no code implementations • 1 Sep 2023 • Rémi Lacombe, Nikolce Murgovski, Sébastien Gros, Balázs Kulcsár
We propose a hierarchical control framework to solve this problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer.
no code implementations • 24 Aug 2023 • Erik Börve, Nikolce Murgovski, Leo Laine
In this paper we treat optimal trajectory planning for an autonomous vehicle (AV) operating in dense traffic, where vehicles closely interact with each other.
no code implementations • 27 Mar 2023 • Ebrahim Balouji, Jonas Sjöblom, Nikolce Murgovski, Morteza Haghir Chehreghani
Finally, the last model is based on two parallel At-LSTMs, where similarly, each At-LSTM predicts time and distance separately through fully connected layers.
no code implementations • 21 Nov 2022 • Ahad Hamednia, Jimmy Forsman, Nikolce Murgovski, Viktor Larsson, Jonas Fredriksson
This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate.
no code implementations • 13 Oct 2022 • Rémi Lacombe, Nikolce Murgovski, Sébastien Gros, Balázs Kulcsár
The rapid adoption of electric buses by transit agencies around the world is leading to new challenges in the planning and operation of bus networks.
no code implementations • 7 Oct 2022 • Ahad Hamednia, Victor Hanson, Jiaming Zhao, Nikolce Murgovski, Jimmy Forsman, Mitra Pourabdollah, Viktor Larsson, Jonas Fredriksson
This paper studies optimal thermal management and charging of a battery electric vehicle driving over long distance trips.
no code implementations • 3 May 2022 • Ahad Hamednia, Nikolce Murgovski, Jonas Fredriksson, Jimmy Forsman, Mitra Pourabdollah, Viktor Larsson
The formulated problem is then transformed into a hybrid dynamical system, where the dynamics in driving and charging modes are modeled with different functions and with different state and control vectors.
no code implementations • 12 Jan 2021 • Victor Eberstein, Jonas Sjöblom, Nikolce Murgovski, Morteza Haghir Chehreghani
In this paper, we present a unified framework for trip destination prediction in an online setting, which is suitable for both online training and online prediction.