How AI and Data Analytics Are Optimizing EV Fleet Management

Artificial intelligence (AI) is being integrated everywhere, including electric vehicle (EV) fleet management. With its predictive capabilities, AI can potentially make fleets faster, safer, and more efficient while saving fleet operators significantly. Specifically, AI can optimize EV fleet routes, charging schedules, and driving behaviors to help fleet managers maximize the benefits of their EV fleets. Here’s what you need to know.

How Can AI Data Help EV Fleets?

The combination of AI and data analytics has many different applications in EV fleet management, including predictive maintenance, charging optimization, route optimization, load management, and driver analysis.

AI and Predictive EV Maintenance

By analyzing a dataset, AI can predict when an EV will require maintenance next. This allows fleet managers to provide proactive service and avoid unexpected breakdowns and disruptions. For example, AI may be able to predict when tires need to be replaced, allowing fleet managers to swap out tires at the depot before they fail.

AI can also help fleet managers automate vehicle inspections to detect any potential mechanical issues before they become serious.

AI and Smart EV Charging Optimization

AI can help fleet managers automate this. Vehicles can be plugged in at any time, but charging only starts once off-peak hours begin.

AI and EV Route Optimization & Range Prediction

AI can use GPS tracking to optimize routes for the lowest energy consumption and reduced delivery times. This also enables the AI technology to predict the optimal routes for an electric fleet vehicle’s range capabilities, suggesting EV charging stations along the way as needed.

In practical applications, autonomous vehicle fleets such as Waymo and Tesla's Robotaxi use AI and machine learning to detect surroundings and determine the best route.

AI and EV Load Management & Energy Distribution

Even with limited power capabilities, fleets can still operate multiple EV charging stations simultaneously, thanks to local load management. Local load management is a software feature that allows a fleet manager to split power between multiple EV charging stations. This feature can help businesses and fleets save money. By using AI and load management, fleet operators can ensure that their plugged-in vehicles receive the maximum amount of charging power available to them.

Fleet managers can adapt their load management usage to their unique needs. For example, you can set the load management to always deliver the same amount of electricity to each vehicle plugged in, or you can prioritize the first vehicle while giving a fraction of the electricity to those that plug in later (First In, First Out, or FIFO). With a FIFO load management policy, once the first vehicle is fully charged, the next vehicle will begin to receive full power, and so on until all vehicles are charged.

Load management is a current feature with Blink Fleet Management, and fleet managers can use AI to design their preferred load management or power-sharing scheme.

AI and EV Fleet Utilization & Driver Behavior Analysis

Analyzing driver behavior with AI can make fleets safer with notifications when unsafe driving behavior is detected. Not only can this make fleets safer, but it can also help you lower your insurance fees.

AI can also help to protect EV battery life by identifying driving patterns that impact battery health and efficiency. For example, to maximize battery longevity, it’s recommended not to fully charge your EV to 100% (nor let it drain to 0%). AI can also make recommendations when it detects driving and/or charging patterns that may harm battery life.

The Future of AI in Fleet Electrification

Using AI and machine learning to increase EV fleet efficiency can spur the adoption of EV fleets. As technology improves, it will further enhance already efficient EV operations. What AI brings to the table is its ability to collect and analyze large amounts of data for vehicles, charging stations, and drivers in real-time to determine the safest and most efficient ways for fleets to operate.

How Machine Learning Can Improve Vehicle-to-Grid (V2G) Integration

For example, a fleet parking lot might have a grid-connected converter (GCC), which enables both V2G and grid-to-vehicle (G2V) operations. It does this by 1. managing the power exchange between the grid and the vehicles, and 2. controlling the bidirectional power flow. The GCC could be made more efficient using AI and machine learning algorithms. Integrating AI and machine learning into this type of system can optimize energy transfer, predict energy demand, and adjust to fluctuating grid conditions. This can significantly improve the overall efficiency and reliability of V2G and G2V systems.

V2G technology enables load shifting, allowing fleets to charge vehicles with electricity generated when demand is lower and the grid requires fewer resources to produce it.

If your goal is to use more clean energy for charging, you can use V2G and AI technologies to schedule your fleet's charging for times when your utility provider is generating electricity with clean energy sources (such as solar).

And if your business is installing solar panels to decrease your electric bills or increase energy resiliency, V2G technology allows compatible fleet EVs to act as energy storage or battery backup.

Emerging Trends in Autonomous EV Fleet Management

With all the data that AI and machine learning collect, fleet managers must ensure their operation is secure from hackers and other cybersecurity threats. They also need to be transparent about their data usage and follow all applicable regulations, such as the California Consumer Privacy Act and future legislation.

The use of encryption and technology like blockchain can help fleet managers make their operations safer. While there may be less risk to fleets with only private charging stations compared to public-facing charging stations, increased security will be a significant emerging trend in autonomous EV fleet management.

Conclusion

The use of AI and machine learning can help EV fleets optimize routes and charging, develop safer driving environments, predict necessary maintenance, and reduce costs. While it will be an initial investment to start using these technologies, the potential in savings is enormous.

Learn more about Blink’s Fleet Solutions here.