Browse Topic: Design Engineering and Styling
The Container trailers are used worldwide to transport goods & materials especially e-commerce applications with valuable materials. These container trailers are presently locked with a mechanical locking system and often broken and unlocked by unauthorized people. During transportation time, the driver stops the vehicle for natural calls, food or any other breakdown, the attempt is made to steal the materials. Many cases were known only after damages are done. It has become a serious issue nowadays in the transportation industry. To avoid these problems, we have designed and developed a system that operates pneumatically with digital locking control. The system is designed to ensure proper safety by rigid mechanical locking. It is actuated by a pneumatic system consisting of Directional control valve & pneumatic cylinders. The lock and unlock inputs are given through digitally and the digital controller provides the appropriate input to solenoid operated direction control valve. Based
During parking conditions of vehicles, the state of the battery is uncertain as it goes through the relaxation process. In such scenarios, the battery voltage may exceed the functional safety limits. If we cross the functional safety limits, it is hazardous to the driver as well as the occupant. In this case, relaxed voltage plays a crucial role in identifying the safe state of the battery. To estimate the relaxed cell voltage there are methods such as RC filter time constat modeling and relaxation voltage error method. The problem with these solutions is the waiting time and accuracy to determine the relaxation voltage. In this manuscript, a solution is proposed which ensures the above problem is reduced. To achieve the reduction of relaxation voltage estimation time, a python sparse identification of nonlinear dynamics (PySindy) is used which identifies and fits an equation model based on observing the battery characteristics at different SOC and temperatures. The implementation is
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
Traditionally, occupant safety research has centered on passive safety systems such as seatbelts, airbags, and energy-absorbing vehicle structures, all designed under the assumption of a nominal occupant posture at the moment of impact. However, with increasing deployment of active safety technologies such as Forward Collision Warning (FCW) and Autonomous Emergency Braking (AEB), vehicle occupants are exposed to pre-crash decelerations that alter their seated position before the crash. Although AEB mitigates the crash severity, the induced occupant movement leads to out-of-position behavior (OOP), compromising the available survival space phase and effectiveness of passive restraint systems during the crash. Despite these evolving real-world conditions, global regulatory bodies and NCAP programs continue to evaluate pre-crash and crash phases independently, with limited integration. Moreover, traditional Anthropomorphic Test Devices (ATDs) such as Hybrid III dummies, although highly
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
The integration of Advanced Driver Assistance Systems (ADAS) into modern vehicles necessitates innovative solutions for interior packaging that balance out safety, performance, and ergonomic considerations. This paper introduces an inverted U-shaped steel tube cross car beam (CCB) as a superior alternative to traditional straight tube designs, tailored for premium vehicle instrument panels. The U-shaped geometry overcomes the limitations of straight tube beams by creating additional packaging space for components such as AR-HUDs, steering columns, HVAC systems, and electronic control units (ECUs). This geometry supports efficient crunch packaging while accommodating ergonomic requirements like H-point, eyeball trajectory, and cockpit depth for optimal ADAS component placement. The vertical alignment of the steering column within the U-shaped design further enhances space utilization and structural integrity. This study demonstrates that the inverted U-shaped CCB is a transformative
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
The design and improvement of electric motor and inverter systems is crucial for numerous industrial applications in electrical engineering. Accurately quantifying the amount of power lost during operation is a substantial challenge, despite the flexibility and widespread usage of these systems. Although it is typically used to assess the system’s efficiency, this does not adequately explain how or why power outages occur within these systems. This paper presents a new way to study power losses without focusing on efficiency. The goal is to explore and analyze the complex reasons behind power losses in both inverters and electric motors. The goal of this methodology is to systematically analyze the effect of the switching frequency on current ripple under varying operating conditions (i.e., different combinations of current and speed) and subsequently identify the optimum switching frequency for each case. In the end, the paper creates a complete model for understanding power losses
Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The
With the rapid adoption of electric vehicles (EVs), ensuring the reliability, safety, and cost-effectiveness of power electronic subsystems such as onboard chargers, DC-DC converters, and vehicle control units (VCUs) has become a critical engineering focus. These components require thorough validation using precise calibration and communication protocols. This paper presents the development and implementation of an optimized software stack for the Universal Measurement and Calibration Protocol (XCP), aimed at real-time validation of VCUs using next-generation communication methods such as CAN, CAN-FD, and Ethernet. The stack facilitates read/write access to the ECU’s internal memory in runtime, enabling efficient diagnostics, calibration, and parameter tuning without hardware modifications. It is designed to be modular, platform-independent, and compatible with microcontrollers across different EV platforms. By utilizing the ASAM-compliant protocol architecture, the proposed system
In recent times, a standard driving cycle is an excellent way to measure the electric range of EVs. This process is standardized and repeatable; however, it has some drawbacks, such as low active functions being tested in a controlled environment. This sometimes causes huge variations in the range between driving cycles and actual on-road tests. This problem of variation can be solved by on-road testing and testing a vehicle for customer-based velocity cycles. On-road measurement may be high on active functions while testing, which may give an exact idea of real-world consumption, but the repeatability of these test procedures is low due to excessive randomness. The repeatability of these cycles is low due to external factors acting on the vehicle during on-road testing, such as ambient temperature, driver behavior, traffic, terrain, altitude, and load conditions. No two measurements can have the same consumption, even if they are done on the same road with the same vehicle, due to the
This paper presents a comprehensive testing framework and safety evaluation for Vehicle-to-Vehicle (V2V) charging systems, incorporating advanced theoretical modeling and experimental validation of a modern, integrated 3-in-1 combo unit (PDU, DCDC, OBC). The proliferation of electric vehicles has necessitated the development of resilient and flexible charging solutions, with V2V technology emerging as a critical decentralized infrastructure component. This study establishes a rigorous mathematical framework for power flow analysis, develops novel safety protocols based on IEC 61508 and ISO 26262 functional safety standards, and presents comprehensive experimental validation across 47 test scenarios. The framework encompasses five primary test categories: functional performance validation, power conversion efficiency optimization, electromagnetic compatibility (EMC) assessment, thermal management evaluation, and comprehensive fault-injection testing including Byzantine fault scenarios
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