Electrical Machines and Drives
Summary
Electrical machines and drives form the cornerstone of modern power conversion, enabling efficient transfer of energy between electrical and mechanical domains. Machines such as induction motors, permanent-magnet synchronous machines and switched-reluctance machines each offer distinct advantages in power density, efficiency and controllability. Drives employ power electronic converters to regulate voltage, frequency and current in accordance with the demands of diverse applications—from sub-watt precision actuators to multi-megawatt turbomachinery. Advances in digital control, real-time estimation and modular converter architectures have led to highly integrated systems that deliver fast dynamic response, robust sensorless operation and seamless connectivity with higher-level automation networks. Thermal, electromagnetic and mechanical design co-optimisation ensures that modern drives meet stringent requirements for efficiency, fault resilience and maintenance intervals across sectors such as transport, industry and renewable energy.
Research from Nature Portfolio
High-precision angle adaptive control methods have been developed for synchronous motors in aerospace platforms, employing coordinate-system modelling and adaptive PID laws to achieve sub-0.15 rad control error under rapid manoeuvres. These algorithms accommodate varying transmission ratios and environmental conditions, demonstrating exceptional adaptability in simulation studies. A sensorless space-vector PWM direct-torque-control scheme for axial-flux permanent-magnet machines has been proposed to minimise torque ripple in electric-vehicle in-wheel applications. By integrating disturbance observers with SVPWM and DTC, the method ensures high performance under standard driving cycles while enabling compact motor placement. In parallel, a physics-informed Bayesian optimisation framework has been introduced for electric-vehicle traction machines. Coupling Gaussian-process surrogates with finite-element models, this approach accelerates exploration of complex slot and winding geometries, achieving 20 % higher slot-filling factors and 12 % torque gains with 45 % less computation than genetic algorithms.
Electrical Machines and Drives publication trend
The graph below shows the total number of articles in electrical machines and drives across all publications each year (not limited to Nature Index journals).
Technical terms
Sensorless control: A drive technique that estimates rotor position or speed using electrical measurements rather than physical sensors.
Space-vector PWM (SVPWM): A modulation method that synthesises three-phase inverter output by mapping voltage vectors in a two-axis reference frame.
Sliding-mode observer: A nonlinear estimation algorithm that forces error trajectories onto a predefined manifold for robust state recovery.
Bayesian optimisation: A sequential design strategy using surrogate models to efficiently locate optimal parameters in expensive design spaces.
Slot-filling factor: The ratio of conductor cross-sectional area to total slot area, reflecting winding compactness and current density.
I-f (current–frequency) method: A low-speed startup technique that controls line frequency and current amplitude to initiate rotor motion without back-EMF feedback.
References
- High-precision angle adaptive control simulation of synchronous motor for automatic lifting and boarding equipment of aircraft platform. Scientific Reports (2023).
- Sensorless based SVPWM-DTC of AFPMSM for electric vehicles. Scientific Reports (2022).
- A physics-informed Bayesian optimization method for rapid development of electrical machines. Scientific Reports (2024).
- Traction motors for electric vehicles: Maximization of mechanical efficiency – A review. Applied Energy (2024).
- Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Fuzzy Sliding Mode Observer. IEEE Access (2022).
- Research on Startup Process for Sensorless Control of PMSMs Based on I-F Method Combined With an Adaptive Compensator. IEEE Access (2020).
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