EV Charging Optimization With Machine Learning And AI

We have recently seen smart grid concepts move from prototypes to implementation. Renewable energy sources and electric mobility pose new challenges to the existing distribution system infrastructure. In demand–response energy forecasting, AI and machine learning play important roles in ensuring the stability of the distribution system.

The general acceptance of electric vehicles (EVs) and their large-scale deployment requires a good charging infrastructure. Overcoming the issue of prolonged charging time simply by adding more EV charging stations does not work due to the limitations of physical space and the grid. Therefore, research should focus on developing smart forecasting and optimization algorithms.

The planning of charging infrastructure

The planning of charging infrastructure involves various tasks, such as charging station placement and demand prediction. The potential extent of utilization plays an important role in EV infrastructure planning., and demandprediction helps estimate the ROI of installing charging stations.

As mentioned above, the impact of EV stations on the existing grid is not negligible, and utility providers are interested in the information about expected load profiles before the installation. The uncoordinated massive installation of new charging points can cause the power distribution network instability. With the ambitious EU Green Deal target of 1 million charging points by 2025, this topic becomes even more urgent.

Linear regression and artificial neural networks (ANN) are common machine learning and predictive analytics methods for predicting load demand. For tasks involving time-series data processing and considering long-term dependencies, a recurrent neural network model known as long short-term memory (LSTM) is used.

Availability information

The planning of long-distance trips using an electric vehicle is complicated by the absence of adequate user information on the availability of charging stations. Since a reasonable amount of charging will take dozens of minutes, it would be very useful for a customer to know how much energy will be available at a given charging station at a given time.

Therefore, an algorithm that can predict the load demand at charging stations will significantly benefit both the provider and the customer. Customers would benefit from receiving availability prediction information on their smartphones, which would give them an estimate of the predicted charging time for the charging points along their route.

Customer behavior analysis

There have been growing attempts to identify customer charging behavior patterns to provide data-driven insights. Here are some examples of valuable insights for the EV charging service providers:

• Statistical analysis of EVs population distribution, for example, battery capacity and charging speed
• Analysis of customer habits, for example, where and how often they charge, what is the typical charging time and period, what is the typical state of charge values at the beginning and at the end of the charging, etc.

CConventionalmachine learning methods, such as ask-mean clustering and k-nearest neighbors., typically segment and classify customers based on their behavior

Understanding the charging behavior of regular EV users is essential for setting up an optimal pricing strategy. Presented analyses support developing and building tailored customer. 

Other considerations

Applications of machine learning and AI can also be considered. For example, a battery health monitoring service evaluates the battery charging process over time and monitors its degradation (or state of health), enabling predictive maintenance strategies for EV batteries.

The prerequisite for the successful deployment of every machine learning model is the collection of data of insufficient quality and quantity. The charging records database should contain historical charging data for a given charging station and EV owner. The data can be enriched for predictive modeling with additional input features like traffic and weather conditions.

Conclusions

Are you ready for the challenges associated with the arrival of electric vehicles and related infrastructure? Have you collected relevant data but not derived business insights from it? Or are you just starting your EV mobility business, and do the presented topics sound interesting to you? In any case, feel free to contact us. Our team is happy to discuss possible cooperation – whether you are interested in getting the most out of your data with machine learning or in the application design and development.