Browse Topic: Computer simulation
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
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
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
Ensuring the safety and efficiency of autonomous vehicles in increasingly complex, dynamic, and structured road environments remains a key challenge. While traditional optimization-based approaches can provide safety guarantees, they often struggle to meet real-time requirements due to high computational complexity. Concurrently, although Control Barrier Functions (CBFs) can ensure instantaneous safety with minimal intervention, their inherent locality makes it difficult to consider global task objectives, potentially leading to mission failure in complex scenarios like lane-change obstacle avoidance. To address this trade-off between safety and mission completion, this paper proposes a hierarchical switching CBF safety framework. The core of this framework is to decompose complex lane-change tasks into multiple logical phases and to activate specialized, pre-designed CBF constraint sets via a top-level logic controller. Finally, we demonstrate the feasibility and safety of the
Rubber components are an important part of the suspension system of high-speed trains, and the complex nonlinear characteristics of rubber parts have a significant impact on the vehicle dynamic performance. This paper establishes a nonlinear dynamics model of the liquid composite swivel arm positioning node, which can reflect the dynamic stiffness and dynamic damping characteristics of the rubber components that change nonlinearly with the frequency and amplitude, and also has a fast calculation speed. The vehicle dynamics simulation model considering the longitudinal stiffness nonlinear characteristics of the arm node is established, and the influence of the stiffness nonlinearity of the liquid composite arm positioning node on the dynamic performance of the vehicle, such as straight-line stability and curve passing ability, is studied in depth through numerical simulation.
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