Chair 1 and 2

  • Chair 1 contract: Improving the performance of electric vehicles
    • Duration: 2016-2022
    • Budget: €4.6M
    • Staff: around thirty people
  • Chair 2 contract: Improving and validating the performance of electric vehicles. Prospects with hydrogen storage
    • Duration: 2022-2025
    • Budget: €1.8M
    • Staff: around twenty people

Scientific research areas

Control of AC machines and static converters for EV powertrains
The first area of the Chair focuses on robust sensorless control of electric drive motors. There are three different parts: position sensor control, torque monitoring, and PWM optimization control of AC machines inverters.

Part 1: Control of AC machines for EV without position/speed sensors


The main goals focus on the development and design of the new self-sensing control strategy for AC machines based on advanced estimation techniques to increase the performance, reliability, robustness of the control chain and reduce risk, cost and implementation complexity.

In the domain of classical (FOC, Feedback militarization, ...) and modern (sliding modes, backstepping, high gain, passivity, ... etc) controls of AC machines making the propulsion/traction of HEV/EV, the knowledge of rotor position and speed (figure 1) are required, which are obtained generally by a physical sensor (encoder or resolver). However, mechanical sensors are expensive, bulky, sensitive to the environment (temperature, noise, mechanical oscillations, electromagnetic compatibility, etc.) and reduce the system reliability. The mechanical sensor can be a sign of defects that can lead to the total loss of information or its degradation, resulting in a significant drop in performance and a malfunction of the traction chain. The position/speed estimation (figure 2) by means observers, or estimators, or generally speaking software sensors, becomes an ongoing need on the first part of the Chair Area 1, namely within EV and HEV, either for fault diagnosis and safety applications, or for the closed-loop self-sensing control. The main goal of software sensors is to estimate the position/speed by using only currents measurements of AC machines.


Figure 1: Control principle of AC machines with position/speed measurement


Figure 2: Control principle of AC machines without position/speed measurement (using observers, i.e. software sensors)

Part 2: Online monitoring of AC machine torque

AC machines like for example surface and interior permanent magnet synchronous machines (SPMSM, IPMSM) are widely used in EV/HEV applications because of their simplicity, reliability and robustness. Online monitoring of machine torque is a mandatory for an EV/HEV for reasons such as safety or/and control. Torque-meters (figure 1) removing in these applications and replacing them with a torque estimator (figure 2) based on measured/available signals such as voltages, currents and rotor position can make the drive system more reliable at less cost. Therefore, designing torque estimators for electric drives is the second part of the Chair Area 1.
 

Figure 1: Test bench of AC machines with torque-meter
Figure 2: Control principle of AC machines without torque measurement (using estimators, i.e. software sensors)

Part 3: PWM control optimization for HEV/EV

Power Electronic Converters are widely used in electric vehicle (EV) and hybrid EV (HEV) powertrains. As the automotive application requirements are pushing the limits of the electrical components (higher power density, cost reduction, noise/vibration reduction, etc.), the control algorithms of the power converters are being challenged on several aspects, including the Pulse-Width Modulation (PWM) optimization. This part deals with the optimization of the PWM strategies of the two main power converters in an electric powertrain: the traction motor voltage source inverter (figure 3), and the battery charger (bidirectional rectifier).
Figure 3: PWM control scheme of EV
Energy management and control of EV charging

Part 1: Optimized Energy Management for Electric Vehicles and Infrastructure

Smart charging of an EV (figure 4) and the smart energy management of an EV fleet, considering the mobility constraints (desired SOC at the end of the charging and departure time), the temperature of the Li-ion batteries, the charging infrastructure, and the power grid, is the first part of this area. Algorithms developed have the advantage of increasing the estimation accuracy of final SOC in very low temperature, and to be embedded on the EV due to the low computational capacity of the algorithms and the speed of execution. On the other hand, the EV fleet charging management algorithms allow the possibility of large-scale integration of electric vehicles on the grid and show the potential of EVs in contributing to the stability of the power grid by offering auxiliary services such as frequency regulation.
Figure 4: Smart charging of an EV

Part 2: Control of bidirectional charger for the electric vehicle

Electric mobility represents a significant transport technology for society, resulting in more efficient mobility. This transport technology is mainly supported by the electric vehicle (EV), where the batteries are charged from the power grid, arises the concept of grid-to-vehicle (G2V). However, it is possible to send energy in the opposite way, i.e., from the EV batteries to the power grid.  This operation mode is defined as vehicle-to-grid (V2G). A V2G electric vehicle offers reactive and active power regulation, load balancing, and current harmonic filtering. It offers also a wide integration with renewable energies, provides an electric grid stability and supports the balancing act between electricity demand and supply.  To enable power transfer in both directions between the grid and an electric vehicle, the battery charger system is needed with bidirectional power flow capability (figure 5). By investigating new control solutions, the aim of this part is to improve the stability of the DC bus voltage around a specified reference with a switching frequency included in a feasible operation zone of the bidirectional converter of the charger in both modes V2G and G2V.
 
Figure 5: Bidirectional charger (V2G and G2V) control 

Part 3: Automatic generation of VHDL code for electric vehicle chargers

In relation to part 2, the two-way chargers use a lot of VHDL code. The goal of this part is to develop a toolbox that allows, from Maltlab / Simulink files, to automatically generate a VHDL code for integrating the control laws developed in part 2 of this second area of the chair on an FPGA target (figure 6).
 
Figure 6: Integration of control laws on an FPGA target
Energy optimization of hybrid powertrain systems
HEVs (Hybrid Electric Vehicles) have gone through several design iterations throughout their development, amongst which: planetary gear-based architecture, dual clutch serial–parallel, continuous Variable Transmission (CVT) and range extender. However, vehicle manufacturers need to innovate on existing architecture in order to improve HEV sales.

This area focuses on proposing control strategies for HEVs in a serial hybrid mode without the main battery (figure 7). In the serial hybrid mode, the internal combustion engine is coupled with an electrical generator to produce power used by a second electric motor to propel the vehicle. In the absence of the main battery, the powertrain has small energy buffer, making the system sensitive to small disturbance.

Figure 7: Serial HEV architecture

Publications and patents

19 Journal Articles

2025

  • [1] Chen, Y., Ghanes, M., Fekik, A., & Maloum, A. (2025). Torque observation of WRSM with model uncertainties for EV applications. IEEE Transactions on Control Systems Technology.

​​​​​​​2024

  • [2] Y. Chen, M. Ghanes and J. -P. Barbot (2024). Implicit Function Theorem for Nonlinear Time-Delay Systems With Algebraic Constraints," in IEEE Transactions on Automatic Control, vol. 69, no. 4, pp. 2629-2636.


2023

  • [3] Rouquet, S., Ghanes, M., Barbot, J. P., Shtessel, Y. B., & Merienne, L. (2023). A batteryless series hybrid electric vehicle energy management. IEEE Transactions on Control Systems Technology. 31(3), 1032-1041.
  • [4] Dahmane, Y., Chenouard, R., Ghanes, M., & Alvarado Ruiz, M. (2023). Optimal Electric Vehicle Fleet Charging  Management with a Frequency Regulation Service. World Electric Vehicle Journal, 14(6), 152.    
  • [5] Alvaro-Mendoza, E., De Leon-Morales, J., Hamida, M.A., & M. Ghanes. "Adaptive sensorless control for interior permanent magnet synchronous motor based on sliding mode approach." ISA Transactions (2023): S0019-0578.
  • [6] Bourgeade, A., Ghanes, M., Jean-Pierre, B., Abdelkader, B., Maurice, F., & Robert, B. (2023). New PWM inverter control based on optimal pulse pattern computation without phases symmetry constraints. Control Engineering Practice, 132, 105390.
  • [7] Attar, H., Hamida, M. A., Ghanes, M., & Taleb, M. (2023). Review on Modeling and Control Strategies of DC– DC LLC Converters for Bidirectional Electric Vehicle Charger Applications. Energies, 16(9), 3946.
  • [8] Y. Chen, M. Ghanes and J. -P. Barbot. (2023). Strong Left-Invertibility and Strong Input-Observability of Nonlinear Time-Delay Systems," in IEEE Control Systems Letters, vol. 7, pp. 805-810

2022

  • [9] Al Attar, H., Hamida, M. A., Ghanes, M., & Taleb, M. (2022). LLC DC-DC Converter Performances Improvement for Bidirectional Electric Vehicle Charger Application. World Electric Vehicle Journal, 13(1), 2.
  • [10] Alvaro-Mendoza, E., Hamida, M., DeLeon, J., & Ghanes, M. (2022).  Angular position estimation error extraction for  speed and angular position estimation of IPMSM using a parameter-free adaptive observe. Accepted in  Journal of Franklin Institute.
  • [11] Ghanes, M., Moreno, J. A., & Barbot, J. P. (2022). Arbitrary order differentiator with varying homogeneity degree. Automatica, 138, 110111.

2021

  • [12]  Dahmane, Y., Chenouard, R., Ghanes, M., & Alvarado-Ruiz, M. (2021). Optimized time step for electric vehicle charging optimization considering cost and temperature. Sustainable Energy, Grids and Networks, 26, 100468.
  • [13] Taherzadeh, M., Hamida, M. A., Ghanes, M., & Maloum, A. (2021). Torque estimation of permanent magnet synchronous machine using improved voltage model flux estimator. IET Electric Power Applications, 15(6), 742-753.

2020

  • [14] Taherzadeh, M., Hamida, M. A., Ghanes, M., & Koteich, M. (2020). A New Torque Observation Technique for a PMSM Considering Unknown magnetic Conditions. IEEE Transactions on Industrial Electronics, 68 (3), 1961-1971.
  • [15] Messali, A., Hamida, M. A., Ghanes, M., & Koteich, M. (2020). An Estimation Procedure Based on Less Filtering and Robust Tracking for a Self-Sensing Control of IPMSM. IEEE Transactions on Industrial Electronics, 68 (4), 2865-2875.
  • [16] Shtessel, Y. B., Ghanes, M., & Ashok, R. S. (2020). Hydrogen Fuel Cell and Ultracapacitor Based Electric Power System Sliding Mode Control: Electric Vehicle Application. Energies, 13(11), 2798.
  • [17] Ghanes, M., Barbot, J. P., Fridman, L., Levant, A., & Boisliveau, R. (2020). A new varying-gain-exponent-based differentiator/observer: An efficient balance between linear and sliding-mode algorithms. IEEE Transactions on Automatic Control, 65(12), 5407-5414.

2019

  • [18] Messali, A., Ghanes, M., Hamida, M. A., & Koteich, M. (2019). A resilient adaptive sliding mode observer for sensorless AC salient pole machine drives based on an improved HF injection method. Control Engineering Practice, 93, 104163.
  • [19] Hamida, M. A., Leon-Morales, D., & Messali, A. (2019). Observer design for nonlinear interconnected systems: experimental tests for self-sensing control of synchronous machine. The International Journal of Advanced Manufacturing Technology, 105(1), 1041-1054.
26 conferences

2024

  • [1] Permiakov, I., Ghanes, M., Shtessel, Y., & Taleb, M. (2024, October). Singular Perturbation Strategy based on Super-Twisting Control: Application to an EV powered by FC-SC. In 2024 17th International Workshop on Variable Structure Systems (VSS) (pp. 23-28). IEEE.

2023

  • [2] Igbokwe, D. & Ghanes, M., "Sensorless HOSM Control based VGE Observer for a 7-Phase PMSM," 2023 IEEE Conference on Control Technology and Applications (CCTA), Bridgetown, Barbados, 2023, pp. 346-351, doi: 10.1109/CCTA54093.2023.10253455.
2022
  • [3] Al Attar, H., Hamida, M. A., Ghanes, M., & Taleb, M., "Model Free Control Design of DC-DC LLC Converter with Phase Shift Modulation in V2X Mode for EV Charger Application," 2022 IEEE Conference on Control Technology and Applications (CCTA), Trieste, Italy, August 22-25, 2022.
  • [4] Al Attar, H., Hamida, M. A., Ghanes, M., & Taleb, M., "Bidirectional Electric Vehicle Charger Control Design with Performance Improvement," IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, pp. 1-62022.
  • [5] Al Attar, H., Hamida, M., Ghanes, M., & Taleb, M. (2022, September). Electric Vehicle Charger Control Design Combining Phase Shift Modulation with an Adaptive Super Twisting Algorithm. In 2022 16th International Workshop on Variable Structure Systems (VSS) (pp. 135-140). IEEE.
  • [6] Alvaro-Mendoza, E., Hamida, M., DeLeon, J., & Ghanes, M., "Sensorless control of an interior permanent magnet synchronous motor based on an adaptive sliding mode observer using position error extraction method," 2022 IEEE Conference on Control Technology and Applications (CCTA), Trieste, Italy, August 22-25, 2022.
  • [7] Alvaro-Mendoza, E., Hamida, M., DeLeon, J., & Ghanes, M., "Application of a sensorless control based on sliding mode in permanent magnet synchronous machine using an angular error extraction," 2022 16th International Workshop on Variable Structure Systems (VSS), Rio de Janeiro, Brazil, 2022, pp. 141-146.

2021

  • [8] Bourgeade, A., Ghanes, M., Fadel, M., Bouarfa, A., & Barbot, J-P., "A New PWM Control based on an Optimized Zero Sequence Component injection: Application in a Two-Level Inverter," IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 2021, pp. 1-6, doi: 10.1109/IECON48115.2021.9589469.
  • [9] Parrot, R., Briday, M., & H. Roux, O., "Pipeline optimization using a cost extension of timed petri nets". In 2021 IEEE 28th Symposium on Computer Arithmetic (ARITH), pages 37–44, Los Alamitos, CA, USA, jun 2021. IEEE Computer Society.
  • [10] Parrot, R., Briday, M., & H. Roux, O., "Réseaux de Petri temporisés pour la conception et vérification de circuits pipelinés". In Modélisation des Systèmes Réactifs (MSR 2021), November 2021.
  • [11] Parrot, R., Briday, M., & H. Roux, O., "Timed Petri Nets with Reset for Pipelined Synchronous Circuit Design". In The 42th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2021), volume 12734 of Lecture Notes in Computer Science. Springer, June 2021.
  • [12] Bourgeade, A., Ghanes, M., Fadel, M., Bouarfa, A., & Barbot, J-P., "Off-line PWM control with a three phases relaxed symmetry applied to a two-level inverter," 2021 IEEE Conference on Control Technology and Applications (CCTA), 2021, pp. 595-600, doi: 10.1109/CCTA48906.2021.9659137.
  • [13] Al Attar, H., Hamida, M. A., Ghanes, M., & Taleb, M., "Control strategies design and comparison of DC-DC LLC converter in V2X mode for electric vehicle charger application," 2021 IEEE Conference on Control Technology and Applications (CCTA), 2021, pp. 1154-1159, doi: 10.1109/CCTA48906.2021.9659000.

2020

  • [14] Rouquet, S., Ghanes, M., Barbot, J. P., Shtessel, Y. B., & Merienne, L. (2020, August). Energy management in a mechatronics system with delay: A series hybrid electric vehicle case. In 2020 IEEE Conference on Control Technology and Applications (CCTA) (pp. 679-684). IEEE.
  • [15] Rouquet, S., Ghanes, M., Barbot, J. P., & Merienne, L. (2020). Power Management in Serial Hybrid Electric Vehicle: A singular perturbation approach. IFAC-PapersOnLine, 53(2), 13775-13780.
  • [16] Dahmane, Y., Chenouard, R., Ghanes, M., & Alvarado-Ruiz, M. (2020, August). Coordinated Charging of Large Electric Vehicle Fleet in a Charging Station With Limited Transformer Power. In 2020 IEEE Conference on Control Technology and Applications (CCTA) (pp. 1054-1059). IEEE.
  • [17] Taherzadeh, M.A. Hamida, M. Ghanes, M. Koteich (2020).A New Approach on Stator Flux Estimation of IPMSMs Considering Magnetic and Cross-Coupling Saturations IFAC-PapersOnLine,53(2), 13763-13768.

2019

  • [18] Dahmane, Y., Ghanes, M., Chenouard, R., & Alvarado-Ruiz, M. (2019, October). Decentralized Control of Electric Vehicle Smart Charging for Cost Minimization Considering Temperature and Battery Health. In 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.
  • [19] Messali, A., Hamida, M. A., Ghanes, M., & Koteich, M. (2019, September). Robust adaptive sliding mode observer for self-sensing IPMSM control based on optimized HF injection method. In 2019 IFAC Symposium on Nonlinear Control Systems (NOLCOS), 52(16), 556-561.
  • [20] Messali, A., Hamida, M. A., Ghanes, M., & Koteich, M. (2019, September). Robust phase-shift estimator for self-sensing control of PM synchronous machines. In 2019 IEEE 10th International Symposium on Sensorless Control for Electrical Drives (SLED) (pp. 1-6). IEEE.
  • [21] Messali, A., Hamida, M. A., Ghanes, M., & Koteich, M. (2019, August). Rotating HF signal injection method improvement based on robust phase-shift estimator for self-sensing control of IPMSM. In 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) (pp. 195-200). IEEE.
  • [22] Messali, A., Hamida, M. A., Ghanes, M., & Koteich, M. (2019, July). Rotor position, speed and acceleration estimation for salient-pole AC machines at all speed and torque ranges. In 2019 IEEE 13th International Conference on Power Electronics and Drive Systems (PEDS) (pp. 1-6). IEEE.
  • [23] Messali, A., Hamida, M. A., Ghanes, M., & Koteich, M. (2019). Robust adaptive sliding mode observer for self-sensing IPMSM control based on optimized HF injection method. IFAC-PapersOnLine, 52(16), 556-561.

2018

  • [24] Messali, A., Ghanes, M., Koteich, M., & Hamida, M. A. (2018, September). A robust observer of rotor position and speed for ipmsm hfi sensorless drives. In 2018 IEEE 9th International Symposium on Sensorless Control for Electrical Drives (SLED) (pp. 90-95). IEEE.
  • [25] Messali, A., Hamida, M. A., Ghanes, M., & Koteich, M. (2018, October). A novel high frequency signal injection strategy for self-sensing control of electric ac machine drives. In  2018-44th Annual Conference of the IEEE Industrial Electronics Society (IECON) (pp. 343-348). IEEE.

2017

  • [26] Koteich, M., Messali, A., & Daurelle, S. (2017, September). Self-sensing control of the externally-excited synchronous machine for electric vehicle traction application. In 2017 IEEE International Symposium on Sensorless Control for Electrical Drives (SLED) (pp. 91-96). IEEE.
22 patents have been filed

Submitted:
  • [19] Chen, Y., Fekik, A., Ghanes, M., & Maloum, A. (2024). Linear Parameter Varying Observer for estimating the electromagnetic torque. PJ-24-0722.
  • [20] Permiakov, I., Ghanes, M., & Taleb, M. (2025). Robust State-of-Charge Hybrid Control for the Secondary Energy Storage System in the Fuel Cell Electric Vehicle System. PJ-25-0810.
  • [21] Igbokwe, D., Ghanes, M.,  Messali, A., & Maloum, A. (2025). Commande Adaptative Discrète par Modes Glissants pour les Machines Synchrones Multiphasées en utilisant des Différenciateurs à Filtrage Hybride. PJ-25-0833.
  • [22] Igbokwe, D., Ghanes, M.,  Messali, A., & Maloum, A. (2025). Méthode et Système pour la Génération Optimale de Courant dans les Moteurs Synchrones à 5 phases en considérant la Contribution du Couple Sous-harmonique. PJ-25-0834. 
Published on November 9, 2026 Updated on November 9, 2026

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