Abstract: Vibrations of thin sheet-metal parts during robotic manipulation on a production line create a number of serious challenges for production process planning. Modeling the behavior of an elastic plate or shell as a function of the robot manipulator trajectory is typically performed using the finite element method (FEM) and requires significant computational effort. The time factor remains a key limitation for integrating operations involving flexible parts into the virtual commissioning process. In this work, a methodology is proposed that enables accurate real-time reproduction of the behavior of an elastic part during linear robotic manipulation. The approach is based on modeling the response of an elastic part to a prescribed base excitation using the FEM and on the development of a reduced model compliant with the FMI/FMU standard. This reduced model computes, in real time, the convolution of the precomputed base response with the acceleration profile corresponding to the robot TCP trajectory. This makes it possible to determine the total cycle duration, which consists of the part transfer time and the time required for vibration decay at the end of the trajectory down to an acceptable threshold, as well as to perform collision checking while accounting for the deformation of the flexible part. As a result, operations involving elastic parts can be integrated into the virtual commissioning process.

Abstract: The main purpose of the paper is to show the possibility of assessing the dynamic properties of crankshaft in the early design phase of engine development, without performing dynamic forced response simulation. This is achieved by carrying out modal analysis of crankshaft under existing boundary conditions, namely by taking the radial stiffness in main bearings and the masses of moving conrods and pistons into account. The spectra of eigenfrequencies and corresponding mode shapes as a result of such supported modal analysis are compared to those of free modal analysis, emphasizing the influence of the boundary conditions. To easily identify the modes and to compare them with each other, kinetic energy-based method is used, alongside visualization and animation of mode shapes. The examples of crankshafts considered in the paper are taken from model catalog of virtual engines of six different sizes and configurations, being compared to that of in-line 4-cylinder engine as the reference case. All types of modal analysis are performed on structured FE models of crankshafts using software tool AVL EXCITE™ Shaft Modeler.

Abstract: Plug-in hybrid electric vehicles (PHEVs) are expected to reduce fleet CO₂ emissions, yet their real-world performance often deviates substantially from type-approval expectations. This study examines whether traction battery capacity provides an independent explanatory signal for the test-to-reality CO₂ gap (gap%), or whether it primarily acts as a proxy for market segmentation and usage patterns. Using European on-board fuel and energy consumption monitoring (OBFCM) records for 457,555 PHEV observations (2021–2023) from 14 manufacturers, we estimate nested fixed-effects models and introduce engineered usage proxies describing charge-depleting operation (EUR), hybrid utilization intensity (HI), energy-into-battery intensity (EDE), and a real-world to type-approval fuel-consumption ratio proxy (ELP). Battery capacity alone explains limited variation in gap% (R² = 0.075), while adding segment/year/manufacturer fixed effects increases R² to 0.203 and adding usage proxies increases it to 0.826, with the battery coefficient attenuating from 19.6 to 8.9 percentage points per kWh. Allowing non-linear battery terms via cubic B-splines yields only a modest additional improvement (R² ≈ 0.829), although the conditional shape is non-monotonic. Importantly, the battery–gap association is strongly segment-dependent, ranging from −22.1 pp/kWh in medium vans to +10.5 pp/kWh in large cars. Robustness checks using model-identifier fixed effects (MS_Cn) with standard errors clustered by MS_Cn further attenuate the battery effect (p ≈ 0.085), whereas ELP remains strongly associated with gap%. Overall, battery capacity is informative for compliance analytics mainly as a proxy variable capturing segmentation and real-world usage, rather than a universal lever of PHEV CO₂ performance.

Abstract: Vehicle–infrastructure cooperative perception (VICP) overcomes the sensing limitations and field-of-view constraints of single-vehicle intelligence by integrating multi-source information from onboard and roadside sensors. However, in complex urban environments, system robustness—particularly regarding blind-spot coverage and feature representation—is severely compromised by occlusion (static and dynamic) and distance-induced point cloud sparsity. To address these challenges, this paper proposes a 3D object detection framework incorporating point cloud feature enhancement and spatial adaptive fusion. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined-SENet (R-SENet) attention module is embedded into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism—across pillars and intra-pillar points—to adaptively recalibrate key geometric features. Concurrently, a Feature Pyramid Backbone Network (FPB-Net) is constructed to enhance unified target modeling across varying distances via multi-scale extraction and cross-layer aggregation. Second, a Spatial Adaptive Feature Fusion (SAFF) module is introduced to resolve feature heterogeneity and spatial misalignment. By explicitly encoding feature origins and leveraging spatial attention, SAFF enables dynamic weighting and complementary fusion of fine-grained vehicle-side features and global roadside semantics. Experiments on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed method outperforms state-of-the-art approaches, achieving Average Precision (AP) scores of 0.762 and 0.694 at IoU 0.5, and 0.617 and 0.563 at IoU 0.7, respectively. Furthermore, the inference speed satisfies real-time requirements, validating the method’s effectiveness and potential for engineering deployment.

Abstract: This paper presents a practical industrial hybrid control architecture that augments the widely deployed 49-rule Mamdani fuzzy supervisory PID controller with a lightweight online meta-tuner based on Soft Actor-Critic (SAC) reinforcement learning. While the inner 1 kHz fuzzy-PID loop remains fully deterministic and identical to the industrial baseline, a separate 10 Hz SAC agent autonomously adapts the three output scaling factors (α_Kp, α_Ki, α_Kd ∈ [0.5, 2.5]) of the fuzzy layer using an ONNX Runtime inference engine. The complete controller is implemented and experimentally validated on a real Siemens S7-1214C PLC (6ES7214-1AG40-0XB0) in a hardware-in-the-loop setup with a high-fidelity 5-DoF manipulator model incorporating measured friction, backlash, sensor noise, and payload variation (0–2.5 kg). Across four demanding scenarios (sinusoidal tracking, sudden payload jumps, sustained disturbances up to 0.76 Nm, and high-speed motions), the proposed method consistently achieves 46–52 % lower RMSE and 28–30 % reduced control energy compared to the fixed-scaling industrial baseline, while preserving strict real-time constraints (inner loop cycle time 0.68–0.89 ms, SAC inference < 0.6 ms). The full PLC program (SCL/FBD), HIL environment, and trained policies will be released open-source upon acceptance (DOI to be provided during revision).The full PLC program, HIL environment, and trained SAC policies will be released open-source as a preprint supplement.

Abstract: The rapid adoption of electric vehicles has fundamentally altered noise, vibration, and harshness (NVH) requirements, as the absence of internal combustion engine noise exposes previously masked drivetrain excitations. In this context, vibroacoustic simulation has become a key enabler for achieving low-noise electric powertrains while reducing development time and physical prototyping. This review provides a comprehensive overview of multiphysics simulation methodologies applied to EV powertrains, covering the full excitation–response–radiation chain from electromagnetic motor forces and gear meshing dynamics to flexible multibody behavior, structural vibration, and acoustic radiation. The literature is systematically analyzed with respect to modeling approaches, numerical methods, and software workflows used to couple electromagnetic analysis, gear contact mechanics, multibody dynamics, finite element structural models, and acoustic FEM/BEM solvers. Particular attention is given to transmission error, time-varying mesh stiffness, and electromagnetic torque ripple as dominant tonal noise sources, as well as to the role of housing dynamics in sound radiation. The review highlights the strengths and limitations of time-domain and frequency-domain formulations, reduced-order models, and high-fidelity numerical simulations, emphasizing the trade-off between accuracy, computational cost, and practical applicability. Beyond summarizing existing methods, this paper critically discusses current limitations in predictive capability, including insufficient treatment of manufacturing variability, limited system-level validation, and the lack of standardized benchmark datasets. Emerging trends such as stochastic modeling, machine-learning-based surrogate models, and digital twin concepts are identified as promising directions to address these challenges. Overall, the review underscores that effective EV NVH prediction requires a holistic, system-level multiphysics approach in which electromagnetic, mechanical, structural, and acoustic phenomena are considered jointly rather than in isolation. From a knowledge-structuring perspective, the reviewed methodologies establish a clear conceptual mapping between classical NVH theory and electric powertrain–specific eNVH simulation. Fundamental concepts such as excitation–transfer–radiation paths, modal superposition, and frequency-order analysis remain valid, while their dominant sources shift from combustion-related mechanisms to electromagnetic forces and gear meshing phenomena. In this sense, electromagnetic excitation and transmission error can be interpreted as the primary counterparts of traditional engine orders in EV applications, propagated through flexible multibody and structural models toward acoustic radiation. This explicit linkage between established NVH principles and EV-specific excitation mechanisms provides a coherent framework that supports both human understanding and machine-learning-based knowledge extraction of multiphysics eNVH simulation workflows.

Abstract: Reverse parking maneuvering of a vehicle with trailer system is a difficult task to complete for human drivers due to the multi-body nature of the system and the unintuitive controls required to orientate the trailer properly. The problem is complicated with the presence of other vehicles that the trailer and its connected vehicle must avoid during the reverse parking maneuver. While path planning methods in reverse motion for vehicles with trailers exist, there is a lack of results that also offer collision avoidance as part of the algorithm. This paper hence proposes a modified Hybrid A*-based algorithm that can accommodate the vehicle-trailer system as well as collision avoidance considerations with the other vehicles and obstacles in the parking environment. One of the novelties of this proposed approach is its adaptability to the vehicle with trailer system, where limits of usable steering input that prevent the occurrence of jackknife incidents vary with respect to system configuration. The other contribution is the addition of the collision avoidance functionality which the standard Hybrid A* algorithm lacks. The method is developed and presented first, followed by simulation case studies to demonstrate the efficacy of the proposed approach.

Abstract: In modern electric vehicles (EVs), where the absence of a combustion engine reveals new acoustic challenges, gear and gearbox noise—especially tonal “whine”—has emerged as a prominent NVH (Noise, Vibration, and Harshness) concern. This review investigates the state-of-the-art multiphysics simulation workflows capable of predicting NVH from root excitation through structural vibration and up to radiated airborne noise. Emphasis is placed on software ecosystems developed between 2015 and 2025, including Romax, AVL EXCITE, Siemens Simcenter, SMT MASTA, MSC Adams/Nastran/Actran, KISSsoft + RecurDyn, and COMSOL Multiphysics. The review explores simulation layers ranging from analytic torsional models to coupled flexible multibody dynamics (MBD), finite-element structural response, and acoustic FEM/BEM methods. Recent trends such as per-tooth microgeometry definition, flank waviness modelling, use of measured topography (e.g., CMM data), and digital twin concepts are discussed in depth. Furthermore, the review highlights validation challenges—especially the limited system-level correlation between predicted and measured noise—and identifies research gaps regarding EV-specific excitations, manufacturing variation modeling, and NVH-oriented design optimization. This work aims to give engineers and researchers a structured overview of integrated CAE methods to “front-load” gearbox NVH prediction in electrified drivetrains, thereby improving design cycles and acoustic performance.

Abstract: This study evaluates the feasibility of using two affordable thermal cameras (UNI-T UTi260M and UTi260T), which are not designed as automotive sensors, for observing pedestrians and warm objects during night-time driving under low-illumination conditions. The experimental setup includes mounting the camera on the vehicle body (e.g., side-mirror area/roof), recording road scenes in urban and rural environments, and selecting representative frames for qualitative and quantitative analysis. The study assesses: (i) observable pedestrian detectability in unlit road sections and under oncoming headlight glare, where visible cameras often lose contrast; (ii) the influence of low ambient temperature and strong cold wind on image appearance (including “whitening”/contrast shifts); and (iii) workflow differences, where UTi260M relies on a smartphone application for streaming/recording, while UTi260T supports PC-based image analysis and temperature-profile visualization. In addition, a calibration-based geometric method is proposed for approximate pedestrian distance estimation from single frames using silhouette pixel height and a regression model based on 1/h_px, valid for a specific mounting configuration and a known subject height. Results indicate that both cameras can highlight warm objects relative to the background and support visual pedestrian identification at low illumination, including in the presence of oncoming headlights, with UTi260M showing more stable behaviour in part of the tests. This work is a feasibility study and does not claim ADAS functionality; it outlines limitations, repeatability considerations, and a minimal set of metrics and procedures for future extension. All quantitative indicators derived from exported frames are explicitly treated as image level proxy metrics not as physical sensor characteristics.

Abstract: Plug-in hybrid electric vehicles (PHEVs) are critical to the EU's decarbonization strategy, yet their real-world climate benefits remain uncertain. This study presents a large-scale analysis of real-world PHEV performance using on-board monitoring data from 457,303 vehicles (2021-2023). The results reveal a profound discrepancy between official test values and actual use. The mean real-world CO₂ emissions were 138 g/km, compared to a test-cycle average of 46 g/km, resulting in a regulatory gap of approximately 300%—significantly higher than for other vehicle types. Performance varied substantially across manufacturers, with gaps ranging over 200 percentage points. Contrary to expectations, larger battery capacity was correlated with a wider performance gap. Real-world electric driving averaged only 45.5% of distance, far below regulatory assumptions. This gap has grown wider each year, indicating test-cycle optimization is outpacing real-world efficiency gains. Policy analysis shows that closing this gap could achieve major CO₂ savings, underscoring the urgent need for regulatory reform, including real-world emissions monitoring and updated test procedures, to ensure PHEVs deliver their promised environmental impact.

Abstract: Lightweight gearbox housings often raise NVH risk, yet full finite-element evaluations are too slow for early design screening. This study tests whether a few frequency-band descriptors of radiated sound are enough to classify housing stiffness. Using an open dataset of electric-vehicle gearbox spectra for three rib-configurations—flexible, intermediate and rigid—we averaged sound-pressure levels in five 1 kHz bands. Principal-component analysis separated the twelve samples into three non-overlapping groups, confirmed by k-means clustering (adjusted Rand index = 1.00). The random-forest model achieved 75 % classification accuracy on the present 12-sample data set (leave-one-out evaluation). Owing to the small sample size this figure should be regarded as explorative, and a larger validation study is required to confirm generalizability; permutation analysis confirmed the 3–4 kHz and 2–3 kHz bands as most important for classification. In contrast, total integrated spectral energy showed no significant group difference (p = 0.81). The results These findings suggest that mid-frequency band energy may encode structural-stiffness differences, although validation on larger datasets is necessary. The workflow—load spectra, compute five band means, classify—offers a rapid, interpretable tool for NVH-aware lightweight design.

Abstract: Battery Thermal Management Systems (BTMS) are critical for maintaining optimal operating temperatures (20-40°C) in lithium-ion batteries, particularly for electric vehicles (EVs) and grid-scale energy storage [1,2]. Phase Change Materials (PCMs) have emerged as a transformative solution, leveraging latent heat absorption/release during phase transitions to provide passive thermal regulation [3]. This review systematically evaluates inorganic (salt hydrates), organic (paraffins, fatty acids), and composite PCMs, analyzing their thermophysical properties, performance characteristics, and implementation challenges in BTMS applications [4,5]. Key findings reveal that advanced composite PCMs with thermal conductivity enhancers (graphene, metal foams) can achieve 3-5× improvement in heat dissipation while maintaining >90% of base latent heat capacity [6,7]. The paper concludes with actionable recommendations for next-generation PCM development and integration strategies.

Abstract: Infrared traffic object detection faces challenges such as low resolution, weak thermal 2 contrast, and inefficiency in detecting small objects. To address these issues, this paper 3 proposes RES-YOLO, an enhanced YOLOv8n-based architecture. It incorporates Receptive 4 Field Adaptive Convolution for improved multi-scale perception, Efficient Multi-scale 5 Attention for better feature representation, and the Scylla-IoU loss for more accurate 6 and faster bounding box regression. Additionally, a pseudo-color infrared dataset is 7 constructed to enrich texture and contrast information beyond conventional white-hot 8 images. Experiments on both the FLIR public dataset and a self-built dataset show RES- 9 YOLO improves accuracy by 4.9% and 5.5% over the baseline while maintaining real-time 10 performance. These results highlight the method’s effectiveness in integrating lightweight 11 deep learning and dataset enhancement for robust perception in intelligent vehicle systems, 12 supporting AI-driven autonomous driving and driver assistance applications.

Abstract: Development of AI-driven automated driving functions requires vast amounts of diverse, high-quality data to ensure road safety and reliability. However, manual collection of real-world data and creation of 3D environments is costly, time-consuming, and hard to scale. Most automatic environment generation methods still rely heavily on manual effort, and only a few are tailored for Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) training and validation. We propose an automated generative framework that learns real-world features to reconstruct realistic 3D environments from a road definition and two simple parameters for country and area type. Environment generation is structured into three modules - map-based data generation, semantic city generation, and final detailing. The overall framework is validated by training a perception network on a mixed set of real and synthetic data, validating it solely on real data, and comparing performance to assess the practical value of the environments we generated. By constructing a Pareto front over combinations of training set sizes and real-to-synthetic data ratios, we show that our synthetic data can replace up to 90% of real data without significant quality degradation. Our results demonstrate how multi-layered environment generation frameworks enable flexible and scalable data generation for perception tasks while incorporating ground-truth 3D environment data. This reduces reliance on costly field data and supports automated rapid scenario exploration for finding safety-critical edge cases.

Abstract: The shift toward Industry 5.0 places human-centred and digitally integrated metrology at the core of modern manufacturing, particularly in the automotive sector, where portable Laser Line Triangulation (LLT) systems must combine accuracy with operator usability. This study addresses the challenge of operator-induced variability by evalu-ating how algorithmic strategies and mechanical support features jointly influence the performance of a portable LLT device derived from the G3F sensor. A comprehensive Measurement System Analysis was performed to compare three feature-extraction al-gorithms—GC, FIR, and Steger—and to assess the effect of a masking device designed to improve mechanical alignment during manual measurements. The results highlight distinct algorithm-dependent behaviours in terms of repeatability, reproducibility, and computational efficiency. More sophisticated algorithms demonstrate improved sensi-tivity and feature localisation under controlled conditions, whereas simpler gradi-ent-based strategies provide more stable performance and shorter processing times when measurement conditions deviate from the ideal. These differences indicate a trade-off between algorithmic complexity and operational robustness that is particu-larly relevant for portable, operator-assisted metrology. The presence of mechanical alignment aids was found to contribute to improved measurement consistency across all algorithms. Overall, the findings highlight the need for an integrated co-design of algorithms, calibration procedures, and ergonomic aids to enhance repeatability and support operator-friendly LLT systems aligned with Industry 5.0 principles.

Abstract: With the rapid development of the automotive industry, autonomous driving has attracted growing research interest, among which path planning and trajectory tracking play a central role. To better understand the evolution, current status, and future directions of this field, this study conducts a comprehensive bibliometric analysis combined with latent Dirichlet allocation (LDA) topic modeling on publications related to autonomous vehicle path planning and trajectory tracking indexed in the Web of Science database. Multiple dimensions are examined, including publication trends, highly cited authors, leading institutions, research domains, and keyword co-occurrence patterns. The results reveal a sustained growth in research output, with trajectory planning, path optimization, trajectory tracking, and model predictive control emerging as dominant topics, alongside a notable rise in learning-based approaches. In particular, reinforcement learning and deep reinforcement learning have become increasingly prominent in complex decision-making and tracking control scenarios. The analysis further identifies core contributors and institutions, highlighting the leading roles of China and the United States in this research area. Overall, the findings provide a systematic overview of the knowledge structure and evolving research trends, offering valuable insights into key opportunities and challenges and supporting future research toward safer and more intelligent autonomous driving systems.

Abstract: We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that manages latency-energy-coherence trade-offs under rapid topology changes3. We introduce (i) an Ideal Information Cycle as an operational abstraction of information injection and validation and (ii) a modular VANET Engine implemented as a real-time control loop in NS-3.354. The Engine monitors normalized Shannon entropies—informational entropy $S$ over active transactions and spatial entropy $H_{spatial}$ over occupancy bins (both on [0, 1])—and adapts the consensus mode (PoW versus signature/quorum-based modes such as PoS/FBA) together with key rigor parameters via calibrated policy maps5. Governance is cast as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, subject to timeliness and ledger-coherence pressure6. Cryptographic cost is explicitly traceable through counted operations, $E_{crypto} = e_h n_{hash} + e_{sig} n_{sig}$, and coherence is tracked via an LCA/LCP-normalized ledger-divergence metric7. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities, Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bootstrap confidence intervals8. In high-disorder regimes ($S \ge 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same latency target9. A consensus-first triggering policy further lowers agreement latency and increases throughput compared with broadcast-first baselines10. Under high mobility ($v=30$ m/s) in the emphasized urban setting, the Engine bounds orphaning ($\le 10\%$), keeps finality within sub-150 ms ranges, and reduces average ledger divergence below 0.07 at high spatial disorder11. Scope and security envelope: the main evaluation is limited to $N \le 100$ vehicles under full PHY/MAC fidelity12. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security and mode-transition considerations are discussed explicitly in Section 413.

Abstract: Background: The automotive industry is undergoing a deep transformation driven by 9 the global green transition. This change follows divergent trajectories in developed and 10 emerging markets due to differences in regulation, infrastructure, and economic con- 11 straints. The research methodology is adapted to incorporate different factors of influence 12 and contraints. The research applies a structured Failure Mode and Effects Analysis 13 (FMEA) based on IEC 60812:2018 and AIAG & VDA (2019), and integrates the Analytic 14 Hierarchy Process (AHP) to prioritize corrective measures. Concepts from adaptive risk 15 management, informed by expert consensus and literature-backed data, are also used to 16 interpret dynamic behavior of supply chains and market volatility. The comparative anal- 17 ysis successfully highlights the systematic RPN Divergence between market types, reveal- 18 ing critical differences in failure mode profiles, risk priorities, and capacity to adopt miti- 19 gation strategies. The hybrid FMEA-AHP approach reduces subjectivity and provides 20 transparent prioritization tailored to market maturity. The integrated methodology sup- 21 ports decision-making in electrification programs and offers a robust framework for 22 benchmarking complex transition processes across regions.

Abstract: The objective of the article is to present investigates the feasibility of driver-state assessment in a real automotive environment using a mobile long-wave infrared (LWIR) thermal camera. Unlike visible-spectrum systems, thermal imaging provides illumination-invariant and temperature-dependent information that is particularly advantageous inside a vehicle, where lighting conditions vary substantially. A handheld microbolometer (UTi260M) was used to record thermal video of a driver during prolonged, monotonous driving with a stabilized cabin temperature. Pixel-wise temperature reconstruction, spatial noise estimation, uniformity analysis, and NETD approximation were applied to evaluate thermal image quality and to quantify thermophysiological changes associated with drowsiness. The thermal recordings revealed characteristic pre-sleep markers, including head droop, reduced neuromuscular correction, elevated and spatially uniform facial temperature, and diminished thermal variability. These patterns correspond to known physiological responses to fatigue, reduced sympathetic activation, and warm cabin exposure. The analysis demonstrates that mobile thermal imaging can reliably capture early indicators of declining vigilance and can support the development of non-contact driver-monitoring systems. The findings further suggest that integrating temperature-driven alerting thresholds into mobile applications may provide an additional preventive mechanism against drowsiness-related accidents.

Abstract: Vehicle exhaust gases remain one of the key sources of atmospheric air pollution and pose a serious threat to ecosystems and public health. This study presents an experimental investigation into reducing the toxicity of gasoline internal combustion engine exhaust using ultrasonic waves and infrared (IR) laser exposure. An original hybrid system integrating an ultrasonic emitter and an IR laser module was developed. Four operating modes were examined: no treatment, ultrasound only, laser only, and combined ultrasound–laser treatment. The concentrations of CH, CO, CO2, and O2, as well as exhaust gas temperature, were measured at idle and under operating engine speeds. The experimental results show that ultrasound provides a substantial reduction in CO concentration (up to 40%), while IR laser exposure effectively decreases unburned hydrocarbons CH (by 35–40%). The combined treatment produces a synergistic effect, reducing CH and CO by 38% and 43%, respectively, while increasing the CO₂ fraction and decreasing O₂ content, indicating more complete post-oxidation of combustion products. The underlying physical mechanisms responsible for the purification were identified as acoustic coagulation of particulates, oxidation, and photodissociation of harmful molecules. The findings support the hypothesis that combined ultrasonic and laser treatment can enhance real-time exhaust gas purification efficiency. It is demonstrated that physical treatment of the gas phase not only lowers the persistence of by-products but also promotes more complete oxidation processes within the flow.