| KEYWORD |
Predictive Charging Recommender for Electric Mobility
Thesis in external company
keywords BATTERIES STATE OF HEALTH, STATE OF CHARGE, ELECTRIC VEHICLES, MODELING AND SIMULATION, OPTIMIZATION
Reference persons ALESSANDRO RIZZO
Thesis type SIMULATIVE AND EXPERIMENTAL
Description Reefilla is a startup that was born from the incubator of the Politecnico in
2021, with the aim of revolutionizing the world of electric charging. The
proposed thesis focuses on the study and design of an ambitious predictive
system capable of suggesting to the users of Reefilla when to schedule the
recharge of their car.
Solving a problem like this would be strategic in the field of electric mobility
where the major limits are dictated by the lack of infrastructure and the time
required for charging.
For this reason, Reefilla's goal is to rely on data analysis and the latest
artificial intelligence algorithms to have a system that is first and foremost
reliable and can be dynamically perfected.
One possible approach could be to combine the training of an algorithm for
user profiling with a model of battery discharge of the vehicle. The use of
hybrid machine learning techniques and physical modeling could allow the
development of a predictive recharge suggestion system that is precise and
personalized for each user.
Naturally, an integral part of studying the problem will be to evaluate multiple
possible solutions and critically analyze the results obtained: the only limit will
be the candidate's imagination.
Required skills The candidate must have an interest in modern applications of artificial
intelligence and data analysis, as well as a strong aptitude for problem solving
and a willingness to take on challenges.
Notes More information about the hosting company at https://www.reefilla.com
Deadline 04/04/2023
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