Design and Control of Electrical Machines for Electric Vehicles

Topic Information

Dear Colleagues,

With the global attention to environmental protection and sustainable energy development, the market demand for electric vehicles (EVs) is gradually increasing. As one of the key components, the electrical machines involved electric vehicles have achieved substantial levels of development and are poised to become integral to further technological innovations. The main directions of innovation and development include being more efficient, lighter, higher-power, smarter and more diversified, and comfort and safety have also become integral elements. The achievement of these objectives may involve the next-generation machine topology, innovative research on control strategy, new techniques in magnetics and field analysis, as well as advanced technologies for power electronic converters and inverters. This special section aims to present current state-of-the-art research addressing the design and control of electrical machines within the context of electric vehicle applications. We encourage researchers in this field to contribute their original papers to share their technical achievements with the readers. The subjects include, but are not limited to:

1. New machine topology of electric machines in EV;
2. Innovative methods of machine design and analysis in EV;
3. Multi-objective optimization of electric machines in EV;
4. Vibration and noise suppression technology of electric machines in EV;
5. Efficient and reliable control of the power train in EV;
6. Power electronic devices in the application of EV machine;
7. New technologies related to motor and drive integration in EV;
8. Applications for hub motors or multi-motor systems in EV;
9. Other advanced technologies related to modeling, design, and control of electric machines in EV.

The application of AI to the electric machines in EV. We look forward to receiving your contributions.

Prof. Dr. Jinhua Du
Prof. Dr. Xuan Wu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400
Batteries batteries	4.8	6.6	2015	19.2 Days	CHF 2700
Electricity electricity	1.8	5.1	2020	26.9 Days	CHF 1200
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600
Machines machines	2.5	4.7	2013	17.6 Days	CHF 2400
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000
Vehicles vehicles	2.2	5.3	2019	21.4 Days	CHF 1800

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Published Papers (3 papers)

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23 pages, 7468 KB  

Open AccessArticle

FPGA-Based Real-Time Simulation of Externally Excited Synchronous Machines

by Yannick Bergheim, Fabian Jonczyk, René Scheer and Jakob Andert

Energies 2026, 19(7), 1661; https://doi.org/10.3390/en19071661 - 27 Mar 2026

Viewed by 302

Abstract

Externally excited synchronous machines (EESMs) are a rare-earth-free solution for traction applications. However, variable field excitation and magnetic coupling increase control complexity. Efficient validation of the resulting control functionalities can be carried out using hardware-in-the-loop (HIL) testing, which requires high-fidelity real-time simulation models. [...] Read more.

Externally excited synchronous machines (EESMs) are a rare-earth-free solution for traction applications. However, variable field excitation and magnetic coupling increase control complexity. Efficient validation of the resulting control functionalities can be carried out using hardware-in-the-loop (HIL) testing, which requires high-fidelity real-time simulation models. This paper presents a semi-analytical, discrete-time EESM model tailored for HIL applications. Nonlinear magnetic saturation and magnetic coupling are captured using an inverted flux–current characteristic combined with a rotating coordinate transformation, which improves resource utilization. Spatial harmonics are included through a Fourier decomposition of the angle-dependent inverse characteristics. Additionally, different loss mechanisms are considered to accurately represent the physical behavior of the machine. The model is parameterized using finite element analysis (FEA) results from a 100$\mathrm{k}$$\mathrm{W}$ salient-pole EESM. It is implemented on a field-programmable gate array to achieve real-time capability at a simulation frequency of $2.5$$\mathrm{M}$$\mathrm{Hz}$. Validation results for the typical operating range show deviations below $0.1$% compared to detailed FEA results, demonstrating accurate real-time simulation of the electromagnetic behavior. Full article

(This article belongs to the Topic Design and Control of Electrical Machines for Electric Vehicles)

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26 pages, 8588 KB  

Open AccessArticle

A High-Precision Torque Control Method for New Energy Vehicle Motors Based on Virtual Signal Injection

by Zhiqiang Wang, Weihao Wang, Wei Chen, Chen Li and Zhichen Lin

Electronics 2025, 14(7), 1443; https://doi.org/10.3390/electronics14071443 - 2 Apr 2025

Cited by 1 | Viewed by 972

Abstract

The operating temperature of new energy vehicles fluctuates significantly, and variations in motor temperature lead to changes in parameters. These changes introduce errors into the motor’s mathematical model, reducing torque accuracy and causing deviations in the Maximum Torque Per Ampere (MTPA). This paper [...] Read more.

The operating temperature of new energy vehicles fluctuates significantly, and variations in motor temperature lead to changes in parameters. These changes introduce errors into the motor’s mathematical model, reducing torque accuracy and causing deviations in the Maximum Torque Per Ampere (MTPA). This paper proposes a Gated Recurrent Unit (GRU) neural network-based torque observer that employs virtual signal injection. Specifically, this method innovatively injects a virtual constant signal into the d-q axis current inputs processed by the neural network to derive the partial derivatives of torque concerning the d-axis and q-axis currents. Subsequently, it calculates the derivative of torque concerning the current vector angle (β) using the total differential equation. By leveraging these partial derivatives, the motor parameters are identified online, and the MTPA current reference value is dynamically adjusted based on the identified parameters. Additionally, the GRU’s internal parameters are fine-tuned in real time using the least mean square (LMS) algorithm, which adjusts based on the derivative of torque concerning the current angle and the error between the observed and actual values, thereby enhancing the accuracy of torque observation, and bringing results closer to the true shaft-end torque. Finally, experimental validation confirms the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Topic Design and Control of Electrical Machines for Electric Vehicles)

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13 pages, 9723 KB  

Open AccessArticle

Demagnetization Fault Diagnosis for PMSM Drive System with Dual Extended Kalman Filter

by Jiahan Wang, Chen Li and Zhanqing Zhou

World Electr. Veh. J. 2025, 16(2), 112; https://doi.org/10.3390/wevj16020112 - 18 Feb 2025

Cited by 1 | Viewed by 1761

Abstract

Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety [...] Read more.

Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety and reliability of permanent magnet motor drive systems. In the proposed scheme, multiple operating states of the motor are acquired by injecting sinusoidal current signals into the d-axis, ensuring that the parameter estimation equation satisfies the full rank condition. Furthermore, the accurate dq-axis inductance parameters are obtained based on a recursive least square method. Subsequently, a dual extended Kalman filter is employed to acquire real-time permanent magnet flux linkage data of PMSMs, and the estimation data between the two algorithms are transferred to each other to eliminate the bias of permanent magnet flux estimation caused by a parameter mismatch. Finally, accurate evaluation of the remanence level of the rotor permanent magnet and demagnetization fault diagnosis can be achieved based on the obtained permanent magnet flux linkage parameters. The experimental results show that the relative estimation errors of the dq-axis inductance and permanent magnet flux linkage are within 5%, which can realize the effective diagnosis of demagnetization fault and high-precision condition monitoring of a permanent magnet health. Full article

(This article belongs to the Topic Design and Control of Electrical Machines for Electric Vehicles)

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