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The Particle Number–Portable Emission Measurement System (PN-PEMS) came into force with Euro VI Phase E regulations starting January 1, 2022. However, positive ignition (PI) engines must comply from January 1, 2024. The delay was due to the unavailability of the PN-PEMS system that could withstand high concentrations of water typically present in the tailpipe (TP) of CNG vehicles, which was detrimental to the PN-PEMS systems. Thus, this study was designed to evaluate the condensation particle counter (CPC)-based PN-PEMS measurement capabilities that was upgraded to endure high concentration of water. The PN-PEMS measurement of solid particle number (SPN23) greater than 23 nm was compared against the laboratory-grade PN systems in four phases. Each phase differs based upon the PN-PEMS and PN system location and measurements were made from three different CNG engines. In the first phase, systems measured the diluted exhaust through constant volume sampler (CVS) tunnel.

With the capability of avoiding failure in advance, failure prediction model is important not only to end users, but also to the service engineers in vehicle industry. This paper proposes an approach based on anomaly detection algorithms and telematic data to predict the failure of the engine air system with Turbo charger. Firstly, the relationship between air system and all obtained features are analyzed by both physical mechanism and data-wise. Then, the features including altitude, air temperature, engine output power, and charger pressure are selected as the input of the model, with the sampling interval of 1 minute. Based on the selected features, the healthy state for each vehicle is defined by the model as benchmark. Finally, the ‘Medium surface’ is determined for specific vehicle, which is a hyperplane with the medium points of the healthy state located at, to detect the minor weakness symptom (sub-health state).

Shell-and-tube heat exchangers, commonly referred to as radiators, are the most prevalent type of heat exchanger within the automotive industry. A pivotal goal for automotive designers is to increase their thermal effectiveness while mitigating pressure drop effects and minimizing the associated costs of design and operation. Their design is a lengthy and intricate process involving the manual creation and refinement of computer-aided design (CAD) models coupled with iterative multi-physics simulations. Consequently, there is a pressing demand for an integrated tool that can automate these discrete steps, yielding a significant enhancement in overall design efficiency. This work aims to introduce an innovative automation tool to streamline the design process, spanning from CAD model generation to identifying optimal design configurations. The proposed methodology is applied explicitly to the context of shell-and-tube heat exchangers, showcasing the tool's efficacy.

The thermal performance of an engine coolant system is efficient when the engine head temperature is maintained within its optimum working range. For this, it is desired that air should not be entrapped in the coolant system which can lead to localized hot spots at critical locations. However, it is difficult to eliminate the trapped air pockets completely. So, the target is to minimize the entrapped air as much as possible during the coolant filling and deaeration processes, especially in major components such as the radiator, engine head, pump etc. The filling processes and duration are typically optimized in an engine test stand along with design changes for augmenting the coolant filling efficiency. However, it is expensive and time consuming to identify air entrapped locations in tests, decide on the filling strategy and make the design changes in the piping accordingly.

Shafaat and Kenley in 2015 identified the opportunity to improve System Engineering Standards by incorporating the design principle of learning. The System Level Assessment (SLA) Methodology is an approach that fulfills this need by efficiently capturing the learnings of a team of subject matter experts in the early stages of product system design. By gathering expertise, design considerations are identified that when used with market and business requirements improve the overall quality of the product system. To evaluate the effectiveness of this approach, the methodology has been successfully applied over 400 times within each realm of the New Product Introduction process, including most recently to a Technology Development program (in the earliest stages of the design process) to assess the viability of various electrification technologies under consideration by an automotive Tier 1 supplier.

The Verband der Automobilindustrie (VDA) 278 is an industry method widely used to measure volatile organic compounds (VOCs). It is most commonly used in the automobile industry to measure and regulate VOC and FOG levels in automotive parts as a safety regulation. The current VDA 278 method has issues from poor accuracy, precision, and reproducibility. There is variability in data due to differences in sample type and handling as well as instrument model. There is little understanding on the reproducibility of measurements of different sample types analyzed on different makes of instruments using VDA 278 analysis. In this work, a round-robin study is performed on diverse sample types, using different makes of instruments in laboratories across the world. It uses improved method conditions developed internally, for better reproducibility, that reduce sources of error.

A multilayer acoustic treatment constructed of fibrous acoustic absorption material combined with dissipative acoustic material achieves a low profile and is lightweight and multi-functional, with properties that are desirable for NVH (noise, vibration, and harshness) treatments. The dissipative material consists of microfibers and acoustically active particles; this material was introduced in the last SAE NVH conference: A Novel Dissipative Acoustic Material [1]. In this paper, the acoustic performance of the multilayer treatment was evaluated by using random incidence absorption and transmission loss measurements, as well as in-vehicle experiments. Absorption and transmission loss were additionally modeled using the transmission matrix method (TMM). In the in-vehicle test, an OEM wheelhouse liner with Trim, for an SUV, was evaluated utilizing this new multilayer 3M treatment.

In the past decades, automotive structure design has sought to minimize its mass while maintaining or improving structural performance. As such, topology optimization (TO) has become an increasingly popular tool during the conceptual design stage. While the designs produced by TO methods provide significant performance-to-mass ratio improvements, they require considerable computational resources when solving large-scale problems. An alternative for large-scale problems is to decompose the design domain into multiple scales that are coupled with homogenization. The problem can then be solved with hierarchical multiscale topology optimization (MSTO). The resulting optimal, homogenized macroscales are de-homogenized to obtain a high-fidelity, physically-realizable design. Even so MSTO methods are still computationally expensive due to the combined costs of solving nested optimization problems and performing de-homogenization.

Diesel oxidation catalyst (DOC) is one of the critical catalyst components in modern diesel aftertreatment systems. It mainly converts unburned hydrocarbon (HC) and CO to CO2 and H2O before they are released to the environment. In addition, it also oxidizes a portion of NO to NO2, which improves the NOx conversion efficiency via fast SCR over the downstream selective catalytic reduction (SCR) catalyst. HC light-off tests, with or without the presence of NOx, has been typically used for DOC evaluation in laboratory. In this work, we aim to understand the influences of DOC light-off experimental conditions, such as initial temperature, initial holding time, HC species, with or without the presence of NOx, on the DOC HC light-off behavior. The results indicate that light-off test with lower initial temperature and longer initial holding time (at its initial temperature) leads to higher DOC light-off temperature.

The lack of inherent security controls makes traditional Controller Area Network (CAN) buses vulnerable to Machine-In-The-Middle (MitM) cybersecurity attacks. Conventional vehicular MitM attacks involve tampering with the hardware to directly manipulate CAN bus traffic. We show, however, that MitM attacks can be realized without direct tampering of any CAN hardware. Our demonstration leverages how diagnostic applications based on RP1210 are vulnerable to Machine-In-The-Middle attacks. Test results show SAE J1939 communications, including single frame and multi-framed broadcast and on-request messages, are susceptible to data manipulation attacks where a shim DLL is used as a Machine-In-The-Middle. The demonstration shows these attacks can manipulate data that may mislead vehicle operators into taking the wrong actions.

A common scenario in engineering design is the evaluation of expensive black-box functions: simulation codes or physical experiments that require long evaluation times and/or significant resources, which results in lengthy and costly design cycles. In the last years, Bayesian optimization has emerged as an efficient alternative to solve expensive black-box function design problems. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition functions that drives the design process. Successful Bayesian optimization strategies are characterized by accurate surrogate models and well-balanced acquisition functions. The Gaussian process (GP) regression model is arguably the most popular surrogate model in Bayesian optimization due to its flexibility and mathematical tractability. GP regression models are defined by two elements: the mean and covariance functions.

Stoichiometric natural gas engines with three-way catalysts emit less NOx and CH4 due to their higher efficiency compared to lean-burn natural gas engines. Although hydrothermal aging of three-way catalysts has been extensively studied, a deeper understanding beyond hydrothermal aging is needed to explain real-world performance, especially for natural gas engines with near-zero NOx emissions. In this investigation, field-aged three-way catalysts were characterized to identify the contribution of chemical aging to their overall performance. It was found that the sulfur species on the field-aged TWCs were entirely distributed along the catalyst length, showing a decreasing trend, whereas phosphorous contamination was mainly observed at the inlet section of the three-way catalysts, and the phosphorous concentration declined sharply along the axial length.

Knock in gasoline engines at higher loads is a significant constraint on torque and efficiency. The anti-knock property of a fuel is closely related to its research octane number (RON). Ethanol has superior RON compared to gasoline and thus has been commonly used to blend with gasoline in commercial gasolines. However, as the RON of a fuel is constant, it has not been used as needed in a vehicle. To wisely use the RON, an On-Board Separation (OBS) unit that separates commercial gasoline with ethanol content into high-octane fuel with high ethanol fraction and a lower octane remainder has been developed. Then an onboard Octane-on-demand (OOD) concept uses both fuels in varying proportion to provide to the engine a fuel blend with just enough RON to meet the ever changing octane requirement that depends on driving pattern.

Safety, fuel economy and uptime are key requirements for the operation of heavy-duty line-haul trucks within a fleet. With the penetration of connectivity and automation technologies, energy optimal and safe operation of the trucks are further improved through Advanced Driver Assistance System (ADAS) features and automated technologies as in truck platooning. Understanding the braking capability of the vehicle is very important for optimal ADAS and platooning control system design and integration. In this paper, the importance of tire connectivity and tire conditions on truck stopping distance are demonstrated through testing. The test data is further utilized to develop tire models for integration in an optimal vehicle automation for platooning. New ways to produce and use the tire related information in real-time optimal control of platooning trucks are proposed and the contribution of tire information in fuel economy is quantified through simulations.

In the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. A LIB is composed of several unit cells. Therefore, one of the most important factors that determine the performance of a LIB are the characteristics of the unit cell. The design of LIB cells is a challenging problem since it involves the evaluation of expensive black-box functions. These functions lack a closed-form expression and require long-running time simulations or expensive physical experiments for their evaluation. Recently, Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition function that guides the optimization.

Commercial automotive diesel engine service and repair, post a diagnostic trouble code trigger, relies on standard troubleshooting steps laid down to identify or narrow down to a faulty engine component. This manual process is cumbersome, time-taking, costly, often leading to incorrect part replacement and most importantly usually associated with significant downtime of the vehicle. Current study aims to address these issues using a novel in-house simulation-based approach developed using a Digital Twin of the engine which is capable of conducting in-mission troubleshooting with real world vehicle/engine data. This cost-effective and computationally efficient solution quickly provides the cause of the trouble code without having to wait for the vehicle to reach the service bay. The simulation is performed with a one-dimensional fluid dynamics, detailed thermodynamics and heat transfer-based diesel engine model utilizing the GT-POWER engine performance tool.

Due to modern trends in the automotive industry, such as vehicle electrification, light-weighting, reduced NVH (Noise, Vibration and Harshness) packaging space, etc., it is desirable to have a low profile and light-weight acoustic material with multi-functionality. If one single layer of a thin acoustic material can provide comparable absorption and transmission loss to a multilayer treatment, it will benefit the industry by saving weight, packaging space and system cost. Acoustic absorption and sound transmission loss performance of a new dissipative material at reduced weight and thickness is introduced in this paper. The acoustic performance of the material was evaluated by using random incidence absorption and transmission loss as well as in-vehicle experiment. Further potential applications for this material have been identified using the Statistical Energy Analysis (SEA) method with panel leakage considered.

Nested planetary gear trains, which consist of two integrated co-axial single-stage planetary gearsets, have recently been widely implemented in automobile transmissions and various other applications. In the current study, a non-destructive vibrational and acoustical monitoring technique is developed to detect a common type of gear grinding defect for a complex nested gear train structure. A nested gear train which has an unground pinion with unpolished teeth profile is used to exemplify the developed methodology. An experimental test stand with an open and vertical mounting configuration has been designed to acquire both vibrational and acoustical data. The measured data are investigated using several signal processing techniques to identify unground pinions in the gear system. A general frequency spectrum analysis is performed initially, which is then followed by a peak finding algorithm to identify the peaks in the spectrum.

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

Electrical connectors and terminals are widely used in the automotive industry. It is desirable to mate the electrical connections using materials or coatings with low friction force to improve the ergonomics of the assembly process while maintaining good electrical conduction over the lifetime of the vehicle. We have previously shown that plasma-enhanced chemical vapor deposition (PECVD) of graphene on gold (Au) and silver (Ag) terminals can significantly reduce the insertion force (friction force during the terminal insertion process). However, the cost of this deposition method is rather high, and its high temperature process (> 400 oC) makes it impractical for materials with low melting temperatures. For example, tin (Sn) coating with a melting temperature of 232 oC is commonly used in electrical connectors, which cannot sustain the high temperature process. In this study, reduced graphene oxide was prepared using a low-cost solution process and applied onto metallic terminals.