融合驾驶习惯与地图信息，精准预测电动车能耗与电量变化。
Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data
- 结合地图特征与驾驶员速度预测，构建个性化能耗模型。
- 在城市、高速和山路路段均实现高精度功率与电量轨迹预测。
- 适合需要精准续航预估的电动车用户与车企开发人员。
本文提出一种个性化电池电动车辆（BEV）能耗估计框架，融合基于地图的上下文特征、驾驶员特定的速度预测以及物理驱动的能耗建模。系统包含路线选择、道路特征处理、基于规则的参考速度生成、基于PID控制器的车辆动力学模拟器，以及训练用于再现个体驾驶行为的双向LSTM模型。预测的个性化速度曲线与准稳态逆向能耗模型耦合，用于计算牵引功率、再生制动及电池荷电状态（SOC）演化。在城市、高速公路和丘陵路段的评估表明，该方法能准确捕捉驾驶员在交叉口减速、限速跟踪及道路坡度响应等关键行为模式，同时生成精确的功率与SOC轨迹。结果证明，将学习到的驾驶行为与地图上下文及物理能耗模型结合，可有效生成准确的个性化BEV SOC衰减预测。
原文摘要 · Abstract (English)
This paper presents a personalized Battery Electric Vehicle (BEV) energy consumption estimation framework that integrates map-based contextual features with driver-specific velocity prediction and physics-based energy consumption modeling. The system combines route selection, detailed road feature processing, a rule-based reference velocity generator, a PID controller-based vehicle dynamics simulator, and a Bidirectional LSTM model trained to reproduce individual driving behavior. The predicted individual-specific velocity profiles are coupled with a quasi-steady backward energy consumption model to compute tractive power, regenerative braking, and State-of-Charge (SOC) evolution. Evaluation across urban, freeway, and hilly routes demonstrates that the proposed approach captures key driver behavioral patterns such as deceleration at intersections, speed-limit tracking, and road grade-dependent responses, while producing accurate power and SOC trajectories. The results highlight the effectiveness of combining learned driver behavior with map-based context and physics-based energy consumption modeling to produce accurate, personalized BEV SOC depletion profiles.
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