Energy Estimation of Last Mile Electric Vehicle Routes

• Main Authors: Snoeck, André, Bhargava, Aniruddha, Merchan, Daniel, Davis, Josiah, Pachon, Julian
• Format: Journal Article
• Language: English
• Published: 21-08-2024
• Subjects: Computer Science - Learning
• DOI: 10.48550/arxiv.2408.12006

Last-mile carriers increasingly incorporate electric vehicles (EVs) into
their delivery fleet to achieve sustainability goals. This goal presents many
challenges across multiple planning spaces including but not limited to how to
plan EV routes. In this paper, we address the problem of predicting energy
consumption of EVs for Last-Mile delivery routes using deep learning. We
demonstrate the need to move away from thinking about range and we propose
using energy as the basic unit of analysis. We share a range of deep learning
solutions, beginning with a Feed Forward Neural Network (NN) and Recurrent
Neural Network (RNN) and demonstrate significant accuracy improvements relative
to pure physics-based and distance-based approaches. Finally, we present Route
Energy Transformer (RET) a decoder-only Transformer model sized according to
Chinchilla scaling laws. RET yields a +217 Basis Points (bps) improvement in
Mean Absolute Percentage Error (MAPE) relative to the Feed Forward NN and a
+105 bps improvement relative to the RNN.