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digital standards systems an integrated approach to engineering standards usage

From SAE International, SAE MobilityRxiv is a free online preprint server and sharing service for English-language preprints in the mobility/transportation field

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Considering the current trend towards the electrification of commercial vehicles, the development of Beam eAxle solutions has become necessary. The utilization of an electric drive unit in heavy-duty solid axle-based commercial vehicles presents unique and demanding challenges. These include the necessity for elevated peak and continuous torque while meeting packaging constraints, structural integrity requirements, and extended service life. One such solution was developed by BorgWarner to address these challenges. This paper offers a comprehensive overview of the design and development process undertaken for this Dual Motor Beam eAxle system. This includes the initial comparison of various eAxle solutions, the specifications of components selected for this design, and the initial results from dyno and vehicle development.

This SAE Standard applies to all forestry machines exposed to the hazard of objects penetrating the front of the operator station (other than the roof). This would include:

Efficient transportation for carrying heavy loads is a common challenge across various applications, from supermarkets to industrial purposes. Conventional trolleys often fall short when loaded with heavy cargo, resulting in increased exertion and diminished productivity. Moreover, these challenges can adversely affect posture and lumbar spine health, especially for elder people and persons with cervical problems. There is a need for more user-friendly, ergonomic, and space-efficient solutions. This project addresses these challenges through an innovative design that encompasses various aspects of trolley functionality, including the study of comfort, wheel selection, and material considerations, drawing from ergonomic research. Multiple methods are employed to optimize the trolley’s dimensions to improve its overall performance. The trolley’s design features a collapsible basket for the transport of smaller-sized items and a base frame for larger goods and luggage.

Commercial vehicle are exposed to harsh environment conditions like dust, mud, wind, rain, extreme sun and winter throughout. Apart from white goods and other conventional loading these vehicles also used in applications which involve Handling of Dirty Loads, Construction Raw materials, Mining Industry etc. which leads to fast deterioration of Interiors. Also, in most cases drivers are not the owners. Hence due to high cost of Cleaning at dealerships and low Product maintenance awareness amongst Commercial Vehicle Users, on Road Washing & Cleaning by riverside is common practice which leads to early deterioration of Interior trims. This paper deals with the retention of newness of soft trim parts such as headliner, wall trims and carpets. Causes of product deterioration and attributes which influence newness like product appeal, NVH, perceived quality, environmental impact, geometry retention over time etc. have been discussed in detail.

Automotive crash data analysis and reconstruction is vital for ensuring automotive safety. The objective of vehicle crash reconstruction is to determine the vehicle's motion before, during, and after the crash, as well as the impact on occupants in terms of injuries. Simulation approaches, such as PC CrashTM, have been developed to understand pre-crash and post-crash vehicle motion, rather than the crash phase behavior. Over the past few decades, crash phase simulations have utilized vehicle finite element models. While multibody simulation tools are suitable for crash simulations, they often require detailed crash test data to accurately capture vehicle behavior, which is not always readily available. This paper proposes a solution to this limitation by incorporating crash test data from databases, such as NHTSA, Global NCAP, consumer rating reports, and videos, along with a multibody-based approach, to conduct crash phase simulations.

This SAE Recommended Practice pertains to liquid level determination for any fluid compartment of off-road work machines as defined in SAE J1116 and ISO 6165.

An optically accessible hydrogen-fueled, heavy-duty engine was used to investigate the impact of mixture formation on the early flame kernel propagation and the resulting combustion cyclic variability. Direct injection from a centrally mounted medium-pressure outward-opening hollow-cone injector created a fuel- air mixture with a global equivalence ratio of 0.33. The engine was operated at 1200 RPM with dry air at an intake pressure and temperature of 1.0 bar and 305 K, respectively. The charge was ignited at three different locations using focused-laser ignition to allow for undisturbed flame evolution, and the fuel injection timing and injection pressure were varied to influence the mixture inhomogeneity.

Abstract Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm.

This SAE Recommended Practice pertains to blast cleaning and shot peening and provides for standard cast shot and grit size numbers. For shot, this number corresponds with the opening of the nominal test sieve, in ten thousandths of inches1, preceded by an S. For grit, this number corresponds with the sieve designation of the nominal test sieve with the prefix G added. These sieves are in accordance with ASTM E11. The accompanying shot and grit classifications and size designations were formulated by representatives of shot and grit suppliers, equipment manufacturers, and automotive users.

The new generation vehicles these days are managed by networked controllers. A large portion of the networks is planned with more security which has recently roused researchers to exhibit various attacks against the system. This paper talks about the liabilities of the Controller Area Network (CAN) inside In-vehicle communication protocol and a few potentials that could take due advantage of it. Moreover, this paper presents a few security measures proposed in the present examination status to defeat the attacks. In any case, the fundamental objective of this paper is to feature a comprehensive methodology known as Intrusion Detection System (IDS), which has been a significant device in getting network data in systems over many years. To the best of our insight, there is no recorded writing on a through outline of IDS execution explicitly in the CAN transport network system.

The paper presents an approach used to generate a customer-oriented drive cycle using the MATLAB-based drive cycle generation tool for EVs developed by Isuzu Technical Center of America. The drive cycle generation tool extracts important features from the customer vehicle data and compares it with the globally used pre-existing candidate cycle to generate a representative drive cycle. The tool can read multiple file formats of preprocessed data or raw data from the vehicle telematics systems. This data is then processed using a unique and efficient algorithm developed by the Isuzu engineers, calculating seven important vehicle dynamic parameters. These selected parameters are compared with the pre-existing candidate cycles used across the globe in multiple iterations to generate a custom representative drive cycle that best fits real-world customer driving behavior.

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles.

Testing was conducted in daytime and nighttime conditions at four speeds – 35, 50, 55, and 60 miles per hour (mph) – to evaluate the performance of the audible and visual forward collision warning (FCW) system in a WABCO OnGuardACTIVE collision mitigation system (CMS) while approaching a foam stationary vehicle target (SVT). Testing measured the time to collision (TTC) values utilizing a VBOX data acquisition system as well as an “analog” system utilizing synced cameras and a reference line painted on the test track. WABCO Toolbox was utilized to download OnGuard data from the Freightliner after each test; this data was then compared to the data acquired by the VBOX data acquisition system. The results of the testing provide valuable information to collision investigators on the performance of the WABCO OnGuardACTIVE Collision Mitigation System on stationary vehicles.

This study aimed to construct driver models for overtaking behavior using long short-term memory (LSTM). During the overtaking maneuver, an ego vehicle changes lanes to the overtaking lane while paying attention to both the preceding vehicle in the travel lane and the following vehicle in the overtaking lane and returns to the travel lane after overtaking the preceding vehicle in the travel lane. This scenario was segregated into four phases in this study: Car-Following, Lane-Change-1, Overtaking, and Lane-Change-2. In the Car-Following phase, the ego vehicle follows the preceding vehicle in the travel lane. Meanwhile, in the Lane-Change-1 phase, the ego vehicle changes from the travel lane to the overtaking lane. Overtaking is the phase in which the ego vehicle in the overtaking lane overtakes the preceding vehicle in the travel lane.

The Daimler Detroit AssuranceⓇ 4.0 collision mitigation system is able to assist a driver in various aspects of safely operating their vehicle. One capability is the Active Brake Assist (ABA), which uses the Video Radar Decision Unit (VRDU) to communicate with the front bumper-mounted radar to provide information about potential hazards to the driver. The VRDU may warn the driver of potential hazards and apply partial or full braking, depending on the data being gathered and analyzed. The VRDU also records event data when an ABA event occurs. This data may be extracted from the VRDU using Detroit DiagnosticLink software. This paper presents an overview of the VRDU functionality and examines aspects of VRDU data such as the range and resolution of data elements, the synchronicity or timing of the recorded data, and application of the data for use in the analysis of crashes.

The transport systems, as large energy consumers and important contributors to greenhouse (GHG), criteria pollutant and noise emissions worldwide, have been permanently challenged by the continuously increased stringent environmental standards to improve its energy efficiency, as well as to reduce its environmental footprint. Transit systems, which operates in urban areas, are particularly subjected to stringent environmental and efficiency regulations, given their proximity to large population concentrations, alongside the urban transport corridors. This is particularly true for bus transit systems, mostly powered by internal combustion engines (ICE), generally fueled with fossil diesel fuel.

The changing mobility landscape of India reveals that the erstwhile transport modes of the 20th century i.e., railways and road buses are making way for airlines, personal vehicles, shared mobility, metro rails. Rapid technological changes, stricter regulations, new transport cultures autonomous, connected, electric and shared (ACES), state-of-the-art and environmental concerns are shaping up the eco-system for automobiles. Despite these challenges roadways and automobiles will continue to be most prominent solution in India for future. But for that, the automobile sector should be agile, innovative, and adaptable to changing eco-system, vigilant to thwart threat of alternate mobility solutions and must provide sustainable solutions for the future. The purpose of this paper to evaluate various mobility solutions, ascertain prominence of upcoming automobile solutions and their sustainability for future in India.