Browse Topic: Energy management
ABSTRACT General Dynamics Land Systems has developed an Auxiliary Power Unit (APU) that provides 508A at 28VDC, for 14.2 KW. It is a stand-alone system, independent of the vehicle systems, except for utilizing vehicle fuel and vehicle batteries. Power is generated by a 570 amp alternator that is belt-driven by a diesel engine. It is load following which improves fuel efficiency and eliminates the probability of “wet stacking.” All the major components are commercially available and the APU is ready for production
ABSTRACT One of the main thrusts in current Army Science & Technology (S&T) activities is the development of occupant-centric vehicle structures that make the operation of the vehicle both comfortable and safe for the soldiers. Furthermore, a lighter weight vehicle structure is an enabling factor for faster transport, higher mobility, greater fuel conservation, higher payload, and a reduced ground footprint of supporting forces. Therefore, a key design challenge is to develop lightweight occupant-centric vehicle structures that can provide high levels of protection against explosive threats. In this paper, concepts for using materials, damping and other mechanisms to design structures with unique dynamic characteristics for mitigating blast loads are investigated. The Dynamic Response Index (DRI) metric [1] is employed as an occupant injury measure for determining the effectiveness of the each blast mitigation configuration that is considered. A model of the TARDEC Generic V-Hull
Over the past twenty years, the automotive sector has increasingly prioritized lightweight and eco-friendly products. Specifically, in the realm of tyres, achieving reduced weight and lower rolling resistance is crucial for improving fuel efficiency. However, these goals introduce significant challenges in managing Noise, Vibration, and Harshness (NVH), particularly regarding mid-frequency noise inside the vehicle. This study focuses on analyzing the interior noise of a passenger car within the 250 to 500 Hz frequency range. It examines how tyre tread stiffness and carcass stiffness affect this noise through structural borne noise test on a rough road drum and modal analysis, employing both experimental and computational approaches. Findings reveal that mid-frequency interior noise is significantly affected by factors such as the tension in the cap ply, the stiffness of the belt, and the properties of the tyre sidewall
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
The next-gen 15-liter diesel engine meets all 2027 EPA emissions regulations while boosting fuel efficiency. Cummins provided extensive details of the design and engineering efforts involved in developing the new HELM version of its X15 diesel engine. The company says its new engine will offer up to a 7% improvement in fuel economy compared to the current EPA 2024-certified X15 while also meeting all 2027 emissions targets. Truck & Off-Highway Engineering was invited to tour the company's headquarters in Columbus, Indiana, where journalists were given a comprehensive update on the hardware powering the latest X15
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
Effective thermal management is crucial for vehicles, impacting both passenger comfort and safety, as well as overall energy efficiency. Electric vehicles (EVs) are particularly sensitive to thermal considerations, as customers often experience range anxiety. Improving efficiency not only benefits customers by extending vehicle range and reducing operational costs but also provides manufacturers with a competitive edge and potential revenue growth. Additionally, efficient thermal management contributes to minimizing the environmental impact of the vehicle throughout its lifespan. Digital twins have gained prominence across various industries due to their ability to accelerate development while minimizing testing costs. Some applications have transitioned to comprehensive three-dimensional models, while others employ model reduction techniques or hybrid approaches that combine different modeling methods. The discovery of unknown working mechanisms, more efficient and effective control
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
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