Browse Topic: Vehicle performance
Tillage, a fundamental agricultural practice involving soil preparation for planting, has traditionally relied on mechanical implements with limited real-time data collection or adjustment capabilities. The lack of real-time data and implement statistics results in fleet managers struggling to track performance, driver behavior, and operational efficiency of the implements. Lack of data on vehicle performance can result in unexpected breakdowns and higher maintenance costs, ensuring compliance with regulations is challenging without proper data tracking, potentially leading to fines and legal issues. Bluetooth-enabled mechanical implements for tillage operations represent an emerging frontier in precision agriculture, combining traditional soil preparation techniques with modern wireless technology. Implement mounted battery powered BLE (Bluetooth Low Energy) modules operated by solar panel based rechargeable batteries to power microcontroller. When Implement is operational turns
Transmission tuning involves adjusting parameters within a vehicle's transmission control unit (TCU) or transmission control module (TCM) to optimize performance, efficiency, and driving experience. Transmission tuning is beneficial for optimizing performance, improving fuel efficiency, smoother shifting and enhancing drivability particularly when a vehicle's power output is increased or for specific driving conditions. Especially in offroad and agricultural machines, transmission tuning is vital to significantly improve vehicle performance during different operations. The process of transmission tuning is quite time consuming as multiple tuning iterations are required on the actual vehicle. A significant reduction in tuning time can be achieved using a simulation environment, which can mimic the actual vehicle dynamics and the real time vehicle behavior. In this paper, tuning during the forward and reverse motion of the tractor is described. A two-level PI control-based shift strategy
This paper offers a state-of-the-art energy-management strategy specifically developed for FCHEV focusing on robustness under uncertain operations. Currently, energy management strategies try to optimize fuel economy and take into account the sluggish response of fuel cells (FCs); however, they mostly do so assuming all system variables are explicit and deterministic. In real-world operations, however, a variety of sources may cause the uncertainty in power generation, energy conversion, and demand interactions, e.g., the variation of environmental variables, estimated error, and approximation error of system model, etc., which accumulates and adversely impacts the vehicle performance. Disregarding these uncertainities can result in overestimation of operating costs, overall efficiency and overstepped performance limitations, and, in serious cases can cause catastrophic system breakdown. To mitigate these risks, the current work introduces a neural network-based energy management
In automotive systems, efficient thermal management is essential for refining vehicle performance, enhancing passenger comfort, and reducing MAC Power Consumption. The performance of an air conditioning system is linked to the performance of its condenser, which in turn depends on critical parameters such as the opening area, radiator fan ability and shroud design sealing. The opening area decides the airflow rate through the condenser, directly affecting the heat exchange efficiency. A larger opening area typically allows for greater airflow, enhancing the condenser's ability to dissipate heat. The shroud, which guides the airflow through the condenser, plays a vital role in minimizing warm air recirculation. An optimally designed shroud can significantly improve the condenser's thermal performance by directing the airflow more effectively. Higher fan capacity can increase the airflow through the condenser, improving heat transfer rates. However, it is essential to balance fan
The integration of digital twins within a digital thread framework offers significant benefits for managing Army ground and surface water vehicles. This paper examines how digital twins can enhance lifecycle management, operational efficiency, and maintenance for mature and new military vehicle programs. Scalable and cost-effective implementation with layered capabilities allows organizations to start with a cost-effective foundational model and phase in additional layers of capability over time. This phased approach allows you to expand your digital twin capabilities as program budgets permit, ensuring that you can adapt to evolving requirements without overwhelming upfront investment. For established programs, digital twins enable real-time monitoring, predictive analytics, and data-driven decisions, improving resource allocation and cutting costs. For new programs, they speed up prototyping, integrate modern technologies, and enhance training capabilities. Case studies demonstrate
This paper aims to explore the application of machine learning techniques to the analysis of road suspension systems, with particular emphasis on mechanical leaf spring suspensions. These systems are essential for vehicle performance, as they guarantee comfort and stability while driving, and they have an intrinsically complex and non-linear dynamic behavior. Because of this complexity, traditional approaches often prove costly and insufficient to represent operating conditions. In this context, machine learning techniques stand out for their ability to learn patterns from experimental data, allowing the modelling of non-linear phenomena that characterize road implement suspensions. One of the main contributions of this study is the demonstration that machine learning algorithms are capable of identifying complex patterns to represent the behavior of the system, as well as facilitating the detection of anomalies and potential faults in the suspension system, contributing to predictive
This SAE Information Report applies to structural integrity, performance, drivability, and serviceability of personally licensed vehicles not exceeding 10000 pounds GVWR such as sedans, crossovers, SUVs, MPVs, light trucks, and van-type vehicles that are powered by gas and alternative fuel such as electric, plug-in hybrid, or hybrid technologies. It provides engineering direction to vehicle modifiers in a manner that does not limit innovation, and it specifies procedures for preparing vehicles to enhance safety during vehicle modifications. It further provides guidance and recommendations for the minimum acceptable design requirements and performance criteria on general and specific structural modifications, thereby allowing consumers and third-party payers the ability to obtain and purchase equipment that meets or exceeds the performance and safety of the OEM production vehicle.
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