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A Learning Bilevel Optimization Approach for the Demand Maximizing Battery Swapping Station Location Problem

Laurenz Tomandl, Thomas Jatschka, Guenther Raidl, Tobias Rodemann, "A Learning Bilevel Optimization Approach for the Demand Maximizing Battery Swapping Station Location Problem", Eurocast, 2024 19th International Conference on Computer Aided Systems Theory, 2024.

Abstract

A problem for the wide-scale adoption of electric vehicles are the usually long battery charging times. To avoid the waiting time for the customer, vehicles with exchangeable batteries and a network of battery swapping stations are a promising solution for smaller-scale vehicles like electric scooters. A customer can drive to a station and exchange their depleted batteries with an already charged battery and thus avoid the waiting that would be necessary otherwise. A good infrastructure is needed to make the implementation of such a system viable. To plan and design this infrastructure we consider the Demand Maximizing Battery Swapping Station Location Problem (DMBSSLP), which is an adaption from the Multi-Period Battery Swapping Station Location Problem (MBSSLP) by Jatschka. We propose a learning bilevel optimization (LBLO) algorithm for solving large-scale instances of the DMBSSLP. This work is an extension of the first author’s master thesis.



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