Conditional Diffusion Models for Energy-Efficient Driving

Hemanth Neelgund Ramesh; André Snoeck; Chyi-Fu Hong; Shijing Sun

cs.LGAug 28, 2026

Conditional Diffusion Models for Energy-Efficient Driving

Authors: Hemanth Neelgund Ramesh, André Snoeck, Chyi-Fu Hong, Shijing Sun

Organizations: Department of Mechanical Engineering, University of Washington · Amazon Science · Department of Materials Science and Metallurgy, University of Cambridge

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 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.

Figures & tables

Figure 1: Overview of the conditional diffusion framework for EV battery-current profile generation . Route features (velocity and ambient temperature) from about 12,000 commercial trips are mapped by a latent encoder (MLP + Transformer) into a shared representation. This representation conditions a temporal 1D U-Net during reverse denoising to generate realistic battery-current time-series profiles evaluated against real-world trajectories.

Figure 2: Model architecture of the latent-conditioned diffusion framework. The clean current trajectory

i_{0}

is progressively corrupted by Gaussian noise during the forward diffusion process to obtain

i_{t}

. During reverse denoising, a temporal 1D U-Net predicts the added noise and reconstructs the current profile while being conditioned on the latent representation

z=f_{\psi}(c)

, encoded from velocity and ambient temperature using the MLP-Transformer conditioning encoder.

Figure 3: Conditional diffusion model generates realistic battery current trajectories . (a) Plot comparing generated trajectories (mean and 95% confidence interval) vs actual current profiles for two held-out test trips for the proposed model. (b) Distribution of Wasserstein distance (WD) computed on a held-out test trips.

Figure 4: Model comparison and ablation results. (a,b)Wasserstein distance (WD) and Mean absolute error (MAE) for the selected model variants, shown in ascending order. The proposed E4 model is highlighted in dark. (c) Generated and actual current trajectories for selected models on the same test sample. D1 and D2 receive only temperature or velocity conditioning, respectively.

Figure 5: Learned latent representation of operating conditions. UMAP projections of the raw condition sequence $c=[v,T]$ (left) and the learned latent condition $z=f_{\psi}(c)$ (right) colored by mean normalized velocity.

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