reactivity-controlled compression ignition (hRCCI) concept is being investigated along with the development of 0D/1D combustion simulation models. The proposed concept employs early direct injection of octanol as the high-reactivity fuel in combination with port fuel injection of ethanol as the low-reactivity fuel. In the previous parts of the publication, testing results and the in-cylinder fuel and temperature distribution predictions were studied. Low-temperature combustion (LTC) concepts such as hRCCI are strongly governed by autoignition chemistry of the fuels and oxidizer, making the explicit consideration of chemical kinetics essential for predictive simulations. In addition, the hRCCI combustion process exhibits high sensitivity to a wide range of control parameters, including intake temperature, intake pressure, excess air ratio, premixed fuel ratio, and direct injection parameters, posing further challenges to model robustness. To address these challenges, a predictive and robust quasi-dimensional multi-zone combustion model for LTC engines is developed. The model couples a 0D/1D flow framework implemented in GT-POWER with detailed chemical kinetics solved using a Python–Cantera interface for user-defined combustion modeling. The combustion framework incorporates dedicated sub-models to account for fuel evaporation, interzonal heat transfer, wall heat transfer, spatial distribution of directly injected octanol, and temperature stratification across the zones. The model’s predictive capability is validated against experimental data from a single-cylinder research engine under variations in intake pressure, intake temperature, premixed ratio, and the start of direct octanol injection. The simulation results show strong agreement with experimental measurements, demonstrating the robustness and predictive accuracy of the developed modeling approach. The proposed simulation framework provides a valuable tool for the future development and optimization of low-temperature combustion engine concepts aimed at improving efficiency and reducing emissions.

Sundaram, Pravin Kumar, Grundl, Larissa Michaela, Trapp, Christian Thorsten, Tinschmann, Georg

Double-Envelope Coordinated Control with Subjective-Objective Consistent Stability Evaluation for Corner-Module Vehicles

10-11-01-0006

10/9/2026

Physical constraints are characterized by vehicle stability boundaries and actuator hardware limits. However, they cannot capture driver perception of vehicle responses or quantify the effects of actuator inputs on vehicle stability. This article proposes a double-envelope coordinated control strategy with subjective–objective consistent stability evaluation for corner-module vehicles. Actuator stability margins are derived from vehicle stability boundaries under four-wheel steering and distributed drive. They are used to prevent vehicle instability in advance by constraining destabilizing actuator inputs. Real-vehicle tests identify driver-perceived boundaries under varying vehicle speeds and road adhesion conditions. Mapping these boundaries to the objective stability index establishes a subjective–objective consistent evaluation. Actuator stability margins and consistent evaluation form a soft envelope, while vehicle stability boundaries and actuator hardware limits define a hard envelope. The resulting double-envelope coordinated control adjusts actuator priorities according to the actuator stability margins and adapts control targets to reflect driver perception. CarSim-Simulink co-simulation and hardware-in-the-loop experiments demonstrate improvements in extreme obstacle avoidance capability and driver handling confidence.

Shi, Wenbo, Ding, Haitao, Xu, Nan, Zhang, Xinjie

Development and Validation of Quasi-Dimensional Multi-Zone Combustion Model for Homogeneous Reactivity-Controlled Compression Ignition with Renewable Fuels—Part 1: Fuel and Temperature Stratification Prediction

03-19-05-0023

10/9/2026

As part of the dtec.bw MORE project, serial hybrid powertrains featuring alternative combustion regimes, specifically homogeneous reactivity-controlled compression ignition (hRCCI), are under investigation to maximize thermal efficiency while minimizing NOx and soot emissions. The proposed hRCCI concept utilizes dual-fuel stratification, employing early direct injection (DI) of octanol (high-reactivity fuel) alongside port fuel injection (PFI) of ethanol (low-reactivity fuel). While 0D/1D engine modeling is essential for developing predictive powertrain simulation frameworks, conventional models often lack the robustness required to capture the complexities of low-temperature combustion (LTC). This study addresses this limitation by developing a multi-zone “onion skin” quasi-dimensional model. Since for LTC, ignition and heat release rates are highly sensitive to thermal and chemical stratification, capturing these gradients is critical. 3D computational fluid dynamics (CFD) simulations are capable of capturing thermal and chemical stratifications to a high degree of accuracy, whereas 0D simulation models, in general, do not consider them. The primary contribution of this work is the translation of high-fidelity 3D CFD data into a computationally efficient quasi-dimensional environment. A simplified 3D CFD architecture was utilized to map the effects of various operating parameters on mixture distribution. Using these results, a regression learning model was trained to predict octanol and temperature stratification within the combustion chamber. Validation against simulation data demonstrates that this coupled machine learning and multi-zone approach provides a robust, predictive tool for evaluating advanced LTC concepts within larger system-level simulations.

Sundaram, Pravin Kumar, Grundl, Larissa Michaela, Trapp, Christian Thorsten, Tinschmann, Georg

Experimental Assessment of Off-Tracking Reduction Using a Split Fifth-Wheel Coupling during Intersection-Type Turning of a Scaled Tractor–Semitrailer

02-20-01-0006

10/9/2026

This article presents an experimental investigation of off-tracking in a tractor–semitrailer during intersection-type turning maneuvers, which typically involve quarter-circle turning paths at urban junctions. Two coupling configurations are examined: a conventional fifth-wheel coupling (CFWC) and a split fifth-wheel coupling (SFWC). A scaled articulated vehicle prototype is developed to conduct controlled low-speed turning tests across multiple radii representative of intersection maneuvering conditions. The measured trajectories of the tractor and semitrailer are compared with predictions from a previously developed zero-speed kinematic model adapted for the present configuration. Maximum off-tracking is quantified for both coupling systems, and agreement between measured and simulated trajectories is evaluated using a normalized root-mean-square error metric. Results show that the SFWC consistently reduces off-tracking relative to the CFWC across all tested radii, while the kinematic model predicts the measured off-tracking profiles with mean-normalized errors below 0.1. The study is positioned as an experimental assessment of a previously proposed SFWC concept and kinematic formulation, rather than as a new coupling concept or a new modeling method. The findings provide controlled experimental evidence on the influence of coupling configuration on low-speed off-tracking behavior, while the practical implementation of the SFWC is identified as requiring further full-scale mechanical design, articulation limit, and durability studies.

Jogi, Ajith, Chandramohan, Sujatha, Krishnapillai, Shankar

Latency-Aware Optimal Scheduling Scheme for Computational Offloading

12-10-01-0002

10/9/2026

Proactive safety and vehicle automation typically requires high energy use from sensors and energy-intensive computing from sensor data processing. For high-quality, reliable perception and localization within a driving environment at the vehicle level, incoming data from multiple sensors need to be fused using advanced computational algorithms, which demand a high compute load. Alternatively, computational offloading of automated driving tasks shifts energy consumption from the vehicle to cloud infrastructure, where renewable energy sources, such as hydropower or solar power, can be utilized more efficiently. Herein, an optimal scheduling strategy for autonomous driving tasks via the cloud layer is formulated as a mixed-integer linear programming (MILP) problem and verified using measurement-informed task graphs. It is shown that cloud-based computational offloading enables energy-efficient operation while maintaining task deadlines to ensure the timely availability of perception and localization information, which is consistent with prior studies in the literature. Simulation results demonstrate that on average, 35.78% of the computational load was offloaded to the cloud, which can achieve significant energy savings for the onboard system. The compute operations achieved an average energy savings of 35.65%, while total system savings ranged from 26.16% to 30.67% under different cloud energy efficiency scenarios, highlighting the advantages of offloading compute-intensive tasks. The framework was further evaluated using multiple directed acyclic graph (DAG) configurations to assess its scalability and adaptability. Critical tasks, such as sensor fusion, were executed exclusively on the vehicle to ensure real-time responsiveness.

Sharma, Sachin, Meyer, Richard, Feinberg, Ben, Asher, Zachary

Effects of Methane Addition to Diesel in Dual-Fuel Mode Combustion Utilizing the Advanced Fuel Ignition Delay Analyzer

04-19-03-0013

10/8/2026

With the rising pressure of a global energy transition to cleaner energy systems, the transportation sector faces a challenge in maintaining its current structure and adapting to all the required changes. Dual-fuel technology emerges as a promising alternative to keep the viability of the sector while utilizing environmentally friendly fuels alongside traditional hydrocarbons, without deep structural changes in the current framework. This work investigates the impact of the use of methane gas coupled with diesel fuel, specifically focusing on its influence on the ignition delay time (IDT) in a dual-fuel combustion setting. The investigation employed the Advanced Fuel Ignition Delay Analyzer (AFIDA), a constant-volume combustion chamber capable of automatically measuring multiple samples in a single run. Methane gas was introduced into the chamber at molar concentrations of 1%, 2%, and 3% and at initial pressures of 5 and 10 bar, with temperature values ranging from 653 to 723 K. With the same amount of diesel being injected for all experiments, it was possible to replace up to 40% of the total energy release with methane during the combustion process. Compared to pure diesel, the IDT values of the methane–diesel mixtures were only affected in low-temperature settings. At higher temperatures, the IDT values remained largely consistent with those of pure diesel, suggesting that higher substitution ratios of methane are feasible without compromising the ignition delay. This work also uncovered that the percentage of energy replaced by methane does not have a direct correlation with IDT or peak pressure values.

Pacheco Virginio, Thiago, Eraqi, Basem, Sakleshpur Nagaraja, Shashank, Sarathy, S. Mani

Importance of Duty Cycle Variability in Estimating Total Cost of Ownership for Vocational Battery Electric and Fuel Cell Vehicles

13-07-02-0007

10/7/2026

Total Cost of Ownership (TCO) is a key metric in commercial vehicle purchase decisions and evaluation of advanced technologies such as battery electric and fuel cell vehicles (BEVs and FCEVs). Inputs derived from aggregate operating data, such as annual miles of travel and average fuel efficiency, as used in most TCO tools, may be inadequate for assessing technologies in specific applications. To provide more accurate estimates, the Transportation Technology Total Cost of Ownership (T3CO) model integrates vehicle simulation using the Future Automotive Systems Technology Simulator (FASTSim™). T3CO incorporates opportunity costs that may arise when technologies do not provide equivalent performance to conventional powertrains, such as reduced payload capacity and added downtime for more frequent and longer duration refueling. While past analyses used single duty cycles representative of average daily distance and energy per mile, fueling dwell time is nonlinear with respect to daily energy requirements and may not be captured with representative cycles. In this study, T3CO is applied to real-world operational data collected over 32 months from conventional Class 5 work trucks in three fleets, covering a variety of use cases and variability in daily demands. Diesel, BEV, and FCEV powertrains were simulated over the full data set, for a total of 1,986 vehicle days and 145,919 miles each. Analysis of duty cycle variability results in up to a 61% difference in fleet-level weighted average TCO compared to analysis over a single representative day. While diesel and FCEV TCO results are sensitive to fuel cost, BEV TCO is sensitive to the downtime from occasional daytime charging present in the total life cycle and not reflected in a subset “representative day.” For the studied fleets, these findings alter conclusions about the relative cost-effectiveness of new technology options at both the vehicle and fleet levels.

Birky, Alicia, Panneer Selvam, Harish, Sigler, Cory, Carow, Kyle, Miller, Eric, Ortmann, Walt, Tascillo, Mark

A Shared-Hardware Battery Management System for Solar-Assisted and Capacitive Balancing in Unmanned Surface Vehicles

12-10-01-0001

10/7/2026

This article presents a hybrid battery management system for unmanned surface vehicles, integrating three operational modes: solar-to-cell balancing, capacitive balancing, and solar-to-pack charging—within a shared architecture. The system prioritizes solar-to-cell balancing when solar energy is available, capacitive balancing when charging without solar input, and solar-to-pack charging when cells are balanced. This approach eliminates additional hardware by utilizing a shared-hardware architecture. A 14.8 V battery pack composed of four 3.7 V Li-ion modules was simulated and prototyped to validate the system. Experimental and simulation results confirm improved cell balancing, reduced state-of-health deviation, and extended operational time, demonstrating superior performance, sustainability, and adaptability over conventional battery management system solutions for unmanned surface vehicles and similar platforms.

Blair, Raymond, Kyger, Kenton, Duan, Chen

A Numerical and Experimental Investigation of Concealed HVAC Air Duct and Vent Aeroacoustics in Electric Vehicles Using Lattice Boltzmann Method

14-16-01-0001

10/7/2026

Concealed HVAC air duct and vent systems are increasingly adopted in modern electric vehicles due to their improved cockpit integration capability and compatibility with intelligent airflow management strategies. However, the complex internal geometry and distributed airflow characteristics of concealed vent configurations often introduce increased broadband aeroacoustic noise, particularly at middle and high frequencies, where cabin masking effects are significantly reduced in electric vehicles. In this work, a high-fidelity aeroacoustic methodology based on the Lattice Boltzmann Method (LBM) was applied using PowerFLOW to investigate concealed HVAC vent aeroacoustic mechanisms and design sensitivities. Detailed vent geometry was preserved using the Precise Wrap tessellation approach, while acoustic porous media and wall absorption treatments were incorporated to represent the acoustic behavior of foam materials inside the duct system. Numerical predictions were correlated with semi-anechoic chamber measurements under representative test conditions. The study hypothesized that concealed vent cavity structures and flap–louver interaction dominate broadband aeroacoustic generation above 1000 Hz and that high-fidelity LBM simulation with detailed geometric representation can capture these mechanisms within engineering-level prediction accuracy. Good agreement between simulation and experiment was achieved for both overall sound pressure level (SPL) and narrow-band spectrum distribution. The average OASPL deviation was approximately 2 dB, with a maximum deviation of 3.3 dB among the investigated microphone locations, while the narrow-band SPL deviation mainly remained within 5 dB for frequencies below 5000 Hz. The predicted broadband hump near 1500–2500 Hz was shown to correlate strongly with vent cavity characteristics and flap–louver interaction. Flow field analysis identified pronounced vortex shedding within concealed vent branches and localized flow separation near the flap leading edge and louvers. Parametric investigations further demonstrated that suppressing leading-edge vortex impingement reduced the broadband hump near 1500 Hz, while removal of flap–louver interaction significantly reduced high-frequency broadband noise above 1500 Hz. The present work demonstrates that high-fidelity LBM simulation can provide reliable engineering-level aeroacoustic prediction for concealed HVAC vent development during the early design stage. The study also provides practical design guidance for developing low-noise concealed HVAC systems for electric vehicle applications.

Hu, Liangbo, Xiong, Fei, Pan, Aicheng, Song, Jinxiang, He, Jianfeng

Critical Review of In-Wheel Motor Systems for Electric Vehicles

10-10-04-0037

10/7/2026

As the technology of electric vehicles (EVs) continues to mature, in-wheel motors (IWMs), as an innovative drivetrain technology, offer significant advantages in enhancing vehicle performance, simplifying design, and optimizing space utilization. It is gradually becoming a key direction in the technological development of EVs. Therefore, this article aims to provide a comprehensive review of IWM drive techniques and recent developments for automotive applications. This article first reviews the historical development of IWMs, followed by an overview of current motor configurations categorized by air-gap flux direction and topological structure, along with a comparative analysis of their performance characteristics. Following this, the coupling dynamic effects between IWMs and overall vehicle dynamics are systematically analyzed, specifically distinguishing between internal and external vibration excitation sources. In terms of control strategies for IWM-driven EVs, recent literature is extensively surveyed across three critical dimensions: noise, vibration, and harshness (NVH), vehicle stability, and energy economy. Finally, based on the current state of the art, the review identifies and discusses future trends in IWM technology aimed at further elevating overall vehicle performance. Ultimately, this article is intended to serve as a valuable reference guide for researchers and engineers engaged in the development of IWMs and related EV technologies.

Guo, Ruixin, Zhu, Yueying, Xing, Chao, He, Yang, Lin, Yier

Effect of High-Energy Ignition and Spark Plug on Combustion and Fuel Economy of a Dedicated Hybrid Engine

03-19-05-0026

10/6/2026

High exhaust gas recirculation (EGR) rates in dedicated hybrid engines (DHEs) cause combustion instability and fuel economy degradation. This study investigates a 2.0L turbocharged DHE with a high-tumble combustion system optimized by computational fluid dynamics (CFD) simulation. At the brake thermal efficiency (BTE) operating point (2750 r/min, brake mean effective pressure [BMEP] 11 bar), engine bench tests evaluated the effects of three ignition energy levels (120 mJ, 150 mJ, 200 mJ) and two spark plug configurations (1.1 mm nickel alloy and 0.7 mm pin-to-pin iridium) on combustion characteristics, fuel economy, the misfire-limited EGR rate (hereinafter the EGR misfire limit), and engine-out emissions. Results show that above 23% EGR, intake condensate weakens ignition and destabilizes combustion. Raising ignition energy to 200 mJ extends the EGR misfire limit from 23% to 28%, shortens ignition delay (CA0-10) and combustion duration (CA10-90) by 4.5°CA and 2.8°CA and reduces BSFC by 3.2 g/kWh. The 0.7-mm iridium plug outperforms the 1.1-mm nickel alloy plug above 27% EGR: CA0-10 and CA10-90 shorten by 2.9°CA and 2.5°CA, the EGR misfire limit extends by 0.5 percentage points, and BSFC improves by 0.9 g/kWh. The combined optimization extends the EGR misfire limit to 28.5% with a cumulative BSFC reduction of 4.5 g/kWh. HC emissions decrease with reduced spark plug gap, while NOx and CO remain insensitive to the ignition strategy. The coupled optimization of high-energy ignition and small-gap spark plug effectively overcomes ignition degradation under high EGR. The reduced breakdown voltage of the small gap and the sufficient energy reserve of high-energy ignition form a synergistic effect, providing quantitative design guidance for ignition systems in next-generation high-efficiency DHEs.

Wang, Peng, Ren, Siming, Cong, Rizhen, Deng, Xiaorong, Liu, Zonghui, Zhu, Yunfeng, Li, Hongzhou, Yan, Pingtao

2025–2026 Reviewers

14-15-03-0021

10/6/2026

2025-2026 Reviewers

Onori, Simona

Thermoelectric Generators for Battery Charging: Waste Heat Utilization with Minimized Back Pressure

13-07-02-0009

10/1/2026

Waste heat recovery has become a critical research area in the quest for improving automotive energy efficiency. Internal combustion engines lose a large amount of their energy as heat through exhaust gases. Thermoelectric generation technology presents a promising approach to capturing and converting this waste heat into useful electrical energy. In this work, a theoretical study on the application of thermoelectric generators (TEGs) for battery charging by converting waste heat from internal combustion engine exhaust into usable electrical energy is proposed. A prototype system incorporating TEG modules was designed focusing on the minimization of back pressure; flow of exhaust gas is ensured through a circular internal cross-section. Thermal simulations were performed using Ansys Workbench, and computational fluid dynamics (CFD) analysis was conducted to quantify the back pressure. Two heat exchanger materials, aluminum and copper alloys, were evaluated for their heat transfer performance. The results indicate that copper achieves superior heat transfer, with hot-side temperatures approximately 8.6% to 23.9% higher than aluminum. However, aluminum remains a viable alternative due to its lightweight and cost-effectiveness. With the cold side being maintained at 91°C to simulate realistic engine coolant conditions, the experiment shows that a series-parallel configuration of six TEG modules (three in series × two in parallel) can effectively generate the necessary voltage (13–15 V) and current (3.5–7 A) to charge a 12-V automotive battery. CFD analysis confirmed that the circular internal geometry produces low back pressure, with pressure drops of 28 Pa, 63 Pa, and 168 Pa for inlet velocities of 25 m/s, 40 m/s, and 70 m/s, respectively. This research underscores the potential of TEG-based battery charging systems in enhancing energy efficiency, though further development is required for real-world automotive integration. Future work could focus on on-vehicle testing, optimizing thermoelectric materials, and integrating advanced cooling mechanisms and maximum power point tracking controllers to improve overall system viability.

Satheesh, Amal, Satheesh, Akul, Pillai, Ashwin S., Mahisankar, J.S., Sreejith, B.J.

2025–2026 Reviewers

10-10-04-0038

10/1/2026

2025–2026 Reviewers

Xu, Peijun

Prognostic Thermal Modeling and Control of a Carbon–Ceramic Brake Disc for Electric Sports Cars under Racetrack Conditions

14-15-03-0020

10/1/2026

Carbon–ceramic brake discs in high-performance electric sports cars are vulnerable to heat fade under racetrack conditions, where repeated high-speed braking can raise disc temperature above the material’s safe limit of 1200°C. Three-dimensional finite-volume analysis is accurate but inefficient for long transient track events. To improve efficiency, a one-dimensional lumped capacitance method (LCM) is proposed to predict brake disc temperature evolution. A speed-dependent cooling coefficient links disc thermal response to vehicle operating conditions. The model is validated against wheel-end temperature measurements of sports cars on the Zhuzhou International Circuit and Nürburgring Nordschleife Circuit. It is then used to assess three thermal control measures: an external air director, increased disc thermal mass, and higher regenerative braking contribution. The model reproduces the measured trend with acceptable error and predicts that the baseline disc temperature can peak at 1445°C in a four-lap Zhuzhou scenario and 1540°C in a Nürburgring scenario. The air director provides substantial cooling but is insufficient on its own. A system-level safe temperature of 1050°C is achieved only when the disc size is increased to 410 mm × 40 mm and regenerative braking deceleration is raised to at least 0.1 g in combination with the air director scheme. The proposed LCM provides a practical and computationally efficient tool for early-stage brake thermal design of sports cars.

Fan, Yang, Huang, Longsheng, Shao, Xingyang, Huang, Taishuo, Liao, Yinsheng

Development and Experimental Study of Vehicle Control System for Pure Electric Construction Machinery

14-15-03-0019

10/1/2026

This article develops a comprehensive vehicle control system for pure electric wheel loaders, covering the entire development process. Based on a dual-motor and two-speed transmission configuration, the collaborative design of the hardware and software for the vehicle control unit (VCU) was completed. Vehicle control strategies were developed, including motor demand torque calculation, an overspeed protection algorithm, a gear shift control algorithm, an energy recovery strategy integrating coasting and braking, and control methods for the working system. Finally, the control strategies were verified and optimized through real vehicle tests. Experimental results indicate that the developed pure electric loader exhibits excellent performance. Compared with a diesel counterpart of the same tonnage, its maximum speed is increased by 19.4%, operational productivity (V-cycle loading cycles per hour) is improved by 8.7%, while the operating cost is only 35% of that of the diesel model. This study provides a solution validated by real vehicle tests for the development of vehicle control systems for pure electric construction machinery.

Xu, Haozhi, Ji, Shaobo, Che, Renhai, Tan, Yuchen, Hou, Defeng, Ren, Shuojin, Zhao, Jiayang

Exploring the Benefits of Multiple Injections in a Premixed Charge Compression-Ignition Engine Operated with Modified Injector Orientation and Diesel–Gasoline Blends

03-19-05-0025

9/22/2026

The current study examines the combined effects of injection strategy, injector configuration, and fuel blending on the combustion performance and emission behavior of a light-duty compression-ignition (CI) engine operated in premixed charge compression-ignition (PCCI) combustion mode. Experiments were conducted in PCCI combustion mode using a diesel–gasoline blend (D80G20, 80% diesel and 20% gasoline by volume). A modified injector configuration, with a split-injection scheme comprising pilot and main injection events, was implemented to enhance mixture preparation and control combustion characteristics. The baseline configuration utilized PCCI mode with diesel (D100) and an inclined injector orientation. The results indicate that blending gasoline into diesel prolongs ignition delay and facilitates charge premixing, hence improving the stability of the PCCI combustion regime. Using a vertically oriented injector with a symmetric spray pattern significantly improves air–fuel mixing, and split-pulse injection enables more accurate control of combustion phasing. Among the tested strategies, the D80G20 blend, combined with a vertical injector and optimized split injection, achieved the highest brake thermal efficiency at 60% load, improving by 10.1% over the baseline case. In addition, unburned hydrocarbon (HC) and carbon monoxide (CO) emissions were significantly reduced by 54.1% and 49.4%, respectively. Additionally, the load extension was increased to 77%, which is limited to 60% of the engine-rated load in PCCI with diesel fuel. The current integrated approach provides a viable pathway to implement the PCCI mode to improve engine thermal efficiency and reduce pollutant emissions without significant hardware modifications, thereby supporting the transition to cleaner combustion technologies.

Ranjan, Ashish Pratap, Krishnasamy, Anand

Response Surface Optimization of Alkylphenolate Additive AKI-152 for High-Performance Motor Oils in Oil and Gas Applications

04-19-03-0012

9/22/2026

The behavior of detergent–dispersant additives in lubricating oils depends upon their chemical composition and synthesis, particularly for lubricants used in rotary equipment under harsh conditions, in the upstream and downstream oil and gas industries. Alkylphenolate-based additives are extensively used, among others, due to their high alkalinity and good dispersion stability. However, their performance is extremely sensitive to synthesis parameters, making experimental optimization time-consuming and resource-intensive. In this study, an alkylphenolate-based additive (AKI-152) was synthesized and optimized systematically via a response surface methodology (RSM), derived from a central composite design (CCD). The influence of three important process variables, namely the alkylphenol/amino mass ratio, reaction temperature, and Ca(OH)2/CO2 mass ratio, was assessed with respect to the total base number (TBN), kinematic viscosity, and corrosivity of the final product. A quadratic regression model was developed and validated, showing good agreement between predicted and experimental results. Analysis of variance (ANOVA) indicated that the alkylphenol/amino ratio and Ca(OH)2/CO2 ratio exert the strongest influence on TBN, while temperature also contributes significantly within the investigated operating range. Numerical optimization identified a stable optimum region corresponding to TBN values of approximately 160 mgKOH/g, viscosity values close to 54 mm2/s, and corrosivity values below 0.76 g/m2 under the selected optimization criteria. No separate experimental run was performed to verify the selected numerical optimum, as the optimization was conducted within the experimentally investigated CCD space using the developed and statistically evaluated response surface models. These results demonstrate that the applied modeling approach can be effectively used to define suitable synthesis conditions for high-performance lubricant additives.

Gasimov, Rahib, Naghiyeva, Elmira, Sujayev, Afsun, Gadirov, Ali, Nasibova, Nigar

Research on Electro-Hydraulic Composite Braking Control Strategy for Four-Axle Electric-Drive Special Vehicles

02-20-01-0005

9/22/2026

Four-axle electric-drive special vehicles are often deployed to execute complex tasks under extreme operating conditions. Their harsh working environments, stringent dynamic-response requirements, and high energy demand for mobility impose higher requirements on both the energy-regeneration efficiency and braking safety of the braking system. To address these issues, this article proposes an electro-hydraulic composite braking control strategy for four-axle electric-drive special vehicles. First, a braking axle-load transfer model for the four-axle electric-drive special vehicle is established, enabling axle-to-axle braking force distribution based on axle loads. Second, considering the motor torque characteristics and battery charging characteristics and by establishing an anti-lock braking prediction model, an electro-hydraulic composite braking torque allocation strategy with safety-range constraints on motor braking torque is designed. Then, to enhance vehicle safety during emergency braking, an adaptive super-twisting sliding-mode variable-structure anti-lock braking system (ABS) controller is proposed, and based on this ABS controller, an emergency braking control strategy is developed in which the motor braking torque is pre-reduced to within a safe range, with motor-dominant and hydraulic auxiliary modulation. Finally, simulation and real-vehicle tests demonstrate that the proposed electro-hydraulic composite braking control strategy improves the motor braking energy recovery rate under service braking conditions, reduces the braking distance, and enhances braking safety under emergency braking conditions.

Jin, Liqiang, Peng, Jinxin, Ke, Yuan, Peng, Silun, Li, Jianhua, Xiao, Feng

Geometric Design Consistency Evaluation Criteria for Horizontal Curves on Two-Lane Rural Highways Based on Vehicle Stability

09-15-01-0003

9/17/2026

Rollovers are among the most severe road crashes, often leading to high fatalities and significant property damage, as reported by government and insurance agencies. This study investigates the impact of curve geometry and loading conditions on the rollover stability of a two-axle truck using validated vehicle dynamics simulations. The research highlights the importance of providing adequate curve radii and shows that larger radii are required to ensure design consistency. The study reveals that a 1 cm increase in center-of-gravity height results in a 0.82% decrease in the margin of safety against rollover, and that loading the truck to 93.75% of its full capacity over an equivalent platform length is the most critical loading condition in terms of rollover stability. To enhance safety, predictive models for lateral acceleration are developed along with geometric design consistency evaluation criteria based on vehicle rollover stability. Design guidelines for consistent curve design are also proposed. These models and criteria guide strategic improvements in road geometry, including optimized placement of rollover caution signage and targeted infrastructure refinements. The study underscores the need for enhanced curve design standards to improve truck stability and driver comfort while providing essential tools for advancing highway safety and mitigating rollover risks for heavy vehicles.

Remya, Y. K., Jacob, Anitha, Subaida, E. A.

Short-Term Load Forecasting of Sustainable Power Systems Based on Newton–Raphson-Based Optimizer–Extreme Gradient Boosting under Extreme Weather Events

13-07-02-0008

9/17/2026

With continuous advancements in load-side resources such as distributed photovoltaic systems, electric vehicles, and virtual power plants, the low-carbon and sustainable development attributes of power systems have been significantly enhanced. Meanwhile, the coupling intensity between sustainable power systems and meteorological conditions has been further consolidated. Considerable impacts are exerted by weather variations, particularly extreme weather events, on the dispatching and operation of sustainable power systems. Accurate load forecasting is critical for enabling sustainable power systems operators to optimize power generation strategy, ensuring supply stability and resilience against extreme weather-induced disruptions. However, the intrinsic non-stationarity and volatility of extreme weather events present significant challenges to conventional forecasting approaches. Herein, we introduce a hybrid algorithm integrating Newton–Raphson-based optimizer (NRBO) with extreme gradient boosting (XGBoost) to enhance short-term load predictions under such conditions. The model uses optimally selected meteorological and load features as inputs, while NRBO systematically tunes XGBoost’s hyper-parameters to maximize performance. Evaluated on an Irish dataset, the proposed framework is quantitatively compared against five baseline models, including traditional decision trees and neural networks. The case studies show that the mean absolute percentage error (MAPE) of the proposed model is 2.57%, which is the lowest among these decision tree and neural network algorithms.

Wang, Yi, Zhou, Jian, Wu, Gang, Ma, Tiannan, Ma, Ruiguang, He, Chuan, Zhu, Huixian

Data-Driven Robust Preview Control of Vehicle Active Suspension Considering Random Controller Area Network Communication Dropouts

10-11-01-0001

9/10/2026

With the shift to full-by-wire chassis architectures, active suspension control is progressively integrated into chassis domain controllers to achieve coordinated chassis management. However, random packet dropouts in controller area network (CAN) communication under high-load conditions can significantly degrade suspension control performance. To address this challenge, this study proposes a novel data-driven robust preview control method. First, the packet-dropout phenomenon in CAN communication is modeled as a Bernoulli random process, and an augmented state-space model of the active suspension system is constructed by incorporating road preview information. Second, based on zero-sum game theory, road disturbances and control inputs are modeled as adversarial players, leading to the formulation of a stochastic game algebraic Riccati equation (SGARE) for the suspension system. To improve data efficiency and reduce design complexity, a data-driven value iteration (VI) reinforcement learning algorithm is employed to approximate the optimal control solution, with rigorous proof of convergence. Simulation results demonstrate that the proposed algorithm provides effective and feasible solutions across different packet-dropout probabilities. Furthermore, hardware-in-the-loop simulations confirm the robustness and reliability of the proposed control scheme, showing that the active suspension system maintains stable performance even in the presence of random CAN communication losses.

Wang, Gang, Duan, Deyang, Zhou, Tingting, Liu, Suqi

Comparison of Two Distinct Automatic Emergency Braking Control Strategies for Heavy Vehicles

02-20-01-0004

9/2/2026

Research on automatic emergency braking (AEB) control algorithms for heavy vehicles is relatively limited. Compared with passenger cars, heavy vehicle AEB algorithms must accommodate both unloaded and fully loaded conditions, with the latter posing higher demands. This study compares two distinct AEB control strategies: the time-to-collision (TTC) algorithm and the professional driver fitted (PDF) algorithm. Using simulation analyses under three regulatory-recommended scenarios—stationary lead vehicle, slow-moving lead vehicle, and decelerating lead vehicle—the results indicate that the PDF-based control system better adapts to both unloaded and fully loaded conditions. It demonstrates significant improvements in braking performance and robustness compared to the TTC-based system. For an unloaded vehicle equipped with the PDF–AEB control system (5500 kg), the final gap to the lead vehicle is the longest (11.5 m) under the scenario of a stationary lead vehicle with an initial ego vehicle speed of 80 km/h and the shortest (3.1 m) under the scenario of a lead vehicle with an initial speed of 50 km/h braking at 0.4 g. For a fully loaded vehicle (12,500 kg), the corresponding final gaps to the lead vehicle are 11.2 m and 2.5 m, respectively.

Lai, Fei, Huang, Chaoqun

Assessment of Heat Transfer Correlations for Shell-and-Tube Heat Exchangers under Periodically Varying Exhaust Flow

03-19-05-0022

9/2/2026

Waste heat recovery from internal combustion engines (ICEs) is one potential option to improve overall vehicle efficiency. Rankine cycles based on engine coolant and exhaust heat sources have demonstrated their effectiveness in enhancing brake thermal efficiency. Critical to their success is the design of the heat exchanger for the evaporator, with shell-and-tube heat exchangers (STHEs) a common hardware choice. However, little experimental data exists evaluating STHEs with the pulsating flow encountered in the exhaust of ICEs. In addition, correlations for periodically varying flow do not appear to be used in the modeling of STHEs. To alleviate this limitation, this study combined experiments using a pulsating exhaust heat source from an ICE under low loads at a single engine speed with a one+one-dimensional model to evaluate tube- and shell-side heat transfer correlations for a STHE without baffles. Four working fluids, water, ethylene glycol, propylene glycol, and a 50/50 ethylene glycol–water mixture, were examined. The combined thermodynamic properties of an ethylene glycol–water mixture were the most effective based on an evaluation of heat exchanger effectiveness, overall heat transfer coefficient, exergetic efficiency, and entropy generation. A Pearson correlation analysis identified the inlet working fluid temperature as the parameter most strongly correlated with STHE performance due to its higher enthalpy. From a modeling perspective, the pulsating flow correlation of Al-Haddad and Al-Binally predicted greater heat transfer rates in the STHE. In combination with all shell-side correlations tested, simulations still underpredict performance relative to experimental results. An optimized correlation developed specifically for the geometry of this unbaffled STHE matched the experimental data more closely but likely overpredicted shell-side heat transfer. Monte Carlo uncertainty propagation based on sensor uncertainties showed that the differences in effectiveness and overall heat transfer coefficient exceeded measurement uncertainty. Furthermore, sensitivity analysis demonstrated the importance of accurate thermophysical property values and indicated that the underprediction likely reflects coupled limitations in both tube- and shell-side formulations, with correlations on each side exerting a comparable influence on predicted heat transfer.

Spickler, Bailey, Segares Dominguez, Maria Luisa, McGowan, Raymond, Depcik, Christopher

An Effective Pathway for Off-Road Tier 5 Emission Compliance

03-19-08-0041

8/31/2026

Proposed Tier 5 off-highway emission regulations for the 19–56 kW engine class pose significant technical and economic challenges. Unlike larger platforms, where selective catalytic reduction (SCR) is the standard nitrogen oxide (NOX) control strategy, engines in this class face cost and packaging constraints that limit complex aftertreatment adoption. This article investigates whether a production Tier 4 diesel engine and its existing aftertreatment can meet proposed Tier 5 limits through calibration and minor hardware changes alone, without major redesign or SCR. The approach combined a cooled exhaust gas recirculation (EGR) strategy with start of injection (SOI) timing optimization to manage the NOX–particulate matter (PM) trade-off, using the stock diesel oxidation catalyst (DOC) and diesel particulate filter (DPF) system for particulate control. An EGR/SOI design-of-experiments (DOE) sweep identified an optimal calibration, validated over both the ramped modal cycle (RMC) and non-road transient cycle (NRTC) per Title 13 California Code of Regulations (CCR) Section 2423 for certification of variable-speed engines in this power category. Results indicate that the system can be a viable pathway of meeting upcoming Tier 5 final emission standards.

Patil, Shubham Vishwanath, Michlberger, Alexander, Bachu, Pruthvi R., Amaral Garcia, Herbert, Smith, Edward M.

Shimmy Dynamics Analysis of a Dual-Wheel Main Landing Gear Considering Side Stay Angular Coupling

01-19-02-0007

8/31/2026

Main landing gear shimmy is jointly affected by tire forces, structural elasticity, damping, and geometric coupling. To investigate the influence of side stay angular coupling on shimmy stability, this article establishes a shimmy dynamic model of a dual-wheel main landing gear considering side stay angular coupling. Numerical continuation bifurcation analysis, Hopf bifurcation frequency mapping, and local sensitivity analysis are then employed to study its influence mechanism on stability boundaries, dominant modes, and multistable behavior. The results show that the horizontal inclination angle of the side stay introduces additional structural coupling between strut torsion and longitudinal bending, causing the longitudinal motion to evolve from a passive response into an important mode participating in shimmy instability. A small horizontal inclination angle can induce the coexistence of multiple stable periodic responses, whereas a larger inclination angle changes the connectivity of Hopf bifurcation curves and forms a new instability branch involving longitudinal motion. Further analysis indicates that adjusting the orientation angle to make the local horizontal inclination angle approach zero can weaken the direct structural coupling between torsion and longitudinal motion and reduce the sensitivity of the longitudinal response to variations in the horizontal inclination angle. These results indicate that the angular design of the side stay should comprehensively consider the coupling effect between the horizontal inclination angle and the orientation angle, so as to avoid multistability and mode transition induced by the side stay angular arrangement.

Wei, Jian, He, Jipeng, Zhang, Jiahao, Zhu, Shixing, Li, Shuangbao, Zhu, Hengjia

Design, Simulation, and Experimental Evaluation of an R290-Based Dual Secondary Loop Thermal Management System for Electric Vehicles

14-15-03-0018

8/28/2026

The transition toward low global warming potential (GWP) refrigerants, driven by increasingly stringent environmental regulations and carbon reduction targets, has imposed new requirements on thermal management systems (TMSs) for electric vehicles (EVs). These systems must ensure efficient operation across a wide range of ambient conditions while maintaining high energy efficiency and environmental compatibility. Among potential alternatives, R290 (propane) has emerged as a promising natural refrigerant due to its favorable thermophysical properties and low environmental impact. In this study, an R290-based dual secondary loop TMS is proposed and evaluated for wide-temperature-range EV applications. A one-dimensional system model was developed using Dymola and validated through experimental testing on a dedicated performance test bench. TMS performance was investigated under multiple steady-state operating conditions, including high-load cooling, battery fast charging, and low-temperature heating, and benchmarked against a conventional R1234yf-based direct TMS. The results demonstrate that the R290-based dual secondary loop system achieves improved performance compared to a conventional R1234yf direct system, with a coefficient of performance (COP) increase of 4.29% under high-load cooling conditions at 43°C and up to 27.27% under high-load heating conditions at −10°C. Furthermore, under extreme low-temperature conditions (−18°C), the system delivers a heating capacity of 7 kW with a COP of 1.8, demonstrating strong low-temperature adaptability without the need for auxiliary heating. The results confirm that the proposed R290-based dual secondary loop system provides significant advantages in energy efficiency and wide-temperature adaptability, offering a promising solution for next-generation EVTMSs.

Zhang, Yunpeng, Mohammed, Mustafa Mudassir, Gu, Yiliang, Zhou, Guoliang

Objective Quantification of Harmonic Impulsive Sounds and Interval Prediction of Sound Quality in Hybrid Vehicle Engines

10-11-01-0003

8/28/2026

Steady advancement is observed in global research on eco-friendly and sustainable transportation. Rapid technological evolution of hybrid electric vehicles (HEVs) is documented. Lower overall noise output and more compact structures are achieved in HEV engines relative to conventional internal combustion engines. The perceptibility of harmonic impulsive sounds is significantly enhanced by these design characteristics. A close correlation is observed between these acoustic phenomena and negative human auditory perceptions. These events are treated as a core focus for HEV noise, vibration, and harshness optimization. Accurate quantification of harmonic impulsive sounds is not achieved by conventional objective indicators. A favorable balance between reliability and accuracy is not established by existing subjective prediction models. Practical engineering applications of these methods are severely restricted. A novel objective quantification method for harmonic impulsive sounds is proposed in this study. The method is established based on time–frequency masking theory and tonal strength. Bench tests in a semi-anechoic chamber and subjective evaluation experiments with standardized rating scales are performed for data collection. Collected sound signals are decomposed through an integrated approach of wavelet transform and variational mode decomposition. Targeted feature extraction is completed for harmonic impulsive sounds. A quantitative index incorporating human auditory temporal and frequency masking effects is developed. The proposed index exhibits a significantly stronger correlation with subjective evaluation results than traditional objective metrics, confirming its superior ability to reflect actual perceived sound quality. An interval prediction model for sound quality evaluation is established based on support vector machines and kernel density estimation. Traditional objective metrics and the proposed index are introduced as key input parameters. Effective and reliable prediction of HEV engine noise subjective satisfaction is achieved by the model.

Lin, Xu, Liang, Xingyu, Shi, Zhiyuan

Study of Port Fuel Injection Engine Cold Start Unburned Hydrocarbons Reduction Using Computational Fluid Dynamics with a Reduced Iso-Octane Mechanism Considering Various Isomers and Thermal Decompositions

03-19-04-0021

8/27/2026

A modeling study was performed to find solutions to reduce the unburned hydrocarbons during cold start of a PFI (port fuel injection) SI (spark ignition) engine. Through modeling, the root cause for the high unburned hydrocarbons of the baseline engine during cold start was found. The slow combustion, which is due to the high amount of exhaust gas flowing back into the intake port and then becoming trapped inside the cylinder, is the root cause. A new valve lift, which can reduce the internal residual by 26%, was designed. Along with a fuel amount decrease of 35%, the UHC (unburned hydrocarbons) before the three-way catalyst can be reduced by 40%. The exhaust temperature using the new valve lift design increases by 400°C, which improves the performance of the three-way catalyst for further reducing UHC. In addition to the adoption of the new valve lift, an active SAI (secondary air injection) strategy was also investigated. Modeling results show that SAI can promote secondary combustion in the exhaust pipes to increase exhaust temperature and thus is beneficial for further oxidizing unburned hydrocarbons. The amount of active SAI mass flow rate should be controlled to less than 25% of the intake air flow rate to avoid the cooling effect dominating over the oxidation process. The duration of SAI should be from EVO (exhaust valve opening) to IVO (intake valve opening). For combustion modeling, a newly reduced iso-octane chemical kinetic mechanism was developed using carbon flux analysis to extract major reaction pathways for a wide range of practical engine temperature conditions. In the new reduced mechanism, a skeletal sub-mechanism for species starting from iso-octane to C4 is coupled with a recently updated H2/O2/CO/C1–C4 detailed sub-mechanism. Including a reduced NOx (oxides of nitrogen) sub-mechanism, the final mechanism has 681 species and 3332 reactions. Before the new reduced iso-octane mechanism was used, it had been validated with available experimental data of ignition delay times, laminar flame speeds, and important species profiles in the literature. Both the investigation of PFI engine unburned hydrocarbons reduction under cold start operating conditions and the development of a reduced chemical mechanism are the objectives of this work.

Guo, Dongshao, Zhang, Licheng, Yang, Shiyou, Bourg, Cyrus, Sun, Yong, Abidin, Zainal, Lin, Shujun

Development of Remote Cab Controller for Construction Machinery

14-15-03-0017

8/27/2026

This article focuses on the research and development of a remote cab controller for pure electric loaders, aiming to address the threats posed by traditional loaders operating in harsh and hazardous environments to drivers’ health and safety. First, the functional requirements of the controller were analyzed, based on which the hardware design with a multicore microprocessor as the core was completed, featuring functions such as signal acquisition, controller area network (CAN) communication, and H-bridge driving. On this basis, a control algorithm framework for remote driving was developed, including modules for signal input, analysis and processing, and signal output. Detailed control strategies were formulated for key components: For the pedal sensor, algorithms for opening degree calculation, automatic zero-position calibration, and dual-signal redundant fault diagnosis were proposed; for the steering module, precise angle calculation and force feedback feel simulation were achieved; and for the electric control handle, a hysteresis control algorithm was developed to suppress shocks caused by overly fast operations. In addition, a hierarchical fault diagnosis mechanism was established to ensure system safety. To verify the controller performance, a complete remote driving system was built. Field test results show that the system exhibits good signal following and control responsiveness in terms of traveling and working functions. Efficiency tests indicate that the remote driving efficiency can reach 80% of that of in-person operation under short-term test conditions, demonstrating the technical feasibility and control effectiveness of the developed controller. While the prototype exhibits promising performance for pilot deployment, long-term reliability metrics such as mean time between failures (MTBF) remain to be validated through extended field operation.

Lu, Yueqi, Ji, Shaobo, Yu, Qiuye, Li, Meng, Xu, Haozhi, An, Meng

Evaluation of the Isolation Efficiency of Quasi–Zero Stiffness Structure in Improving Seat Comfort of Different Commercial Vehicles

02-20-01-0003

8/26/2026

Different commercial vehicles, such as the sunflower harvester, tracked vehicle, and vibratory roller, operate across off-road and on-road environments, often encountering rough and poorly maintained surface conditions. Thus, their comfort and working efficiency are very low. To solve this problem, a quasi–zero stiffness structure (QZSS) is investigated and added to traditional seat suspensions in the sunflower harvester, tracked vehicle, and vibratory roller to improve their comfort and working efficiency. From their established dynamic models, the isolating efficiencies and stabilities of QZSS are then analyzed in detail under different conditions of speed and seat mass. Reducing the root-mean-square values of the seat acceleration (aw) and displacement (zw) is used to evaluate the results. The research shows that the comfort of the sunflower harvester and vibratory roller is very poor compared with the tracked vehicle under the same simulation conditions. By adding QZSS to their seat suspension system, the values of {aw and zw} in the sunflower harvester, tracked vehicle, and vibratory roller are strongly reduced by {67.9% and 38.8%}, {54.7% and 23.9%}, and {65.4% and 34.3%} in comparison without QZSS. Therefore, the comfort of the three vehicle models is greatly improved in comparison without QZSS. Besides, QZSS improves the vibratory roller’s comfort better than the tracked vehicle, while QZSS improves the sunflower harvester’s comfort to be the best. These study results further strengthen the isolation efficiency of QZSS on different commercial vehicles. This contributes to providing more applicability of QZSS in real vehicle conditions.

Nguyen, Vanliem, Zhang, Li, Liu, Yaxi

Performance and Emission Control Enhancement Using Green-Synthesized Additives in Diesel Engine

03-19-04-0020

8/24/2026

Cashew nut shell oil–based biodiesel (BD) is an environmentally friendly and sustainable alternative energy source that can help decrease the depletion of fossil fuels and reduce environmental pollution. In this research, the BD extracted from cashew nut shell was enriched with green-synthesized nanoparticles with various blends and evaluated for its performance. The BD20A blend recorded the best thermal efficiency of the brake, 29.5%, which was a boost of about 20.4% over diesel when using a medium load of 2.7 kW. Furthermore, the decrease in brake-specific fuel consumption was 36.2%, and exhaust gas temperature improved by 26.1% due to enhanced combustion, indicating better combustion and utilization of heat. The BD10A and BD20A recorded a considerable decrease in emissions compared to diesel under full-load conditions, with carbon monoxide and hydrocarbons reducing by 35.7% and 33.3%, respectively, and a moderate increase of nitrogen oxides. Among the multi-objective optimization approaches, the Jaya algorithm exhibited the fastest convergence rate and identified the optimum operating condition that achieved the best trade-off between engine performance and exhaust emissions. BD blends, particularly BD20A, provide greater thermal performance and better combustion behavior as well as lower exhaust emissions, making them viable as green alternatives to the traditional diesel fuel.

Victor Soosai Irudayaraj, S., Thanigaivelan, V., Brucely, Y., Lenin, N.

Resilient Trajectory Tracking Static Output-Feedback Control for Automated Vehicles under Denial-of-Service Attacks

10-11-01-0002

8/24/2026

The safety and reliability of autonomous vehicles are critically linked to network communication quality. Consequently, threats such as Denial-of-Service attacks pose significant risks by disrupting communication between essential components and jeopardizing driving safety. This article presents a robust trajectory tracking control method resilient to such attacks. Utilizing a Linear Parameter–Varying control framework, the method effectively addresses time-varying vehicle speeds and incorporates a norm-bounded strategy to manage tire behavior uncertainties. By employing a Static Output-Feedback scheme, it avoids the need of costly sensors while maintaining a straightforward control structure suitable for real-time implementation. Using Lyapunov’s method, we analyze the system stability under attack conditions, ensuring the controller remains robust against disturbances. The design of the resilient controller is formulated as a convex optimization problem with Linear Matrix Inequalities constraints. The effectiveness of the proposed controller is validated through co-simulation in MATLAB/CarSim®, where it outperforms several state-of-the-art controllers across different driving scenarios, maintaining consistent tracking performance despite varying attack severity levels.

Meléndez-Useros, Miguel, Viadero-Monasterio, Fernando, Nguyen, Anh-Tu, López-Boada, María Jesús

Hierarchical Yaw–Roll Stability Control of Semi-Trailers Based on Joint Estimation of Equivalent Parameters

02-20-01-0001

8/19/2026

Semi-trailers are widely used in highway freight transportation because of their large payload capacity and high transport efficiency. However, structural characteristics such as a high center of gravity (CG), heavy loads, and the dynamic coupling between the tractor and trailer make them prone to yaw instability and rollover under complex conditions. To solve these problems, this article proposes a hierarchical stability control architecture for semi-trailers based on the joint estimation of equivalent parameters. First, a six-degree-of-freedom (6-DOF) theoretical dynamic model is established. This model includes the lateral, yaw, and roll motions of both the tractor and trailer to provide desired reference states. Second, a parameter estimation method combining a genetic algorithm (GA) with a forgetting-factor recursive least squares (RLS) algorithm is designed. It dynamically identifies eight unknown equivalent parameters, specifically the tire cornering stiffness and suspension damping. Next, a hierarchical controller is developed. The upper layer uses model predictive control (MPC) to calculate the required additional yaw moments, while the lower layer allocates these moments through quadratic programming (QP) based on vehicle steering characteristics. Co-simulation results, evaluated using error metrics that compare control outputs directly against TruckSim reference outputs, show that the fusion GA-RLS method offers better accuracy and adaptability than a standalone GA. Furthermore, the stability controller prevents rollover in high-speed maneuvers and reduces peak state indicators by over 41.2% in low-speed scenarios. Robustness tests also confirm its effectiveness under low-adhesion road conditions and heavy payloads. Compared with a conventional fixed-parameter MPC, the proposed adaptive architecture improves key stability indicators by 19% to 25%, effectively enhancing the dynamic safety of semi-trailers.

Song, Dafeng, Ni, Lixin, Duan, Chaosheng, Zeng, Xiaohua

A Comprehensive Analysis of a Truck–Semi-Trailer Performance Based on Hierarchical Controller Design to Enhance Driver Comfort and Dynamic Stability

02-20-01-0002

8/18/2026

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

Evaluating the Applicability of Advanced Physical Vapor Deposition Coatings through Characterization and Dry Machining

05-19-04-0031

8/18/2026

This study investigates the characterization and dry machining performance of advanced physical vapor deposition (PVD) aluminum titanium nitride (AlTiN) and aluminum chromium titanium nitride (AlCrTiN) coatings deposited using three techniques: cathodic arc evaporation (CAE), high-power impulse magnetron sputtering (HiPIMS), and scalable pulse power plasma (S3p). The coatings were evaluated for thickness, microstructure, surface roughness, coefficient of friction (CoF), adhesion strength, and microhardness. Among the tested coatings, the S3p-deposited AlCrTiN showed the best performance, exhibiting the highest microhardness (40 GPa), the strongest adhesion (108 N), and the lowest CoF (0.25), along with a defect-free microstructure. Under the selected dry turning condition of 150 m/min cutting speed, 0.15 mm/rev feed rate, and 0.7 mm depth of cut, the S3p-deposited AlCrTiN coating achieved a maximum tool life of 10,800 mm, nearly three times higher than the CAE-deposited AlTiN coating. In contrast, CAE coatings showed comparatively lower hardness and weaker adhesion, with minimum values of 25 GPa and 68 N for C1, along with higher CoF values of 0.58–0.60. Furthermore, AlCrTiN coatings produced by HiPIMS and S3p provided 20–30% longer tool life than AlTiN coatings under identical cutting conditions, highlighting the importance of deposition technique.

Sonawane, Gaurav Dinkar

Pelvis Lap Belt Engagement of Small-Sized Females in Frontal Impacts Using Seat-Integrated Restraints and Vehicle Seats for Alternative Seating Positions

2026-22-0008

8/14/2026

Non-traditional vehicle seating postures challenge traditional occupant protection paradigms that promote pelvis lap belt engagement. Seat-integrated restraints may promote pelvis lap belt engagement in alternative seating postures but have not been evaluated with post-mortem human subjects (PMHS) in vehicle seats. The goal of this research was to perform three 38.8 g, 56 km/h frontal impact sled tests with small-sized female PMHS in a crash environment with a vehicle seat designed for alternative seating positions. PMHS pelvis kinematics, lap belt engagement, and submarining response were compared to that of the Hybrid III 5th female (HIII-5F) in the same environment. The seat was in the rearmost seat track position, reclined 40° from vertical, and incorporated a leg rest, which elevated the feet off the floor. A seat cushion airbag (SCAB), large passenger airbag (PAB), shoulder belt pretensioner (SB P/T), and seat-integrated belt (BIS) were incorporated into the testing environment. The SCAB restricted initial downward translation of the pelvis and induced 7.7°–11.8°of initial pelvis rearward rotation. Lap belt loading of the abdominal soft tissue occurred in each test via three distinct interactions: (1) initial pelvis lap belt engagement followed by pelvis fracture and subsequent submarining; (2) lack of initial pelvis engagement and direct abdominal loading; (3) initial pelvis lap belt engagement followed by submarining. In a matched test environment, the HIII-5F did not submarine nor reproduce the entire lap belt pelvis interactions observed by the PMHS. Future research must develop better tools for predicting lap belt engagement in alternative seating positions.

Newman, Rachel, Shin, Jeesoo, Sochor, Sara, Morgan, Neal R., Gepner, Bronislaw D., Kerrigan, Jason R., Kim, Yongtae, Kim, Sung Rae

Relevant vehicle front-end components for pedestrian laboratory testing to address real-world pedestrian injuries when struck by passenger vehicles with high hood heights

2026-22-0009

8/14/2026

In the United States, pedestrian deaths account for 18% of roadway fatalities and have increased 78% since their lowest point in 2009. U.S. consumers are increasingly purchasing larger vehicles that are responsible for a disproportionate number of pedestrian injuries. This study examined a dataset of pedestrians struck by passenger vehicles in Michigan from 2015 to 2024 to identify the unique characteristics of the tallest vehicles, large SUVs and pickups, which are contributing to increased injury. Vehicle height was categorized as the hood leading edge (HLE) height compared with the estimated pedestrian hip and waist heights from anthropometric measures. Maximum abbreviated injury scale and injury sources by body region were tabulated for three vehicle height categories. Typical kinematic patterns were observed for each relative height category and the corresponding injury frequency and impact locations. For vehicles with high hood heights, head and torso injuries were commonly from the front of the vehicle —the grille, headlights, and HLE. In contrast, head injuries sustained when pedestrians were struck by medium-height and short vehicles were primarily from the vehicle hood and windshields. Even among the tallest vehicles where the bumper was much higher than the pedestrian’s knee, leg injuries from the vehicle bumper and valance were frequent, suggesting that evaluating these vehicle components is also necessary to address lower extremity injuries. This study identified the unique pedestrian impact locations associated with the tallest vehicles, which can help guide vehicle designers when considering impact attenuation strategies to reduce injury in crashes with pedestrians.

Mueller, Becky, Jermakian, Jessica

Review of Semi-Active Shock Absorption Mechanisms in Landing Systems

01-19-02-0006

8/13/2026

Effective shock absorption is essential for maintaining stability during landing events. Aerospace systems traditionally rely on oleo-pneumatic struts, while robotic platforms utilize lightweight compliant joints for impact mitigation. Recent advances have shifted attention toward adaptive solutions, including magnetorheological and electrorheological dampers, which can adjust their damping characteristics in real time through sensor feedback and control algorithms. By integrating established mechanical design principles with advanced materials and intelligent control strategies, modern landing systems can achieve improved energy dissipation and enhanced performance under variable and unpredictable conditions. This work evaluates the transition from passive to adaptive shock absorption technologies by examining landing dynamics, the mechanical architectures of conventional and semi-active systems, and the control strategies that enable adaptive damping. The findings indicate that, although passive systems offer reliability and simplicity, they lack the adaptability required for highly variable environments, while semi-active systems provide enhanced performance through real-time modulation enabled by advanced control algorithms. However, challenges related to power requirements, system complexity, material durability, and long-term reliability continue to limit widespread implementation of adaptive technologies. Overall, this review highlights the limitations of passive designs, evaluates the tradeoffs between MR and ER damping technologies, examines the evolution of semi-active control strategies, and identifies the key technical barriers that must be addressed before adaptive shock absorption systems achieve broader operational adoption.

Shah, Rajesh, Patel, Parth, Mittal, Vikram

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

03-19-04-0019

8/7/2026

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

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

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

02-19-04-0027

8/5/2026

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

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

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

05-19-04-0030

7/30/2026

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

Samanta, Avishek, Maji, Kuntal

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

02-19-04-0026

7/29/2026

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

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

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

12-09-04-0029

7/29/2026

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

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

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

01-19-01-0005

7/29/2026

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

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

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

2026-22-0007

7/28/2026

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

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

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

15-19-03-0012

7/27/2026

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

Kumar, Manik, Srinivasan, Sriram

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

02-19-04-0025

7/27/2026

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

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

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