Browse Topic: Control systems
A futuristic vehicle chassis rendered in precise detail using state-of-the-art CAD software like Blender, Autodesk Alias. The chassis itself is sleek, low-slung, and aerodynamic, constructed from advanced materials such as high-strength alloys or carbon-fibre composites. Its polished, brushed-metal finish not only exudes performance but also emphasizes the refined form and engineered details. Underneath this visually captivating structure, a sophisticated system of self-hydraulic jacks is seamlessly integrated. These jacks are situated adjacent to the four shock absorber mounts. These jacks are designed to lift the chassis specifically at the tyre areas, and the total vehicle, ensuring that underbody maintenance is efficient and that, in critical situations, vital adjustments or emergency lifts can be performed quickly and safely. The design also incorporates an intuitive control system where the necessary buttons are strategically placed to optimize driver convenience. Whether
Tippers transporting loose bulk cargo during prolonged descents are subject to two critical operational challenges: cargo displacement and rear axle lifting. Uncontrolled cargo movement, often involving loose aggregates or soil, arises due to gravitational forces and insufficient restraint systems. This phenomenon can lead to cabin damage, loss of control, and hazardous discharge of materials onto roadways. Simultaneously, load imbalances during descent can cause rear axle lift, increasing stress on the front steering axle, resulting in tire slippage and compromised maneuverability. This study proposes a dynamic control strategy that adjusts the tipper lift angle in real time to align with the descent angle of the road. By synchronizing the trailer bed angle with the slope of the terrain, the system minimizes cargo instability, maintains rear axle contact, and enhances braking performance, including engine and exhaust braking systems. Computational modelling is employed to assess the
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 studies an important industrial controls engineering problem statement on mitigating vibrations in a mechanical boom structure for an off-highway agricultural vehicle. The work discusses the implementation of an active force control concept to efficiently dampen out vibrations in a boom. Through rigorous simulation comparison with respect to an existing PID mechanism, the efficacy of the AFC is demonstrated. A notable reduction of 60 % to 70 % in the boom vibrations was observed.
Modern battery management systems, as part of Battery Digital Twin, include cloud-based predictive analytics algorithms. These algorithms predicts critical parameters like Thermal runaway events, state of health (SOH), state of charge (SOC), remaining useful life (RUL), etc. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. This is a very critical and safety function and delay is not acceptable, but automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud and similarly delay in return of the detected warning to the driver back in the vehicle. As a result, the inherent lag in data transfer between the cloud and vehicles challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes application of Federated Learning and applying to a thermal runaway model in low
In both Internal Combustion Engine Vehicles (ICEVs) and Electric Vehicles (EVs), the refrigerant charge is essential for efficient climate control and energy consumption. An accurate refrigerant charge allows the system to regulate cabin temperature effectively and optimizing energy use. In ICEVs, this prevents the wastage of engine power. In EVs, it preserves battery life by minimizing energy drain by the climate control systems. Undercharging or Overcharging has adverse effects on the Heat Ventilation Air-Conditioning (HVAC) systems and the energy usage associated with it. Undercharging leads to poor cabin cooling which reduces heat absorption by refrigerant whereas overcharging leads to higher energy consumption by compressor, and potential damage to components, which can lead to wear, leaks, and system failures. Hence it is crucial to use optimum refrigerant charge quantity in Mobile Air-Conditioning (MAC) system both in ICEVs and EVs. Previous work on refrigerant charge
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