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arXiv Machine Learning By Hemanth Neelgund Ramesh, Andr\'e Snoeck, Chyi-Fu Hong, Shijing Sun

Conditional Diffusion Models for Energy-Efficient Driving

Read the original on arXiv Machine Learning →

The paper presents a conditional diffusion model that generates electric vehicle battery‑current profiles conditioned on route features such as velocity and ambient temperature. Using a latent conditioning encoder and a temporal 1D U‑Net denoising backbone, the model produces realistic current trajectories that capture both the overall envelope and sharp transient events. On a dataset of 12,000 trips from nine vehicles, the model achieves a Wasserstein distance of 0.0029, outperforming direct condition injection by 89.1% in Wasserstein distance and 52.8% in MAE.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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