Tanks play a pivotal role in swiftly deploying firepower across dynamic battlefields. The core of tank mobility lies within their powertrains, driven by diesel engines or gas turbines. To better understand the benefits of each power system, this study uses geo-location data from the National Training Center (NTC) to understand the power and energy requirements from a main battle tank over an 18-day rotation. This paper details the extraction, cleaning, and analysis of the geo-location data to produce a series of representative drive cycles for an NTC rotation. These drive-cycles serve as a basis for evaluating powertrain demands, chiefly focusing on fuel efficiency. Notably, findings reveal that substantial idling periods in tank operations contribute to diesel engines exhibiting notably lower fuel consumption compared to gas turbines. Nonetheless, gas turbines present several merits over diesel engines, notably an enhanced power-to-weight ratio and superior power delivery.
Lithium-ion batteries (LIBs) serve as the main power source for contemporary electric vehicles (EVs). Safeguarding these batteries against damage is paramount, as it can trigger accelerated performance deterioration, potential fire hazards, environmental threats, and more. This study explores the damage progression of a commercial vehicle LIB module containing prismatic cells under crush loading. We employed computational simulations of mechanical loading tests to investigate this behavior. Physical tests involved subjecting modules to low-speed (0.05 m/s) indentations using a V-shaped stainless-steel wedge, under 6 unique loading conditions. During the tests, the force and voltage change with wedge displacement were monitored. Utilizing experimental insights, we constructed a finite element (FE) model, which included the key components of the battery module, such as the prismatic cells, steel frames and various plastic parts.
Since the early 1990’s, commercial vehicles have suffered from repeated vulnerability exploitations that resulted in a need for improved automotive cybersecurity. This paper describes the strategies and challenges involved in securing vehicle networks through the implementation of an automotive Zero Trust Architecture (ZTA). Zero Trust, originally an IT principle of “never trust, always verify”, is the concept that a network must never assume assets can be trusted regardless of their ownership or location within the network. This research focused on drastically improving security of the cyber-physical vehicle network, with minimal performance impact, measured as timing, bandwidth, and processing power. Impacts to safety and cost were also considered. The automotive ZTA was tested using a software-in-the-loop vehicle simulation paired with resource constrained hardware that closely emulated a production vehicle network.
Nowadays, Bismuth (Bi) is being applied as an overlay material for engine bearings instead of Lead (Pb) which is an environmentally harmful material. Bi overlay has already been a solid performer in some automotive engine sectors due to its superior load carrying capacity and good robustness characteristic which are necessary to maintain its longevity during the lifetime of engines. The replacement is also seen on relatively larger size engines, such as Trucks and Off-highway heavy duty applications. Basically, these applications require higher power output than passenger cars, and the expected component lifecycle becomes longer. Even Bi has similar material characteristic with traditional Pb, it becomes challenging for the material alone to satisfy these requirements. Polymer overlay is known for its superior anti-wear performance and longer lifetime due to less adhesion against a steel counterpart than metal materials (included Bi).
Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems are complicated, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to heavy vehicle event data recorders (HVEDRs) to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly injected into the HVEDR to trigger an event record.
Aluminium composites are remarkably used in automotive, aerospace, and agricultural sectors because of their lightweight with definable mechanical properties. The stir casting route was followed to fabricate cylindrical samples with base aluminium alloy LM4, LM4/SiC, LM4/Al2O3, and LM4/SiC/Al2O3. The tensile strength, compressive strength, hardness, and micro-structural analysis were performed on samples and Finite element analysis (FEA) was adopted to predict the failure modes of composites. The composites experimental results were found to be in line with the FEA results, however, the LM4/SiC/Al2O3 revealed better results on the mechanical properties when compared with other composite configurations. The mechanical properties improvement like hardness 5%-11%, tensile strength 10.26%-20.67%, compressive strength 15.19% - 32.58% and 71.52 - 82.1% reduction in dimension have been achieved in LM4/SiC/Al2O3 composite comparing to base metal.
Lightweight materials are in great demand in the automotive sector to enhance system performance. The automotive sector uses composite materials to strengthen the physical and mechanical qualities of light weight materials and to improve their functionality. Automotive elements such as the body shell, braking system, steering, engine, battery, seat, dashboard, bumper, wheel, door panelling, and gearbox are made of lightweight materials. Lightweight automotive metals are gradually replacing low-carbon steel and cast iron in automobile manufacture. Aluminium alloys, Magnesium alloys, Titanium alloys, advanced high-strength steel, Ultra-high strength steel, carbon fiber-reinforced polymers, and polymer composites are examples of materials used for light weighing or automobile decreased weight. The ever-present demand for fuel-efficient and ecologically friendly transport vehicles has heightened awareness of lowering weight and performance development.
Aluminum alloys are employed in agricultural equipment, aerospace sectors, medical instruments, machinery, automobiles, etc. due to their physical and mechanical characteristics. The geometrical shape and size of the parts are modified in turning operation by using a single-point cutting tool. A356 aluminum alloy is widely used in various engineering sectors, hence there is a necessity to produce A-356 components with quality. The inappropriate cutting parameters used in turning operation entail high production costs and reduce tool life. Box–Behnken design (BBD) based on response surface methodology (RSM) was used to design the experiments such that the experiment trials were conducted by varying cutting parameters like N-spindle speed (rpm), f-feed rate (mm/rev), and d-depth of cut (mm). The multi-objective responses, such as surface roughness (SR) and metal removal rate (MRR) were analyzed with the desirability method.
For commercial vehicles, reliability is key since the vehicle is typically linked to the daily earnings of the owner. To ensure continuous vehicle operation, early diagnostics of critical issues and proactive maintenance are important. However, an electric vehicle is a complex and dynamic system consisting of numerous components interacting with each other and with external environments such as road conditions, traffic, weather, and driving behavior. Thus, vehicle operation and performance are highly contextual and for identifying an abnormal operation (diagnostics) the solution must consider the conditions under which it is driven. To address this, the paper proposes an AI-based digital twin of an electric three-wheeler vehicle. TabNet a deep-learning based model is used to learn and generate near-ideal vehicle behavior. The focus of the paper is motor subsystem. The model is trained using appx 200 vehicles first 1500 km driven data.
With the rise of worldwide trends towards light weighting and the move towards electric vehicles, it is now more important than ever for the automotive industry to develop and implement lightweight materials that will result in significant weight reduction and product improvements. A great deal of research has been done on how to best combine and configure honeycomb cores with the right face sheets for Truck-Mounted Container Applications. Honeycomb structures possess the ability to bring about superior structural rigidity when the core parameters are selected and optimized based on the automotive application requirements.
Rapid Urbanisation, in recent times, has created an exponential demand for light commercial vehicles. Electric vehicles are seen as a way to reduce the impact of emissions due to transportation in urban areas. Due to the growth of e-commerce, commercial transportation, and particularly last-mile delivery, is anticipated to increase. In this context, electric light commercial vehicles (eLCVs) have the potential to be a promising solution by tackling the emission impacts, ensuring faster delivery along with ideal running costs and payload capacity. To increase the range of electric vehicles, it has to be designed for lighter weight with optimal performance in order to meet the user requirements. Cargo capacity and payload have to be taken into account while design & validating the vehicle structure under static and dynamic conditions. Simulation driven product development will help the design team to account for the possible design failure cases at system and vehicle level.
Commercial transportation is the key pillar of any growing economy. Light and Small commercial vehicles are increasing every day to cater the logistics demand, but there is always a gap between customer’s actual and desired operational efficiency. This is because of lack of organized fleet and efficient fleet operation. The major requirement of fleet owners is timely delivery, high productivity, downtime reduction, real time tracking, etc., Automakers are now providing fleet management application in modern LCV & SCV to satisfy the fleet operator requirement. However, any feature malfunction, consignment mismatch, wrong notification, missed alerts, etc., can incur huge loss to fleet operator and disrupt the entire supply chain. Hence it is very critical to extensively validate the telematics features in fleet management application. This paper explains the approach for exhaustive validation strategy of fleet management applications (B2B) from end user perspective.
Abstract This study underscores the benefits of refining the intralogistics process for small- to medium-sized manufacturing businesses (SMEs) in the engineer-to-order (ETO) sector, which relies heavily on manual tasks. Based on industrial visits and primary data from six SMEs, a new intralogistics concept and process was formulated. This approach enhances the value-added time of manufacturing workers while also facilitating complete digital integration as well as improving transparency and traceability. A practical application of this method in a company lead to cutting its lead time by roughly 11.3%. Additionally, improved oversight pinpointed excess inventory, resulting in advantages such as reduced capital needs and storage requirements. Anticipated future enhancements include better efficiency from more experienced warehouse staff and streamlined picking methods. Further, digital advancements hold promise for cost reductions in administrative and supportive roles.
Abstract Being an engineer-to-order (ETO) operating industry, the control cabinet industry faces difficulties in process and workplace optimizations due to changing requirements and lot size one combined with volatile orders. To optimize workplaces for employees, current literature is focusing on ergonomic designs, providing frameworks to analyze workplaces, leaving out the optimal design for productivity. This work thus utilizes a Kano analysis, collecting empirical data to identify essential design requirements for assembly workplaces, incorporating input from switchgear manufacturing employees. The results emphasize the need for a balance between ergonomics and efficiency in workplace design. Surprisingly, few participants agree on the correlation between improved processes and workspaces having a positive impact on their well-being and product quality.
Perkins bets big on smaller engine The new 2600 Series 13-liter engine for off-highway machines will do more with less thanks to variable geometry turbocharging. BorgWarner targets more- sustainable e-motors System optimization and lifecycle analysis are key to taking heavy rare earths out of next-gen motors for commercial EVs. Enhancing digital platforms with CT data analysis TE Connectivity gains critical insights using Volume Graphics software throughout design, simulation and manufacturing.
In the current scenario, manufacturing of heavier products generates colossal waste, generates more CO2 emission, and negatively affects the environment. Customers not only pay higher product costs but also higher operational costs. This in turn demands the need for more recycling. Advanced high strength materials are a key solution to applications demanding higher strength, stiffness, durability & wear requirement, whereas low density materials like aluminium and magnesium won’t be a sustainable choice. With more and more battery electric & fuel cell vehicles, “light weighting” is a key priority. Austempered Ductile Iron (ADI) has a great advantage of superior mechanical properties compared to conventional ductile iron, aluminium alloys and even some steel forgings. Typically, ADI is used for high wear applications, whereas this paper will demonstrate the potential of using ADI for Structural applications.