Department of Electrical Engineering and Automation


The Research Group of Electromechanics focuses on advancing research in electromechanics at three different aspects: theoretical, numerical, and experimental. The group develops its own models and numerical softwares and has the facilities to test electrical machines with power up to 150 kW.
Stator Machine only Picture Group electromechanics
Electromechanics Group Laboratory

Loading table of contents

About the group

The Group of Electromechanics is led by Professor Anouar Belahcen.

The group aims to address timely research topics related to electrical machines with the integration of electrical, mechanical, and thermal aspects.

We develop our own methods and related softwares and validate the methodology through experimental procedures in our own facilities. Some of the methods we develop are also incorporated in commercial softwares for better transfer of knowledge to our stakeholders. The group has the facilities to test electrical machines with power up to 150 kW. Test results are typically needed for the validation of the numerical methods and models developed.

The group is conducting research on the design and optimization of machines for industrial applications. This research includes transport electrification by designing next generation of machines dedicated to More Electric Aircraft (MEA) and Electric Vehicles (EV).

Yet another major topic is the numerical modeling of coupled problems that occur in magnetic materials and especially electrical steel sheets used to construct electrical machines. These problems range from energy dissipation in the material to vibrations and noise of electrical machines and devices.

The group is involved in various national, european, and international research projects and collaborates with industrial and academic partners around the globe.

The group is open for meaningful collaboration: Contact us.

Follow our latest research updates:

Research Interests

The PhD research work previously defended within the group can be accessed here. The group is actively engaged on various research topics. Among these topics:

  1. Optimization of Electric Machines: Through evolutionary optimization techniques, such as Particle Swarm or Genetic Algorithms, machines can be geometrically fine-tuned for given specifications achieving higher efficiency and power density.
  2. Modeling and Prototyping of Machines: Multi-physics models (electromagnetic, thermal, and mechanical) are crucial for designing machines with best performances and cost-effectiveness in a shorter timeframe, allowing for efficient prototyping.
  3. High-Speed Machines: High-speed machines allow for the achievement of high power density, making them ideal for various applications like automotive and aerospace industries. These machines require fine design to account for high frequency phenomena occurring in machine components.
  4. Transport Electrification: Electric machines facilitate the transition to transport electrification by ensuring key specifications such as power, efficiency, and compact design. They are key enablers of greener transportation, getting closer to zero carbon emissions by 2050.
  5. Inverse Modeling of Core Losses: Novel inverse modeling techniques allow estimating core losses and localized losses in the machine using short-term transient temperature measurements validated through a forward model.
  6. Magnetically Levitated Rotors: This machine utilizes magnetic fields to suspend and rotate rotors without physical contact, eliminating friction, and enhancing efficiency. It finds applications in high-speed transportation, energy storage, and advanced industrial processes.
  7. Axially Laminated Rotors: Rotors are composed of insulated thin laminations stacked together along the axis of rotation, which offer various advantages at high-speed: reduced eddy current losses, improved thermal performances, and enhanced mechanical strength.
  8. Topology Optimization of Machines: Through topology optimization techniques, such as solid isotropic material with penalization (SIMP) method or level set method, machine’s structure can be designed for high torque density and mechanical reliability.
  9. Condition Monitoring: Combining machine learning and data augmentation helps advance the condition monitoring system of electrical machines by generating synthetic data for unmeasured conditions, improving fault detection and diagnosis using fewer measurements and more simulation data.
  10. Magneto-Mechanical Coupling: The influence of punching on electrical steel sheets is described using a thermodynamical multi-axial formalism.
    optim machines flowchart

    Optimization of Electric Machines: Optimization of machines for given specifications has noticeable implications across various industries and applications such as automotive and aeronautics sectors. By optimizing the geometry, materials, and winding configurations, machines can meet specifications and requirements using a multi-physics model (electromagnetic, mechanical, and thermal). Multi-objective optimization is often performed to obtain a Pareto Front representing a balance between different performances which can be conflicting such as efficiency and power density. Although time consuming, large-scale optimization using finite element models can be performed with powerful computers at Aalto University allowing the exploration of vast solutions in record time. Research on the optimization can make machines competitive and sustainable alternatives for green transports. 

    High Speed Machine 0

    High-Speed Machines: High-speed machines are highly desirable in applications with space-constrained environments. Various high-frequency phenomena manifest in different components of the machine at various aspects. Electromagnetic challenges include eddy currents in the core and the windings. Thermal challenges include high losses density, necessitating innovative cooling techniques, and high frequency phenomena in the airgap. Mechanical challenges include increased stresses in the rotor and vibrations. Most of these phenomena can be finely modeled and included in the design and optimization process for given application.

    Read more:

    Why High-Speed Machines?High-Speed Machines: Economic Impact

    transport electrification

    Transport Electrification: Electric machines play a crucial role in propelling various electrified transportation systems, including automotive, aerospace, rail, and marine applications. Machines can be designed to meet various specifications dictated by industries, such as fault tolerance, maximal power, torque ripple, cogging torque, size, cost, and others. These specifications are essential factors considered during the design and optimization process. Two primary considerations for industries are enhancing efficiency and minimizing size, which can be achieved by proposing the appropriate machine type, exploring new materials, and performing optimizations.

    Read more:

    Why Electric Transports?

    axially laminated rotor

    Axially Laminated Rotors: Designing high-speed axial-laminated synchronous reluctance machines poses significant challenges due to their intricate nature. This research delves into the intricate domains of electromagnetic and mechanical design methodologies while also considering cooling techniques with CFD analysis integration. Noteworthy focus is dedicated to comprehensive 3D analysis, precise rotor loss approximation methods, and the influence of electrical machine malfunctions and frequency converter operation on losses. 3D and 2D simulations are performed using supercomputers at Aalto University, employing cluster computing for efficient data processing and analysis. This work paves the way for advancements in high-speed synchronous reluctance machine technology.

    inverse modeling 0

    Inverse Modeling of Core Losses: Accurate prediction and measurement of losses are vital for evaluating the machine's efficiency, temperature distribution, and cooling requirements. This research introduces a novel technique based on inverse modeling to estimate core losses and localized losses in the core regions of an electrical machine. The approach involves using short-term transient temperature measurements from the machine's core and validating them through a forward model. Two primary measurement methods were employed: thermal sensors embedded in a printed circuit board within the stator core, and surface thermographic measurements with an infrared camera. The results demonstrate the effectiveness of the inverse modeling technique in predicting core losses based on short-time transient temperature rise measurements.

    SynRM prototype, torque transducer, load motor

    Topology Optimization of Machines: The topology optimization is the design optimization to automatically solve the problem of placing material in the design domain to achieve best performances. In order to address the design requirements for high electromagnetic output torque, lightweight, and mechanical stiffness and strength, this research develops a topology optimization approach for the rotor design of synchronous reluctance machines (SynRMs). Considering the high dimensional constraints caused by the multi-physics performances, the augmented Lagrangian method based optimization framework is developed to solve the minimization problem. Effectiveness of the proposed method is verified by performing analysis and simulations of the topology optimization of SynRMs. Performances of the optimized structure are also verified with experiments on the prototype.

    Electrical motor

    Magnetically Levitated Rotors: The control segment of a six-phase induction motor is studied considering various windings configurations. The machine is modeled using analytical model, magnetic equivalent circuit (MEC), and finite element model (FEM). Different aspects are studied: optimization of electromagnetic performances (torque and forces in the x-y directions), vibration modeling, and mitigation of torque and force ripples as well as vibrations. Models are validated by performing experiments on the induction machine prototype.

    Condition Monitoring of machines

    Condition Monitoring of Machines: Condition monitoring of electrical machines has witnessed significant advancements due to the deployment of data-driven machine learning models, addressing the increasing demand for reliable and efficient operation. This research project aims to explore the synergistic integration of machine learning and innovative data augmentation methods to enhance the accuracy of condition monitoring in electrical machines. The main objective is to develop precise and efficient data augmentation techniques by utilizing a reduced amount of measurement data and incorporating simulation data, thereby generating a large number of synthetic data. The ultimate goal is to develop novel data augmentation methods capable of replicating measured data by minimizing the deviation between measured and simulated data caused by noise and uncertainty.

    magneto mechanical coupling 5

    Magneto-Mechanical Coupling: Thermodynamic couplings and in particular magneto-mechanical coupling in electromagnetic devices allows developing non-destructive testing methods. The model of magneto-elastic behaviour are proposed based on the writing of a Gibbs free energy, the terms of which have been determined using the theory of invariants. This project is focused on the formulation of the magneto-plastic coupling using the previous techniques and conducting an experimental campaign. This project aims also to describe the influence of plasticity on the performances of electrical steel sheets, which are subjected to multi-axial loading.

    Current Research Projects

    Past Research Projects

    Previous PhD


    Brijesh Upadhaya

    PhD 2022

    Asad Bilal

    PhD 2021

    Victor Mukherjee

    PhD 2020

    Ravi Sundaria

    PhD 2020

    Sabin Sathyan

    PhD 2020

    Mehrnaz Farzam Far

    PhD 2019

    Ugur Aydin

    PhD 2018

    Antti Lehikoinen

    PhD 2017

    Bishal Silwal

    PhD 2017

    Deepak Singh

    PhD 2016

    Javier Martinez

    PhD 2015

    Paavo Rasilo

    PhD 2012

    Jenni Pippuri

    PhD 2010

    Zlatko Kolondzowski

    PhD 2010

    Katarzyna Anna Fonteyn

    PhD 2010

    Emad Ali Dlala

    PhD 2008


    Latest publications

    Magnetic Material Modelling of Electrical Machines

    Anouar Belahcen, Armando Pires, Vitor Fernão Pires 2023 Energies

    Special Issue "Magnetic Material Modelling of Electrical Machines"

    Anouar Belahcen, Armando Pires, Victor Fernão Pires 2023 Energies

    Generation of Unmeasured Loading Levels Data for Condition Monitoring of Induction Machine Using Machine Learning

    Md Masum Billah, Alireza Nemat Saberi, Ahmed Hemeida, Floran Martin, Karolina Kudelina, Bilal Asad, Muhammad U. Naseer, Victor Mukherjee, Anouar Belahcen 2023 IEEE Transactions on Magnetics

    An Energy-Based Anisotropic Vector Hysteresis Model for Rotational Electromagnetic Core Loss

    Ruiying Chen, Floran Martin, Yongjian Li, Shuaichao Yue, Anouar Belahcen 2023 IEEE Transactions on Industrial Electronics

    Multiaxial validation of a magneto-elastic vector-play model

    Luiz Guilherme Da Silva, Laurent Bernard, Floran Martin, Anouar Belahcen, Laurent Daniel 2023 IEEE Transactions on Magnetics

    AC Loss Analysis Approaches for Hairpin Winding Configuration: Analytical, Hybrid Model, and FEA

    Payam Shams Ghahfarokhi, Ants Kallaste, Andrejs Podgornovs, Antonio J.Marques Cardoso, Anouar Belahcen, Toomas Vaimann 2023 CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering

    The Oil Spray Cooling System of Automotive Traction Motors: The State of the Art

    Payam Shams Ghahfarokhi, Andrejs Podgornovs, Ants Kallaste, Antonio J. Marques Cardoso, Anouar Belahcen, Toomas Vaimann 2023 IEEE Transactions on Transportation Electrification

    Magnetic Equivalent Circuit and Lagrange Interpolation Function Modeling of Induction Machines Under Broken Bar Faults

    Ahmed Hemeida, Md Masum Billah, Karolina Kudelina, Bilal Asad, Muhammad U. Naseer, Baocheng Guo, Floran Martin, Paavo Rasilo, Anouar Belahcen 2023 IEEE Transactions on Magnetics

    Energy-Efficient Control of Bearingless Linear Motors

    Reza Hosseinzadeh, Floran Martin, Marko Hinkkanen 2023 Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023

    Investigation of Six-Phase Surface Permanent Magnet Machine with Typical Slot/Pole Combinations for Integrated Onboard Chargers Through Methodical Design Optimization

    M. Y. Metwly, M. Ahmed, A. Hemeida, A. S. Abdel-Khalik, M. S. Hamad, A. Belahcen, S. Ahmed, N. A. Elmalhy 2023 IEEE Transactions on Transportation Electrification
    More information on our research in the Aalto research portal.
    Research portal
    • Published:
    • Updated: