Energy Estimation of Last Mile Electric Vehicle Routes

Josiah Davis, Andre Snoeck, Aniruddha Bhargava, Daniel Merchan, Julian Pachon

Keywords: Neural Networks, Transformers, Electric Vehicles

TL;DR: Energy Estimation of Last Mile Electric Vehicle Routes with Deep Learning and Transformer

Abstract: 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 includ- ing 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.

Submission Number: 7