Automotive Science and Engineering

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This study develops and validates a simplified testing methodology aligned with UNECE Regulation No. 49 to quantify particle number (PN) emissions from diesel vehicles. A modified World Harmonized Vehicle Cycle (WHVC) was implemented, incorporating steady-state operational segments (urban: 21.3 km/h, rural: 43.6 km/h, motorway: 76.7 km/h), and applied to evaluate 51 Iranian-manufactured diesel vehicles. The tested fleet comprised heavy-duty trucks, buses, and pickup trucks equipped with diverse propulsion systems (e.g., ISF3.8s5154, OM457LA.IV) and after-treatment technologies, including SCR, DOC, and DPF. Results demonstrate that original equipment manufacturer (OEM)-installed DPFs reduced PN emissions by 7000-fold compared to non-DPF-equipped vehicles (2.49 × 10¹⁰ vs. 1.74 × 10¹⁴ particles/km; p < 0.001). Euro VI-compliant vehicles exhibited the lowest emissions (6.01 × 10¹⁰ particles/km), outperforming Euro V and Enhanced Environmentally Friendly Vehicle (EEV) standards. These findings underscore the necessity of adopting OEM-grade filtration systems and enforcing stringent emission regulations, such as Euro VI, to mitigate particulate pollution in urban environments. The methodology provides a replicable framework for emerging markets to align with global emission compliance protocols.
 

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Ensuring that ethically sound decisions are made under complex, real-world conditions is a central challenge in deploying autonomous vehicles (AVs). This paper introduces a human-centric risk mitigation framework using Deep Q-Networks (DQNs) and a specially designed reward function to minimize the likelihood of fatal injuries, passenger harm, and vehicle damage. The approach uses a comprehensive state representation that captures the AV’s dynamics and its surroundings (including the identification of vulnerable road users), and it explicitly prioritizes human safety in the decision-making process. The proposed DQN policy is evaluated in the CARLA simulator across three ethically challenging scenarios: a malfunctioning traffic signal, a cyclist’s sudden swerve, and a child running into the street. In these scenarios, the DQN-based policy consistently minimizes severe outcomes and prioritizes the protection of vulnerable road users, outperforming a conventional collision-avoidance strategy in terms of safety. These findings demonstrate the feasibility of deep reinforcement learning for ethically aligned decision-making in AVs and point toward a pathway for developing safer and more socially responsible autonomous transportation systems.

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A new idea is applied for damping of driveline oscillation (shunt and shuffle) created because of sudden changes in transmitting engine torque. In this case, by an actuator the dry clutch slides due to decreasing in clutch clamp force. By regulating the clutch force, it prevents the excessive torque from entering the driveline, which causes vibrations. This strategy eliminates the oscillations. Hence, the driveline dynamics which consists of the clutch model, driveline elements, backlash, and tire slip is modeled comprehensively in the MATLAB software and validated. When the vibrations are sensed by sensors, the electronic control unit directs the clutch actuator by changing the normal clutch force so the dry clutch slides and dampens the vibrations. For this idea, three controlling approaches are suggested: a rule-based controller, a pulse controller, and a linear predictive controller. Essential tests for examining driveline control performance are chosen and then the driveline performance is assayed for it. Also, one of the priorities in selecting a clutch control strategy is to increase the clutch lifetime. From the results, driveline vibrations including shunt and the shuffle can be reduced well. In addition, this idea doesn’t have the harms of the other solutions such as spark advance engine control that pollutes the air and electronic throttle control which lessens the vehicle performance. Moreover, the usage of this system is simple, especially in vehicles that have automatic manual transmission.

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This paper presents a novel investigation into the free vibration of porous folded plates using the differential transformation method (DTM). The porosity is functionally graded (FG) along the thickness of the plate, resulting in material properties that vary with the z-coordinate. The motion equations for each plate segment are derived based on classical plate theory (CPT), with simply-supported boundary conditions applied at the front edges, allowing the transformation of partial differential equations into ordinary differential equations. The differential transformation method is then employed to discretize the motion equations in the x-direction. By applying boundary conditions at the remaining edges and ensuring continuity at the joints, the eigenvalue problem is formulated, leading to the calculation of natural frequencies and mode shapes of the folded plate. The mathematical model is validated through comparisons with finite element method (FEM) results and existing literature. Results indicate that Type C porosity distributions exhibit the highest stiffness and resonant frequency compared to other porosity types. While frequency behavior is consistent across mode numbers regardless of porosity distribution and plate length, the impact of the porosity parameter on the frequency of Type C plates is demonstrably less significant than on other porosity types.

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This study investigates the performance of Reactivity-Controlled Compression Ignition (RCCI) engines under varying engine speeds using a 4E approach (Evaporation, Energy, Emissions, Exergy) and introduces innovative multidimensional efficiency indices. A 1.9-liter TDI Volkswagen engine was modeled in CONVERGE CFD software to analyze spray dynamics, combustion processes, and emissions across different engine speeds. New indices, including Evaporation-Energy Performance Index (EvEPI), Emission-Energy Synergy Index (EmESI), and Exergy-Emission Balance Index (ExEmBI), were developed to evaluate engine performance comprehensively. Results reveal that optimal performance occurs within 1600–2200 RPM, where fuel evaporation, combustion efficiency, and exergy utilization are maximized while emissions are minimized. For instance, at 3100 RPM, EvEPI increases sharply to 9857.17 mg/ms, reflecting enhanced evaporation but also highlighting risks of non-uniform fuel-air mixing at high speeds. Conversely, EmESI for HC rises from 33.04 gr/kW.h at 1000 RPM to 284.90 gr/kW.h at 3100 RPM, indicating increased unburned hydrocarbons due to incomplete combustion. NOx emissions decrease from 11.51 gr/kW.h at 1600 RPM to 2.28 gr/kW.h at 3100 RPM, aligning with reduced combustion temperatures. Higher speeds lead to elevated HC and CO emissions due to shorter mixing times, while lower speeds increase NOx due to prolonged combustion durations. Exergy analysis shows total and second-law efficiencies peak at lower speeds, emphasizing the importance of optimizing operational parameters. These findings provide valuable insights for designing efficient, low-emission RCCI engines.

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The high penetration of renewable energy sources (RES) makes the power system unreliable due to its uncertain nature. In this paper, the quantifying impact of electric vehicles (EV) charging and discharging on power system reliability and relieving the congestion is analyzed. The proposed reliability assessment is formulated by considering generation and demand interruption costs for N-1 contingency criteria. The proposed algorithm manages the optimal scheduling of EV to mitigate the uncertainties associated with RES and relieving the congestion. The impact of EV charging and discharging on expected energy not supplied (EENS) and expected interruption cost (ECOST) for generating companies (GENCOs), transmission companies (TRANSCOs), customers, and entire power system are calculated. The charging station of EV is selected by the trade-o_ between investment cost of EV and percentage change in EENS and ECOST value for the entire power system, GENCOs, TRANSCOs, and customers. The effectiveness of the proposed approach is tested on the modified IEEE RTS 24 bus system. The impact of EV charging stations on system reliability has been evaluated by quantifying the EENS and the ECOST across all available EV capacities. The results clearly demonstrate the improvement of system reliability and minimize the objective function consisting of generator re-dispatch and load curtailment considering N-1 contingency in the face of uncertainties of wind and solar generation sources by considering EV. The results show that EV can improve the reliability by about 40%. The problem is modeled in GAMS environment and solved using CONOPT as a nonlinear programming (NLP) solver.
 

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The liquid metal droplets in the mercury magnetic reciprocating micropump are actuated by Lorentz force and reciprocated inside some sub-channels. The droplets in sub-channel act as pistons to pump the working fluid. The initial step in establishing the performance of the mercury magnetic reciprocating micropumps is to study the motion of droplet inside the channel. The extraction of the analytic equation governing the droplet motion inside the channel is complicated due presence of electromagnetic fields and three dimensional effects of the flow. Further, the existence of a pumped fluid in contact with the droplet and the adhesion force due to small dimensions are considered as the other reasons. In this study, the forces operating on the droplet were figured out by the Lagrangian approach and lumped mass assumption for the droplet. Accordingly, forces less than 5% of the actuation force were eliminated from the motion equation of droplet employing dimensional analysis. The simplified equation was presented as an ordinary differential equation and solved numerically. In addition to the analytic solution, the issue was experimentally investigated for a case study. The analytic and empirical results accord well with one another. The method pointed out in this study can be applied to predict the droplet motion in various microsystems.

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This research proposes a robust fuzzy adaptive fractional-order proportional-integral-derivative (PID) controller for an active suspension system of a quarter-car model. For this, the research first designed the PID controller using chassis acceleration and relative displacement. Next, it utilized the chain derivative rule and the gradient descent mechanism to formulate adaptation rules based on integral sliding surfaces. In the next step, the control parameters were regulated by employing a fuzzy system comprising the product inference engine, singleton fuzzifier, and center average defuzzifier. Eventually, the optimum gains of the proposed controller were determined by running a multi-objective material generation algorithm (MOMGA). Simulation results implied the superiority of the proposed controller over other controllers in handling road irregularities.

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This study examines how different porosity levels and perforation patterns affect the crushing performance of thin-walled cylindrical tubes under axial loading. Nonlinear explicit finite element simulations, validated by experiments, were performed on tubes with varying porosity ratios to assess deformation modes, peak crushing forces, and energy absorption efficiencies. The study's results indicate that perforated tubes have better energy absorption characteristics than non-perforated tubes, with a 7.83% improvement in the Specific Energy Absorption (SEA) value. The straight-type tube demonstrated a 1.75% higher Specific Energy Absorption (SEA) and 1.23% greater total energy absorption compared to the staggered arrangement. These findings suggest the effectiveness of the straight-type design for load-bearing and energy dissipation. This research offers insights into optimizing energy-absorbing structures for impact mitigation, suggesting that the straight-type configuration may be better when structural integrity and energy absorption are crucial.

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The escalating proliferation of electric vehicles (EVs) as a pivotal solution to address energy consumption and air pollution challenges within the transportation sector necessitates a comprehensive understanding of the factors influencing their performance and driving range. Among these factors, driving patterns exert a direct and significant impact on energy consumption and battery state. This study aims to quantify the influence of diverse driving cycles on the performance of an electric vehicle, specifically the Audi e-tron 50.   Utilizing Simcenter Amesim software, a longitudinal vehicle dynamics model, coupled with an equivalent circuit model (ECM) for the lithium-ion battery, was developed for simulation purposes. The vehicle's performance was evaluated under five distinct driving cycles, including global standards (WLTC, NEDC, HWFET) and two real-world driving cycles recorded in Tehran (Route1, Route2). Key parameters such as state of charge (SoC), depth of discharge (DoD), battery temperature, and estimated driving range were analyzed. The results revealed a significant impact of driving cycles on all investigated parameters. Driving cycles characterized by higher speeds and accelerations (e.g., WLTC and HWFET) led to increased specific energy consumption, accelerated temperature rise, and a notable reduction in estimated driving range (with the lowest range observed in WLTC). Conversely, milder urban driving cycles (particularly Route1) resulted in improved energy efficiency, minimal thermal stress, and the highest estimated driving range. These findings underscore the critical importance of considering real-world and localized driving patterns for accurate performance evaluation, range estimation, and the development of optimized energy management strategies in electric vehicles.
 

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Conventional diesel engine, hydraulic hybrid, and fully electric powertrain structures were modeled to assess fuel consumption in a sample urban refuse collection truck. The components utilized in the modeling include an internal combustion engine, transmission, electric motor, and battery. To this end, the vehicle's driving cycle is initially analyzed and characterized. The target vehicle is a light duty N series Isusu 8 tones truck. Based on the simulations conducted in the MATLAB/Simulink environment, the hydraulic hybrid configuration demonstrated the lowest fuel consumption for the Refuse truck vehicle, achieving 27.6 liters of diesel fuel per 100 kilometers. The fully electric configuration exhibited a fuel consumption value closely approaching that of the hydraulic hybrid. Eventually, based on the obtained results, the layout of the equipment for the finalized configurations was designed in the Autodesk Inventor software environment.

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Automotive radar systems operating in the 24 GHz band are widely used in Advanced Driver Assistance Systems (ADAS) due to their cost-effectiveness and robust performance across diverse environmental conditions. However, these systems face critical vulnerabilities from electromagnetic interference (EMI) and high-power microwave (HPM) threats, which can degrade detection accuracy. This study presents a novel plasma-based limiter employing a Gas Discharge Tube (GDT) within an optimized K-band waveguide (10.668 × 4.318 mm) filled with Rogers RO3035 dielectric (εr = 3.6). The design achieves exceptional metrics: 0.9 dB insertion loss and 21.5 dB return loss during normal operation, while providing over 30 dB isolation against HPM signals with a sub-100 ns response time. These characteristics position this solution as an industry-leading protection mechanism for next-generation automotive radars. 

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This study proposes a hierarchical nested cascade control framework to enhance voltage regulation and current management in fuel cell hybrid electric vehicles (FCHEVs). The architecture addresses limitations of conventional cascade control by reducing design complexity and improving resilience under dynamic and uncertain conditions. It integrates three coordinated layers: an outer control level (OCL) employing an adaptive proportional–integral controller for DC bus voltage regulation, and two internal layers, middle (MCL) and inner (ICL), implemented via backstepping controllers for precise current control of fuel cells, batteries, and supercapacitors. By combining nonlinear control with model reference adaptive control, the system dynamically tunes parameters to maintain voltage stability across variable load profiles. Simulations using the WLTC-Class 3 cycle show that the proposed strategy (Case 1) achieves superior battery sustainability, with a final SOC of 74.2%, compared to 71% and 72.5% in benchmark strategies (Cases 2 and 3). Under battery aging (20% increased resistance, 15% reduced capacity), DC bus voltage remains within ±3.5 V of the 380 V reference, with only 18% ripple increase and 0.8% additional SOC depletion. A resilience index of 96.5% confirms robustness, outperforming benchmarks (84.2%, 89.7%). To further validate performance under real-world urban conditions, date-specific driving cycles tailored for Shiraz city were employed. Results confirm the framework’s effectiveness in sustaining stability, efficiency, and scalability for next-generation FCHEV energy systems.

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Target detection using cameras or automotive radar to identify traffic or prevent collisions is an important issue in Autonomous Vehicles (AV) research. Traditional Constant False Alarm Rate (CFAR) methods are commonly employed. Although these methods are suitable for lightweight hardware, improving the target detection process often leads to losing real-time performance. The method proposed in this paper improves detection accuracy. It reduces response time by modifying the position of guard cells in the first stage and employing harmonic averaging (inverse of the sum of the inverse of data) while eliminating data sorting in the second stage. Moreover, this approach exhibits better performance in the presence of interfering targets. Since the proposed method is more applicable to the Range-Doppler map, it has been named RD-CFAR. The proposed method also enhances target detection in Synthetic Aperture Radar (SAR) images. Simulation results demonstrate that the proposed algorithm improves detection probability by nearly 40% compared to conventional methods (like CA-CFAR), while maintaining comparable computational time.

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Conventional suspension systems exhibit performance limitations when encountering road irregularities and specific surface profiles, often failing to attenuate road-induced disturbances effectively. This functional deficiency reduces ride comfort and compromises vehicle dynamic stability under various driving conditions. In contrast, active suspension systems, utilizing hydraulic or pneumatic actuators in combination with feedback control strategies, have demonstrated a significant potential for disturbance suppression and considerable improvement in ride comfort and vehicle stability. Previous studies have identified that vertical (bounce) and rotational (roll) motions are among the primary factors influencing passenger comfort and vehicle stability in dynamic scenarios. Therefore, controlling these motions is essential to enhance ride quality and handling performance. In this study, a half-car dynamic model equipped with an active suspension system is developed, focusing on controlling bounce and roll motions. All modeling and simulation tasks are conducted within the MATLAB environment, where two control strategies fuzzy control and optimal control are designed and implemented for the active suspension system. Finally, the dynamic performance of these two approaches is compared and analyzed. The simulation results indicate that the optimal control strategy outperforms the fuzzy control method regarding disturbance rejection and overall ride comfort and vehicle stability improvement.

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The rising demand for sustainable transportation has intensified research on Fuel Cell Hybrid Electric Vehicles (FCHEVs). Integrating fuel cells with lithium-ion batteries provides a pathway to enhance energy efficiency and driving performance, but ensuring the durability of both components under real operating conditions remains a critical challenge. This work proposes an integrated framework to improve FCHEV performance and lifetime through combined modeling, degradation analysis, and optimized energy management. Dynamic vehicle simulations were conducted using the ADVISOR platform under both the Urban Dynamometer Driving Schedule (UDDS) and a real-world cycle based on Tehran traffic data. Degradation models were implemented to capture platinum dissolution in the Proton Exchange Membrane Fuel Cell (PEMFC) and capacity loss in the lithium-ion battery, incorporating the effects of state of charge, temperature, and current rate. An energy management strategy was developed using a Fuzzy Logic Controller (FLC) for fuel cell–battery power distribution, which was further refined with a Genetic Algorithm (GA). The optimization objectives included reducing hydrogen consumption and extending component lifetimes. The GA-optimized FLC extended PEMFC lifetime by 50.6% Tehran and 12.9%  UDDS and reduced battery capacity fade by 10% and 4.9%, respectively. While direct hydrogen consumption increased in Tehran due to more aggressive regenerative-energy routing to the battery, the Equivalent Fuel Consumption (EFC) decreased from 971.32 → 937.21 g/100 km (Tehran) and 794.41 → 782.24 g/100 km (UDDS), reflecting a net efficiency gain once SOC restoration is accounted for.

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The deployment of autonomous robots in unstructured, cluttered environments remains a significant challenge, particularly for low-cost platforms. While the Dynamic Window Approach (DWA) provides a robust foundation for reactive navigation, its performance is often suboptimal due to a lack of historical context, leading to oscillatory behavior and entrapment in local minima. This paper presents a novel, cost-effective mechatronic system that enhances DWA with a real-time spatial memory module and optimizes its performance using a Bayesian Optimization strategy. Our platform integrates a Raspberry Pi 4 with a fused ultrasonic and infrared sensor suite. The core innovation is a Local Occupancy History Map that provides a short-term, decaying memory of obstacle locations. This memory influences the DWA’s trajectory evaluation, discouraging paths through recently occupied space. Furthermore, we employ Bayesian Optimization loop to automatically tune the critical hyperparameters of the navigation system—the memory decay rate and the history weight—to maximize efficiency and safety. We validate our system in complex indoor environments, comparing the baseline DWA, the DWA with Spatial Memory (DWA-SM), and the optimized DWA-SM (DWA-SM-Opt). Quantitative results demonstrate that the optimized system (DWA-SM-Opt) achieves a 40% reduction in average path completion time and a 65% decrease in collisions compared to the baseline DWA. Qualitative analysis confirms more intelligent, fluid navigation and a consistent ability to escape trapping configurations. This work establishes that the fusion of a lightweight spatial memory with an AI-driven optimization routine, implemented on low-cost hardware, can yield a level of performance previously associated with more complex and expensive systems.

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This study investigates the dynamic response of a lithium‑ion battery pack subjected to environmental vibrations. Considering the widespread use of such packs in electric vehicles and energy storage systems, and the adverse effects of vibrations on their performance and safety, both numerical and experimental approaches are employed. In the numerical simulation phase, a detailed three-dimensional model of the battery pack, including all components and joints, is developed in Abaqus, and a full modal analysis is performed to extract the natural frequencies and mode shapes of the system. In the experimental phase, modal testing is conducted using an impact hammer and an accelerometer on a physical battery-pack sample under free‑free boundary conditions to validate the simulation results. A systematic comparison between the two approaches demonstrates a good agreement, with the maximum deviation in the primary natural frequencies being less than 10%. This level of consistency confirms the accuracy and reliability of the proposed model. The developed model can serve as an effective tool during the early design stages for mechanical optimization, dynamic behavior prediction, and mitigation of vibration‑induced failures in battery packs. The results of this study mark an important step toward improving the reliability and safety of battery packs in operational environments.

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In lightweight body-in-white design, joints must not only provide strength but also allow for ductility and sufficient energy absorption. In this study, Single Lap Joints (SLJs) made with adhesive bonding are compared experimentally with those joined by Resistance Spot Welding (RSW) in low-carbon steel sheets. The influence of overlap length (15 and 25 mm) and weld number (one or two spots) is examined. Tensile force–displacement tests, conducted at room temperature with a crosshead speed of 1 mm/min, revealed that extending the overlap from 15 to 25 mm improved the peak load, final displacement, and fracture energy of the adhesive joints. Among the tested configurations, double spot welds (2RSW) provided the greatest capacity and toughness.  However, adhesive joints with a 25 mm overlap (AB25) exhibited higher strength than single spot welds (1RSW), while their ductility was comparable. The observed failure modes varied across the joint types. In resistance spot welds, failure occurred mainly through button pull-out, whereas adhesive joints exhibited a mixed adhesive–cohesive failure mode.  In contrast, the 2RSW specimens displayed pull-out and necking sequences, reflecting load sharing between the weld nuggets. Overall, the findings suggest straightforward design guidelines. When maximum strength and energy absorption are required, two Spot Welds (2RSW) are the best choice. On the other hand, AB25 joints, with a 25 mm overlap, provide higher strength than single Spot Welds (1RSW).