Machine Learning Based Charging Decision Policy for a Fleet of Electric Vehicles
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2022
Författare
Johannesson, Gustav
Lindhardt, Alexander
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In a fleet of autonomous electric vehicles, each vehicle must frequently charge its
battery. The charging of the vehicles should ideally be as efficient as possible without
causing roadblocks or battery depletion. The aim of this thesis, which is performed in
collaboration with Volvo Autonomous Solutions, is to develop such charging policies
using reinforcement learning methods. More precisely, the problem is formulated
as a Markov decision process (MDP), and tabular Q-learning and deep Q-learning
are used to learn (optimal) charging policies. The learned policies are evaluated
and compared against each other, as well as against a rule-based policy used as a
benchmark. The robustness and generalizability of the learned policies are tested by
adjusting a number of site-specific parameters, such as charging time and number
of vehicles at the site. The results indicate that machine learning-based charging
policies can increase the efficiency for a fleet of vehicles compared to a simple rulebased
charging policy. Moreover, the importance of such policies is also shown to
vary depending on the site configuration and its complexity.
Beskrivning
Ämne/nyckelord
charging policy, machine learning, reinforcement learning, Q-learning, DQN, deep Q-learning, Markov decision process