Automotive and Engine Technology最新文献

{"title":"Development of a passive noise control approach for vibroacoustic and acoustic reduction in electric vehicle inverters using particle dampers","authors":"Braj Bhushan Prasad,&nbsp;Tommy Luft,&nbsp;Hermann Rottengruber","doi":"10.1007/s41104-026-00170-4","DOIUrl":"10.1007/s41104-026-00170-4","url":null,"abstract":"<div><p>The shift from internal combustion engines to electric powertrains has redefined the noise, vibration, and harshness (NVH) profile of contemporary vehicles. The reduction of engine-generated broadband noise has led to the increased perceptibility of high-frequency tonal components, particularly those associated with inverter operation. The purpose of this study is to examine the effectiveness of a passive noise mitigation strategy, utilizing the particle damping technique, in addressing the emerging issue of inverter-induced acoustic phenomena. A particle damper filled with rubber granulate was integrated into the existing lid structure of an inverter enclosure without requiring any structural or geometric modifications. Experimental validation was conducted under full-load and stationary conditions, simulating critical NVH scenarios typical of electric drive systems. Vibration and sound pressure measurements were performed to assess the effectiveness of the damping strategy. The implementation of the particle damper led to a substantial reduction in structural vibration amplitudes within the frequency range of 800 - 1100 Hz. Notably, a peak vibration attenuation of 9.7 dB was recorded at the resonance frequency of 897 Hz. Complementary sound pressure level measurements revealed a noise reduction of approximately 6 dB, confirming the acoustic benefits of the damping intervention. The findings highlight the effectiveness of particle dampers as a passive, lightweight, and non-invasive solution for mitigating inverter-induced noise in electric vehicles. This approach not only enhances acoustic comfort for vehicle occupants but also addresses a critical NVH issue in the context of future electric mobility.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-026-00170-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical diagnostics of fuel induced soot formation: color ratio pyrometry of in-cylinder soot in a DISI engine","authors":"Lukas Heinz,&nbsp;Uwe Wagner,&nbsp;Thomas Koch","doi":"10.1007/s41104-026-00169-x","DOIUrl":"10.1007/s41104-026-00169-x","url":null,"abstract":"<div><p>The rapid diversification of gasoline-derived fuels demands a deep understanding of in-cylinder soot formation mechanisms, especially under the transient and low-temperature conditions characteristic of cold-start operation. Reliable, high-resolution diagnostics enable spatially resolved investigation of soot formation and allow a detailed analysis of mixture formation and soot-generation processes. This study evaluates Color-Ratio-Pyrometry (CRP) as an optical soot-measurement technique in a state of the art direct-injection spark-ignition (DI-SI) engine operating at steady-state load points for both cold-start (engine coolant temperature 20 °C) and warm-engine (95 °C) conditions. Three fuel families were examined: (i) a worst-case low-volatility gasoline (LV-G) representing a high-soot baseline, (ii) a low-soot, alkylate gasoline (ALK-G), and (iii) binary blends of LV-G or ALK-G with ethanol or methanol in mass fractions of up to 30% CRP signals were acquired using high speed imaging through an optical access at cylinder one, synchronized with cylinder pressure and heat release data to validate the temperature estimation. The results of soot volume fractions are compared to particle concentration measurements in the raw exhaust gas using an electric mobility particle spectrometer.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-026-00169-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid modeling approach combining machine learning and physical phenomenological methods to predict highly transient engine emissions","authors":"Tobias Gehra,&nbsp;Michael Günthner","doi":"10.1007/s41104-026-00168-y","DOIUrl":"10.1007/s41104-026-00168-y","url":null,"abstract":"<div><p>Climate change has made low-emission propulsion technologies an increasingly important focus of development. Battery-electric vehicles (BEVs), which operate locally emission-free and offer high overall efficiency, are one possible solution. However, they are limited by charging infrastructure and comparatively short ranges in the compact and midsize segments. Hybrid-electric vehicles (HEVs) can mitigate these disadvantages and can reduce fuel consumption and emissions relative to purely internal-combustion-engine vehicles. Full exploitation of these benefits requires dedicated operating strategies that can be developed efficiently in simulation environments—provided that precise models of the vehicle subsystems and the environment are available. This work investigates various approaches for modeling emissions during highly transient engine operation, with an emphasis on forecasting emissions under real-driving conditions. Purely physics-based models are computationally expensive and lose fidelity during transients, whereas purely data-driven models require large, carefully curated data sets. Accordingly, this paper proposes a novel single hybrid architecture that combines a physical-phenomenological combustion/emission model with a long short-term memory (LSTM) network through a lightweight gray-box fusion layer to predict NO<span>(_x)</span>, CO, CO<span>(_2)</span>, and THC. Machine-learning methods outperform the physical-phenomenological model in dynamic scenarios, achieving an RMSE of 0.0401 on the test cycle, computed on min–max normalized emission signals (scaled to the range [0, 1] based on the training data). The model is therefore suitable for real-time control and optimization tasks. The parallel hybrid model further improves this result by approximately 25%, demonstrating that a data-based model can be enhanced with physical information without increasing the training effort.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-026-00168-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147341335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph-based encoding of curve driving using spatial keypoints","authors":"Jannes Iatropoulos,&nbsp;Adrian Prueggler,&nbsp;Maximilian Flormann,&nbsp;Roman Henze","doi":"10.1007/s41104-026-00167-z","DOIUrl":"10.1007/s41104-026-00167-z","url":null,"abstract":"<div><p>Current accident statistics show that the highest rate of fatal traffic accidents in Germany occurs on rural roads, particularly as a result of vehicles leaving the road. Advanced driver assistance systems (ADAS) and highly automated driving functions therefore have high potential to improve safety in this domain. A key challenge is lateral vehicle control, especially the selection of an appropriate trajectory when cornering in automated driving mode (SAE Level 3+). The aim of this work is to derive characteristic driving variants from real-world measurement data, which serve as a basis for the design of automated lateral vehicle control and contribute to achieving high customer acceptance at the same time. For this purpose, extensive data from real world field tests was collected, standardized, and segmented at defined nodes (curve entry, apex, curve exit). A subsequent cluster analysis identified typical driving styles. Based on this, various trajectory variants were systematically generated using graph theory methods. These variants differ in terms of vehicle class, curve radius, and preference for corner-cutting. In addition, environmental influences such as the presence of oncoming traffic were considered. The outcome is a catalog of reality-based trajectories that serves as the basis for future driving functions. This enables further investigations in which the influence of the variants on driving comfort and safety will be evaluated, both in the Dynamic Vehicle Road Simulator (DVRS) and in real-world driving tests with test vehicles.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-026-00167-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147337244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential analysis of a predictive energy management strategy designed to increase the efficiency of the powertrain in a hybrid vehicle with use of online available route information","authors":"Christian Riegelbeck,&nbsp;Raja Sangili Vadamalu,&nbsp;Alexander Stalp,&nbsp;Lea Schwarz","doi":"10.1007/s41104-025-00166-6","DOIUrl":"10.1007/s41104-025-00166-6","url":null,"abstract":"<div><p>Hybrid vehicles, with their combined use of internal combustion engines and electric motors, present a unique opportunity to leverage intelligent control strategies for optimal performance. The method presented in this paper aims to improve the efficiency of a hybrid powertrain through an increased usage of the advantages of the electric components. While conventional hybrid operation strategies must determine the torque-split on either rule-based decision logics or on strategies that are optimized for certain test scenarios a predictive strategy can optimize the torque-split under consideration of the upcoming load requirements. Therefore, this paper explores the development and implementation of real-time predictive driving and operating strategies for hybrid vehicles to enhance fuel efficiency and reduce environmental impact. The data pertaining to road networks and traffic conditions, currently accessible from numerous map providers, can be effectively utilized to further amplify the benefits offered by hybrid vehicles. This information will be used to improve the accuracy of the predicted driving situations, which in turn improves the effectiveness of the predictive hybrid strategy by increasing the accuracy of the predicted load requirements. Advancements in prediction model accuracy have been shown to enhance the effectiveness of predictive hybrid control strategies, leading to higher energy efficiency and lower emissions. The present study develops and evaluates enhanced prediction models and a real-time predictive energy management strategy in simulation and Hardware-in-the-Loop (HiL) testing. We position the contribution at the system level, integrating forecasting, horizon supervision, receding-horizon P-ECMS with on-horizon EF adaptation and a state-change cost, and ΔSOC-fair evaluation under HiL-level timing. For a representative urban/rural route, the enhanced predictive strategy achieves a ΔSOC-fair, fuel-equivalent reduction of up to 12.18%, approaching the 14.4% obtained under perfect prediction, while reducing engine state transitions from 110 to 30, thereby improving both efficiency and driving comfort. This study is framed as a potential analysis: in this recuperation-rich case the strategy delivers high reduction potential, whereas on a Real Driving Emissions (RDE) trip with limited recuperation opportunity the improvement is ≈ 1% even under perfect prediction, indicating route-dependent potential rather than route-agnostic savings.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-025-00166-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146083196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulation and experimental validation of NO emissions in a heavy-duty H2DI engine considering injector needle dynamics and multi-cycle analysis","authors":"Marcel Reinbold,&nbsp;Mingyi Liang,&nbsp;Manuel Bucherer,&nbsp;Sameera Wijeyakulasuriya,&nbsp;Thai An Bui,&nbsp;Kelly Senecal,&nbsp;Thomas Koch","doi":"10.1007/s41104-025-00165-7","DOIUrl":"10.1007/s41104-025-00165-7","url":null,"abstract":"<div><p>Hydrogen direct-injection engines offer a promising pathway for decarbonizing heavy-duty transportation, but accurate prediction of mixture formation and NO<span>(_x)</span> emissions remains challenging due to complex injector dynamics and strong cycle-to-cycle variability. This work presents a comprehensive computational and experimental investigation of supersonic H<span>(_2)</span> direct injection, mixing, combustion, and NO formation in a single-cylinder heavy-duty hydrogen engine operated at 1100 rpm and <span>(lambda )</span> = 2.6. A detailed three-dimensional CFD model is developed, coupling a pressure-based injection boundary condition with a realistic <i>Bosch</i> F2 prototype injector needle-lift profile to capture valve-bounce effects. The model is validated against measured in-cylinder pressure, fuel and air mass, and NO emission data. Multi-cycle combustion behavior and NO emission variability are analyzed using the concurrent perturbation method (CPM), with 20 statistically independent realizations at reduced computational cost. Results show that near-spark mixtures with higher fuel concentration accelerate flame propagation and increase peak NO by a factor of two (76 ppm vs. 32 ppm). Simulations reveal that NO forms predominantly in local pockets of high fuel concentration, with turbulent flame speeds of 11–22 m/s during the early combustion phase. Predicted exhaust-port NO levels agree qualitatively with experiments, though unsteady RANS tends to overpredict NO due to limited small-scale mixing. The study demonstrates that resolving the injector flow rather than approximating boundary conditions, combined with CPM can effectively capture hydrogen combustion dynamics and emission variability.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-025-00165-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145930684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power optimal torque distribution for overactuated electric vehicles: analysis and experimental validation","authors":"Daniel Klein,&nbsp;Philipp Mandl,&nbsp;Manfred Plöchl,&nbsp;Florian Klinger,&nbsp;Johannes Edelmann","doi":"10.1007/s41104-025-00164-8","DOIUrl":"10.1007/s41104-025-00164-8","url":null,"abstract":"<div><p>The optimal integration of redundant actuators in an overactuated vehicle may be found by considering energy efficiency in the actuator allocation strategy. This paper investigates the potential to reduce the drive power demand of an electric vehicle with wheel individual drive through optimal allocation of drive torques and steering angles at the front and rear wheels. The actuator allocation problem is considered for the entire feasible normal and tangential acceleration range of the vehicle. The influences of electrical power losses of the motor-inverter system and tyre slip power losses on power demand are examined. By just considering the tyre slip power losses, the optimal control allocation reduces the drive power demand up to 20%, with the most significant reduction in the region of medium to high normal and tangential accelerations. The potential power reduction with respect to suboptimal strategies is validated in simulation and experimental tests by implementing the optimal torque distribution strategy as a feed-forward control on a demonstrator vehicle. The theoretically found power reduction gains are experimentally validated.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-025-00164-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145930435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive battery thermal management for fast charging of electric vehicles using nonlinear model predictive control and dynamic programming","authors":"Lukas Acker,&nbsp;Peter Hofmann,&nbsp;Johannes Konrad","doi":"10.1007/s41104-025-00157-7","DOIUrl":"10.1007/s41104-025-00157-7","url":null,"abstract":"<div><p>This paper addresses the thermal management of batteries during fast charging of electric vehicles. Using comprehensive measurement data from a state-of-the-art battery electric vehicle (BEV), a control-oriented model of the battery and its thermal system is developed and parameterized. The existing thermal management strategy for fast charging is first analyzed, after which a predictive strategy specifically for this use case is proposed. The approach consists of two steps: offline setpoint optimization via dynamic programming and optimal control allocation using nonlinear model predictive control (NMPC). The strategy’s performance is evaluated using a validated high-fidelity simulation model. Compared to the existing state-of-the-art strategy, the proposed predictive approach reduces energy consumption by up to 0.41  kWh at moderate ambient temperatures through efficient cooling, and shortens charging time by up to 4.5% at low ambient temperatures through aggressive heating.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-025-00157-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical investigation on spark ignition and flame kernel formation of lean premixed hydrogen/air flame under ICE condition","authors":"Chunkan Yu,&nbsp;Brijesh Kinkhabwala,&nbsp;Chunwei Wu,&nbsp;Sven Eckart,&nbsp;Thomas Koch","doi":"10.1007/s41104-025-00162-w","DOIUrl":"10.1007/s41104-025-00162-w","url":null,"abstract":"<div><p>The present work investigates the spark ignition and flame kernel formation processes in hydrogen–air mixtures under real engine-relevant conditions from a chemical-physical perspective. A one-dimensional numerical model based on the <i>INSFLA</i> solver with cylindrical geometry is used to resolve the coupled effects of detailed chemical kinetics and molecular transport during the ignition phase. The initial and boundary conditions (e.g. initial pressure, initial temperature, equivalence ratio, spark width and duration) are directly taken from a hydrogen-fueled internal combustion engine (H2-ICE) operation point. The simulations capture both failed and successful spark ignition events. Sensitivity analyses identify chain-branching and chain-termination reactions as the dominant chemical kinetic factors controlling the speed of flame kernel formation, while molecular transport effects become significant only after the onset of flame propagation. Furthermore, the evolution of NO emissions is analyzed in detail, showing that thermal NO dominates at high temperatures, whereas the NNH pathway contributes substantially during the early ignition stage, with the <span>(text {N}_2)</span>O route playing a minor role. Overall, the study provides mechanistic insights into the chemical and transport processes governing early flame development and NO formation in hydrogen-fueled engines, offering a scientific foundation for optimizing spark ignition strategies and reducing NOx emissions under realistic engine conditions.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-025-00162-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An experimental and computational analysis of backfire initiation and propagation in a single-cylinder hydrogen port-fuel-injection engine","authors":"Brijesh Kinkhabwala,&nbsp;Koushal Krishna,&nbsp;Florian Reppert,&nbsp;Uwe Wagner,&nbsp;Thomas Koch","doi":"10.1007/s41104-025-00163-9","DOIUrl":"10.1007/s41104-025-00163-9","url":null,"abstract":"<div><p>The global push for defossilization necessitates the advancement of hydrogen internal combustion engines as a key solution for the heavy-duty transport sector. However, the distinct combustion properties of hydrogen, particularly its high reactivity, introduce operational challenges for port-fuel-injected (PFI) engines, most critically the risk of backfire—the uncontrolled ignition in the intake system. This phenomenon not only makes the engine potentially unsafe for operation but also severely limits the achievable power density and combustion stability. Addressing this barrier requires a comprehensive understanding of the complex interactions between various engine control parameters. This study presents a coordinated experimental and computational fluid dynamics (CFD) investigation focusing on strategies to mitigate backfire in a single-cylinder, heavy-duty hydrogen PFI engine. The influence of engine parameters such as injector location, start of injection (SOI) timing, backpressure, engine valve timing and injection pressure and duration on mixture formation, and backfire onset were also analyzed. The findings establish critical guidelines for defining the stable operating window, demonstrating how the tuning of key control variables can effectively promote mixture preparation, reduce backfire instances and potentially increase engine efficiency. This research provides an essential framework for the reliable, safe and efficient deployment of hydrogen PFI technology in future low-carbon transportation applications.</p></div>","PeriodicalId":100150,"journal":{"name":"Automotive and Engine Technology","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41104-025-00163-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}