Search Results

This paper presents a feedback control strategy to minimize noise during dog clutch engagement in a hybrid transmission. The hybrid transmission contains an internal combustion engine(ICE) and 2 electric motors in P1 and P3 configurations. For efficiency during driving, at high vehicle speeds ICE is connected to wheels, via the dog clutch, hence shifting the vehicle from series to parallel hybrid mode. It is shown by experimental results that if the speed difference between the two sides of the dog clutch is below a certain level the engagement will be without clonk noise. In this paper the designed state feedback Linear Quadratic Integral (LQI) control provides the synchronization torque request to the P1 motor, hence matching the speed of one side of dog clutch with the other under the disturbance from combustion torque of the engine.

In torque converters, a lockup clutch is used for direct torque transfer from the engine to the gearbox. Nowadays, earlier lockup engagement is necessary to reduce fuel consumption. It introduces noise and vibration issues in the transmission that are solved by clutch slipping. However, the clutch experiences much heat because of earlier engagement, which needs to be adequately dissipated by ATF oil. To overcome this issue, multi-plate clutches are commonly used for efficient torque transfer and clutch slipping. On the other side, packaging space for torque converters is reducing at the vehicle level, especially in hybrid vehicles, which reduces the efficient cooling of clutches. So, accurate modeling of clutch slipping is necessary to improve the clutch performance and durability of the product. Clutch slipping is a transient phenomenon that involves conjugate heat transfer and rotational flow modeling. There are different ways to model clutch slipping in CFD simulations.

Recently, there has been a new method for setting bearing preload on tapered roller bearings in a power transmission system. To move this new method into production, an analytical model that relates the bearing preload to the stiffness of the bearing was developed. This work develops an analytical model that links the preload on multi-row tapered roller bearings to the stiffness of the power transmission system. This study also validates the proposed analytical model by comparing it to both previous work and commercially available simulation software. The analytical model has shown that it is highly sensitive to the number of rollers in the bearing, which is discussed in this work.

When compared to traditional cars with internal combustion engines (ICEs), electric vehicles (EVs) are seen as a more environmentally friendly option. However, the widespread acceptance of EVs in India faces several obstacles, including the high cost of the technology, inadequate charging infrastructure, and limited driving range. Additionally, potential customers are concerned about the actual range of EVs, which often falls short of the certified range. The certified range is determined based on a standardized driving cycle so selecting the appropriate driving cycle for range estimation is of utmost importance. In India, the modified Indian drive cycle (MIDC) has been implemented, which is comparable to the New European Driving Cycle (NEDC).

Internal combustion (IC) engines still power most of the vehicles on road and will likely to remain so in the near future, especially for heavy duty applications in which electrification is typically more challenging. Therefore, continued improvements on IC engines in terms of efficiency and longevity are necessary for a more sustainable transportation sector. Two important design objectives for heavy duty engines with wet liners are to reduce friction loss and to lower the risks of cavitation damages, both of which can be greatly influenced by the piston-liner clearance and the design of the piston skirt. However, engine design optimization is difficult due to the nonlinear interactions between the key design variables and the design objectives, as well as the multi-physics and multi-scale nature of the mechanisms that are relevant to the design objectives.

A semi-active suspension system provides superior safety, ride, and handling performance for a vehicle by continuously varying the damping based on vehicle motions, where semi-active hydraulic damper (SAHD) is the most critical component. Today, SAHD’s are standard in most of the premium segments of vehicles and optional extras in mid-size and compact vehicle segments. Electric vehicles require larger sized SAHD’s to meet heavier vehicle loads and meet ride and handling requirements. The aim of this paper is to highlight the design and development methodology of a base valve for larger bore-size for semi-active hydraulic damper. The workflow follows to present a process for base valve design to meet structural strength and, the key steps of design calculations of the hydraulic performance. The design of the base valve and suction disks architecture was engineered with the aid of Computer Aided simulations.

As part of the development of its new powertrain consisting of two electric motors, a combustion engine and a gearbox, Renault SAS followed an original approach to achieve an assembly with an optimized, robust, and reliable link between the main electric motor and the gearbox. The running operation optimization as well as the high reliability is achieved by processing the following topics: filtration of vibrations and operating jolts; solving of tribological problems specific to splined connections, such as fretting corrosion and abrasive tooth wear; avoidance of potential seizure of elements with cyclic relative slippage under load; and eventually, control of wear and tear on the sealing and damping O-rings, which must accept oscillating translational movements at the same time as torque transfer. The aim of this article is to retrace the main steps taken to achieve the desired reliability and performance targets for this type of product.

Thermoacoustic heat engines convert heat into useful energy by generating acoustic waves from a heat source that can then be extracted as useful work. These engines are inexpensive, robust, versatile, and capable of extracting energy from a wide variety of heat sources ranging from waste heat from power plants to exhaust heat of vehicles. In this article, our investigation focuses on using simulation workflows to improve the performance of thermoacoustic engines. We begin with validating the workflows with published data for both traveling wave and standing wave thermoacoustic engines. Following that, we investigate the effect of changing the working fluid and the operating pressure to increase acoustic power. This study uses a coupled PowerFLOW™ and PowerTHERM™ methodology to simulate the buoyancy-driven flows that generate acoustic pressure waves. Good correlations were observed for both traveling and standing wave thermoacoustic engines.

Model-based Development (MBD) has been employed for engine development to reconcile the contradictory relationship between numerous functions and systems at a high level and in a short span of time. However, in actuality, as engines have become more advanced, it has become challenging to even satisfy the requirements of individual components. Moreover, reconciling multiple contradictory functions like engine power and strength and durability performance, as well as coordinating many related systems, requires an even higher level of skill. Such harmonization techniques require total optimization studies that cover a wide range of designs, and which requires several years of examination with current development processes. Multi-objective Design Exploration (MODE) methods [1] using parametric models [2] and surrogate models [3] are being used to shorten the development period and achieve more balanced designs.

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.

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.

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.