Recruitment

PhD Proposal, CN-Ampère/Renault 2026

Subject: Temperature estimation/monitoring in the electric vehicle powertrain

Context:

The transition to sustainable mobility (decarbonized or green mobility) places electric vehicles (EVs) at the heart of strategies to reduce greenhouse gas emissions. However, the performance of electric powertrains, including the electric machine, battery, power converters and auxiliaries, is highly dependent on their thermal behavior. Indeed, temperature variations, particularly in extreme climatic conditions, have a significant impact on overall energy efficiency, component durability (especially the battery) and vehicle range.

A promising area of research lies in the on-line estimation of the temperature of EV subsystems (electric machine rotors, power modules, batteries, etc.) to anticipate thermal drifts and thus optimize the GMPe. Observer-based techniques (Luenberger, Kalman, observers with adaptive correctors, etc.) offer robust solutions for estimating temperature-dependent magnetic fluxes, from which the internal temperature can be reconstructed via simplified equivalent electrical models. These methods will be combined with hybrid approaches combining analytical models and data based on artificial intelligence (AI) techniques. Validation of these models using MATLAB/Simulink simulations, and if possible, data from test benches, is essential to compare their accuracy and robustness in the face of variations in operating conditions (speed, load, measurement noise, etc.).

The challenge is therefore to design thermal estimation methods that will help make EV operation more reliable, extend the service life of key components and guarantee their performance under a wide range of usage conditions, while promoting the rapid industrial integration of innovations resulting from research.

Goals:

The main objective of this research is to develop robust methods for real-time temperature estimation in the energy conversion chain of electric powertrains (GMPe) within the framework of on-board thermo-management. To achieve this, the approach will focus on the following areas:

  1. Design of thermal observers, based on flux and voltage estimation, taking into account electrothermal couplings and using data. Model-based techniques will be combined with AI-based techniques.
  2. Development and comparative analysis of observer architectures (classical, adaptive or hybrid), aiming at a compromise between accuracy, robustness and complexity.
  3. Numerical and experimental validation of the performance of the proposed method on representative cases of engine operation.
  4. Optimization for on-board implementation in a real-time thermal management system.
  5. Contribution to the electrothermal eco-design of GMPe, with a view to improving the reliability, autonomy and safety of electric vehicles.

Thesis roadmap:

  1. First year:
  • Review of the literature on temperature observers (based on models and IA) and the thermoelectric behavior of GMPe.
  • Development of simplified models for the EV drive train (electric machine, power module, battery) and its thermal behavior.
  • First design of observers (flux and temperature) in MATLAB/Simulink.
  1. Second year:
  • Implementation of improved observers (adaptive, hybrid) combined with AI techniques.
  • Simulation validation on different usage profiles (load, speed, temperature).
  • Optimization for embedded applications (robustness, real-time).
  1. Third year:
  • Validation on experimental data or engine test bench.
  • Integration into an overall EV thermal management strategy.
  • Performance analysis, writing of publications and thesis.
  • Drafting of thesis manuscript.

Required skills and candidate profile:

  • Knowledge and skills in control and optimization.
  • Knowledge and skills in energy sources/storage, power electronics-electric motors.
  • The candidate must hold a Research Master 2 in the following fields: electric propulsion and its control/optimization.

Contacts and funding:

Pr. Malek GHANES. Director of the Chair. Centrale Nantes, LS2N, CNRS UMR 6004
Tel: 02 40 37 69 13, Email: Malek.Ghanesd8b89beb-37a8-44ec-b8ce-48b8f72af0fa@ec-nantes.fr

Funding is available through the Chair. 3 years. Start: 01/10/2026.

Non exhaustives references:

  • Previati, G., Mastinu, G., & Gobbi, M. (2022). Thermal Management of Electrified Vehicles—A Review. Energies, 15(4), 1326. https://doi.org/10.3390/en15041326
  • Wang, X., Li, B., Gerada, D., Huang, K., Stone, I., Worrall, S., & Yan, Y. (2022). A critical review on thermal management technologies for motors in electric cars. Applied thermal engineering, 201, 117758.
  • Schaut, S., Arnold, E., & Sawodny, O. (2021). Predictive thermal management for an electric vehicle powertrain. IEEE Transactions on Intelligent Vehicles, 8(2), 1957-1970.
  • Erazo, D. E. G., Wallscheid, O., & Böcker, J. (2019). Improved fusion of permanent magnet temperature estimation techniques for synchronous motors using a Kalman filter. IEEE Transactions on Industrial Electronics, 67(3), 1708-1717.
  • Joshi, H., Burkhardt, Y., Seilmeier, M., & Hofmann, W. (2020, August). Error compensation in initial temperature estimation of electric motors using a kalman filter. In 2020 International Conference on Electrical Machines (ICEM) (Vol. 1, pp. 840-846). IEEE.

Post-Doc Proposal, Ampère/Renault-Centrale Nantes chair, 2026

Hybrid Thermal Estimation (Physics + AI) for Electric Machines and Powertrains of Electric Vehicles

Context and objectives:

The transition to sustainable mobility places electric vehicles (EVs) at the heart of industrial strategies. The performance, reliability, and durability of the electric powertrain (EPU), including the electric motor, converters, battery, and auxiliary components, depend heavily on their thermal behavior.

Temperature drifts significantly influence the vehicle's efficiency, range, lifespan, and safety. Furthermore, the prioritization of thermal processes (heating, battery or motor cooling, etc.) can significantly impact the overall efficiency of the EV.

In this context, the work conducted within the Renault–Centrale Nantes Chair (projects E7A, 6AK, HV95, INW) has demonstrated the value of hybrid Physics + AI approaches for online estimation of stator, rotor, and permanent magnet temperatures. These estimations must be robust, fast, inexpensive, and suitable for embedded deployment.

The proposed postdoctoral position aims to optimize an existing hybrid strategy, integrating it into a comprehensive approach to thermal estimation and monitoring of the entire powertrain, including motors, power modules, and battery. The ultimate goal is to improve observer accuracy, reduce computational complexity (CPU), and eliminate the use of sensors (NTC) while maintaining a very high level of reliability.

Required skills:

  • Control/Estimation with a solid foundation in stability and robustness testing
  • Machines, converters
  • Knowledge of AI
  • Design/Analysis of electrical machines
  • Control of power converters
  • Signal processing
  • Matlab/Simulink, rapid prototyping

Candidate Profile:

  • PhD in Automation or/and Electrical Engineering.
  • Motivation for work combining physical modeling, AI, and real-time implementation.
  • Excellent communication skills (good level of English).

Contacts:

Prof. Malek GHANES
Chairholder – Ampère/Renault–Centrale Nantes,
Ecole Centrale Nantes (CN), LS2N, CNRS UMR 6004, Nantes, France
Phone: +33 2 40 37 69 13 — Email: Malek.Ghanes@ec-nantes.fr

Salary: Based on experience. Funded by the Ampère/Renault–CN Chair.
Duration: 12 months (renewable once). Starting in March 2026.

Application Process:

Please send your application via the link: https://jobs.ec-nantes.fr/o/post-doctorat-physique-ia-estimation-thermique-des-systemes-de-traction-electrique-hf-ref-1069

References:

  • [1] Previati, G., Mastinu, G., & Gobbi, M. (2022). Thermal Management of Electrified Vehicles—A Review. Energies, 15(4), 1326. https://doi.org/10.3390/en15041326.
  • [2] Wang, X., Li, B., Gerada, D., Huang, K., Stone, I., Worrall, S., & Yan, Y. (2022). A critical review on thermal management technologies for motors in electric cars. Applied thermal engineering, 201, 117758.
  • [3] Schaut, S., Arnold, E., & Sawodny, O. (2021). Predictive thermal management for an electric vehicle powertrain. IEEE Transactions on Intelligent Vehicles, 8(2), 1957-1970.
  • [4] Joshi, H., Burkhardt, Y., Seilmeier, M., & Hofmann, W. (2020, August). Error compensation in initial temperature estimation of electric motors using a kalman filter. In 2020 International Conference on Electrical Machines (ICEM) (Vol. 1, pp. 840-846). IEEE.
  • [5] Erazo, D. E. G., Wallscheid, O., & Böcker, J. (2019). Improved fusion of permanent magnet temperature estimation techniques for synchronous motors using a Kalman filter. IEEE Transactions on Industrial Electronics, 67(3), 1708-1717.
  • [6] D. Reigosa, D. Fernández, M. Martínez, J. M. Guerrero, A. B. Diez and F. Briz, (2019). Magnet Temperature Estimation in Permanent Magnet Synchronous Machines Using the High Frequency Inductance. In IEEE Transactions on Industry Applications, vol. 55, no. 3, pp. 2750-2757, doi: 10.1109/
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Published on June 15, 2022 Updated on June 17, 2026

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