A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles. Moawad, A., Gurumurthy, K. M., Verbas, O., Li, Z., Islam, E., Freyermuth, V., & Rousseau, A. arXiv:2111.12861 [cs], November, 2021. arXiv: 2111.12861![A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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.
@article{moawad_deep_2021,
title = {A {Deep} {Learning} {Approach} for {Macroscopic} {Energy} {Consumption} {Prediction} with {Microscopic} {Quality} for {Electric} {Vehicles}},
url = {https://anl.box.com/s/fsexy08ee86c5r5l4pv9fglxt0dignjc},
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.},
urldate = {2021-12-10},
journal = {arXiv:2111.12861 [cs]},
author = {Moawad, Ayman and Gurumurthy, Krishna Murthy and Verbas, Omer and Li, Zhijian and Islam, Ehsan and Freyermuth, Vincent and Rousseau, Aymeric},
month = nov,
year = {2021},
note = {arXiv: 2111.12861},
keywords = {AI, Autonomie, DOE SMART, Transportation systems},
}
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