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The context for real-world emissions compliance has widened with the anticipated implementation of EU7 emissions regulations. The more stringent emissions limits and deeper real-world driving test fields of EU7 make compliance more challenging. While EU6 emissions legislation provided clear boundaries by which vehicle and powertrain Original Equipment Manufacturers (OEMs) could develop and calibrate against, EU7 creates additional challenges. To ensure that emissions produced during any real-world driving comply with legal limits, physical testing conducted in-house and in-field to evaluate emissions compliance of a vehicle and powertrain will not be sufficient. Given this, OEMs will likely need to incorporate some type of virtual engineering to supplement physical testing.

To promote real time monitoring, In use performance ratio monitoring “IUPRm” checks has been enforced in India from Apr’23 as a part of BS6-2 regulation. Since IUPRm is representative of diagnostic frequency in real driving conditions and usage pattern. therefore, a clear understanding of real-world driving is required to define IUPRm targets. This paper shares methodology and Validation steps for defining IUPRm routes for Indian market. Methodology objective is to standardize the market operating conditions over a particular region. Selected Methodology consist of three steps: For defining IUPRm route framework, first step is to have a pre-market survey to know current In use performance ratio “IUPR” status and improvement areas in existing market vehicles. Second step is to define market representative localized on road routes based on the finding of Pre-market survey.

Design of internal combustion (IC) engine pistons is dependent on accurate prediction of the temperature field in the component. Experimental temperature measurements can be taken but are costly and typically limited to a few select locations. High-fidelity computer simulations can be used to predict the temperature at any number of locations within the model, but the models must be calibrated for the predictions to be accurate. The largest barrier to calibration of piston thermal models is estimating the backside boundary conditions, as there is not much literature available for these boundary conditions. Bayesian model calibration is a common choice for model calibration in literature, but little research is available applying this method to piston thermal models. Neural networks have been shown in literature to be effective for calibration of piston thermal models.

The proposed Euro 7 regulation includes On Board Monitoring, or OBM, to continuously monitor vehicles for emission exceedances. OBM relies on feedback from existing or additional sensors to identify high emitting vehicles, which poses many challenges. Currently, sensors are not commercially available for all emissions constituents, and the accuracy of available sensors is not capable enough for in use compliance determination. On board emissions models do not offer enough fidelity to determine in use compliance and require new complex model innovation development which will be extremely complicated to implement on board the vehicle. The stack up of multi-component deterioration leading to an emissions exceedance is infeasible to detect using available sensors and models.

The present study was motivated by a need to expand information for consumers offered through the FuelEconomy.Gov website. To that end, a power-based modeling approach has been used to examine the effect of steady-speed driving on estimated range for model year 2020 – 2023 battery electric vehicles (BEVs). This approach allowed rapid study of a broader range of BEV models than could be accomplished through vehicle tests. Publicly accessible certification test results and other data were used to perform a regression between cycle-average tractive power requirements and the resulting electrical power. This regression enabled estimation of electric power and energy use over a range of steady highway speeds. These analyses in turn allowed projection of vehicle range at differing speeds. The projections agree within 6% with available 65 MPH manufacturer test data.

Crankshaft bearings function to maintain the lubrication oil films needed to support crankshaft journals in hydrodynamic regime of rotation. Discontinuous oil films will cause the journal-bearing couple to be in a mixed or boundary lubrication condition, or even a bearing seizure or a spun bearing. This condition may further force the crankshaft to break and an engine shutdown. Spun bearings have been identified to be one of the top reasons in field returned engines. Excessive investigations have found large, embedded hard debris particles on the bearings are inevitably the culprit of destroying continuity of the oil films. Those particles, in particular the suspicious steel residues, in the sizes of hundreds of micrometers, are large enough to cause oil film to break, but rather fine and challenging for materials engineers to characterize their metallurgical features. This article presents the methodology and steps of debris analyses on bearings at different stages of engine build.

This study delves into the microstructural and mechanical characteristics of AlSi10Mg alloy produced through the Laser Powder Bed Fusion (L-PBF) method. The investigation identified optimal process parameters for AlSi10Mg alloy based on Volume Energy Density (VED). Manufacturing conditions in the L-PBF process involve factors like laser power, scan speed, hatching distance, and layer thickness. Generally, high laser power may lead to spattering, while low laser power can result in lack-of-fusion areas. Similarly, high scan speeds may cause lack-of-fusion, and low scan speeds can induce spattering. Ensuring the quality of specimens and parts necessitates optimizing these process parameters. To address the low elongation properties in the as-built condition, heat treatment was employed. The initial microstructure of AlSi10Mg alloy in its as-built state comprises a cell structure with α-Al cell walls and eutectic Si.

The handling of flexible components creates a unique problem set for pick and place automation within automotive production processes. Fabrics and woven textiles are examples of flexible components used in car interiors, for air bags, as liners and in carbon-fiber layups. These textiles differ greatly in geometry, featuring complex shapes and internal slits with varying material properties such as drape characteristics, crimp resistance, friction, and fiber weave. Being inherently flexible and deformable makes these materials difficult to handle with traditional rigid grippers. Current solutions employ adhesive, needle-based, and suction strategies, yet these systems prove a higher risk of leaving residue on the material, damaging the weave, or requiring complex assemblies. Pincer-style grippers are suitable for rigid components and offer strong gripping forces, yet inadvertently may damage the fabric, and introduce wrinkles / folded-over edges during the release process.

As additive manufacturing technology advances, it is becoming a more feasible option for fabricating highly complex, lightweight structures in the automotive industry. To take advantage of the improved design freedom and to reduce support structures for the selected printing orientation, components must be designed specifically for additive manufacturing. A new approach for accomplishing this process combines topology and build orientation optimization, which aims to simultaneously determine the ideal build direction and component design to maximize stiffness and reduce additive manufacturing costs. Current techniques in literature are formulated for specific categories of additive manufacturing: either methods that print on a support structure raft or print directly on the build plate. However, these two categories have very different relationships between part orientation and support structure, resulting in distinct optimal orientations for each additive manufacturing category.

Literature has shown that 3D printed composites may have highly anisotropic mechanical properties due to variation in microstructure as a result of filament deposition process. Laminate composite theory, which is already used for composite products, has been proposed as an effective method for quantifying these mechanical characteristics. Continuous fiber composites traditionally have the best mechanical properties but can difficult or costly to manufacture, especially when attempting to use additive manufacturing methods. Traditionally, continuous fiber composites used specialized equipment such as vacuum enclaves or labor heavy hand layering techniques. An attractive alternative to these costly techniques is modifying discontinuous fiber additive manufacturing methods into utilizing continuous fibers. Currently there exist commercial systems that utilize finite-deposition (FD) techniques that insert a continuous fiber braid into certain layers of the composite product.

Driver steering feature clustering aims to understand driver behavior and the decision-making process through the analysis of driver steering data. It seeks to comprehend various steering characteristics exhibited by drivers, providing valuable insights into road safety, driver assistance systems, and traffic management. The primary objective of this study is to thoroughly explore the practical applications of various clustering algorithms in processing driver steering data and to compare their performance and applicability. In this paper, principal component analysis was employed to reduce the dimension of the selected steering feature parameters. Subsequently, K-means, fuzzy C-means, the density-based spatial clustering algorithm, and other algorithms were used for clustering analysis, and finally, the Calinski-Harabasz index was employed to evaluate the clustering results. Furthermore, the driver steering features were categorized into lateral and longitudinal categories.

Driver Assistance and Autonomous Driving features are becoming nearly ubiquitous in new vehicles. The intent of the Driver Assistant features is to assist the driver in making safer decisions. The intent of Autonomous Driving features is to execute vehicle maneuvers, without human intervention, in a safe manner. The overall goal of Driver Assistance and Autonomous Driving features is to reduce accidents, injuries, and deaths with a comforting driving experience. However, different drivers can react differently to advanced automated driving technology. It is therefore important to consider and improve the adaptability of these advances based on driver behavior. In this paper, a human-centric approach is adopted to provide an enriching driving experience. We perform data analysis of the naturalistic behavior of drivers when performing lane change maneuvers by extracting features from extensive Second Strategic Highway Research Program (SHRP2) data of over 5,400,000 data files.

To meet the ever-increasing demands of the engineering industry, novel approaches to design optimization are essential, especially in fast-paced production environments. Conventional CAD and simulation tools often struggle to keep up with the complexity and speed required for designing critical components. In this context, leveraging Deep Learning technologies presents a promising solution by integrating knowledge from simulations and designs to drastically accelerate product development. With the drive for Electrification, conventional power electronics and systems are becoming more energy dense and hence requires compact and efficient thermal management solutions. Higher energy density is attributed to high power electrical components fitted in packs with shrinking characteristic dimensions and hence needs more efficient and compact thermal management solutions.

The phenomenon of drop-wall interaction plays a crucial role in a wide range of industrial applications. When liquid droplets come into contact with a high-temperature surface, it can lead to thermal shock due to rapid temperature fluctuations. This abrupt temperature change can generate thermal stress within the solid wall material. If the thermal stress exceeds the material's strength in that specific stress mode, it can result in material failure. Therefore, it is imperative to delve into the evolving temperature patterns on high-temperature surfaces to optimize material durability. This study focuses on investigating drop-wall interactions within the context of engine environments. To achieve this, the Smoothed Particle Hydrodynamics (SPH) method is employed to simulate the impact of fuel droplets on a silicon nitride wall. The goal is to understand the heat transfer mechanisms, thermal penetration depths, and temperature distributions within the heated wall.

The discussed invention is centered on the evaporative cooling of a vehicle cabin, introducing a novel concept of humidity control. Unlike conventional Air Conditioning (AC) systems that operate on the Vapor Compression Refrigeration Cycle (VCRC), which tend to be costly and contribute to higher fuel consumption due to the engine-driven compressor in automobiles, there is currently no other Original Equipment Manufacturer (OEM) fitted cabin cooling option available to address this issue. This paper introduces the idea of a humidity-controlled evaporative cooler. The objective of humidity control is achieved through a controller unit that receives feedback from a humidity sensor, subsequently regulating the operation of the water pump. The ambient air is passed through a humidified honeycomb pad, cooling through the principle of evaporation. To prevent any leftover water droplets from entering the cabin, a polyester nonwoven filter has been integrated into the system.

Upcoming, increasingly stringent greenhouse gas (GHG) as well as emission limits demand for powertrain electrification throughout all vehicle applications. Increasing complexity of electrified powertrain architectures require an overall system approach combining modular component technology with integration and industrialization requirements when heading for further significant efficiency optimization. At the same time focus on reduced development time, product cost and minimized additional investment demand reuse of current production, machining, and assembly facilities as far as possible. Up to date additive manufacturing (AM) is an established prototype component, as well as tooling technology in the powertrain development process, accelerating procurement time and cost, as well as allowing to validate a significantly increased number of variants. The production applications of optimized, dedicated AM-based component design however are still limited.

The author has developed UV based photocatalytic air purification system (Mathur, 2021, 2122, 2023) that can eliminate all pathogens from the cabin air including COVID-19. In this study, the focus is to determine the risk of infection due to pathogens/germs in the cabin of an automobile. Author has determined the risk of infection by using Wells-Riley model and conducted CFD analysis to determine propagation of virus in cabin as a function of: 1 Cabin Volume & Number of Occupants (Wells-Riley Model in OSA mode): (i) Cabin volume from: Small Sedan, Large Sedan and a SUV; with 4 occupants (males & females); Number of infector 1; Air flowrate (m3/min); (ii) A 15-seater minibus – with 10 occupants (males); Number of infectors 1 & 2; Air flowrate (m3/min) 2 CFD to simulate 4 occupants and 1 infector in an automotive cabin – Current investigation is for talking, coughing and sneezing with blower off in Recirc mode wit (i) Infector in the front seat; (ii) Infector in the rear seat.

Fiber reinforced additive manufacturing (FRAM) is a fused deposition modelling (FDM) additive manufacturing (AM) process which produces composite print layers - polymer matrix and reinforcing fiber. This work proposes a novel method which utilizes FRAM design freedom and simultaneously optimizes 3D print orientation and component topology to improve the response of a mass minimization problem statement. The method is robust and is designed to solve industry-applicable problem statements (mass minimization) with complex geometry and loading. Design sensitivities of 3D print orientation design variables, (θ1, θ2, θ3), are calculated using finite differencing and gradient descent is used to converge to an optimized print orientation. Changing 3D print orientation alters anisotropic material properties to improve the structural response of the component in the prescribed load-cases.

A systematic review based on the PRISMA protocol was used to evaluate compounds developed for 3D printing with the incorporation of cellulose nanofibrils into polymers to be used in the automotive sector. The processing parameter is a data of great relevance for the development of durable structural parts and this study can be carried out using the state of the art on this subject. This way, the research was carried out using a search strategy from three different databases (Web of Science, Scopus and Science Direct) limiting studies between the years 2019 and 2023. The keywords used in these searches were: "3D Print" OR "FDM" OR "Fused Deposition Modeling" OR "FFF" OR "Fused Filament Fabrication" AND "Natural Nanofiber" OR "Natural Nanofibril" OR "Cellulose Nanofiber" OR "Cellulose Nanofibril". The same criteria described were also used to search for patents on the PatentScope, Google Patents and Espacenet platforms.

The next generation of digital cockpits requires modern architectures to be successful and affordable. This paper provides an in-depth view on the future of digital cockpit architectures. The currently emerging architectures are explored with two main points in focus: The key experiences that drive customer expectations and the options to cost-effectively meet those expectations—while keeping the vehicle affordable. Modern architectures rely on middleware services. Well-designed middleware services allow for an efficient and reusable approach across different model lines and market segments. The paper presents this approach. The new architectures also lead to a transformation of the partner ecosystem between original equipment manufacturers (OEMs) and component suppliers. OEMs try to lever this system while maintaining control over their offerings. These changes transform the traditional semiconductor industry as a whole.