Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction
Abstract: Electrifying heavy-duty trucks offers a substantial opportunity to curtail carbon emissions, advancing toward a carbon-neutral future. However, the inherent challenges of limited battery energy and the sheer weight of heavy-duty trucks lead to reduced mileage and prolonged charging durations. Consequently, battery-swapping services emerge as an attractive solution for these trucks. This paper employs a two-fold approach to investigate the potential and enhance the efficacy of such services. Firstly, spatial-temporal demand prediction models are adopted to predict the traffic patterns for the upcoming hours. Subsequently, the prediction guides an optimization module for efficient battery allocation and deployment. Analyzing the heavy-duty truck data on a highway network spanning over 2,500 miles, our model and analysis underscore the value of prediction/machine learning in facilitating future decision-makings. In particular, we find that the initial phase of implementing battery-swapping services favors mobile battery-swapping stations, but as the system matures, fixed-location stations are preferred.
- A3t-gcn: Attention temporal graph convolutional network for traffic forecasting. ISPRS International Journal of Geo-Information, 10(7):485, 2021.
- Commercial vehicle traffic detection from satellite imagery with deep learning. ICML 2021 Workshop on Tackling Climate Change with Machine Learning Workshop, 2021.
- Heavy-duty truck electrification and the impacts of depot charging on electricity distribution systems. Nature Energy, 6(6):673–682, 2021.
- Net zero by 2050: A roadmap for the global energy sector. 2021.
- Evgen: Adversarial networks for learning electric vehicle charging loads and hidden representations. arXiv preprint arXiv:2108.03762, 2021.
- Using lstm and gru neural network methods for traffic flow prediction. In 2016 31st Youth academic annual conference of Chinese association of automation (YAC), pages 324–328. IEEE, 2016.
- Impact of transport electrification on critical metal sustainability with a focus on the heavy-duty segment. Nature communications, 10(1):5398, 2019.
- Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
- Ashish Kapoor. Helping reduce environmental impact of aviation with machine learning. arXiv preprint arXiv:2012.09433, 2020.
- Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
- Planning and establishment of battery swapping station-a support for faster electric vehicle adoption. Journal of Energy Storage, 51:104351, 2022.
- Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012.
- Infrastructure planning for electric vehicles with battery swapping. Management science, 59(7):1557–1575, 2013.
- Transitioning to zero-emission heavy-duty freight vehicles. ICCT Washington DC, 2017.
- Forecasting freeway link travel times with a multilayer feedforward neural network. Computer-Aided Civil and Infrastructure Engineering, 14(5):357–367, 1999.
- Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption. Nature Energy, 7(10):932–945, 2022.
- Scaling up electric-vehicle battery swapping services in cities: A joint location and repairable-inventory model. Management Science, 2023.
- Machine learning for activity-based road transportation emissions estimation. In NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, 2022.
- Overview of integration of power electronic topologies and advanced control techniques of ultra-fast ev charging stations in standalone microgrids. Energies, 16(3):1031, 2023.
- Optimal operation and services scheduling for an electric vehicle battery swapping station. IEEE transactions on power systems, 30(2):901–910, 2014.
- Attention is all you need. Advances in neural information processing systems, 30, 2017.
- Traffic flow prediction via spatial temporal graph neural network. In Proceedings of the web conference 2020, pages 1082–1092, 2020.
- Optimal policies for the management of an electric vehicle battery swap station. Transportation Science, 52(1):59–79, 2018.
- Toward “net zero” emissions in the road transport sector in china. World Resources Institute: Beijing, China, 2019.
- Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review. Renewable and Sustainable Energy Reviews, 167:112671, 2022.
- T-gcn: A temporal graph convolutional network for traffic prediction. IEEE transactions on intelligent transportation systems, 21(9):3848–3858, 2019.
- Does the battery swapping energy supply mode have better economic potential for electric heavy-duty trucks? ETransportation, 15:100215, 2023.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.