Methodology development for analytical modeling of hybrid vehicle’s electrical components: Energy performance optimization

Status
closed
Research division
Location

The hybrid electric vehicle (HEV) is a complex system consisting of several energy sources. Depending on their configuration typically combining an internal combustion engine, electrical machinery and a storage system, significant benefits in terms of energy consumption as well as other  features can be achieved as compared to a conventional vehicle. In order to achieve this goal, optimization is required at different levels. For a given use, the energy performance of the HEV  mainly depends on three highly interdependent aspects:
-    The topology : series, parallel ...
-    The sizing of the components
-    The energy management strategy between the different sources

The sizing and optimization of a powertrain are known problems in the automotive field. They cannot be solved independently : a system is well sized only in relation to specifications and a control strategy. Indeed, the best control cannot respond optimally if the system is not  accurately sized. Similarly, the design of a system can be optimal only depending on the chosen control strategy. This problem of simultaneous optimization of the control and the sizing of the components is complex, and it must be based on models having just the right  to yield accurate predictions of physical performances, and being sufficiently easy to compute to be compatible with optimization algorithms. Too complex models complexify or even render impossible the optimization process, which requires computing them  at each evaluation of the criterion to be optimized. Too simplistic, they do not allow taking accurately into account key physical parameters  of the system.
The ambition of this thesis is to develop a methodology for the joint optimization of the design and control of a hybrid powertrain with an improved calculation efficiency, based on an analytical modeling of all the electrical components, such as the electric motor, the battery and the power electronics (inverter and DC-DC converters). The models that will be developed should be of a limited complexity in order to allow using them in an optimization process   to be performed in a limited time compatible with a practical usage.

Keywords: HEV, Modeling, Optimization, Power electronics, Electrical engineering, Control 

  • Academic supervisor    Dr. (HDR)  Antonio SCIARRETTA, IFPEN, Control, Signals & Systems Dept.
  • Doctoral School    ED 589 STIC, , Information and communication science and technology, Paris-Saclay University
  • IFPEN supervisor    Dr, Laid  KEFSI  IFPE? Research Engineer on power electronics and drive control, Electric Systems Department, laid.kefsi@ifpen.fr 
  • PhD location    IFP Energies nouvelles, Rueil-Malmaison, France
  • Duration and start date    3 years, starting not earlier than October 2020
  • Employer    IFP Energies nouvelles, Rueil-Malmaison, France
  • Academic requirements    University Master degree or engineering school in Electrical engineering
  • Language requirements    Fluency in English, ability for speak French or willingness to learn it appreciated
  • Other requirements    Good knowledge in Matlab/Simulink, skills in electrical machines, power electronics and control, knowledge in optimization methodologies will be appreciated
     
Contact
Encadrant IFPEN 
Dr. Laïd KEFSI
Ingénieur de recherche en électronique de puissance et contrôle, Département système électrifiés
Texte libre

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see https://www.ifpen.com. 
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions.