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Injector nozzle deposits can have a profound effect on particulate emissions from vehicles fitted with Gasoline Direct Injection (GDI) engines. Several recent publications acknowledge the benefits of using Deposit Control Additives (DCA) to maintain or restore injector cleanliness and in turn minimise particulates, but others claim that high levels of DCA could have detrimental effects due to the direct contribution of DCA to particulates, that outweigh the benefits of injector cleanliness. Much of the aforementioned work was conducted in laboratory scenarios with model fuels. In this investigation a fleet of 7 used GDI vehicles were taken from the field to determine the net impact of DCAs on particulates in real-world scenarios. The vehicles tested comprised a range of vehicles from different manufacturers that were certified to Euro 5 and Euro 6 emissions standards.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

The LEV IV FTP PM limit in the recently approved CARB ACC II regulations for passenger cars and light duty trucks will be 1 mg/mile starting in 2025. Gravimetric PM measurement at these levels is very challenging as the net mass of PM on the filter in full flow tunnel testing ranges between 8 to 32 micrograms depending on amount of dilution. This is approaching tunnel background levels which, in combination with filter handling, static charge removal and microbalance instability, compounds the uncertainty. One major source of the uncertainty at these low levels is the tunnel contamination resulting in high variability from test to test and cell to cell. This tunnel background is mostly HC artifact which cannot be easily controlled and can be significantly higher than the 5-μg CFR allowable correction limit in some test cells.

As more electric vehicles (BEV, HEV, PHEV, etc.) are adopted in the upcoming decades, it is becoming increasingly important to conduct vehicle-level thermal simulations under different drive-cycle conditions while incorporating the various subsystem thermal losses. Thermal management of the various heat sources in the vehicle is essential both in terms of ensuring passenger safety as well as maintaining all the subsystems within their corresponding safe temperature limits. It is also imperative that these thermal simulations include energy consumption prediction, while considering the effect of battery degradation both in terms of increased thermal losses as well as reduction in the vehicle’s range. For this purpose, a three-dimensional transient thermal analysis framework was coupled with an electrochemical P2D-based battery model and a vehicle dynamics model to test different scenarios and their effect on a hybrid vehicle’s range and the lithium-ion battery life.

ISO/SAE 21434 [1] Final International Standard was released September 2021 to great fanfare and is the most prominent standard in Automotive Cybersecurity. As members of the Joint Working Group (JWG) the authors spent 5 years developing the 84 pages of precise wording acceptable to hundreds of contributors. At the same time the auto industry had been undergoing a metamorphosis probably unmatched in its hundred-year history. A centerpiece of the metamorphosis is the adoption of the Agile development method to meet market demands for time-to-market and flexibility of design. Unfortunately, a strategic decision was made by the JWG to focus ISO/SAE 21434 on the V-Model method. Agile does not break ISO/SAE 21434. Agile is a framework that can be adapted to suit any process. In the end the goals are the same regardless of development method; security by design must be achieved.

Exhaust Gas Recirculation (EGR) is employed in diesel engines to reduce engine-out NOx emissions. Despite the concerted design efforts of manufacturers, high-pressure Exhaust Gas Recirculation (HP-EGR) systems can be susceptible to fouling as the particulate matter, hydrocarbons and other entrained species deposit from the exhaust gas flow as it cools on its passage through the EGR system. Such deposits can lead to a number of problems including deterioration of emissions, fuel efficiency, performance and drivability, as well as breakdowns. The development of an engine test method to enable the study of the impact of fuel on deposits in the HP-EGR system was reported in 2020. In the test, a 4-cylinder light-duty diesel engine of 1.6L displacement runs at conditions conducive to EGR deposit formation over 24 hours and the impact of fuels on deposit formation is determined through weighing of the EGR system components before and after the test.

Design efforts to improve the hydraulic efficiency of high-pressure diesel fuel systems and thus further improve overall engine efficiency have resulted in the utilisation of low-spill control valves and reduced injector component clearances to reduce general leakage losses. Overall, these advances have contributed significantly to the high efficiency diesel engines of today. However, the combination of very high fuel pressures, cavitation and low fuel leakage volumes increases the heating of the remaining fuel, increasing temperature and, in turn, the propensity for deposits to form inside the injector. This deposit phenomenon is commonly known as Internal Diesel Injector Deposits (IDID) and can cause rough engine running and failed engine starts requiring injector cleaning or replacement. Methods studying this phenomenon are under development in the industry.

Accurate battery state of charge (SOC) estimation is essential for safe and reliable performance of electric vehicles (EVs). Lithium-ion batteries, commonly used for EV applications, have strong time-varying and non-linear behaviour, making SOC estimation challenging. In this paper, a processor in the loop (PIL) platform is used to assess the execution time and memory use of different SOC estimation algorithms. Four different SOC estimation algorithms are presented and benchmarked, including an extended Kalman filter (EKF), EKF with recursive least squares filter (EKF-RLS) feedforward neural network (FNN), and a recurrent neural network with long short-term memory (LSTM). The algorithms are deployed to two different NXP S32Kx microprocessors and executed in real-time to assess the algorithms' computational load. The algorithms are benchmarked in terms of accuracy, execution time, flash memory, and random access memory (RAM) use.

In the light duty diesel segment, the need persists for an advanced control system to monitor the health of an aftertreatment system throughout a vehicle’s life in order to maintain compliance with ever tightening emissions levels. In on-board diagnostics (OBD), every diagnostic is validated during development stages to detect when a system under monitoring of that diagnostic has failed. This necessitates the need to create parts which represent a failure that would be observed on the vehicle. These failed parts, referred to as limit or threshold parts, are developed through a limit part creation process. Although there are commonalities amongst Original Equipment Manufacturers (OEM), each OEM has their own detection logic which will require a unique and specific limit part. Various methods exist for creating these limit parts, and each method produces a different combination of ability to detect the failure and its associated tailpipe emissions.

Autoignition delay times of two full blend gasoline fuels (high and low RON) were explored in a rapid compression machine. CO2 dilution by mass was introduced at 0%, 15%, and 30% levels with the O2:N2 mole ratio fixed at 1:3.76. This dilution strategy is used to represent exhaust gas recirculation (EGR) substitution in spark ignition (SI) engines by using CO2 as a surrogate for major EGR constituents(N2, CO2, H2O). Experiments were conducted over the temperature range of 650K-900K and at 10 bar and 20 bar compressed pressure conditions for equivalence ratios of (Φ =) 0.6-1.3. The full blend fuels were admitted directly into the combustion chamber for mixture preparation using the direct test chamber (DTC) approach. CO2 addition retarded the autoignition times for the fuels studied here. The retarding effect of the CO2 dilution was more pronounced in the NTC region when compared to the lower and higher temperature range.

The Press-in-Place (PIP) gasket is a static face seal with self-retaining feature, which is used for the mating surfaces of engine components to maintain the reliability of the closed system under various operating conditions. Its design allows it to provide enough contact pressure to seal the internal fluid as well as prevent mechanical failures. Insufficient sealing pressure will lead to fluid leakage, consequently resulting in engine failures. A test fixture was designed to simulate the clamp load and internal pressure condition on a gasket bolted joint. A sensor pad in combination with TEKSCAN equipment was used to capture the overall and local pressure distribution of the PIP gasket under various engine loading conditions. Then, the test results were compared with simulated results from computer models. Through the comparisons, it was found that gasket sealing pressure of test data and CAE data shows good correlations in all internal pressure cases when the bolt load was 500 N.

The reduction of automotive emissions is instrumental in the fight against air pollution and its impact on global warming. This realization has empowered governments around the world to mandate lower levels of vehicle emissions requiring the Original Equipment Manufacturers (OEMs) to implement advanced aftertreatment technologies in their applications. Achieving emission levels as low as SULEV30 or SULEV20 would have been impossible only a couple of decades ago, however, these lower levels of emissions are now a possibility through advanced control strategies and aftertreatment systems. As a part of this mandate to lower emissions, OEMs are also continuously monitoring the health and performance of their aftertreatment and control components. The implementation of On Board Diagnostics (OBD) ensures that control systems are functioning robustly and the emission levels are achieved and maintained to high mileages for the life of the vehicle.

Mixture formation and combustion dynamics are the primary contributors to the performance and emission characteristics of direct-injected spark ignition (SI) engines. This requires assessing the benefits and tradeoffs of the design and control factors that influence mixing and the subsequent combustion event. In this study, Taguchi's L18 orthogonal array design of experiment (DoE) methodology has been applied to assess contributions and tradeoffs of varied compression ratio, piston bowl design, intake port tumble design, injector spray pattern, injection timing, injection pressure, exhaust gas recirculation (EGR) rate, and intake valve closing timing in a single-cylinder boosted gasoline direct injection (GDI) SI engine. This multiparameter study has been carried out across three speed-load conditions representative of typical automotive application operating ranges.

In response to demands for higher fuel economy and stringent emission regulations, OEMs always strive hard to improve component/system efficiency and minimize losses. In the driveline system, improving the efficiency of an automotive rear-axle is critical because it is one of the major power-loss contributor. Optimum oil-fill inside an axle is one of the feasible solutions to minimize spin losses, while ensuring lubrication performance and heat-dissipation requirements. Thus, prior to conducting vehicle development tests, several dyno-level tests are conducted to study the thermal behavior of axle-oil (optimum level) under severe operating conditions. These test conditions represent the axle operation in hot weather conditions, steep grade, maximum tow capacity, etc. It is important to ensure that oil does not exceed its thermal limits (disintegration of oil leading to degradation).

Vibrations affect vehicle occupants and should be prevented early in design process. Powertrain (PT) wiggle is one of the well-known issues. It is the 3rd order lateral vibration, forced by half shaft inner LH/RH plunging tripod joints [1,2]. Lateral PT resonance (7-15Hz) occurs at certain vehicle speed during acceleration and may excite lateral, pitch and roll PT modes. Typically, PT wiggle occurs in speed range of 5-25kph. Vibration is noticeable on driver and passenger seats mostly in lateral direction. The inner half shaft joints are the major source of vibration. Unfortunately, existing MBD tools like Adams [3] are missing detailed tripod joint representation because of complex mechanical interactions inside the joint. At least three sliding contacts between tripod rollers and joint housing, lubricant inside the can and combination of rotation and plunging make the modeling too complicated.

Advances in diesel engine and catalyst technologies have enabled light passenger vehicles in meeting the most stringent Tier 3/LEV III emission levels and durability requirements. The advancements in diesel aftertreatment catalyst technology have made catalysts more susceptible to low levels of impurities, typically referred to as poisons. Published studies over the last two decades, have shown a significant impact on the performance of catalysts, to the presence of sulfur and other inorganics in fuels and oils. The design of an aftertreatment system (ATS) typically sets limits for lubricant and fuel quality, specific to the geographical region and availability of certain level of regulated fuels. In this study, we investigate a real-world aged diesel vehicle which exhibited deterioration in tailpipe emissions, beyond levels targeted during engineering development.

A new method is demonstrated for rating the “severity” of a hypoid gear set duty cycle (revolutions at torque) using the intercept of T-N curve to support gearset selection and sizing decision across vehicle programs. Historically, it has been customary to compute a cumulative damage (using Miner's Rule) for a rotating component duty cycle given a T-N curve slope and intercept for the component and failure mode of interest. The slope and intercept of a T-N curve is often proprietary to the axle manufacturer and are not published. Therefore, for upfront sizing and selection purposes representative T-N properties are used to assess relative component duty cycle severity via cumulative damage (non-dimensional quantity). A similar duty cycle severity rating can also be achieved by computing the intercept of the T-N curve instead of cumulative damage, which is the focus of this study.

Artificial intelligence (AI) is dramatically changing multiple industries. AI’s potential to transform Computer-Aided Engineering (CAE) cannot be overlooked. Conventionally, Finite Element Analysis (FEA) is the simulation of any given physical phenomenon to obtain an approximate solution to a group of problems governed by Partial Differential Equations (PDE). Implementation of AI methods in this area combines human intelligence with numerical solutions to make them more efficient. This paper attempts to develop a Deep Neural Network (DNN) model to solve an elasto-plastic impact problem of a symmetric short crush tube made of three materials impacted by a moving wall. A structured learning database was established to train and validate the model using finite element simulations. Tube size, gauge and elasto-plastic material properties were used as input attributes or features. The maximum axial displacement of the tube is the target label to predict.

Lithium-ion battery (LIB) terminal voltage models are investigated using two modelling approaches. The first model is a third-order Thevenin equivalent circuit model (ECM), which consists of an open-circuit voltage in series with a nonlinear resistance and three parallel RC pairs. The parameters of the ECM are obtained by fitting the model to hybrid pulse power characterization (HPPC) test data. The parametrization of the ECM is performed through quadratic-based programming. The second is a novel modelling approach based on long short-term memory (LSTM) recurrent neural networks to estimate the battery terminal voltage. The LSTM is trained on multiple vehicle drive cycles at six different temperatures, including −20°C, without the necessity of battery characterization tests. The performance of both models is evaluated with four automotive drive cycles at each temperature. The results show that both models achieve acceptable performance at all temperatures.

Simplified vehicle dynamics models used to study the driveline durability are typically limited to the longitudinal dynamics and do not account for vertical and pitch dynamics. The influence of suspension on the vehicle ride and handling characteristics is studied extensively in the literature but its impact on the driveline torques is often not considered. In this paper, an effort is made to investigate the influence of suspension compliance on the driveline torque using a planar (longitudinal, pitch and vertical) vehicle dynamics model. An AWD vehicle is studied to understand its impact on the torque levels of both axles (primary and secondary). Subsequently the planar dynamics is explored in the context of anti-squat/anti-dive suspension. The primary focus of the paper is to predict the driveline torque.