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In farm tractors, the available drawbar power, and Power Take-Off (PTO) power are generally lower than the engine power due to parasitic losses. These losses are caused by engine-driven auxiliary loads such as cooling fans, hydraulic pumps for power steering, alternators, etc. Minimizing these parasitic losses can increase the available drawbar power and PTO power, resulting in direct fuel savings by reducing fuel consumption. The continuous increase in fuel costs and the environmental impact of emitted gases from burned fuel into the atmosphere have necessitated the replacement of hydraulic power steering and mechanical fans with Electric Power Steering (EPS) and electric fans, respectively, to improve efficiency. The existing battery has been replaced with a higher capacity battery to provide power to the electric fan, electric power steering, and other electrical components.

In recent years due to significant increased cost of raw material, fuel and energy, vehicle cost is increased. As vehicle cost is one of the major factors that attracts prospective buyers, it has created specific demand for low weight and low-cost components than traditional components with better performance to meet customer expectations. Suspension is one of the critical aggregates where lot of material is used and reduction in weight tends to give lot of cost benefit. As suspension system derives vehicle’s handling performance, it has to be ensured that handling performance of vehicle is maintained the same or made better while reducing weight of the suspension. Advancements in simulation capabilities coupled with manufacturing technology has enabled development non-traditional leaf springs. One of such springs is mono-leaf spring without shackle. This type of leaf spring provides advantages such as low weight and nonlinear stiffness.

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.

In order to meet future emission targets and to achieve better fuel efficiency, closed loop air mass control strategies have become essential across all vehicle segments. Closed loop airmass control mandates measuring fresh air mass entering the engine combustion chamber. However, in Naturally Aspirated (NA) engines, while measuring airmass using conventional air mass sensors (AMS), heavy pulsations in the Air-intake results in errors which would impact closed loop airmass control and lead to inconsistencies in emissions. To address this issue, we studied different approaches using AMS with Resonator, differential pressure sensor across the intake air filter and Lambda based airmass control. Based on this empirical study we found that modelling air mass with differential pressure sensor (Delta-P) using Bernoulli’s principle (Flow rate ∝ √Differential pressure) results in higher accuracies compared to conventional methods.

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.

Toe misalignment detection and its correction are important periodic tasks recommended by Original Equipment Manufacturers (OEMs) for Heavy Commercial Road Vehicles (HCRVs) to prevent premature tyre wear and improve fuel economy. Existing misalignment detection methods need skilled professionals to operate sophisticated equipment, while automated methods require additional sensors, which are not readily available in most trucks, making their implementation challenging. This study explores the effectiveness of a data-driven method to detect toe misalignment in single-unit twin-axle trucks with symmetric and asymmetric toe configurations. This method involves continuous monitoring of lateral dynamics variables measurable using sensors present in most trucks making it practically tractable.

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.

Sustainability has evolved from being just a niche engagement to a fundamental necessity. The reduction of carbon emissions from aspects of human activity has become desirable for its ability to mitigate the impact of climate change. The Transportation industry is a critical part of the global economy – any effort to curb emissions will have a significant impact on CO2 reduction. Engine lubricant can play an efficient and key role to enhance powertrain performance that have undergone significant hardware changes to reduce emissions. As part of a significant collaborative programme between Tata Motors and Infineum, a new engine oil formulation SAE 5W-30 API FA-4 has been developed and commercially introduced for use in the modern Bharat Stage 6 Phase 2 engines.

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.

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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.

Abstract The drone logistics distribution method, with its small size, quick delivery, and zero-touch, has progressively entered the mainstream of development due to the global epidemic and the rapidly developing global emerging logistics business. In our investigation, a drone and a delivery man worked together to complete the delivery order to a customer’s home as quickly as possible. We realize the combined delivery network between drones and delivery men and focus on the connection and scheduling between drones and delivery men using existing facilities such as ground airports, unmanned stations, delivery men, and drones. Based on the dynamic-vehicle routing problem model, the establishment of a delivery man and drone with a hybrid model, in order to solve the tarmac unmanned aerial vehicle for take-out delivery scheduling difficulties, linking to the delivery man and an adaptive large neighborhood search algorithm solves the model.

It is a kind of range-extended electric vehicle (REEV) using a lithium-titanate battery but can use as a hybrid electric vehicle (HEV). The proposal aims to use a battery of just the right size as the power core of the system to help the engine maintain maximum efficiency. The performance of the lithium-titanate battery is between battery and capacitor. It also has anti-overcharge, anti-over-discharge, and high-temperature stability, suitable for use in vehicles. The engine only works when the SOC is low, independent of the power requirement. By increasing the battery to the extent that it can supply the motor, the system directly suppresses the influence of power on the engine speed, and the engine runs completely at the optimal BSFC. The lithium-titanate battery has a rate of up to 5C, which directly shortens the working time of the engine. The battery will not be as large as an EV, and the vehicle still has a certain amount of pure electric mileage.

This study intends to improve the design of front axles for heavy commercial vehicles, with a major goal of reducing weight while maintaining mechanical strength. The front axle is critical in supporting the weight of the vehicle and facilitating steering while effectively absorbing shocks generated by differences in road surfaces. To achieve these requirements, a front axle beam that minimizes weight, fuel consumption, and stress on the load-carrying member must be designed. In this work, finite element analysis (FEA) techniques are used using CATIA software to assess the structural and mechanical attributes of several front axle designs. The purpose is to pick the best front axle shape depending on specific load situations and driving torque needs. The influence of alternative component shapes on stress and strain distribution is evaluated using surface changes and ANSYS Workbench numerical simulation software.