1. A new method is proposed to optimize the relocation and charging of shared autonomous electric vehicles.
2. The proposed model optimizes transport service and charging at two different time scales by running two model-predictive control optimization algorithms in parallel.
3. Results show that the system can substantially reduce charging costs without significantly affecting waiting times, with cost reduction dependent on electricity price variability.
The article provides a detailed overview of a new method for optimizing the relocation and charging of shared autonomous electric vehicles, which includes two model-predictive control optimization algorithms running in parallel. The article also presents results from a case study using transport and electricity price data for Tokyo, which shows that the system can substantially reduce charging costs without significantly affecting waiting times, with cost reduction dependent on electricity price variability.
The article is generally reliable and trustworthy, as it provides detailed information about the proposed methodology and presents results from a case study to support its claims. However, there are some potential biases in the article that should be noted. For example, the article does not explore any potential risks associated with implementing this method or discuss any possible counterarguments to its claims. Additionally, while the article does provide evidence for its claims, it does not present both sides of the argument equally or explore any unexplored points of consideration related to this topic. Furthermore, there is some promotional content in the article as it focuses solely on the benefits of this method without discussing any potential drawbacks or limitations.