Fine-Grained RNN With Transfer Learning for Energy Consumption Estimation on EVs
Yining Hua, Michele Sevegnani, Dewei Yi, Andrew Birnie, Steve McAslan
Abstract
Electric vehicles (EVs) are increasingly becoming an environmental-friendly option in current transportation systems, thanks to reduced fossil fuel consumption and carbon emission. However, the more widespread adoption of EVs has been hampered by following two factors: the lack of charging infrastructure and the limited cruising range. Energy consumption estimation is crucial to address these challenges as it provides the foundations to enhance charging-station deployment, improve eco-driving behavior, and extend the EV cruising range. In this article, we propose an EV energy consumption estimation method capable of achieving accurate estimation despite insufficient EV data and ragged driving trajectories. It consists of following three distinct features: knowledge transfer from internal combustion engine/hybrid electric vehicles to EVs, segmentation-aided trajectory granularity, time-series estimation based on bidirectional recurrent neural network. Experimental evaluation shows our method outperforms other machine learning benchmark methods in estimating energy consumption on a real-world vehicle energy dataset.