Electric Vehicle Charge Scheduling on Highway Networks from an Aggregate Cost Perspective

TL;DR

The paper addresses the problem of scheduling electric vehicle charging along highway networks to minimize the aggregate cost borne by utilities, station operators, and EV users. It adopts a hybrid-systems framework with two finite-state machines to model driving/charging/waiting and edge navigation, formulating the planning problem as a receding-horizon mixed-integer quadratic program with switched affine dynamics $x_{k+1}^{ct} = A^{i_k} x_k^{ct} + B^{i_k} eta^{ct}_{k}$ over horizon $H_p$. Key contributions include a graph-based highway model with fixed charging locations, a comprehensive cost structure combining $C_{stations}$, $C_{customers}$, and $C_{electricity}$, and a formalization of the system as a domain-preserving, non-blocking hybrid automaton, accompanied by a discussion of practical challenges such as nontrivial indexing and scalability observed in preliminary results. The work lays groundwork for centralized trajectory optimization under real-time constraints, highlighting the potential benefits for grid stability, charging infrastructure utilization, and user experience while acknowledging the need for debugging, scalability-focused heuristics, and validation with real highway data.

Abstract

In this paper, we attempt to optimally schedule the charging of long-range battery electric vehicles (BEVs) along highway networks, in order to minimize aggregate costs to the overall system consisting of utilities or electricity providers, station operators and other infrastructure, as well as EV users. Thus, we approach the problem from the perspective of both customers (EV car owners), as well as charging station operators and utilities using a hybrid systems based formulation.

Figures (7)

• Figure 1: Nodes represent charging stations. Node sizes correspond to station capacities. Edge weights represent distances in miles. Assume all nodes connected through two-way highways/roads results in an undirected graph
• Figure 2: Finite-state machine of an EV with the extended logic finite-state machine
• Figure 3: Charging power graph from kongetal
• Figure 4: Example trajectories of two cars on a five-node network. The current edge is characterized as the vector shown in Section \ref{['sec: appendix']}.
• Figure 5: The local edge length is kept track of the and the local distance is encoded by the counter state denoted as $\epsilon$.