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Optimal Battery Swapping Operations Using Reinforcement Learning
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DOI：10.1109/icinpro47689.2019.9092262.png)
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Abstract
En 中文
Battery swapping has attracted attention from transportation companies, Original Equipment Manufacturers (OEM) and government authorities as a thriving solution to enable faster adoption of electric vehicles in the recent days. However, in order to facilitate a large scale electric vehicle operation that allows multiple independent stakeholders to engage in various different aspects of battery swapping operations, multiple challenges are required to be solved in order to ensure sustainable business models for all stakeholders. In this paper, we explore how the operations of a battery swapping station could be optimized to maximize it's profit, which depends on a trade-off among the revenue earned from swapping operations and the cost incurred due to charging We propose reinforcement learning based charging model that takes into account, this trade-off and adapts to the incoming vehicle arrival rate. We present simulation results to show that the proposed mechanism achieves higher profits than a greedy mechanism.
Keywords:
Electric vehicles
Battery Swapping
Reinforcement Learning
Swap-Charge stations
Optimized charging model
Connected Mobility
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