Browse Topic: Energy consumption
In non-cooperative environments, unmanned aerial vehicles (UAVs) have to land without artificial markers, which is a key step towards achieving full autonomy. However, the existing vision-based schemes have the common problems of poor robustness and generalization, and the LiDAR-based schemes have the disadvantages of low resolution, high power consumption and high weight. In this paper, we propose an UAV landing system equipped with a binocular camera to preform 3D reconstruction and select the safe landing zone. The whole system only consists of a stereo camera, and the innovation of the solution is fusing the stereo matching algorithm and monocular depth estimation(MDE) model to get a robust prediction on the metric depth. The whole landing system consists of a stereo matching module, a monocular depth estimation (MDE) module, a depth fusion module, and a safe landing zone selection module. The stereo matching module uses Semi-Global Matching (SGM) algorithm to calculate the
ABSTRACT A sudden increase in microgrid electrical power consumption requires the fast supply of energy from different generating sources to guarantee microgrid voltage stability. This paper presents the results of simulations investigating the integration of an electric supercharger into a Heavy Duty Diesel (HDD) genset connected to a microgrid for reducing engine speed droop in response to an abrupt power demand requested from the grid. First, a mean value model for the 13 L HDD engine is used to study the response of the baseline turbocharged engine during a fast load increase at low engine speed. The limited air mass in the cylinder during the transient results in engine lugging and ultimately engine stall. Then, an electrical supercharger is integrated before the turbocharger compressor to increase the engine air charge. During steady state operation, the simulation results indicate that the supercharger is able to increase the air-charge by approximately 50% over the lower half
With increasing emphasis on sustainable mobility and efficient energy use, advanced driver assistance systems (ADAS) may potentially be utilized to improve vehicles’ energy efficiency by influencing driver behavior. Despite the growing adoption of such systems in passenger vehicles for active safety and driver comfort, systematic studies examining the effects of ADAS on human driving, in the context of vehicle energy use, remain scarce. This study investigates the impacts of a driver speed advisory system on energy use in a plug-in hybrid electric vehicle (PHEV) through a controlled experiment using a driving simulator. A mixed urban highway driving environment was reconstructed from digitalizing a real-world route to observe the human driver’s behavior with and without driving assistance. The advisory system provided drivers with an optimized speed profile, pre-calculated for the simulated route to achieve maximum energy efficiency. Participants were instructed to navigate the
Artificial Intelligence (AI) has emerged as a transformative force across various industries, revolutionizing processes and enhancing efficiency. In the automotive domain, AI's adaption has ushered in a new era of innovation and driving advancements across manufacturing, safety, and user experience. By leveraging AI technologies, the automotive industry is undergoing a significant transformation that is reshaping the way vehicles are manufactured, operated, and experienced. The benefits of AI-powered vehicles are not limited to their manufacturing, operation, and enhancing the user experience but also by integrating AI-powered vehicles with smart city infrastructure can unlock much more potential of the technology and can offer numerous advantages such as enhanced safety, efficiency, growth, and sustainability. Smart cities aim to create more livable, resilient, and inclusive communities by harnessing innovation through technologies like Internet of Things (IoT), devices, data
Energy efficiency in both internal combustion engine (ICE) and electric vehicles (EV) is a strategic advantage of automotive companies. It provides a better user experience that emanates amongst others from the reduction in operation expenses, particularly critical for fleets, and the increase in range. This is especially important in EVs where customers may experience range anxiety. The energetical impact of using the air conditioning system in vehicles is not negligible with power consumptions in the range of kilowatts, even with a stopped vehicle. This becomes particularly important in areas with high temperature and humidity levels where the usage of the air conditioning systems becomes safety factor. In such areas, drivers are effectively forced to use the air conditioning system continuously. Hence, the air conditioning system becomes an ideal choice to deploy control strategies for optimized energy usage. In this paper, we propose and implement a control strategy that allows a
Tracking of energy consumption has become more difficult as demand and value for energy have increased. In such a case, energy consumption should be monitored regularly, and the power consumption want to be reduced to ensure that the needy receive power promptly. Our objective is to identify the energy consumption of an electric vehicle from battery and track the daily usage of it. We have to send the data to both the user and provider. We have to optimize the power usage by using anomaly detection technique by implementing deep learning algorithms. Here we are going to employ a LSTM auto-encoder algorithm to detect anomalies in this case. Estimating the power requirements of diverse locations and detecting harmful actions are critical in a smart grid. The work of identifying aberrant power consumption data is vital and it is hard to assure the smart meter’s efficiency. The LSTM auto-encoder neural network technique is used here for predicting power consumption and to detect anomalies
One of the challenges of Electric Vehicles (EVs) is to provide thermal comfort for the occupants while minimizing the energy consumption and the impact on the driving range. Conventional heating systems, such as Positive Temperature Coefficient (PTC) heaters, consume a large amount of battery power and reduce the efficiency of the EVs. Heat Pumps (HPs) are an alternative heating system that can divert heat from the ambient air and transfer it to the cabin. HPs can achieve higher Coefficient of Performance (COP) than PTC heaters and save energy. However, for Indian sub-continent conditions HPs have some drawbacks, such as low heating capacity at low ambient temperatures, and variable performance depending on the operating conditions. Therefore, it is important to design and control the HP system optimally. This study employs 1D Computer-Aided Engineering (CAE) modelling and simulation techniques to analyse the performance of heat pump systems within the confined environment of an EV
Penn Engineers have developed a new chip that uses light waves, rather than electricity, to perform the complex math essential to training AI. The chip has the potential to radically accelerate the processing speed of computers while also reducing their energy consumption
Space lasers are transforming the world. Not the far-off future of science fiction, but the universe of how data and communications flow today - everywhere from deep space missions to countless applications here on earth, including consumer internet services, military operations, and banking transactions. Lasers can transmit vast amounts of data over great distances at the speed of light, 100 times faster than previously possible in space. The narrowness of the light beams makes laser communication remarkably efficient. The highly focused light is aimed at the receiver, resulting in minimal beam divergence and signal loss and allowing for reduced power consumption
Vehicle powertrain electrification is considered one of the main measures adopted by vehicle manufacturers to achieve the CO2 emissions targets. Although the development of vehicles with hybrid and plug-in hybrid powertrains is based on existing platforms, the complexity of the system is significantly increased. As a result, the demand for testing during the development and calibration stages is getting significantly higher. To compensate that, high-fidelity simulation models are used as a cost-effective solution. This paper aims to present the methodology followed for the development of a rule-based energy management controller for a plug-in hybrid electric vehicle (PHEV), and to describe the experimental campaign that provided the necessary input data. The controller is implemented in a vehicle simulation model that is parametrized to replicate the real operation of the vehicle. Using such a model it is possible to carry out virtual tests, aiming towards energy management
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