Electrical Vehicle Fleet Routing Accounting for Dynamic Battery Degradation

TL;DR

This paper presents a novel vehicle routing model that incorporates battery degradation dynamics, enabling better fleet management by considering how route choices affect battery health and degradation costs.

Contribution

It introduces a mixed-integer nonlinear programming model that monitors battery DoD across a fleet, integrating degradation considerations into route planning.

Findings

Accounting for battery degradation influences route choices.

Forcing DoD boundaries affects fleet-wide battery health.

Battery degradation impacts overall route planning costs.

Abstract

The increasing uptake of electrical vehicles (EVs) has increased the awareness of battery degradation costs and how they can be minimized. However, from a planning perspective it is difficult to integrate battery degradation models into existing route planning models and to assess how policies that aim at reducing battery degradation affect route planning costs and degradation across the fleet. In this paper, a simple transportation vehicle routing problem (VRP) is formulated as a mixed-integer nonlinear problem (MINLP), with a modification that allows monitoring the maximum and minimum depth-of-discharge (DoD) of the entire fleet. This allows us to measure the battery health degradation during the online optimization process. The results show that accounting for the impact of different route characteristics on battery degradation can have an impact on the route planning of the entire fleet as well as the battery degradation for all vehicles. The latter is achieved by forcing vehicles to adapt to certain DoD boundaries in the long term.