Thesis: Reduced CO2 by Reinforcement learning (RL)

Background
Within Volvo Group Trucks Technology, the Powertrain Strategic Development department drives the development of new innovative powertrains for our vehicles of the next decade. One of the main challenges is to reduce the CO2 emissions by 30% from 2019 up to 2030. Reinforcement Learning (RL) is identified as one emerging techniques with potential to minimize the energy consumption in vehicles, and hence the CO2. Simulation environments play a crucial role in the development of Reinforcement Learning (RL) algorithms. Using mainly matlab and Simulink, Volvo Group has developed a virtual platform representing a complete vehicle on road and a driver (model or real). The utilization of this could for instance be to evaluate fuel consumption, vehicle speed, acceleration and gear shifting when developing new hardware or control strategies.
The Purpose
The overall goal of this project is to demonstrate the potential of RL within a virtual environment. The study will start from one or more use cases already identified, but during the work new use cases can be addressed as well. The actual implementation will take off from Volvo Group’s in-house simulation platform. Apart from the implementation and the algorithm training challenges, other topics such as optimization between computer power and model details will be explored. In addition, literature survey and benchmarking of alternative tools are important tasks.
Suitable background:
  • Master students in Control, Automotive, Physics, Computer science or similar
  • Advanced knowledge in matlab and Simulink
  • Reinforcement Learning and Machine Learning knowledge
  • ​Vehicle motion knowledge
  • Analytic and open mind
  • Python is a plus
Thesis Level: Master Thesis
Language: English
Starting date: January/February
Number of students: 1-2
Tutor: Rickard Andersson, Simulation & Analysis Lead Research Engineer, +46313233054

About Us

The Volvo Group is one of the world’s leading manufacturers of trucks, buses, construction equipment and marine and industrial engines under the leading brands Volvo, Renault Trucks, Mack, UD Trucks, Eicher, SDLG, Terex Trucks, Prevost, Nova Bus, UD Bus and Volvo Penta.

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