Browse Topic: Electronic control systems
Electric vehicles (EVs) present a distinct set of challenges in noise, vibration, and harshness (NVH) compared to traditional internal combustion engine (ICE) vehicles. As EVs operate with significantly reduced engine noise, other sources of noise, such as motor whine, power electronics, and road and wind noise, become more noticeable. This review paper explores the key NVH issues faced by EVs, including high-frequency tonal noise from electric motors, gear meshing, and vibrations. Additionally, it examines recent advancements and trends in NVH mitigation techniques, such as active noise control, improved material insulation, and advanced vibration isolation systems. Furthermore, this paper discusses the role of computational tools, simulation technologies, and testing methodologies in predicting and addressing NVH concerns in EVs. By providing an in-depth analysis of the challenges and the latest innovations, this review aims to contribute to the ongoing development of quieter and
Due to the high-power density, high torque rating, low torque ripples and fault-tolerant capability, the Dual Three-Phase Permanent Magnet Synchronous Motor (DTP-PMSM) has recently emerged as a feasible alternative for automotive applications. However, it comes with its own challenge of increased losses at low torque due to the use of 6-phase inverter or two three-phase inverters. The DTP-PMSM drive model can be designed to function in two operating modes, double-channel (dual three-phase) mode with both the inverters operating, and single-channel (three-phase) with one of the two inverters shut down. This paper proposed an efficiency analysis between single channel and double channel modes in a DTP-PMSM drive. A simulation model is prepared to calculate efficiency, and the losses associated with different parts of battery fed DTP-PMSM drive system operated in both modes. Detailed loss model is simulated to represent efficiency of a battery-fed DTP-PMSM drive system. Both single
Software Defined Vehicle (SDV) is gaining attraction in the automotive industry due to its wide range of benefits like remote software/feature upgrade, scalable functionality, Electronic Control Unit (ECU) commonization, remote diagnostics, increased safety, etc. To obtain all these benefits, ECUs need to be designed accordingly. ECU hardware must be designed to support a range of vehicles with a variety of loading, scalable features, power distribution, levels of processing, and networking architecture. Each domain has unique challenges to make the ECU economical and robust to operating conditions without compromising performance. This paper illustrates the critical hardware design challenges to accommodate a scalable SDV architecture. This paper focuses electrical interface design to support wide range of input/output port loads, scalable functionality, and robust diagnostics. Also, flexibility of microprocessor processing capability, ECU networking, and communication complexity are
The driving capability and charging performance of electric vehicles (EVs) are continuously improving, with high-performance EVs increasing the voltage platform from below 500V to 800V or even 900V. To accommodate existing low-voltage public charging stations, vehicles with high-voltage platforms typically incorporate boost chargers. However, these boost chargers incur additional costs, weight, and spatial requirements. Most mature solutions add a DC-DC boost converter, which results in lower charging power and higher costs. Some new methods leverage the power switching devices and motor inductance within the electric drive motor to form a boost circuit using a three-phase current in-phase control strategy for charging. This approach requires an external inductor to reduce charging current ripple. Another method avoids the use of an external inductor by employing a two-parallel-one-series topology to minimize current ripple; however, this reduces charging power and increases the risk
Unmanned Underwater Vehicles (UUVs) are used around the world to conduct difficult environmental, remote, oceanic, defense and rescue missions in often unpredictable and harsh conditions. A new study led by Flinders University and French researchers has now used a novel bio-inspired computing artificial intelligence solution to improve the potential of UUVs and other adaptive control systems to operate more reliability in rough seas and other unpredictable conditions.
As automotive technology advances, modern vehicles increasingly rely on complex electronics such as cameras, sensors, radar and lidar. These components are critical for advanced driver-assistance systems (ADAS) and automated driving. With the growing complexity of these systems, automotive manufacturers face challenges in efficiently transmitting both power and data while minimizing weight and system complexity. Power over Coaxial (PoC) technology offers a solution by allowing the transmission of power and data over a single coaxial cable, significantly simplifying vehicle design. With the integration of more electronic systems, especially those required for ADAS and autonomous driving, the demand for power and high-speed data transmission in vehicles has surged. Modern cars now use multiple cameras and sensors, and as vehicle systems continue to evolve, the number of electronic components is expected to increase. This shift places significant demands on the transmission of both data
Researchers have developed a gel polymer-based triboelectric nanogenerator (TENG) that generates electrical signals from body movement to power electronics like LEDs and functions as a self-powered touch panel for user identification. The device can stretch up to 375 percent of its original size and withstand rigorous mechanical deformations, making it suitable for wearable applications. TENGs that convert mechanical energy such as body movement to electrical energy offer a solution to power wearable devices without relying on batteries.
The advancement of the automotive industry towards automation has fostered a growing integration between this field and automation. Future projects aim for the complete automation of the act of driving, enabling the vehicle to operate independently after the driver inputs the desired destination. In this context, the use of simulation systems becomes essential for the development and testing of control systems. This work proposes the control of an autonomous vehicle through fuzzy logic. Fuzzy logic allows for the development of sophisticated control systems in simple, easily maintainable, and low-cost controllers, proving particularly useful when the mathematical model is subject to uncertainties. To achieve this goal, the PDCA method was adopted to guide the stages of defining the problem, implementation, and evaluation of the proposed model. The code implementation was done in Python and validated using different looping scenarios. Three linguistic variables were used, one with three
Emergence of Software Defined Vehicles (SDVs) presents a paradigm shift in the automotive domain. In this paper, we explore the application of Model-Based Systems Engineering (MBSE) for comprehensive system simulation within the SDV architecture. The key challenge for developing a system model for SDV using traditional methods is the document centric approach combined with the complexity of SDV. This MBSE approach can help to reduce the complexity involved in Software-Defined Vehicle Architecture making it more flexible, consistent, and scalable. The proposed approach facilitates the definition and analysis of functional, logical, and physical architecture enabling efficient feature and resource allocation and verification of system behaviour. It also enables iterative component analysis based on performance parameters and component interaction analysis (using sequence diagrams).
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