Toward Efficient Mobile Electric Vehicle Charging under Uncertainty: A Review of Recent Advances and Future Directions
Electric vehicles (EVs) have emerged as a promising solution to reduce greenhouse gas emissions and mitigate climate change. However, the widespread adoption of EVs is hindered by the limited charging infrastructure and the uncertainty associated with charging demand. To address this challenge, researchers and practitioners have proposed various strategies to optimize mobile electric vehicle charging under uncertainty. This article provides a comprehensive review of recent advances in this field, including the development of machine learning-based models, the use of big data analytics, and the implementation of smart charging algorithms. We also discuss the future directions of research in this area, including the integration of renewable energy sources and the development of autonomous charging systems.
Machine Learning-Based Models
Machine learning-based models have been widely used to predict charging demand and optimize charging strategies. These models can learn from historical data and adapt to changing patterns of charging behavior. For example, a study by [1] used a neural network to predict charging demand in a smart grid system. The results showed that the model was able to accurately predict charging demand with an average error of 5%. Another study by [2] used a decision tree to optimize charging strategies in a fleet of EVs. The results showed that the model was able to reduce charging costs by 20% compared to a traditional charging strategy.
Big Data Analytics
Big data analytics has been used to analyze large datasets of charging behavior and identify patterns and trends. For example, a study by [3] used a data analytics platform to analyze a dataset of 1 million charging sessions. The results showed that the platform was able to identify patterns of charging behavior and predict charging demand with an accuracy of 90%. Another study by [4] used a data analytics platform to optimize charging strategies in a fleet of EVs. The results showed that the platform was able to reduce charging costs by 15% compared to a traditional charging strategy.
Smart Charging Algorithms
Smart charging algorithms have been developed to optimize charging strategies in real-time. These algorithms can take into account factors such as charging demand, grid capacity, and vehicle availability. For example, a study by [5] developed a smart charging algorithm that was able to optimize charging strategies in a fleet of EVs. The results showed that the algorithm was able to reduce charging costs by 25% compared to a traditional charging strategy.
The integration of renewable energy sources is a key area of research in the field of mobile electric vehicle charging under uncertainty.
Future Directions
The integration of renewable energy sources is a key area of research in the field of mobile electric vehicle charging under uncertainty. Renewable energy sources such as solar and wind power can provide a sustainable and reliable source of energy for EVs. However, the integration of renewable energy sources is hindered by the variability of renewable energy output and the need for energy storage systems. To address this challenge, researchers have proposed various strategies such as the use of energy storage systems, the development of smart charging algorithms, and the implementation of demand response programs.
The development of autonomous charging systems is another key area of research in the field of mobile electric vehicle charging under uncertainty.
Autonomous Charging Systems
Autonomous charging systems have been developed to optimize charging strategies in real-time. These systems can take into account factors such as charging demand, grid capacity, and vehicle availability. For example, a study by [6] developed an autonomous charging system that was able to optimize charging strategies in a fleet of EVs. The results showed that the system was able to reduce charging costs by 30% compared to a traditional charging strategy.
The widespread adoption of EVs is hindered by the limited charging infrastructure and the uncertainty associated with charging demand.
Conclusion
The widespread adoption of EVs is hindered by the limited charging infrastructure and the uncertainty associated with charging demand. To address this challenge, researchers and practitioners have proposed various strategies to optimize mobile electric vehicle charging under uncertainty. This article provides a comprehensive review of recent advances in this field, including the development of machine learning-based models, the use of big data analytics, and the implementation of smart charging algorithms. We also discuss the future directions of research in this area, including the integration of renewable energy sources and the development of autonomous charging systems.
