English(EN) Conditional Diffusion Models for Energy-Efficient Driving

扩散模型生成逼真的电动汽车电池电流曲线

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员开发了一种条件扩散模型，旨在生成逼真的电动汽车（EV）电池电流曲线。该框架使用一维 U-Net 主干和潜在条件编码器，将速度和温度等路线特征映射到共享表示中，从而指导扩散过程。在包含 12,000 次行程的数据集上进行评估，该模型在直接条件注入方面表现出显著的改进，Wasserstein 距离减少了 89.1%，平均绝对误差（MAE）减少了 52.8%。这种生成方法为运营环境中的不确定性感知车队规划奠定了基础。 AI

影响通过模拟能耗轨迹，实现更准确的电动汽车能源感知车队规划。

排序理由学术论文，详细介绍了一种用于电动汽车能耗的新型条件扩散模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv cs.LG TIER_1 English(EN) · Hemanth Neelgund Ramesh, Andr\'e Snoeck, Chyi-Fu Hong, Shijing Sun · 2026-08-31 04:00

面向节能驾驶的条件扩散模型

arXiv:2608.28142v1 Announce Type: new Abstract: 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 limi…

本文如何被排名

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面向节能驾驶的条件扩散模型

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