Incorporating Bounded Rationality into Electric Vehicle Highway Charging Decisions: A Bayesian Game Analysis
2026-08-17 • Computer Science and Game Theory
Computer Science and Game Theory
AI summaryⓘ
The authors studied how electric vehicle (EV) drivers decide when and where to charge on highways. They created a new model using ideas from psychology and game theory to explain why drivers might charge more than needed due to fear of running out of power. By analyzing real data, they showed that drivers' cautious behavior affects how busy charging stations get and overall costs. Their approach helps reduce costs for both drivers and charging station operators compared to existing methods.
Electric VehiclesInternet of ThingsProspect TheoryBayesian GameBayesian Nash EquilibriumRange AnxietyCharging StationsQueuing TheoryRisk AversionHighway Network
Authors
Huanyu Yan, Xiaoying Tang
Abstract
Electric vehicles (EVs) represent a critical intelligent terminal within the Internet of Things (IoT). Despite the year-on-year growth in EV penetration, the highway driving experience still requires improvement. Accurate prediction of EV highway charging behavior is crucial to addressing this issue. This paper introduces a novel bounded rationality framework to analyze highway charging decisions. Specifically, we utilize prospect theory to capture the tendency of drivers to reserve more electricity than theoretically necessary. We then propose a Bayesian game in which EV drivers, unaware of others' decisions, aim to minimize costs, including range anxiety, charging fees, and queuing time. To gain insights into the game, we prove the existence and uniqueness of the Bayesian Nash Equilibrium in two practical scenarios. Our numerical experiments, based on real-life data, demonstrate that drivers' risk aversion tendency significantly influence EV charging decisions, charging demand, queuing lengths at charging stations, and the departure rate on the highway network. Furthermore, our strategy reduces cumulative EV cost and CSs' charging costs compared to other benchmarks.