Federer,MarikaMüssig,DanielLenk,SteveLässig,JörgDemmler, DanielKrupka, DanielFederrath, Hannes2022-09-282022-09-282022978-3-88579-720-3https://dl.gi.de/handle/20.500.12116/39602To reduce $CO_2$ emissions in the mobility sector, battery electric service vehicles might play an important role in the future. Here, an optimal charging scheduling use case will be presented which includes local solar power generation for minimizing the power grid usage for electric service vehicles. Different formulations of the use case are given to illustrate the differences for classical and quantum-based optimization using a mixed integer linear program and a quadratic unconstrained binary optimization program, respectively. Addtionally, we study the complexity of our benchmark experiments by characterizing the respective QUBO matrices and the optimization landscapes. It is shown how the setting of the parameters of a certain experiment and its penalty function influences the complexity for a quantum-based optimizer. Additionally, we present a comparison of the computing times and summarize the current state of gate-based quantum computing for electromobility.enQAOAElectromobilityQuantum ComputingEnergy10.18420/inf2022_971617-5468