考虑实时驾驶因素和电池容量指标的电动汽车续航里程预测

IF 7.7 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment Pub Date : 2025-05-10 DOI:10.1016/j.trd.2025.104795

Xinwei Ma , Jiaao Li , Hongjun Cui , Long Cheng , Yanjie Ji , Jianbiao Wang

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引用次数: 0

摘要

准确预测电动汽车的剩余续驶里程（RDR）对于缓解续驶里程焦虑至关重要。然而，目前大多数研究仅基于当前状态预测RDR，未能捕捉实时驾驶行为和电池老化对RDR的影响。该研究使用了中国天津100辆电动汽车的数据集，这些数据集从2024年3月30日至4月7日每10秒收集一次，包括详细的驾驶行为和电池状态。引入了一种新的度量，电池容量指数（BCI），用于量化电池的健康和老化，反映每单位充电状态（SOC）所保留的电量。采用新颖的Kolmogorov-Arnold网络（KAN）集成时间序列模型，BiLSTM-KAN模型具有较好的预测精度。SHapley加性解释（SHAP）分析确定了观察到的SOC、BCI和驾驶行为是影响RDR的关键因素。这些发现有助于电动汽车技术的发展，并支持可持续交通发展。

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Electric vehicle range prediction considering real-time driving factors and battery capacity index

Accurate prediction of the Remaining Driving Range (RDR) of Electric Vehicles (EVs) is crucial for alleviating range anxiety. However, most current studies predict RDR based solely on the current state, failing to capture the impact of real-time driving behaviors and battery aging on RDR. This study uses a dataset from 100 EVs in Tianjin, China, collected every 10 s from March 30 to April 7, 2024, encompassing detailed driving behavior and battery status. A new metric, the Battery Capacity Index (BCI), is introduced to quantify battery health and aging, reflecting the charge retained per unit of State of Charge (SOC). Novel Kolmogorov-Arnold Networks (KAN)-integrated time series models are applied, with the BiLSTM-KAN model demonstrating superior prediction accuracy. SHapley Additive exPlanations (SHAP) analysis identifies observed SOC, BCI, and driving behavior as key factors influencing RDR. These findings contribute to EV technology and support sustainable transportation development.

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来源期刊

Transportation Research Part D-transport and Environment 工程技术-运输科技

CiteScore

14.40

自引率

9.20%

发文量

314

审稿时长

39 days

期刊介绍： Transportation Research Part D: Transport and Environment focuses on original research exploring the environmental impacts of transportation, policy responses to these impacts, and their implications for transportation system design, planning, and management. The journal comprehensively covers the interaction between transportation and the environment, ranging from local effects on specific geographical areas to global implications such as natural resource depletion and atmospheric pollution. We welcome research papers across all transportation modes, including maritime, air, and land transportation, assessing their environmental impacts broadly. Papers addressing both mobile aspects and transportation infrastructure are considered. The journal prioritizes empirical findings and policy responses of regulatory, planning, technical, or fiscal nature. Articles are policy-driven, accessible, and applicable to readers from diverse disciplines, emphasizing relevance and practicality. We encourage interdisciplinary submissions and welcome contributions from economically developing and advanced countries alike, reflecting our international orientation.