The University of Southampton
University of Southampton Institutional Repository

Personalised electric vehicle routing using online estimators

Personalised electric vehicle routing using online estimators
Personalised electric vehicle routing using online estimators
In this paper, we develop a novel approach to help drivers of electric vehicles (EVs) plan charging stops on long journeys. A key challenge here is eliciting the highly heterogeneous preferences of drivers. Here we develop an intelligent personal agent that learns preferences through multiple interactions. To minimise the cognitive burden on the driver, we propose a novel technique which applies a small-scale discrete choice experiment to interact with the driver. Specifically, the agent provides drivers with several routes with possible combinations of charging stops based on their latest beliefs about the driver's preferences. Then, through subsequent iterations, the personal agent learns and refines its beliefs about the driver's preferences. It suggests better routes closer to the driver's preferences. We evaluated our novel algorithm with real preference data from EV drivers, showing that our approach converges quickly to the optimal routes after only a small number of queries.
Preference Elicitation, Electric Vehicles, Online Planning
Shafipour, Elnaz
a2e1dea9-d3c0-4288-afdc-197df65f2556
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Ahipasaoglu, Selin
d69f1b80-5c05-4d50-82df-c13b87b02687
Shafipour, Elnaz
a2e1dea9-d3c0-4288-afdc-197df65f2556
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Ahipasaoglu, Selin
d69f1b80-5c05-4d50-82df-c13b87b02687

Shafipour, Elnaz, Stein, Sebastian and Ahipasaoglu, Selin (2023) Personalised electric vehicle routing using online estimators. ECAI 2023 Workshop on Artificial Intelligence for Sustainability, Poland. 8 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we develop a novel approach to help drivers of electric vehicles (EVs) plan charging stops on long journeys. A key challenge here is eliciting the highly heterogeneous preferences of drivers. Here we develop an intelligent personal agent that learns preferences through multiple interactions. To minimise the cognitive burden on the driver, we propose a novel technique which applies a small-scale discrete choice experiment to interact with the driver. Specifically, the agent provides drivers with several routes with possible combinations of charging stops based on their latest beliefs about the driver's preferences. Then, through subsequent iterations, the personal agent learns and refines its beliefs about the driver's preferences. It suggests better routes closer to the driver's preferences. We evaluated our novel algorithm with real preference data from EV drivers, showing that our approach converges quickly to the optimal routes after only a small number of queries.

Text
Personalised_Electric_Vehicle_Routing_Using_Online_Estimators - Accepted Manuscript
Download (324kB)

More information

Accepted/In Press date: 2023
Venue - Dates: ECAI 2023 Workshop on Artificial Intelligence for Sustainability, Poland, 2023-09-30
Keywords: Preference Elicitation, Electric Vehicles, Online Planning

Identifiers

Local EPrints ID: 481645
URI: http://eprints.soton.ac.uk/id/eprint/481645
PURE UUID: db93de56-8d21-4d94-b2a7-2c83219c5fde
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857
ORCID for Selin Ahipasaoglu: ORCID iD orcid.org/0000-0003-1371-315X

Catalogue record

Date deposited: 05 Sep 2023 16:49
Last modified: 03 Nov 2023 03:03

Export record

Contributors

Author: Elnaz Shafipour
Author: Sebastian Stein ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×