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Browse AllThis paper explains the method of precooling of electric vehicle from grid connected charger reduce load on HVAC and improve the range. HVAC systems are integral part of a commercial EV bus. With the rise of ambient temperatures during various seasons, the load on HVAC System is increasing. Once an Electric vehicle is released from a depot for service, with an initial soaked up ambient vehicle, the HVAC system demands peak power for cooling the interiors which consumes a lot of battery power thus affecting the range. That cause the additional energy consumption required for precooling, which cannot be estimated as it is highly dependent on ambient temperature and range of the vehicle is also dependent on HVAC consumption during summer and peak loads. This paper is proposing a method that uses a special precooling mode which is activated depending on the selection of the vehicle route based on backend application running on cloud. The Application in the cloud checks if the vehicle is
Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test
A passenger vehicle's front-end structure's structural integrity and crashworthiness are crucial to ensure compliance with various frontal impact safety standards (such as those set by Euro NCAP & IIHS). For a new front-end architecture, design targets must be defined at a component level for crush cans, longitudinal, bumper beam, subframe, suspension tower and backup structure. The traditional process of defining these targets involves multiple sensitivity studies in CAE. This paper explores the implementation of Physics-Informed Neural Networks (PINNs) in component-level target setting. PINNs integrate the governing equations into neural network training, enabling data-driven models to adhere to fundamental mechanical principles. The underlying physics in our model is based upon a force scheme of a full-frontal impact. A force scheme is a one-dimensional representation of the front-end structure components that simplifies a crash event's complex physics. It uses the dimensional and
Powertrain is the most prominent source of Noise and Vibration in the vehicle. Improvement in Powertrain Noise and Vibration is a multifaceted topic due to the complex architecture of the powertrain and the critical role of calibration in defining combustion inputs. Hence, a method to clearly distinguish these aspects is required in order to address the exact problem and decide on course of actions to improve NVH performance of powertrains. This paper discusses a post-processing technique through which experimentally acquired ICE Powertrain Noise can be further segregated in order to identify and address the root source. The segregation methodology requires as input - noise, vibration and cylinder pressure values at various torque conditions across multiple operating points. A MATLAB based code developed by the authors is used to generate correlation between the Cylinder Pressure, Torque and Noise Parameters. The transfer coefficient at every frequency point is calculated using
With the expansion of compressed natural gas (CNG) filling station in India, bi-fuel vehicles are gaining popularity in recent times. Bi-fuel engine runs on more than one fuel, say in both CNG and petrol. Hence, the engine must be optimized in both the fuel modes for performance and emissions. However, due to the inherent differences in combustion characteristics: ignition dynamics and fuel properties, they pose a significant challenge in case of detection of misfires. Misfires are caused because of faulty injection systems and ignition systems and incorrect fuel mixture. Accurate detection is essential as misfires deteriorate the catalysts performance and may impacts emission. Misfires (or engine roughness) is calculated from engine crankshaft speed signal. In this study, the effectiveness of crankshaft-based misfires detection method, comparison of misfire signals magnitude in bi-fuel modes and practices developed for accurate detection of misfires is presented.
The growing environmental, economic, and social challenges have spurred a demand for cleaner mobility solutions. In response to the transformative changes in the automotive sector, manufacturers must prioritize digital validation of products, manufacturing processes, and tools prior to mass production. This ensures efficiency, accuracy, and cost-effectiveness. By utilizing 3D modelling of factory layouts, factory planners can digitally validate production line changes, substantially reducing costs when introducing new products. One key innovation involves creating 3D models using point cloud data from factory scans. Traditional factory scanning processes face limitations like blind spots and periodic scanning intervals. This research proposes using drones equipped with LiDAR (Light Detection and Ranging) technology for 3D scanning, enabling real-time mapping, autonomous operation, and efficient data collection. Drones can navigate complex areas, access small spaces, and optimize