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.
Honda is promoting mobility electrification to realize a carbon-neutral society by 2050. Hybrid vehicles will remain advantageous over electric vehicles in terms of manufacturing cost and driving range until renewable energy usage increases, charging infrastructure is sufficiently developed, and battery costs are reduced. In response to this situation, Honda has developed a new control system, “Honda S+ Shift”, which further enhances the “emotional value of driving pleasure” inherent to the e:HEV system and creates new value for hybrid vehicles. Honda S+ Shift synchronizes the engine and vehicle speed and selects a virtual gear position according to the driver's operation such as acceleration, cornering, and deceleration. Subsequently, the system achieves the required system output in cooperation with a dedicated energy management system. It also works with each vehicle system, such as drive force control, sound control, and meter cluster, to stimulate all five senses of the driver, greatly enhancing synchronization between the driver's senses and vehicle behavior. This paper explains how the concept of Honda S+ Shift is realized with the e:HEV system. Since there are no mechanical gear restrictions, Honda S+ Shift can select each gear ratio freely and enables fast shifting. On the other hand, there are difficulties in realizing realistic shifting behavior without direct connection between an engine and tires. Furthermore, e:HEV has three specific operation modes: EV, Hybrid and Engine, and Honda S+ Shift has to utilize them to keep its high efficiency. The first part of this paper describes the setting of the gear ratios, drive force and deceleration. Next, it explains engine speed control to realize sharp shifting and traction motor control to produce a realistic shift feel. Then, it describes how Honda S+ Shift utilizes three modes of e:HEV. It also refers to cooperation with sound and meter cluster control to maximize its effects.
Monitoring power device temperature in an electric vehicle propulsion drive converter is extremely important to achieve full power delivery within the maximum power capability envelope. Usually, on-die temperature sensors are installed on Si-IGBT power devices in electric vehicle propulsion drive converters to enable monitoring device temperature and achieve over-temperature protection. Currently, SiC MOSFET is a promising power device in power converters of electric drives because of its lower loss, higher switching speed, higher voltage capability, and higher junction temperature limit in comparison with the widely used Si-IGBT. However, SiC MOSFET is a more expensive device, installation of an on-die temperature sensor on SiC MOSFET will significantly increase its cost and complexity. So presently, there is no junction temperature sensor installed in SiC MOSFET due to which there is great difficulty protecting SiC MOSFET from over temperature. When a junction temperature estimation method is used to monitor SiC MOSFET temperature, the power loss computation of SiC MOSFET is a key factor. However, the existing loss calculation method assumes electric motors operate at high speed and power loss is computed/estimated with rms current. When the motor operates at low speed, the existing method is not practical because of a long fundamental period of phase current. The instantaneous power losses and hence the junction temperatures of the six power converter switches are not the same under such operating conditions. Hence, the junction temperature obtained with the loss computed from the rms current value does not reflect the actual device junction temperature, which may result in a failure of power device over-temperature (OT). This paper proposes a converter power device OT protection method under low motor speed to solve the above issues. The proposed method allows system cost and complexity reduction. It computes/estimates instantaneous loss and junction temperature for each individual power device of the power converter, which enables the OT detection at the motor low speed and/or imbalance phase operation. The paper presents the technical principle of the proposed instantaneous power loss and junction temperature estimation method, and OT protection for SiC MOSFET. Its model details, simulation, and experimental test results verify the new method.
Conventional inverter control uses a fixed switching frequency, which leads to high-pitched switching noise in electric vehicles (EVs) that does not vary with vehicle speed. Although EVs are much quieter than traditional internal combustion engine (ICE) vehicles, some EV owners complain about the lack of dynamic driving sound feedback. A new patented technology has been developed to enhance EV sound quality by dynamically controlling the inverter switching frequencies. This technology generates dynamic propulsion sound with new "switching order" features at multiple harmonics, with the pitch proportional to vehicle speed. A constant pulse ratio between the switching frequency and the electric motor RPM is implemented to control the switching order. This reduces switching losses during low-speed operation and provides boosted acoustic feedback to the driver during acceleration, which enhances driving experience during sports driving. Furthermore, a special "EV shifting" sound that mimics the sound of gear shifting is generated by controlling different pulse ratios at each shifting RPM zone. High switching orders are dropped between neighboring zones to boost lower frequencies for an enhanced dynamic driving experience. These new switching sound features have been validated through electric drive unit and electric vehicle tests. Jury tests confirm the new EV sound with switching orders is authentically generated from electric propulsion system and can be tuned by controlling the switching pulse ratio. This technology has been successfully implemented in the "Watts-to-Freedom" drive mode of the Hummer EV, providing enhanced EV sound feedback, and improving the driver's experience during high-acceleration events.
Inverters are typically integrated into electric drive units for electric vehicles (EVs) to reduce packaging size and cost. However, coupled vibrations from the electric motor and gears are transmitted to the inverter, which can become a dominant noise source due to its large radiative panel. Metal panels are required for electromagnetic interference (EMI) compliance, yet these covers usually lack sufficient stiffness or damping for noise control. Adding ribs and applying damping treatments result in excessive mass, cost, and packaging challenges. A new bubble sheet panel design has been developed to enhance the structural strength and damping performance of the inverter cover while significantly reducing its mass. A thin sheet of aluminum is welded onto the cover in an optimized pattern that enhances stiffness and damping performance while accommodating packaging requirements. The welding pattern can include logos or artistic designs to improve the panel’s appearance. The metal sheets are blown apart between the welds to form a 3D structure that is three times stiffer and twice as strong as a flat monolithic sheet of the same thickness. The composite dual bubble sheets can effectively reduce structural vibration. Damping materials, such as liquid-applied sound deadener (LASD), can be injected into the bubble sheet cavities to significantly improve its damping performance. Prototype bubble sheets have been designed, analyzed, and tested for an integrated inverter used in front-wheel-drive (FWD) electric drive unit for EV applications. Analysis and modal tests show a reduction of 10 to 15 dB at panel resonances with close to 30% reduction in mass. The bubble sheet panel is installed on an integrated inverter and tested in the electric drive unit. Sound power measurements confirm a reduction of up to 10 dB in inverter panel vibration.
Hybrid electric vehicles (HEVs) with an increasing level of electrification, are becoming a major part of the global energy transition. To achieve lower engine tailpipe exhaust emissions and improve total fuel consumption, typically the HEV control system expertly and frequently switches between the internal combustion engine and electric motor drive, with multiple stops and restarts of the internal combustion engine (ICE). As a consequential result of this switching, are typically slower or even incomplete engine warm-up times, depending on the engine speed, load pattern and run time of the vehicle drive cycle. Along with the speed and load transient control, the engine stop and start processes are also challenging to control, with respect to cold start fuel and combustion by-products entering the oil. Consequently, contamination enters the engine oil but may not completely leave. These effects are highly transient over the drive cycle. Contaminants and in particular, fuel dilution, will affect the engine oil viscosity. To demonstrate this whilst yielding insights, a precisely controlled engine test cell, running the cold start Worldwide Harmonized Light Duty Transient Cycle (WLTC) for both, a non-hybridized ICE only vehicle and a HEV in charge sustaining mode operation is described. This also has on-line viscosity sensing and oil sampling. Typical data is shared along with engine oil comparisons. For complimentary insights, the impact of the fuel dilution on engine friction was investigated using a novel, precise, fully transient engine friction test rig, which measures gasoline direct injection high pressure fuel-pump friction and engine oil viscosity accurately. The cycle is based on measured data from vehicles tested on a chassis dynamometer. On-line friction data, with oil comparisons is used to show real-time data of the effect of fuel dilution on the frictional energy required, thus CO2 over the full WLTC.
Improving the energy efficiency of electrified vehicles remains a central objective in modern electric powertrains. Multi-level converters (MLCs) are widely recognised for lowering conversion losses relative to two-level inverters and improving total harmonic distortion (THD) in the sinusoidal supply to motors with a consequent reduction in motor losses. Despite this, sustained production-oriented validation at the integrated system level remains limited. This work introduces a multi-level converter architecture of the Battery Integrated Modular Multi-Level Converter (BIMMC) topology using Cascaded H-Bridge (CHB) architecture. It offers improvements in all key metrics of performance, cost, package size, mass and robustness compared to the current state-of-the-art two-level inverter system with distributed functions for charging available in the market today. The overall solution is highly functionally integrated. It supports four major functions required in electric vehicles without the need for additional hardware. Firstly, supply to and control of a three-phase electric motor without the need for a separate, standalone inverter. Secondly, all usual battery management system (BMS) functionality including energy and State of Charge (SOC) management to module level enabling usable energy and robustness improvements. Thirdly, the ability to charge from both alternating current (AC) (single phase and three-phase) and direct current (DC) sources without the need for separate on-board charger (OBC) hardware whilst also enabling an innovative pulse charging approach which benefits both charging time and battery ageing compared to conventional DC charging. Finally, the ability to deliver a controlled DC supply to non-traction loads on the vehicle with high efficiency and redundancy. The BIMMC topology proposed has been designed, built at prototype level and tested in order to collect performance data to empirically validate empirical study of the performance and functional benefits of the approach for traction motor drive, battery stored energy management and charging. Measured results demonstrate that improved inverter waveform quality correlates with lower motor harmonic losses and measurable drive-cycle efficiency gains, consistent with prior MLC assessments. Battery SOC depletion can be managed actively within the complete battery yielding increased usable energy and further driving range gains. Pulse charging shortens charge time whilst maintaining battery health metrics within acceptable limits, aligning with experimental evidence on pulse-based fast charging. The topology has also demonstrated the ability to pulse charge cells in a complete battery pack whilst consuming incoming DC supply current from a standard commercially available DC charger (Electric Vehicle Supply Equipment - EVSE). This potential to offer the benefits associated with pulse charging without requiring change to existing deployed charging infrastructure. Overall, proposed CHB-BIMMC architecture offers a practical blueprint for next-generation electric vehicles (EVs), and is compatible with ongoing integration trends that converge traction, charging and battery management functions within a unified power electronics and control platform.
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).
Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range. This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning, zonal climate control, and integration with renewable energy sources. By implementing such energy-efficient solutions, EVs can achieve better range performance, improved user satisfaction, and greater environmental benefits. Modern EV climate control systems increasingly rely on intelligent features such as Auto mode, which dynamically adjusts fan speed, airflow direction, and temperature settings based on real-time cabin and ambient conditions. By leveraging sensor data and adaptive control algorithms, Auto mode optimizes thermal comfort while minimizing unnecessary energy expenditure. This automated regulation plays a crucial role in reducing HVAC-related power consumption, thereby contributing to overall range improvement and enhancing system efficiency without compromising passenger comfort. This study focuses on the development of three distinct Auto mode calibration levels for each set condition, designed to achieve the same cabin temperature with varying dynamic responses and energy consumption profiles. In Auto mode, the cabin temperature is regulated through intelligent control of compressor speed, blower speed, and evaporator temperature. While all Auto levels can maintain the desired setpoint, the time required to reach this temperature and the system’s responsiveness to sudden thermal loads can vary significantly. This study introduces three distinct calibration profiles, each engineered to achieve the same cabin temperature under different dynamic conditions and energy consumption levels. These profiles allow users to choose between faster thermal response or reduced power usage, effectively enabling a trade-off between immediate comfort and extended driving range
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.
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