A Novel Multi-Criteria Local Latin Hypercube Refinement System for Commutation Angle Improvement in IPMSMs

Asef, Pedram; Denaï, Mouloud; Paulides, Jjh Johannes; Marques, Bruno Ricardo; Lapthorn, Andrew

doi:10.1109/tia.2022.3225786

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…Among all types of synchronous motors without electric current in their rotor, permanent magnet synchronous machines (PMSM)s gained significant consideration in the electric vehicle (EV) market recently 1 , 2 . This is due to their high performance, such as high torque at low operational speeds and efficient and reliable inverters.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers used deterministic methods 2 , 5 , 15 – 18 , stochastic 19 (e.g. genetic 20 – 22 and particle swarm 23 – 25 algorithms) methods, and machine learning (ML) methods 1 , 26 – 33 , such as Bayesian optimization (BO) algorithms 31 , 34 , to improve different aspects of motor performance. Most probabilistic ML methods offer a promising framework for understanding the uncertainty of the design problem and its performance predictions.…”

Section: Introductionmentioning

confidence: 99%

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A physics-informed Bayesian optimization method for rapid development of electrical machines

Asef,

Vagg

2024

Sci Rep

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Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A physics-informed Bayesian optimization method for rapid development of electrical machines

Asef,

Vagg

2024

Sci Rep

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Resilient Multi-range Radar Detection System for Autonomous Vehicles: A New Statistical Method

Enayati

Asef

Wilson

2023

J. Electr. Eng. Technol.

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Critical issues with current detection systems are their susceptibility to adverse weather conditions and constraint on the vertical field view of the radars limiting the ability of such systems to accurately detect the height of the targets. In this paper, a novel multi-range radar (MRR) arrangement (i.e. triple: long-range, medium-range, and short-range radars) based on the sensor fusion technique is investigated that can detect objects of different sizes in a level 2 advanced driver-assistance system. To improve the accuracy of the detection system, the resilience of the MRR approach is investigated using the Monte Carlo (MC) method for the first time. By adopting MC framework, this study shows that only a handful of fine-scaled computations are required to accurately predict statistics of the radar detection failure, compared to many expensive trials. The results presented huge computational gains for such a complex problem. The MRR approach improved the detection reliability with an increased mean detection distance (4.9% over medium range and 13% over long range radar) and reduced standard deviation over existing methods (30% over medium range and 15% over long-range radar). This will help establishing a new path toward faster and cheaper development of modern vehicle detection systems.

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Coordinated configuration of hybrid energy storage for electricity-hydrogen integrated energy system

Liu,

Zhang,

Zhang

2024

Journal of Energy Storage

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A Novel Multi-Criteria Local Latin Hypercube Refinement System for Commutation Angle Improvement in IPMSMs

Cited by 7 publications

References 42 publications

A physics-informed Bayesian optimization method for rapid development of electrical machines

A physics-informed Bayesian optimization method for rapid development of electrical machines

Resilient Multi-range Radar Detection System for Autonomous Vehicles: A New Statistical Method

Coordinated configuration of hybrid energy storage for electricity-hydrogen integrated energy system

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