Browse Topic: Vehicle performance
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
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
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
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