Researchers have developed a conditional diffusion model designed to generate realistic electric vehicle (EV) battery-current profiles. This framework uses a 1D U-Net backbone and a latent conditioning encoder to map route features like velocity and temperature into a shared representation, guiding the diffusion process. Evaluated on a dataset of 12,000 trips, the model demonstrated significant improvements over direct condition injection, reducing Wasserstein distance by 89.1% and MAE by 52.8%. This generative approach provides a foundation for uncertainty-aware fleet planning in operational settings. AI
IMPACT Enables more accurate energy-aware fleet planning for electric vehicles by modeling consumption trajectories.
RANK_REASON Academic paper detailing a new conditional diffusion model for EV energy consumption. [lever_c_demoted from research: ic=1 ai=1.0]
- 1D U-Net
- alphaXiv
- arXiv
- CatalyzeX
- cs.LG
- DagsHub
- electric vehicle
- Gotit.pub
- Hemanth Neelgund Ramesh
- Hugging Face
- ScienceCast
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