Browse Topic: Electric motors
Electric vehicle subsystems, including powertrains, electric motors, and gearboxes, pose new challenges in achieving stringent acoustic performance targets for both interior and exterior noise. These challenges are intensified by increasingly demanding customer expectations regarding interior acoustic comfort, which encompasses the reduction of intrusive noise sources and the enhancement of overall sound quality across a broad frequency spectrum. A primary concern associated with electric vehicles subsystems is the generation of high-frequency tonal noise, commonly referred to as whine noise, which can significantly impact acoustic performance and passenger comfort. High-frequency whine noise propagates through multiple transmission paths and can be effectively attenuated at the source through encapsulation strategies, which also contribute to broadband noise reduction across a wide frequency spectrum. To predict the acoustic performance of encapsulation, a coupled simulation approach combining the Boundary Element Method (BEM), the Finite Element Method (FEM) and the Poroelastic Finite Element Method (PEM) has been developed. This methodology has been already presented and validated through experimental measurements, demonstrating its acoustic effectiveness in the encapsulation of a generic electric motor housing. While BEM is well-suited for modeling exterior acoustic propagation, standard implementations encounter limitations at high frequencies due to mesh density requirements and computational cost. This work presents hybrid parallelization strategies that integrate frequency-domain decomposition with multi-threading to accelerate BEM H-matrix computations. Frequency decomposition enables parallel processing by distributing independent frequency tasks across multiple processes, while multi-threading enhances performance for fine-grained operations such as matrix assembly and H-matrix compression within each frequency. The processes and improvements enabled by these strategies are discussed and presented within an adapted high-performance computing (HPC) environment.
To enhance the grinding quality of spiral bevel gears, an intelligent control model for the grinding process of automotive helical conical gears based on force feedback has been designed. This model outputs the control voltage for the machine tool's permanent magnet synchronous motor (PMSM), ensuring that the motor speed constantly tracks the desired value. By adjusting the grinding generating speed, the grinding force is controlled, and the tooth surface roughness is reduced. Firstly, the state equation of a permanent magnet synchronous AC servo motor is established. By employing the second method of Lyapunov, an RM adaptive control algorithm is developed. It is found that the model output can efficiently track the reference model (RM) and adjust to variations in torque due to load. To further enhance the controller, a generalized regression neural network (GRNN) was developed; subsequently, training data were generated using the output voltage of the RM self-adjusting controller to achieve velocity regulation of the machine tool's servo motor. Finally, the results indicate that the GRNN controller is superior. It uses RM self-adjusting control data as samples for regression analysis, outputs control signals, and controls the angular velocities of each axis of the machine tool to control the grinding force within a reasonable threshold range, reducing the complexity of the controller and achieving lightweight. At the same time, the feasibility of the controller has been experimentally verified. This improves the roughness of the tooth surface during the grinding of spiral bevel gears and enhances the quality of vehicle operation.
The objective of NASA's 4th New Frontiers Mission, Dragonfly, is to explore the surface chemistry and habitability of Saturn's largest moon, Titan. With its thick nitrogen atmosphere, liquid methane cycle, and rich, organic surface materials, Titan holds clues to prebiotic chemistry to answer fundamental scientific questions about the building blocks of life. The combination of high fluid density (4.4x) and low gravity (1/7th) compared to Earth makes exploration of this cryogenic ocean world in the outer solar system feasible by means of a relocatable lander - this is Dragonfly, a multi-rotor vehicle designed for the unique atmospheric conditions and environment at Titan. Dragonfly enables flight in a quad-rotor configuration with two counter-rotating, canted rotors mounted on each of four sting arms. All eight rotors are three-bladed, stiff metal rotors that are controlled by variable-speed electric motors. The objective of this paper is to tell the story of Dragonfly's rotor blade design and optimization, starting with the conceptual design based on flight requirements for Titan, preliminary design iterations of the rotor blades, and detailed design and optimization of the final configuration. Details are given with respect to design constraints driven by the cryogenic Titan environment, resulting from scientific instruments located on Dragonfly, and the overall mission flight profile. Design tools ranged from momentum theory to free-wake methods, hybrid computational fluid dynamics (CFD), and blade-resolved CFD analyses compared to wind tunnel measurements of rotor and lander combinations.
Emerging technologies in the field of electrified propulsion systems offer a promising solution to reduce the dependence on fossil fuels and improve efficiency. However, the design of high-power density electric machines introduces new challenges, including limited passive cooling potential and the issue of the weight of electric motors. To address these challenges, this paper considers analysis and design methods for high torque-to-weight ratio axial flux motors. A magnetic equivalent circuit model coupled with a lumped parameter thermal network is developed for design space exploration and optimization. This inexpensive analytical model predicts the performance of a single-stator dual-rotor axial flux motor based on geometry, loading condition, and slot and pole pair combination. To enable comparisons against real-world data, the optimization study was demonstrated using the hover mission requirements from the Research Aircraft for eVTOL Enabling techNologies (RAVEN) vehicle to minimize the mass of the motor. In tandem with the analytical model, a higher-fidelity finite element model was also developed, and good agreement between predicted power and efficiency was demonstrated across a range of axial flux motor designs. The lightest weight design that satisfied the hover mission requirements was the 12 pole pair 27 slot (12PP 27S) configuration with a fixed weight of 9.28 kg. The analytic model undersized the output power of the electric motor by approximately 9% across a range of slot and pole pair combinations.
Precision control in Level 4 Automated Vehicles is essential for enhancing operational efficiency, accuracy, and safety. This work, conducted as part of ARPA-E’s NEXTCAR program, focuses on developing a robust hardware and software control solution to enable drive-by-wire functionality. A previous publication by the authors presented the hardware solutions for overtaking stock vehicle controls. This paper focuses on a model-based and data-driven control algorithm to enable drive-by-wire functionality for longitudinal and lateral motion control for a 2021 Honda Clarity Plug-In Hybrid Electric Vehicle. This vehicle was equipped with a set of sensors and an onboard processing unit to enable Level 4 automation. For lateral controls, an algorithm was developed to command steering torque to the electronic power steering module, ensuring the vehicle could attain the desired steering angle position at varying speeds. The system leveraged feedforward and feedback mechanisms. Feedback controller gains were identified through frequency response analysis of the steering torque assist electric motor and were further refined during track testing. To optimize the controller’s response time, a feedforward function was developed using a physics-aware model of the vehicle's steering system. The independent feature selection for the model was guided by using the physics of the system. For longitudinal control, the control inputs included the positions of the brake and accelerator pedals sent to the stock ECU, with the desired speed as the setpoint. The setup used a combination of feedforward and feedback control to achieve the target acceleration or deceleration. These algorithms underwent extensive dynamometer and track testing to perform various maneuvers in conjunction with the automated driving system.
At the U.S. headquarters for Aumovio SE (formerly Continental Auto Group), the company showed its new remote temperature sensor for EV motors as part of its post-CES tech day presentations. The tech, which provides a more accurate reading of the rotor temperature of an EV motor, could lead to more sustainable motor designs by reducing the amount of rare earth materials used to increase the heat resistance of magnets. It can also improve potential motor performance. The e-motor rotor temperature sensor (e-RTS) is placed directly near the rotor, improving its tolerance range from 15 degrees C (59 F) to 3 degrees C (37 F). It communicates wirelessly to a wired transceiver elsewhere on the motor module (it can be moved around for better packaging).
Vertical Take-Off and Landing (VTOL) aircraft introduce complex monitoring challenges due to distributed propulsion, lightweight structures, and variable operating conditions. This paper presents advanced Frequency and Orders domain techniques that repurpose existing flight control, propulsion, and structural sensor data to enhance observability without additional instrumentation. By transforming vibration, acoustic, and electrical signals into frequency and order domains, the approach enables detection of harmonics, resonance, and fault signatures tied to rotor dynamics, supporting adaptive control and predictive maintenance. Beyond rotor systems, these techniques are equally effective for monitoring electric motor health, gearbox wear, bearing degradation, and structural coupling effects in composite airframes. They also provide insight into power electronics and thermal management systems by identifying spectral anomalies linked to electrical imbalance or cooling inefficiencies. Aggregated fleet data strengthens prognostic capabilities, enabling early detection of systemic issues and trend analysis. Applications include mitigating ground resonance and modal instabilities, as well as improving reliability of propulsion and structural subsystems. Integration into avionics emphasizes computational efficiency, scalability, and compliance with standards such as DO-160 [1], DO-178 [2], ARP4761 [3] and ARP4764 [4]. Simulation and bench testing confirm feasibility, demonstrating potential to enhance safety, reliability, and lifecycle cost for next-generation urban air mobility platforms.
Electric vehicle (EV) transmission efficiency is crucial for optimizing energy use and enhancing performance. It minimizes power losses during energy transfer from the motor to the wheels, directly impacting the vehicle's range and battery life. High efficiency ensures smoother acceleration and better driving dynamics, improving the overall user experience. Unlike internal combustion engine (ICE) transmissions, EV transmissions often employ simpler, single-speed systems, reducing complexity and energy loss. Efficient transmissions help reduce energy usage, lower costs, and minimize environmental impact. As a result, transmission efficiency plays a vital role in ensuring the sustainability and reliability of EV designs. This paper proposes a simulation model based methodology to estimate EV transmission efficiency based on modelica models developed on simulation X. A single speed EV model is developed which contains whole transmission layout discretized into simple components which include shafts, gears, bearing inertias and power loss components. The developed model considers load dependent losses which occur due to frictional losses because of surface contact between gear teeth, bearings, shafts and inertial losses based on operating conditions of the transmission required to accelerate or decelerate rotating components. Other losses due to oil churning, bearing drag and drag due to gears spinning in gear oil can be modelled using elements present in default library provided in simulation X. In the initial simulation runs, efficiency under operating region of torque speed curve of the electric motor are estimated by considering equidistant points and in subsequent runs overall power loss and efficiency over a duty cycle is estimated. Simulation results show good co-relation with measurements carried out at bench level on physical prototypes. The developed model is capable of modification to suit other single-speed EV transmissions with room left out for developing the same for multi-speed EV transmissions.
Electric motor benchmarking is often constrained by limited availability of motor-specific data, particularly when dealing with commercially available or third-party electric motors. This paper presents a streamlined and scalable methodology for characterizing unknown E-Motors using a configurable universal inverter platform. The proposed approach is specifically designed for OEMs and Tier 1 suppliers seeking to evaluate performance metrics such as torque accuracy, peak and continuous capability, efficiency, and control behavior—without prior access to key motor parameters or simulation data. A central challenge in this context is the stepwise electromagnetic characterization required to determine the phase current needed for accurate speed and torque control, especially under a Maximum Torque per Ampere (MTPA) or Maximum Torque per Watt (MTPW) strategy. As this requirement is highly dependent on the motor’s topology and electromagnetic properties, most conventional approaches rely on finite element method (FEM) simulations to derive the necessary control parameters. In contrast, the presented methodology assumes no such prior knowledge and instead utilizes only inverter-internal voltage and current measurements, complemented by control-side estimations. Apart from a standard external torque meter, no additional sensor instrumentation is required. The approach enables a low-threshold and efficient setup process, allowing rapid E-Motor commissioning and performance benchmarking. The methodology is based on high level testbed automation and smart optimization solutions. Experimental results demonstrate torque estimation errors within 2% of the reference demand, even in the absence of detailed motor models or simulation input. In the current study we demonstrated the methodology on a single motor within a standard E-Motor testbench environment. The methodology was proven over a wide range of motor types. This solution significantly reduces the barrier to performance analysis of unknown motors, enabling faster design iterations and informed decision-making regarding inverter topology, control strategy, and system-level cost-performance trade-offs.
As the brain and the core of the electric powertrain, the traction inverter is an essential part of electric vehicles (EVs). It controls the power conversion from DC to AC between the electric motor and the high-voltage battery to enable effective propulsion and regenerative braking. Strong and scalable inverter testing solutions are becoming more essential as EV adoption rises, particularly in developing nations like India. In India, traditional testing techniques that use actual batteries and e-motors present several difficulties, such as significant safety hazards, inadequate infrastructure, expensive battery prices, and a shortage of prototype-grade parts. This paper presents a comprehensive approach for traction inverter validation using the AVL Inverter TS™ system incorporating an advanced Power Hardware-in-the-Loop (PHiL) test system based on e-motor emulation technology. It enables safe, efficient, and reliable testing eradicating the need for actual batteries or mechanical loads. Testing across signal and power levels and the validation of both inverter hardware and software under real-world driving scenarios can be facilitated with proposed test system. Indian OEM challenges like reduction in battery development costs, ensuring high replication precision, and managing thermal and power instability in early-stage prototypes are primary focus areas for this test system. With the Inverter TS, various motor types (IM, EESM, PMSM), switching strategies, and SiC based 800V architectures with different control architectures can be emulated and validated, which can further be optimized for powertrain efficiency. Inverter efficiency maps can be derived and fast control strategy can be iterated which facilitates the the overall drivetrain optimization. This paper focus on how adopting such emulation test methodologies can help EV developers to overcome infrastructure gaps, reduce time-to-market, and enhance powertrain efficiency at a lower cost.
Electric vehicle (EV) transmissions play a vital role in powering EVs by channeling energy from the electric motor to the wheels. Recently, the focus has shifted to multi-speed transmissions in the EV sector due to their potential to improve efficiency and performance. By utilizing various gear ratios, these transmissions enable the motor to function within its most efficient range across different speeds. Most of these transmissions need electric control unit (ECU) with software for optimal functionality and smoother gear shifting. These controllers incorporate controller area network (CAN) communication protocol to operate along with other ECUs. Thus validation of these transmissions is a challenge as they are clutch less, motor has to be controlled for speed matching and have electro mechanical systems replacing conventional systems for operation. This paper proposes a methodology to validate multispeed EV transmissions on a test bench. The validation setup consists of electric motor at the input of a two speed EV transmission and inertia at the output of the transmission to simulate vehicle along with control unit flashed with vehicle level software. Scripts based on C-language and panels are developed which use CAN database (dbc) file of the vehicle to communicate between electric motor, transmission and vehicle control unit. Using the panels the user either controls the gear shift actuation manually or automate the gear shifting process as per vehicle operating conditions for evaluating the gear shift process. Using the mentioned methodology various vehicle scenarios can be simulated and validated on test bench at early stages, thus providing important feedback in development stage for software refinement for optimal operation of the motor and transmission actuator during gear shift process. The developed scripts can be modified to match for other vehicle configurations.
The automotive industry is undergoing a transformational shift with the addition of Virtual ECU in the development of software and validation. The Level 3 Virtual ECU concept will lead to the transformation in the SDLC process, as early detection of defects will have a significant impact on cost and effort reduction. This paper explains the application of a Level 3 virtual ECU which can enable to perform testing in initial period considering the Shift Left Strategy, which will significantly reduce development time. This paper demonstrates various development and validation strategies of virtual ECU and how it can impact project timeline.
This paper elucidates the implementation of software-controlled synchronous rectification and dead time configuration for bi-directional controlled DC motors. These motors are extensively utilized in applications such as robotics and automotive systems to prolong their operational lifespan. Synchronous rectification mitigates large current spikes in the H-bridge, reducing conduction losses and improving efficiency [1]. Dead time configuration prevents shoot-through conditions, enhancing motor efficiency and longevity. Experimental results demonstrate significant improvements in motor performance, including reduced thermal stress, decreased power consumption, and increased reliability [2]. The reduction in power consumption helps to minimize thermal stress, thereby enhancing the overall efficiency and longevity of the motor.
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