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EURAXESS

MSCA-COFUND-CLEAR-Doc-PhD Position#CD22-63: A machine learning based rolling resistance prediction model for electric vehicles

17/10/2022

Job Information

Organisation/Company
Université Gustave Eiffel
Department
AME-EASE
Research Field
Engineering
Engineering » Civil engineering
Engineering » Mechanical engineering
Mathematics
Researcher Profile
First Stage Researcher (R1)
Country
France
Application Deadline
Type of Contract
Temporary
Job Status
Full-time
Hours Per Week
35
Is the job funded through the EU Research Framework Programme?
H2020 / Marie Skłodowska-Curie Actions COFUND
Marie Curie Grant Agreement Number
101034248
Is the Job related to staff position within a Research Infrastructure?
No

Offer Description

Worldwide governmental bodies and authorities are adopting policies and allocating resource to promote the development of cleaner and more efficient vehicle technologies with the objective of replacing the internal combustion engines that consume large amounts of fossil fuels and emit huge quantities of air pollutants and greenhouse gases (GHG). As a result, the increasing penetration of electric vehicles (EV) in the transportation market witnessed in the last years offers a promising outlook for the diversification of the sector’s fuel sources.

One of the factors affecting vehicle’s fuel economy is rolling resistance. Rolling resistance is a physical phenomenon related to the dissipation of energy that occurs during the passage of a tire on a road pavement. This loss of energy generates forces opposed to the vehicle movement, which in turn increase fuel consumption. Three physical phenomena can be identified that explain rolling resistance: 1) deformation of the tire in the tire/road contact area, 2) aerodynamic drag of the rotating tire, and 3) slip between the tire tread and the pavement surface. Rolling resistance can represent until 30% of the resistive forces depending on the vehicles’ characteristics and the driving conditions (rural or urban roads, motorways). According to several studies, rolling resistance is responsible for 5 to 20% of the fuel consumption of a passenger car and 15 to 40% of trucks’ fuel consumption. It depends on multiple factors related to the vehicle type (load, suspension), tyre properties (rubber, inflation pressure, rubber temperature), vehicle operating conditions (speed), atmospheric conditions (wind, temperature) and road pavement characteristics (roughness, macrotexture). From state of the art, it is clear that all the factors described previously can have opposite effects on rolling resistance. This makes it harder to understand and decouple their individual contribution. Moreover, other factors related to vehicle dynamics can also affect the generation of rolling resistance (e.g., tire camber, torque, etc.) but are less analysed in literature.

In recent decades several approaches for estimating rolling resistance have been studied and developed. They proposed models suitable for diesel and gasoline vehicles based on numerical approaches (dynamical equations) or statistical approaches (correlation). The overwhelming majority of these approaches requires prior knowledge on the road pavement characteristics, usually roughness and macrotexture. Additionally, they exhibit accuracy problems, robustness issues, require sometimes long calibration of the parameters and are limited on their field of application. However, so far, few research studies have been performed on the quantification of the effect of rolling resistance on the energy consumption of EV, let alone while considering the dynamic states of vehicle, tyre and based on the real time perception of pavement characteristics.

Thus, by harnessing the potentialities of data analytics and machine learning this thesis aims to develop a data-driven rolling resistance prediction model based on the acquisition of data related to the dynamic states of the vehicle, tyre and pavement characteristics. To achieve this purpose, an instrumented EV will be used both on test tracks exhibiting a wide range of pavement mixes and on real road sections. The EV is equipped with multiple sensors allowing to measure continuously rolling resistance forces in the tire/road contact area, temperature (rubber and pavement), dynamical behaviour of the vehicle (speeds, accelerations), tire deformation and pavement characteristics. These sensors are controlled periodically to check their accuracy and robustness and the data is acquired at a frequency of 100 Hz.

Lastly, the PhD thesis includes both wide experimental work to collect data and theoretical mathematical development to clean dataset, select relevant parameters and propose accurate and robust model based on machine learning technics. The candidate will have access to instrumented EV, to test tracks and a team of technicians to implement experimental campaigns. He will be hosted in the EASE laboratory (Environmental Assessment, Planning, Safety, Ecodesign)

Requirements

Research Field
Engineering
Education Level
Bachelor Degree or equivalent
Skills/Qualifications
  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
  • At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
Languages
FRENCH
Level
Basic
Languages
ENGLISH
Level
Good

Additional Information

Benefits
  • High-quality doctoral training rewarded by a PhD degree, delivered by Université Gustave Eiffel
  • Access to cutting-edge infrastructures for research & innovation.
  • Appointment for a period of 36 months based on a salary of 2 700 € (gross salary per month).
  • Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year.
  • International mobility will be mandatory
  • An international environment supported by the adherence to the European Charter & Code.
  • Access to dedicated CLEAR-Doc trainings with a strong interdisciplinary focus, together with a Career development Plan.
Eligibility criteria

Applicants must fulfil the following eligibility criteria:

  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
  • At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
  • At the time of the deadline, applicants must be in the first four years (full-time equivalent research experience) of their research career (career breaks excluded) and not yet been awarded a doctoral degree. Career breaks refer to periods of time where the candidate was not active in research, regardless of his/her employment status (sick leave, maternity leave etc). Short stays such as holidays and/or compulsory national service are not taken into account.
  • At the time of the deadline, applicants must fulfil the transnational mobility rule: incoming applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 previous years.

One application per call per year is allowed.

Applicants must be available full-time to start the programme on schedule (November 1st 2023).

Application rules are enforced by the French doctoral system which specifies a standard duration of 3 years for a full-time PhD together with the MSCA standards and the OTM-R European rules as follows.

Citizens of any nationality may apply to the programme.

There is no age limit.

Selection process

Please refer to the Guide for Applicants available on the CLEAR-Doc website: https://clear-doc.univ-gustave-eiffel.fr/how-to-apply/useful-documents/

Additional comments

The First step before applying is contacting the PhD supervisor. You will not be able to apply without an acceptation letter from the PhD supervisor.

International mobility planned: 6 months International mobility planned at the University of Twente (Netherlands)

Please contact the PhD supervisor for any additional detail on job offer.

There are no restrictions concerning the age, gender or nationality of the candidates. Applicants with career breaks or variations in the chronological sequence of their career, with mobility experience or with interdisciplinary background or private sector experience are welcome to apply.

Support service is available during every step of the application process by email: clear-doc@univ-eiffel.fr

Website for additional job details

Work Location(s)

Number of offers available
1
Company/Institute
Université Gustave Eiffel
Country
France
City
Bouguenais
Postal Code
44340
Street
Allée des Ponts et Chaussées
Geofield

Contact

City
Marnel-la-Vallée
Website
Street
5, Boulevard Descartes
Postal Code
77454
E-Mail
veronique.cerezo@univ-eiffel.fr
j.m.oliveiradossantos@utwente.nl