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In recent years, the urgent need to fully exploit the fuel economy potential of the Electrified Vehicles (xEVs) through the optimal design of their Energy Management System (EMS) have led to an increasing interest in Machine Learning (ML) techniques. Among them, Reinforcement Learning (RL) seems to be one of the most promising approaches thanks to its peculiar structure, in which an agent is able to learn the optimal control strategy through the feedback received by a direct interaction with the environment. Therefore, in this study, a new Soft Actor-Critic agent (SAC), which exploits a stochastic policy, was implemented on a digital twin of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The SAC agent was trained to enhance the fuel economy of the PHEV while guaranteeing its battery charge sustainability.

Nowadays, the push for more ecological low-carbon propulsion systems is high in all mobility sectors, including the recreational or light-commercial boating, where propulsion is usually provided by internal combustion engines derived from road applications. In this work, the effects of replacing conventional fossil-derived B7 diesel with Hydrotreated Vegetable Oil (HVO) were experimentally investigated in a modern Medium-Duty Engine, using the advanced biofuel as drop-in and testing according to the ISO 8178 marine standard. The compounded results showed significant benefits in terms of NOx, Soot, mass fuel consumption and WTW CO2 thanks to the inner properties of the aromatic-free, hydrogen-rich renewable fuel, with no impact on the engine power and minimal deterioration of the volumetric fuel economy.

Reducing consumption of fossil fuels and resulting emissions remains a goal of the worlds’ population. Perhaps as an aid to encourage more fuel-efficient driving style, many modern motor vehicles are equipped with digital displays of average and instantaneous display of fuel efficiency in miles per gallon (mileage) or liters per 100 kilometers. The display may be interesting to drivers concerned with fuel efficiency, but may not yield desired or best results. What is missing is impact on time of travel; what is the fuel- and time-efficient way to accelerate, what steady speed to travel, and how to decelerate? The author has defined a new fuel efficiency measure described as Dynamic Fuel Cost, in units of currency such as dollars, per travel time in hours, and a microprocessor to compute and display same. Using this display, vehicle operators can choose a maximum steady highway speed, accelerate and decelerate in ways that are fuel-, time- and cost-effective and minimize emissions.

The paper presents a robust adaptive control technique for precise regulation of a port fuel injection + direct injection (PFI+DI) system, a dual fuel injection configuration adopted in modern gasoline engines to boost performance, fuel efficiency, and emission reduction. Addressing parametric uncertainties on the actuators, inherent in complex fuel injection systems, the proposed approach utilizes an indirect model reference adaptive control scheme. To accommodate the increased control complexity in PFI+DI and the presence of additional uncertainties, a nonlinear plant model is employed, incorporating dynamics of the exhaust burned gas fraction. The primary objective is to optimize engine performance while minimizing fuel consumption and emissions in the presence of uncertainties. Stability and tracking performance of the adaptive controller are evaluated to ensure safe and reliable system operation under various conditions.

Over the past decade, significant progress has been made in developing algorithms and improving hardware for automated driving. However, conducting research and deploying advanced algorithms on automated vehicles for testing and validation remains costly, especially for academia. This paper presents the efforts of our research team to integrate the newest version of the open-source Autoware software with the commercially available DataSpeed Drive-by-Wire (DBW) system, resulting in the creation of a versatile and robust automated vehicle research platform. Autoware, an open-source software stack based on the 2nd generation Robot Operating System (ROS2), has gained prominence in the automated vehicle research community for its comprehensive suite of perception, planning, and control modules. The DataSpeed DBW system directly communicates with the vehicle's CAN bus and provides precise vehicle control capabilities.

To address the issue of PID control for automotive vibration, this paper supplements and develops the evaluation of automotive vibration characteristics, and proposes a vibration response quantity for evaluating the energy dissipation characteristics of automotive vibration. A two-degree-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined. This paper uses the Adaptive Differential Evolution (ADE) algorithm to tune the PID control parameters and introduces an adaptive mutation factor to improve the algorithm's adaptability. Several commonly used adaptive mutation factors are summarized in this paper, and their effects on algorithm improvement are compared.

Net-Zero emission ambitions coupled with availability of oxygenated fuels like ethanol encouraged the Government towards commercial implementation of fuels like E20. In this background, a study was taken up to assess the impact of alcohol blended fuels on performance and emission characteristics of a BS-VI complaint motorbike. A single cylinder, 113-cc spark ignition, ECU based electronic fuel injection motorbike was used for conducting tests. Pure gasoline (E0), 10% ethanol-gasoline (E10), 20% ethanol-gasoline (E20) and 15% methanol-gasoline (M15) blends meeting respective IS standards were used as test fuels. The oxygen content of E10, E20 and M15 fuels were 3.7%, 7.4% and 8.35% by weight respectively. Experiments were conducted following worldwide motorcycle test cycle (WMTC) as per AIS 137 standard and wide-open-throttle (WOT) test cycle, using chassis dynamometer.

The present study aims to determine the comparative performance evaluation in terms of fuel economy (kmpl) and wide open throttle (WOT) power derived from set of different blends of high octane gasoline fuel(s) i.e., Neat Gasoline (E0), E10 & E20 (With different dosages of additives) in high compression ratio (HCR) motorcycle on chassis dynamometer facility. With the Government of India focus on use of alcohol as co-blend of gasoline with the endeavour to save foreign exchange and also to reduce greenhouse gases (GHG) emissions. The commercially available blended fuels, E10 & E20, have high research octane number (RON, 92-100) and as per the available literature high RON fuel have the better anti-knocking tendencies thereby lead to higher fuel economy. There are various routes to formulate high octane fuel (refining technologies, additive approach & ethanol blending route) in the range of 92-100 octane number which are currently commercialized in Indian market.

Recently, as part of the effort to enhance fuel efficiency and reduce costs for eco-friendly vehicles, the R-gearless system has been implemented in the TMED (P)HEV system. Due to the removal of the reverse gear, a distinct backward driving method needs to be developed, allowing the Electronic Motor (e-Motor) system to facilitate backward movement in the TMED (P)HEV system. However, the capability of backward driving with the e-Motor is limited because of partial failure in the high-voltage system of an R-gearless system. Thus, we demonstrate that it is possible to improve backward driving problems by applying a new fail-safe strategy. In the event of a high-voltage battery system failure, backward driving can be achieved using the e-Motor with constant voltage control by the Hybrid Starter Generator (HSG), as proposed in this study.

The Battery Performance and Cost Model (BatPaC), developed by Argonne National Laboratory, is a versatile tool designed for lithium-ion battery (LIB) pack engineering. It accommodates user-defined specifications, generating detailed bill-of-materials calculations and insights into cell dimensions and pack characteristics. Pre-loaded with default data sets, BatPaC aids in estimating production costs for battery packs produced at scale (5 to 50 GWh annually). Acknowledging inherent uncertainties in parameters, the tool remains accessible and valuable for designers and engineers. BatPaC plays a crucial role in National Highway Transportation Traffic Safety Administration (NHTSA) regulatory assessments, providing estimated battery pack manufacturing costs and weight metrics for electric vehicles. Integrated with Argonne's Autonomie simulations, BatPaC streamlines large-scale processes, replacing traditional models with lookup tables.

Recently, with the advancement of autonomous driving technology, the function of external lamps has been changed. Previously, the focus was on the visibility of drivers, but with the advancement of autonomous driving technology, the concept of autonomous driving systems has been developed. Accordingly, the trend of automotive lamp lighting systems has been developed in terms of design, e-HMI (exterior-human machine interface), It is developing in accordance with three major fields such as sensor connection. Therefore, this paper will cover the prior development of road content projection headlamps that enable e-HMI implementation to reflect these new trends. Since the technology is mass-produced and sold by several manufacturers, our company also needs to quickly develop and apply the technology in advance. Only four types of symbols are allowed in European law.

To ensure adequate visibility without excessive glare, vehicle headlights are designed to use a specific source of illumination. The optical designs of headlights gather the luminous flux produced by the light source to produce a useful beam pattern that meets the relevant requirements and standards for vehicle forward lighting. With the advent of solid state, light emitting diode sources for general illumination, an increasing number of LED replacement headlight bulb products has emerged over the past decade. In most cases, these LED replacement bulbs are not permitted for legal use on public roadways, but some countries have begun to permit specific LED replacement bulbs to be used legally on the road for specific makes, models and production years of certain vehicles. If they can be demonstrated to produce a beam pattern that meets the photometric requirements for a legal headlight, they are permitted to be used legally for on-road use.

The National Highway Traffic Safety Administration (NHTSA) plays a crucial role in guiding the formulation of Corporate Average Fuel Economy (CAFE) standards, and at the forefront of this regulatory process stands Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy (DOE) research institution, has developed Autonomie—an advanced and comprehensive full-vehicle simulation tool that has solidified its status as an industry standard for evaluating vehicle performance, energy consumption, and the effectiveness of various technologies. Under the purview of an Inter-Agency Agreement (IAA), the DOE Argonne Site Office (ASO) and Argonne have assumed the responsibility of conducting full-vehicle simulations to support NHTSA's CAFE rulemaking initiatives. This paper introduces an innovative approach that hinges on a large-scale simulation process, encompassing standard regulatory driving cycles tailored to various vehicle classes and spanning diverse timeframes.

Optimizing the specifications of the parts that make up the vehicle is essential to develop a high performance and quality vehicle with price competitiveness. Optimizing parts specifications for quality and affordability means optimizing various factors such as engineering design specifications and manufacturing processes of parts. This optimization process must be carried out in the early stages of development to maximize its effectiveness. Therefore, in this paper, we studied the methodology of building a database for parts of already developed vehicles and optimizing them on a data basis. A methodology for collecting, standardizing, and analyzing data was studied to define information necessary for specification optimization. In addition, AI technology was used to derive optimization specifications based on the 3D shape of the parts. Through this study, body parts specification optimization system using AI technology was developed.

The IncQuery AUTOSAR-UML Bridge is an innovative solution for Assisted Documentation Creation and Automated Handover, aiming at driving a paradigm shift in integrated digital engineering in the automotive domain. The AUTOSAR-UML Bridge is addressing a well-known gap in the engineering ecosystem of automotive design, where the co-design of AUTOSAR models and other model-based artifacts is often hampered by tedious workflows involving manual syncing of model contents between AUTOSAR and UML/SysML tools. The Bridge is aiming at streamlining the workflow by generating high-quality UML models from AUTOSAR projects, with built-in ISO26262 and ASPICE compliance. Automotive software architects and systems engineers spend a lot of time with creating ISO26262-compliant documentation, by creating UML models from AUTOSAR architecture designs, or establishing traceability between requirements captured in SysML and design artefacts that exist in both modeling languages.

The design of lightweight vehicle structures has become a common method for automotive manufacturers to increase fuel efficiency and decrease carbon emission of their products. By using aluminum instead of steel, manufacturers can reduce the weight of a vehicle while still maintaining the required strength and stiffness. Currently, Resistance Spot Welding (RSW) is used extensively to join steel body panels but presents challenges when applied to aluminum. When compared to steel, RSW of aluminum requires frequent electrode cleaning, higher energy usage, and more controlled welding parameters, which has driven up the cost of manufacturing. Due to the increased cost associated with RSW of aluminum, Refill Friction Stir Spot Welding (RFSSW) is being considered as an alternative to RSW for joining aluminum body panels. RFSSW consumes less energy, requires less maintenance, and produces more consistent welding in aluminum as compared to RSW.

Mechanical drawing plays an important role in managing, designing and implementing engineering projects, especially in the field of the automotive industry. The need for accuracy in element design and manufacturing is greater now than ever before in engineering industries. In order to increase accuracy, the part design and function must be clearly communicated between the design engineer and the manufacturing technicians, especially in automotive industry and feeder industries projects. Geometric Dimensions and Tolerances (GD&T) system of elements determines the quality, importance and price of the designed product. The standard used in the United States to define GD&T methodology is ASME Y14.5-2009 while the standard used in Europe is ISO 1101-2017. This article discussed the importance of using GD&T system including the types of geometrical features, limitations and accuracy, datum references frame and feature control frame to handle these symbols seamlessly.

The wear of the piston ring-cylinder liner system in gasoline engines is inevitable and significantly impacts fuel economy. Utilizing a custom-built linear reciprocating tribometer, this study assesses the wear resistance of newly developed engine cylinder coatings. The custom device offers a cost-effective means for tribological evaluation, optimizing coating process parameters with precise control over critical operational factors such as normal load and sliding frequency. Unlike conventional commercial tribometers, it ensures a more accurate simulation of the engine cylinder system. However, existing research lacks a comprehensive comparative analysis and procedure to establish precision limits for such modified devices. This study evaluates the custom tribometer's repeatability compared to a commercial wear-testing instrument, confirming its potential as a valuable tool for advanced wear testing on engine cylinder samples.

As part of Nissan’s strategy of electrification and the shift to smart technologies, our powertrain department has two main pillars: zero emissions and ICE Evolution. As a core unit of ICE Evolution, we have developed a brand new 3.5L V6 Twin turbocharged gasoline engine for Nissan’s next generation full-size flagship SUV to deliver luxury and toughness at the highest level. This brand-new engine will be applied to vehicles in all corners of the world and must have strong performance in every corner. More specifically, it has to meet the latest emissions and fuel efficiency regulations, have strong power performance beyond expectation, and provide reliable drivability on rough roads and deserts. To achieve these requirements, the new engine is incorporating many cutting-edge technologies.

It is widely recognized that internal combustion engines (ICE) are needed for transport worldwide for years to come, however, demands on ICE fuel efficiency, emissions, cost, and performance are extremely challenging. Gasoline compression ignition (GCI) is one approach to achieve demanding efficiency and emissions targets. At Aramco Research Center-Detroit, an advanced, multi-cylinder GCI engine was designed and built using the latest combustion system, engine controls, and lean aftertreatment. The combustion system uses Aramco’s PPCI-diffusion process for ultra-low NOx and smoke. A P2 48V mild hybrid system was integrated on the engine for braking energy recovery and improved cold starts. For robust low-load operation, a 2-step valvetrain system was used for exhaust rebreathing. Test data showed that part-load fuel consumption was reduced 7 to 10 percent relative to a competitive 2.0L European diesel engine.