发表机构
University of Washington; Amazon Science; University of Cambridge（华盛顿大学; 亚马逊科学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出潜在条件扩散模型，基于1.2万次商用EV行程遥测数据生成电流轨迹，其性能优于直接条件注入，为不确定性感知的大规模车队规划奠定基础。
AI 中文摘要
商用配送车队的电气化正将车队路径规划从基于距离和时间的优化转向基于能耗的决策。现有序列模型主要提供确定性点估计或有限的不确定性摘要，无法覆盖运营决策所需的合理能耗轨迹范围。本研究提出一种条件扩散框架，用于生成以车辆速度、环境温度等路径特征为条件的电动汽车（EV）电池电流曲线。该模型结合潜在条件编码器与时序1D U-Net去噪主干网络，可将行程相关条件映射为共享表示并引导反向扩散过程。我们在包含9辆车1.2万次行程的开放商用电动汽车遥测数据集上评估该框架，所提潜在条件扩散模型生成的真实电流轨迹既覆盖主导时序包络，又捕捉到尖锐瞬态事件。生成电流分布与实测分布的Wasserstein距离为0.0029，低于真实-真实参考距离0.0085，表明生成样本处于测试集的经验变异范围内。我们进一步验证，学习到的潜在条件相比直接注入条件大幅提升性能，使Wasserstein距离降低89.1%，平均绝对误差（MAE）降低52.8%。本研究提出的生成建模框架可表征真实运行条件下的电动汽车能耗，为大规模运营场景下的不确定性感知车队规划提供重要基础。
英文摘要
Electrification of commercial delivery fleets is shifting fleet routing from distance- and time-based optimization toward energy-aware decision-making. Existing sequence models primarily provide deterministic point estimates or limited uncertainty summaries, which do not capture the range of plausible energy-consumption trajectories required for operational decision-making. In this work, we introduce a conditional diffusion framework that generates EV battery-current profiles conditioned on route features such as vehicle velocity and ambient temperature. The model combines a latent conditioning encoder with a temporal 1D U-Net denoising backbone that enables trip-related conditions to be mapped into a shared representation and guides the reverse diffusion process. We evaluate the framework on an open-access commercial EV telemetry dataset containing 12k trips from 9 vehicles. The proposed latent-conditioned diffusion model generates realistic cur- rent trajectories that capture both the dominant temporal envelope and sharp transient events. The model achieves a Wasserstein distance of 0.0029 between generated and measured current distributions below the real vs real reference distance of 0.0085 indicating that generated samples lie within the empirical variability of the test set. We further demonstrate that learned latent conditioning substantially improves performance over direct condition injection, reducing the Wasserstein distance by 89.1% and MAE by 52.8%. This work demonstrates a generative modeling framework for characterizing EV energy consumption under real-world operating conditions, providing an essential foundation for uncertainty-aware fleet planning in large-scale operational settings.
