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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.04440 |
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| _version_ | 1866907910777339904 |
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| author | Liu, Linyu Dai, Zhen Song, Shiji Li, Xiaocheng Chen, Guanting |
| author_facet | Liu, Linyu Dai, Zhen Song, Shiji Li, Xiaocheng Chen, Guanting |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_04440 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction Liu, Linyu Dai, Zhen Song, Shiji Li, Xiaocheng Chen, Guanting Systems and Control Artificial Intelligence 90B06, 68T07 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. |
| title | Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction |
| topic | Systems and Control Artificial Intelligence 90B06, 68T07 |
| url | https://arxiv.org/abs/2310.04440 |