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.

翻译：本文提出一种个性化纯电动汽车（BEV）能耗估算框架，该框架融合基于地图的上下文特征、驾驶员特定车速预测以及基于物理模型的能耗建模。系统整合了路线选择、详细道路特征处理、基于规则的参考车速生成器、基于PID控制器的车辆动力学仿真器，以及用于复现个体驾驶行为的双向长短期记忆（Bidirectional LSTM）模型。预测的个体化车速曲线与准稳态逆向能耗模型耦合，用以计算牵引功率、再生制动能量及电池荷电状态（SOC）演变。在城市道路、高速公路及丘陵路线的评估表明，所提方法能够捕捉驾驶员关键行为模式（如交叉口减速、限速跟随及坡度响应），同时生成精确的功率与SOC轨迹。结果验证了将学习得到的驾驶员行为与地图上下文及基于物理模型的能耗建模相结合，可有效生成精确且个性化的BEV SOC消耗曲线。