Browse Topic: Energy consumption
Electrification in off-highway vehicles faces several challenges due to the unique requirements and operational features of heavy-duty applications. Key challenges include power demand, limited range, weight, size, and the costs associated with electrification. Lithium-based batteries have limited capacity and range, and heavy-duty operations can rapidly drain the battery's power. Managing battery power for these operations requires careful planning and optimization of the vehicle's energy consumption to ensure efficient utilization of the battery's capacity. In electric off-highway vehicles, the remaining battery discharge run-time is closely related to the management of operational applications in the field. The utilization of battery power for heavy operations can be enhanced by estimating battery run-time and run distance during operation, which can then be displayed on the vehicle’s display unit. This facilitates the operator's understanding of how much longer the battery can
This SAE Recommended Practice provides instructions and test procedures for measuring air consumption of air braked vehicles equipped with Antilock Brake Systems (ABS) used on highways
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
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
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
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
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
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
The Internet of Military Things (IoMT), sometimes referred to as the Internet of Battlefield Things (IoBT), is gaining momentum for applications that improve defensive and battlefield capabilities. Like its civilian counterpart, the IoMT are networks of sensors, wearables, and imaging devices using edge and cloud computing to improve military operations and safety. However, battery failure in an IoMT device can have serious consequences in applications such as unmanned aerial drones that are used to patrol border areas or secure military bases. Battery life requirements are also high for the sensors and surveillance cameras that can be used to send real-time intelligence back to the command center for strategic decisions. Likewise, predictable battery life for IoMT devices used for vehicle management, battlefield supply chains, and weapon control are critical for efficient operations. Therefore, optimizing the device design and software to reduce power consumption and increase battery
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