将有限理性引入电动汽车高速公路充电决策：贝叶斯博弈分析
Incorporating Bounded Rationality into Electric Vehicle Highway Charging Decisions: A Bayesian Game Analysis
AI总结：
该研究针对EV高速公路充电决策问题，构建有限理性框架结合前景理论与贝叶斯博弈，证明均衡存在性，经真实数据实验验证策略可降低双方成本。
AI中文摘要：
电动汽车（EV）是物联网（IoT）中的关键智能终端。尽管EV渗透率逐年增长，但高速公路驾驶体验仍需改善。准确预测EV高速公路充电行为对解决该问题至关重要。本文提出一种新型有限理性框架以分析高速公路充电决策，具体而言，我们利用前景理论捕捉驾驶员保留比理论所需更多电量的倾向，随后提出一种贝叶斯博弈，其中不了解他人决策的EV驾驶员旨在最小化成本，包括里程焦虑、充电费用和排队时间。为深入理解该博弈，我们在两种实际场景中证明了贝叶斯纳什均衡的存在性与唯一性。基于真实数据的数值实验表明，驾驶员的风险厌恶倾向显著影响EV充电决策、充电需求、充电站（CSs）的排队长度以及高速公路网络的离开率。此外，与其他基准方法相比，我们的策略可降低EV的累积成本和CSs的充电成本。
英文摘要：
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.
