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Ducted Fuel Injection (DFI) engines have emerged as a promising technology in the pursuit of a clean and efficient combustion process. This article aims at elucidating the effect of piston geometry on the engine performance and emissions of a metal DFI engine. Three different types of pistons were investigated and the main piston design features including the piston bowl diameter, piston bowl slope angle, duct angle and the injection nozzle position were examined. To achieve the target, computational fluid dynamics (CFD) simulations were conducted coupled to a reduced chemical kinetics mechanism. Extensive validations were performed against the measured data from a conventional diesel engine. To calibrate the soot model, genetic algorithm and machine learning methods were utilized. The simulation results highlight the pivotal role played by piston bowl diameter and fuel injection angle in controlling soot emissions of a DFI engine.

Android applications have historically faced vulnerabilities to man-in-the-middle attacks due to insecure custom SSL/TLS certificate validation implementations. In response, Google introduced the Network Security Configuration (NSC) as a configuration-based solution to improve the security of certificate validation practices. NSC was initially developed to enhance the security of Android applications by providing developers with a framework to customize network security settings. However, recent studies have shown that it is often not being leveraged appropriately to enhance security. Motivated by the surge in vehicular connectivity and the corresponding impact on user security and data privacy, our research pivots to the domain of mobile applications for vehicles. As vehicles increasingly become repositories of personal data and integral nodes in the Internet of Things (IoT) ecosystem, ensuring their security moves beyond traditional issues to one of public safety and trust.

The US EPA and the California Air Resources Board (CARB) require electric vehicle range to be determined according to the Society of Automotive Engineers (SAE) surface vehicle recommended practice J1634 - Battery Electric Vehicle Energy Consumption and Range Test Procedure. In the 2021 revision of the SAE J1634, the Short Multi-Cycle Test (SMCT) was introduced. The proposed testing protocol eases the chassis dynamometer test burden by performing a 2.1-hour drive cycle on the dynamometer, followed by discharging the remaining battery energy into a battery cycler to determine the Useable Battery Energy (UBE). Opting for a cycler-based discharge is financially advantageous due to the extended operating time required to fully deplete a 70-100kWh battery commonly found in Battery Electric Vehicles (BEVs).

The paper solves the problem of increasing the accuracy of measuring torque and use of an electronic torque meter as a feedback sensor of synchronous and induction machines of an electric traction drive. Relevance of the problem and methods for its solving by indirect means using simulation models of electric machines are considered. The theoretical development of the meter is based on the energy model of electric machines in the form of differential equations for active and reactive power balance. This eliminates the direct influence of instability and nonlinearity of inductive elements and takes into account electrical, magnetic, mechanical, and harmonic losses using the simplest algorithms. Key variables of the model in the form of total active power and angular velocity are measured directly, and the high nominal efficiency of traction machines (95%) provides a wide tolerance range for calculating total losses.

Accurate estimation of battery state of health (SOH) has become indispensable in ensuring the predictive maintenance and safety of electric vehicles (EVs). While supervised machine learning excels in laboratory settings with adequate SOH labels, field-based SOH data collection for supervised learning is hindered by EVs' complex conditions and prohibitive data collection costs. To overcome this challenge, a battery SOH estimation method based on semi-supervised regression is proposed and validated using field data in this paper. Initially, the Ampere integral formula is employed to calculate SOH labels from charging data, and the error of labeled SOH is reduced by the open-circuit voltage correction strategy. The calculation error of the SOH label is confirmed to be less than 1.2%, as validated by the full-charge test of the battery packs.

In the field of automobile development, sufficient structure strength is the most basic objective to be accomplished. Typically, method of strength analysis could be divided into static strength and dynamic strength. Analysis of static strength constitutes the major part of the development, but the supplement of dynamic strength is also dispensable to assure structural integrity. This paper presents a methodology about analyzing the impact strength of body structure based on a Multi-body Dynamics (MBD) and Finite Element Analysis (FEA) combined method. Firstly, the full vehicle MBD model consists of Curved Regular Grid (CRG) road model, Flexible Ring Tire (FTire) model and dynamic deflection-force bump stop model was built in Adams/Car. Next, Damage Initiation and Evolution Model (DIEM) failure criteria was adopted to describe material failure behavior.

A special spot weld element (SWE) is presented for simplified representation of spot joints in complex structures for structural durability evaluation using the mesh-insensitive structural stress method. The SWE is formulated using rigorous linear four-node Mindlin shell elements with consideration of weld region kinematic constraints and force/moments equilibrium conditions. The SWEs are capable of capturing all major deformation modes around weld region such that rather coarse finite element mesh can be used in durability modeling of complex vehicle structures without losing any accuracy. With the SWEs, all relevant traction structural stress components around a spot weld nugget can be fully captured in a mesh-insensitive manner for evaluation of multiaxial fatigue failure.

Level 2 (L2) partial driving automation systems are rapidly emerging in the marketplace. L2 systems provide sustained automatic longitudinal and lateral vehicle motion control, reducing the need for drivers to continuously brake, accelerate and steer. Drivers, however, remain critically responsible for safely detecting and responding to objects and events. This paper summarizes variations of L2 systems (hands-on and/or hands-free) and considers human drivers’ roles when using L2 systems and for designing Human-Machine Interfaces (HMIs), including Driver Monitoring Systems (DMSs). In addition, approaches for examining potential unintended consequences of L2 usage and evaluating L2 HMIs, including field safety effect examination, are reviewed. The aim of this paper is to guide L2 system HMI development and L2 system evaluations, especially in the field, to support safe L2 deployment, promote L2 system improvements, and ensure well-informed L2 policy decision-making.

The transition from combustion engines to electric propulsion is accelerating in every coordinate of the globe. The engineers had strived hard to augment the engine performance for more than eight decades, and a similar challenge had emerged again for electric vehicles. To analyze the performance of the engine, the vector engine operating point (EOP) is defined, which is common industry practice, and the performance vector electric vehicle motor operating point (EVMOP) is not explored in the existing literature. In an analogous sense, electric vehicles are embedded with three primary components, e.g., Battery, Inverter, Motor, and in this article, the EVMOP is defined using the parameters [motor torque, motor speed, motor current]. As a second aspect of this research, deep learning models are developed to predict the EVMOP by mapping the parameters representing the dynamic state of the system in real-time.

Often, when assessing the distraction or ease of use of an in-vehicle task (such as entering a destination using the street address method), the first question is “How long does the task take on average?” Engineers routinely resolve this question using computational models. For in-vehicle tasks, “how long” is estimated by summing times for the included task elements (e.g., decide what to do, press a button) from SAE Recommended Practice J2365 or now using new static (while parked) data presented here. Times for the occlusion conditions in J2365 and the NHTSA Distraction Guidelines can be determined using static data and Pettitt’s Method or Purucker’s Method. These first approximations are reasonable and can be determined quickly. The next question usually is “How likely is it that the task will exceed some limit?”

Methanol is one of the most promising fuels for the decarbonization of the off-road and transportation sectors. Although methanol is typically seen as an alternative fuel for spark ignition engines, mixing-controlled compression ignition (MCCI) combustion is typically preferred in most off-road and medium-and heavy-duty applications due to its high reliability, durability and high-efficiency. In this paper, the potential of using ignition enhancers to enable methanol MCCI combustion was investigated. Methanol was blended with 2-ethylhexyl nitrate (EHN) and experiments were performed in a single-cylinder production-like diesel research engine, which has a displacement volume of 0.83 L and compression ratio of 16:1. The effect of EHN has been evaluated with three different levels (3%vol, 5%vol, and 7%vol) under low- and part-load conditions. The injection timing has been swept to find the stable injection window for each EHN level and load.

Ethanol blending is one method that can be used to reduce knock in spark ignition engines by decreasing the autoignition reactivity of the fuel and modifying its laminar flame speed. In this paper, the effects of ethanol blending on knock propensity and flame speed of petroleum and low-carbon gasoline fuels is analyzed. To do so, surrogate fuels were formulated for methanol-to-gasoline (MTG) and ethanol-to-gasoline (ETG) based on the fuels’ composition, octane number, and select physical properties; and 0-D and 1-D chemical kinetics simulations were performed to investigate reactivity and laminar flame speed, respectively. Results of MTG and ETG were compared against those of PACE-20, a well-characterized surrogate for regular E10 gasoline. Similarly to PACE-20, blending MTG and ETG with ethanol increases the fuel’s research octane number (RON) and sensitivity.

Low-temperature gasoline combustion (LTGC) engines can provide high efficiencies with very low NOx and soot emissions, but rapid control of the combustion timing remains a challenge. Partial Fuel Stratification (PFS) was demonstrated to be an effective approach to control combustion in LTGC engines. PFS is produced by a double-direct injection (DI) strategy with most of the fuel injected early in the cycle and the remainder of the fuel supplied by a second injection at a variable time during the compression stroke to vary the amount of stratification. Adjusting the stratification changes the combustion phasing, and this can be done on cycle-to-cycle basis by adjusting the injection timing. In this paper, the ability of PFS to control the combustion during wide engine load sweeps is assessed for regular gasoline and gasoline doped with 2-ethylhexyl nitrate (EHN). For PFS, the load control range is limited by combustion instability and poor combustion efficiency at low loads.

Methanol emerges as a compelling renewable fuel for decarbonizing engine applications due to a mature industry with high production capacity, existing distribution infrastructure, low carbon intensity and favorable cost. Methanol’s high flame speed and high autoignition resistance render it particularly well-suited for spark-ignition (SI) engines. Previous research showed a distinct phenomenon, known deflagration-based knock in methanol combustion, whereby knocking combustion was observed albeit without end-gas autoignition. This work studies the implications of deflagration-based knock on noise emissions by investigating the knock intensity and combustion noise at knock-limited operation of methanol in a single-cylinder direct-injection SI engine operated at both stoichiometric and lean (λ = 2.0) conditions. Results are compared against observations from a premium-grade gasoline.

With the development of vehicles equipped with automated driving systems, the need for systematic evaluation of AV performance has grown increasingly imperative. According to ISO 34502, one of the safety test objectives is to learn the minimum performance levels required for diverse scenarios. To address this need, this paper combines two essential methodologies - scenario-based testing procedures and scoring systems - to systematically evaluate the behavioral competence of AVs. In this study, we conduct comprehensive testing across diverse scenarios within a simulator environment following Mcity AV Driver Licensing Test procedure. These scenarios span several common real-world driving situations, including BV Cut-in, BV Lane Departure into VUT Path from Opposite Direction, BV Left Turn Across VUT Path, and BV Right Turn into VUT Path scenarios.

Ensuring spray robustness of gasoline direct injection (GDI) is essential to comply with stringent future emission regulations for hybrid and internal combustion engine vehicles. This study presents experimental and numerical assessments of spray for lateral-mounted GDI sprays with two different plume arrangements to analyze spray collapse characteristics, which can significantly deteriorate the atomization performance of fuel sprays. Novel spray characterization methods are applied to analyze complex spray collapse behaviors using diffusive back-illuminated extinction imaging (DBIEI) and 3D computed tomographic (CT) image reconstruction. A series of computational fluid dynamics (CFD) simulations are performed to analyze the detailed spray characteristics besides experimental characterization. Spatio-temporal plume dynamics of conventional triangle-pattern spray are evaluated and compared to a plume pattern with an inversed T pattern that has more open space between plumes.