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Autonomous Driving is being utilized in various settings, including indoor areas such as industrial halls. Additionally, LIDAR sensors are currently popular due to their superior spatial resolution and accuracy compared to RADAR, as well as their robustness to varying lighting conditions compared to cameras. They enable precise and real-time perception of the surrounding environment. Several datasets for on-road scenarios such as KITTI or Waymo are publicly available. However, there is a notable lack of open-source datasets specifically designed for industrial hall scenarios, particularly for 3D LIDAR data. Furthermore, for industrial areas where vehicle platforms with omnidirectional drive are often used, 360° FOV LIDAR sensors are necessary to monitor all critical objects. Although high-resolution sensors would be optimal, mechanical LIDAR sensors with 360° FOV exhibit a significant price increase with increasing resolution.

This research effort is to optimize the conditions to minimize carbon monoxide (CO) gas emissions utilizing activated carbon derived from rice husks, an abundant agricultural waste. In the automobile industry, addressing vehicular emissions is crucial due to environmental ramifications and stringent regulatory mandates. This study presents an innovative and potentially cost-effective solution to capture CO emissions, mainly from motorcycles. The eco-friendly nature of using rice husks and the detailed findings on optimal conditions (20 m/s gas flow rate, 0.47 M citric acid concentration, and 30 g mass of activated carbon) make this research invaluable. These conditions achieved a commendable CO adsorption rate of 54.96 ppm over 1250 s. Essentially, the insights from this research could spearhead the development of sustainable automobile exhaust systems.

A road test on semi-trailers is carried out, and accelerations of some characteristic points on the braking system,axles,and truck body is measured,also brake pressure and noise around the support frame is acquired.The measured data was analyzed to determine the causes of the brake noise, and the mechanism of the noise of the drum brake of semi-trailers during low-speed braking was investigated. The following conclusions are obtained: (1) Brake noise of the drum brake of the semi-trailer at low-frequency is generated from vibrations of the brake shoes, axle, and body, and the vibration frequency is close to 2nd natural frequency of the axle. (2) Brake noise is generated from stick-slip motion between the brake shoes and the brake drum, where the relative motion between the brake drum and the brake shoes is changed alternately with sliding and sticking, resulting in sudden changes in acceleration and shock vibration.

The Bendix Wingman Fusion – a radar and camera collision mitigation system (CMS) available on commercial vehicles – was evaluated in two separate test series to determine its performance in simulated rear collision scenarios. In the first series of tests, evaluations were conducted in daytime, nighttime, and rainy conditions between 15 to 58 miles per hour (mph) to evaluate the performance of the audible and visual forward collision warning (FCW) system in a first-generation Bendix Wingman Fusion CMS while approaching a stationary live vehicle target (SLVT) in a 2017 Kenworth T680. A second test series was conducted with a 2017 Kenworth T680 traveling at 50 mph in daytime conditions approaching a decelerating vehicle to evaluate the Bendix Wingman Fusion CMS on the truck. Both test series sought to determine the maximum distance the system would warn prior to the test driver swerving around the SLVT or moving vehicle target.

This paper details the advancements and outcomes of the NEXTCAR (Next-Generation Energy Technologies for Connected and Automated on-Road Vehicles) program, an initiative led by the Advanced Research Projects Agency-Energy (ARPA-E). The program focusses on harnessing the full potential of Connected and Automated Vehicle (CAV) technologies to develop advanced vehicle dynamic and powertrain control technologies (VD&PT). These technologies have shown the capability to reduce energy consumption by 20% in conventional and hybrid electric cars and trucks at automation levels L1-L3 and by 30% L4 fully autonomous vehicles. Such reductions could lead to significant energy savings across the entire U.S. vehicle fleet.

A variety of structures resonate when they are excited by external forces at, or near, their natural frequencies. This can lead to high deformation which may cause damage to the integrity of the structure. There have been many applications of external devices to dampen the effects of this excitation, such as tuned mass dampers or both semi-active and active dampers, which have been implemented in buildings, bridges, and other large structures. One of the active cancellation methods uses centrifugal forces generated by the rotation of an unbalanced mass. These forces help to counter the external excitation force coming into the structure. This research focuses on active force cancellation using centrifugal forces (CFG) due to mass imbalance and provides a virtual solution to simulate and predict the forces required to cancel external excitation to an automotive structure. This research tries to address the challenges to miniaturize the CFG model for a body-on-frame truck.

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.

The context for real-world emissions compliance has widened with the anticipated implementation of EU7 emissions regulations. The more stringent emissions limits and deeper real-world driving test fields of EU7 make compliance more challenging. While EU6 emissions legislation provided clear boundaries by which vehicle and powertrain Original Equipment Manufacturers (OEMs) could develop and calibrate against, EU7 creates additional challenges. To ensure that emissions produced during any real-world driving comply with legal limits, physical testing conducted in-house and in-field to evaluate emissions compliance of a vehicle and powertrain will not be sufficient. Given this, OEMs will likely need to incorporate some type of virtual engineering to supplement physical testing.

Reducing criteria pollutants while reducing greenhouse gases is an active area of research for commercial on-road vehicles as well as for off-road machines. The heavy duty on-road sector has moved to reducing NOx by 82.5% compared to 2010 regulations while increasing the engine useful life from 435,000 to 650,000 miles by 2027 in the United States (US). An additional certification cycle, the Low Load Cycle (LLC), has been added focusing on part load operation having tight NOx emissions levels. In addition to NOx, the total CO2 emissions from the vehicle will also be reduced for various model years. The off-road market is following with a 90% NOx reduction target compared to Tier 4 Final for 130-560 kW engines along with greenhouse gas targets that are still being established. The off-road market will also need to certify with a Low Load Application Cycle (LLAC), a version of which was proposed for evaluation in 2021.

Dust testing of vehicles on unpaved roads is crucial in the development process for automotive manufacturers. These tests aim to ensure the functionality of locking systems in dusty conditions, minimize dust concentration inside the vehicle, and enhance customer comfort by preventing dust accumulation on the car body. Additionally, deposition on safety-critical parts, such as windshields and sensors, can pose threats to driver vision and autonomous driving capabilities. Currently, dust tests are primarily conducted experimentally at proving grounds. In order to gain early insights and reduce the need for costly physical tests, numerical simulations are becoming a promising alternative. Although simulations of vehicle contamination by dry dust have been studied in the past, they have often lacked detailed models for tire dust resuspension. In addition, few publications address the specifics of dust deposition on vehicles, especially in areas such as door gaps and locks.

The basic needs of people are met by the building, fabric, and farming sectors. In addition, the automobile industry significantly contributes to human mobility and is essential to India’s economic expansion. There are numerous research strategies available to improve the bus body building industries. Several investigative approaches for enhancing bus body building industries are available. However, several of these studies merely look at it from the perspective of shop floor activity. Accordingly, when it comes to the execution of process design approaches, there is little practical evidence for accepting Gemba kaizen’s attitude. Hence, the purpose of this article is to present a continuous improvement redesign framework tailored to a specific bus body building industrial sector. The proposed model is structured after a critical examination of Gemba and Kaizen.

The Baja Electrical All-Terrain Vehicle eATV is a versatile off-road vehicle designed to tackle challenging terrains and endure extreme conditions. Suspension system in a car connects the chassis to its wheels and it comprises of a system of springs, dampers and linkages. Independent suspension systems typically offer better handling and ride quality. This paper focuses on the optimization of the eATV’s suspension and steering systems to enhance its performance, stability, and maneuverability. For explanation purpose the design methodology that has been chosen for the suspension system of an all-terrain vehicle. A double wishbone independent suspension is designed for the front half, and an H-arm independent suspension is designed for the rear half. The steering system uses a Rack & Pinion gearbox along with this Ackerman geometry being used for the steering assembly. Theoretical values were validated with the help of ‘Lotus Shark’ software.

Aluminium alloys enrolled their applications in automobile sectors, agricultural equipment, machine tools and aerospace because of their weight-to-strength ratio. Aluminium alloy 7075-T651 is an inevitable material used in engineering sectors. Turning is a metal removal process, to obtain net geometrical aspects and better surface finish of the products. The machinability of the turning operation is based on different factors; however, turning factors and material of tool plays a significant position in the turning process. To identify the truthful cutting parameters to achieve multi-responses in turning operation, the experiment was designed via Response Surface Methodology (RSM) Central Composite Design (CCD) and the experimental results are analyzed under the desirability approach.

Mining dozers are heavy-duty machines that operate in harshenvironments and under extreme loads. The durability of their powertraincomponents is critical for their performance and reliability. To ensure thedurability of these components, they need to be tested under realisticoperating conditions. However, field testing is time-consuming, costly andoften impractical due to the limited availability of test sites and machines.Therefore, there is a need to develop an accelerated durability cycle that cansimulate the field conditions in a laboratory setting. This paper presents amethodology to develop an accelerated powertrain durability cycle for miningdozers based on the integration of customer usage patterns and road load data.

The recent progress in camera-based technologies has prompted the development of prototype camera-based video systems, intended to replace conventional passenger vehicle mirrors. Given that a significant number of collisions during lane changes stem from drivers being unaware of nearby vehicles, these camera-based systems offer the potential to enhance safety. By affording drivers a broader field of view, they facilitate the detection of potential conflicts. This project was focused on analyzing naturalistic driving data in support of the Federal Motor Vehicle Safety Standard 111 regulatory endeavors. The goal was to assess the effectiveness and safety compatibility of prototype camera-based side-view systems as potential replacements for traditional side-view mirrors.

In the commercial vehicle business, vehicle availability is a pivotal factor for the profitability of the customer. Nonetheless, the intricate nature of the technologies embedded in modern day engines and exhaust after-treatment systems coupled with the variability of the duty cycles of end applications of the vehicles imposes added challenges on the vehicle's sustained performance and reliability. In this context, the ability to predict potential failures through tools like telematics and real-time data analytics presents a significant opportunity for original equipment manufacturers (OEMs) to deliver distinctive value to their customers.

The implementation of TREM/CEV 5 emission norms on farm equipment will bring in cost pressure due to the need for exhaust after treatment systems. This cost increase needs to be reduced by bringing in more efficient and effective processes to shorten the development phase and to provide better fuel efficiencies. In this work ETAS ASCMO Online DoE with Constraint Modelling (ODCM) was applied to execute smart online DoE on a new common rail diesel engine with EGR, whose exact bounds of operation was not available. A Global test plan with ASCMO Static was created without much focus on detailed constraints of engine operation, other than the full load curve. The parameters which were selected were Speed, Torque, Rail Pressure, Main Timing, EGR Valve Position, Pilot Separation and Quantity and Post Quantity and Separation. For these parameters, the safe operating bounds were not available. This ASCMO Static test plan is automated and executed on engine test cell with ETAS INCAFlow.

The Construction & Mining field is continuously upgrading, reshaping under the stimulus of technical enhancement. India is considered one of fastest growing country in the word. Requirement for Construction Equipment Vehicles in India is continuously growing due increased rate infrastructure development. To promote development of the Construction Equipment Vehicles (CEV’s) manufacturing sector it was also necessary to build a new governance architecture. Every vehicle plying on road has to comply with Central Motor Vehicle Regulatory requirements as per CMVR act 1989. Earlier 2021 CEV’s were required to go through performance trials like brake, steering effort, turning circle measurement, speedometer calibration as dynamic tests as per regulations.

Ride comfort is a critical factor to customer perception of vehicle quality as it is related to vehicle experience when driving. It adds value to the product and, consequently, to vehicle brand. It has become a demand not only for passenger unibody vehicles but also to larger segments including mid-size trucks. Ride quality is usually quantified as harshness which is a measure of how the vehicle transmits the road irregularities to the customer at the tactile points such as the steering wheel and seats. Improving harshness requires tuning of different parts including tires, chassis frame/subframe and suspension mounts and bushings. This paper describes the methodology to enhance the harshness performance for a mid-size truck using a full vehicle CAE model. The influence of stiffnesses of body mounts and control arms bushings to harshness response is investigated through sensitivity analysis and the optimal configuration is found.

Product validation time reduction and limit number of physical testing is major challenge all over the world OEMs are facing and they are trying to use latest technologies to fill gap between design parameters, simulated results, and physical validation results. Automotive industry is going through a major transformation with use of artificial intelligence and machine learning and especially in the area of transmission system design and development where lot of data is available from physical testing. Clutch is still being used in internal combustion engines vehicles. Clutch is an important part in transmission system in vehicle, which transmits power generated from engine to transmission and changes the gears at different speed. Design and validation of clutch is a critical and laborious task. Clutch failure occurs due to excessive rise in temperature.