Browse Topic: Vehicle drive systems
The problem of monitoring the parametric failures of a traction electric drive unit consisting of an inverter, a traction machine and a gearbox when interacting with a battery management system has been solved. The strategy for solving the problem is considered for an electric drive with three-phase synchronous and induction machines. The drive power elements perform electromechanical energy conversion with additional losses. The losses are caused by deviations of the element parameters from the nominal values during operation. Monitoring gradual failures by additional losses is adopted as a key concept of on-board diagnostics. Deviation monitoring places increased demands on the information support and accuracy of mathematical models of power elements. We take into account that the first harmonics of currents and voltages of a three-phase circuit are the dominant energy source, higher harmonics of PWM appear as harmonic losses, and mechanical losses in the rotor and gearbox can be
E-mobility is revolutionizing the automotive industry by improving energy-efficiency, lowering CO2 and non-exhaust emissions, innovating driving and propulsion technologies, redefining the hardware-software-ratio in the vehicle development, facilitating new business models, and transforming the market circumstances for electric vehicles (EVs) in passenger mobility and freight transportation. Ongoing R&D action is leading to an uptake of affordable and more energy-efficient EVs for the public at large through the development of innovative and user-centric solutions, optimized system concepts and components sizing, and increased passenger safety. Moreover, technological EV optimizations and investigations on thermal and energy management systems as well as the modularization of multiple EV functionalities result in driving range maximization, driving comfort improvement, and greater user-centricity. This paper presents the latest advancements of multiple EU-funded research projects under
The design of drive units in electric vehicles (EVs) presents challenges due to the need to pass multiple linear and non-linear load cases. This can result in inefficient design. Therefore, optimization plays a critical role in improving the design efficiency. However, setting up the optimization process itself can be challenging, especially when dealing with complex design variables and different load cases that require the use of various computer-aided engineering (CAE) solvers. The drive unit, being a casting component, presents additional challenges in setting up Multidisciplinary Design Optimization (MDO) process. This paper introduces an efficient process for addressing these challenges by presenting a sample Multidisciplinary Design Optimization (MDO) problem. The problem involves the manipulation of discrete design variables, such as the number of ribs, and incorporates five different load cases that require the utilization of different CAE solvers. The proposed process
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
From humble Chevrolet Bolts to six-figure Lucid Airs, every EV can reverse its electric motors to slow the vehicle while harvesting energy for the battery, the efficient tag-team process known as regenerative braking. Today's EVs do this so well that traditional friction brakes, which clamp onto a spinning wheel rotor or drum, can seem an afterthought. Witness Volkswagen's decision to equip its ID.4 with old-fashioned rear drum brakes, with VW claiming drums reduce EV rolling resistance and offer superior performance after long periods of disuse.
Customers are expecting higher level of refinement in electric vehicle. Since the background noise is less in electric vehicle in comparison with ICE, it is challenging for NVH engineers to address even minor noise concerns without cost and mass addition. Higher boom noise is perceived in the test vehicle when driven on the coarse road at a speed of 50 kmph. The test vehicle is rear wheel driven vehicle powered by electric motor. Multi reference Transfer Path Analysis (TPA) is conducted on the vehicle to identify the path through which maximum forces are entering the body. Based on the findings from TPA, solutions like reduction in the dynamic stiffness of the suspension bushes are optimized which resulted in reduction of noise. To reduce the noise further, Operational Deflection Shape (ODS) analysis is conducted on the entire vehicle to identify the deflection shapes of all the suspension components and all the body panels like floor, roof, tailgate, dash panel, quarter panel and
In Electric vehicle Drive Unit Gears, high mesh misalignments result in shift in load distribution of a gear pair that can increase contact and bending stresses. It can move the peak bending and contact stresses to the edge of the face width and increase gear noise as well. Lower misalignment value is often required to reduce the peak bending and contact stresses and have a balanced load distribution along the gear flank, which in turn helps in reducing noise and improving durability of drive unit. This paper delineates Prescriptive Analytics method that combines virtual simulations, Machine learning (ML) and optimization techniques to minimize different gear misalignments for the electric vehicle drive units. Generally, the manual optimization process is carried out by sequential modifications of stiffness of individual components. However, this process is time consuming and does not account for interactions between the components. In this study, firstly, Machine learning models are
The inductance parameter is important for the flux regulation performance of the hybrid excitation motor, and the axial structure leads to the change in the inductance parameter of the axial-radial hybrid excitation motor (ARHEM). To clarify the inductance characteristic of the ARHEM with different winding construction and the mutual coupling effect between the axial excitation and permanent magnet excitation on the inductance. Firstly, the structure of the ARHEM is presented. Secondly, the self and mutual inductance characteristics of ARHEM are analyzed using the winding function method. Then, the influence of the axial excitation structure on the armature reaction field and saliency ratio of ARHEM. On this basis, the mechanism of the mutual coupling, between the axial excitation and permanent magnet field under different excitation currents on the main air gap magnetic field, and the inductance of ARHEM with fractional slot are revealed.
Design validation plays a crucial role in the overall cost and time allocation for product development. This is especially evident in high-value manufacturing sectors like commercial vehicle electric drive systems or e-axles, where the expenses related to sample procurement, testing complexity, and diverse requirements are significant. Validation methodologies are continuously evolving to encompass new technologies, yet they must be rigorously evaluated to identify potential efficiencies and enhance the overall value of validation tests. Simulation tools have made substantial advancements and are now widely utilized in the development phase. The integration of simulation-based or simulation-supported validation processes can streamline testing timelines and sample quantities, all the while upholding quality standards and minimizing risks when compared to traditional methods. This study examines various scenarios where the implementation of advanced techniques has led to a reduction in
This project presents the development of an advanced Autonomous Mobile Robot (AMR) designed to autonomously lift and maneuver four-wheel drive vehicles into parking spaces without human intervention. By leveraging cutting-edge camera and sensor technologies, the AMR integrates LIDAR for precise distance measurements and obstacle detection, high-resolution cameras for capturing detailed images of the parking environment, and object recognition algorithms for accurately identifying and selecting available parking spaces. These integrated technologies enable the AMR to navigate complex parking lots, optimize space utilization, and provide seamless automated parking. The AMR autonomously detects free parking spaces, lifts the vehicle, and parks it with high precision, making the entire parking process autonomous and highly efficient. This project pushes the boundaries of autonomous vehicle technology, aiming to contribute significantly to smarter and more efficient urban mobility systems.
In highly populated countries two-wheelers are the most convenient mode of transportation. But at the same time, these vehicles consume more fuel and produces emissions in urban driving. This work is aimed at developing a hybrid two-wheeler for reducing fuel consumption and emissions by incorporating electric vehicle technology in a conventional two-wheeler. The hybrid electric scooter (HES) made consisted of an electric hub motor in the front wheel as the prime mover for the electrical system. The powertrain of the HES was built using a parallel hybrid structure. The electric system is engaged during startup, low speeds, and idling, with a simple switch facilitating the transition between electric and fuel systems. The HES was fabricated and tested through trial runs in various operating modes. Before conversion to a hybrid system, the two-wheeler achieved a mileage of 34 km/liter. After conversion, the combined power sources resulted in an overall mileage of 55 km. It was observed
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