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Time-Optimal Race Strategies for Hybrid Electric Race Cars

Student(en):

Betreuer:

Mauro Salazar, Joe Warrington, Camillo Balerna
Beschreibung:

Background

The power unit of hybrid electric race cars consists of a turbocharged internal combustion engine (ICE), a kinetic motor/generator unit (MGU-K) connected to the traction system and of a heat motor/generator unit (MGU-H) attached to the turbocharger shaft. With the given racing regulations, the operation of such a system has become an extremely interesting and challenging control engineering problem.

Problem Definition

So far, the optimal control strategies for the energy management system of the power unit are computed solving a lap-time optimization problem off-line. In the existing approach it is assumed that the boundary conditions in every lap are identical. In reality, vehicle and track characteristics evolve during the course of a race. Therefore, it is of interest to understand to which extent these changes affect the optimal solution.

Task

The main target of this project is to develop a full-race optimization framework starting from an existing single-lap optimization tool. Phenomena such as variations in the grip limitations due to tires consumption and mass loss due to fuel consumption should be taken explicitly into account. Sequential convex optimization approaches in combination with methodologies inspired by dual dynamic programming could be used to solve the arising problem. Finally, the solution obtained should be compared to the current one, in order to quantify the potential of improvement if the variations in the vehicle characteristics during a race would be explicitly taken into account.

Weitere Informationen
Professor:

Christopher Onder
Projektcharakteristik:

Typ:
Art der Arbeit: 50% theory, 50% simulation
Voraussetzungen: Control Theory. Knowledge in Optimization and Hybrid Electric Vehicles is a plus, but not mandatory. Analytical and programming skills, initiative and independence.
Anzahl StudentInnen: 1
Status: open
Projektstart: September 2017
Semester: Autumn 2017