A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles

TL;DR

This paper introduces a deep learning model that accurately predicts macroscopic electric vehicle energy consumption using high-fidelity microscopic data, enabling improved real-time transportation decision-making.

Contribution

It presents a novel deep learning approach that infers aggregate energy consumption from masked microscopic vehicle dynamics data, integrating it into transportation simulation tools.

Findings

High-fidelity trip data enables accurate energy prediction.

Deep learning models can recover latent information from masked microscopic data.

Model deployment supports real-time EV routing and charging decisions.

Abstract

This paper presents a machine learning approach to model the electric consumption of electric vehicles at macroscopic level, i.e., in the absence of a speed profile, while preserving microscopic level accuracy. For this work, we leveraged a high-performance, agent-based transportation tool to model trips that occur in the Greater Chicago region under various scenario changes, along with physics-based modeling and simulation tools to provide high-fidelity energy consumption values. The generated results constitute a very large dataset of vehicle-route energy outcomes that capture variability in vehicle and routing setting, and in which high-fidelity time series of vehicle speed dynamics is masked. We show that although all internal dynamics that affect energy consumption are masked, it is possible to learn aggregate-level energy consumption values quite accurately with a deep learning approach. When large-scale data is available, and with carefully tailored feature engineering, a well-designed model can overcome and retrieve latent information. This model has been deployed and integrated within POLARIS Transportation System Simulation Tool to support real-time behavioral transportation models for individual charging decision-making, and rerouting of electric vehicles.