Automation For EV Charging Networks: 6 Tactics to Lower Costs

Now, imagine having to do it all manually and replicate the same process every time you open a new station. This is when automation comes in handy to reduce manual tasks, work with a lean team, and save money. In this guide, we’ll walk you through six ways in which you can rely on automation for EV charging networks to lower your operational costs. 

Understanding EV charging station costs

The charging infrastructure goes beyond connectors and cables. “It’s about orchestrating a constant flow of information—session data, station health, grid signals—to create a frictionless charging experience,” says Matías Alvarez Duran, CEO at NaNLABS.

These operational challenges are compounded by the constant stream of real-time data that needs to be captured, processed, and acted on immediately. This data includes: 

• Session telemetry

• Equipment diagnostics

• Power usage and capacity

Without automation, managing this data at scale becomes both inefficient and error-prone.

If you’re a new CPO launching your first station, you need to get familiar with EV charging networks' requirements and general costs that go beyond the electricity bill. These include: 

• Software licensing fees for charging management systems 

• Hardware charging equipment

• Infrastructure maintenance and repairs

• Site rental or lease payments

• Utility demand charges and time-sensitive rates

• Employee salaries for operations and customer support

• EV charging station regulatory permits and compliance costs

• Insurance coverage

• Ongoing network connectivity fees

Manually managing the grid and load is another major expense for EV charging stations. Doing so could lead to inefficient resource allocation and reactive maintenance, which increases the base costs of your operation and requires you to have a dedicated team to manually:

• Monitor individual units

• Respond to alerts

• Coordinate field technicians

While this is manageable for a single station, manual tasks quickly impact your finances as you scale. And, since you want to avoid having high operational costs before increasing revenue, leveraging automation and a lean operation is the way to go. 

6 Ways to automate EV charging networks to improve uptime and reduce costs

As a CPO, you can implement many automation strategies to reduce the operational overhead while improving the service reliability of your station. These practices leverage AI and machine learning to:

• Automate manual and recurrent tasks

• Analyze data patterns and identify clear optimization opportunities

• Offer better customer service

Some automation for EV charging networks initiatives include:

1. Smart load management

Smart dynamic load balancing systems monitor real-time power consumption and automatically adjust individual charging rates to stay within facility limits without affecting charging speeds. This process spares you the cost of upgrading your infrastructure and reduces utility demand charges.

Another idea is to integrate with renewable energy sources, such as on-site solar installations. This enables you to automatically switch between grid power and renewable sources based on availability. All of these automated load management initiatives can reduce expensive peak loads costs by up to 40%.

To implement this effectively, your system needs to process real-time data from multiple chargers simultaneously, tracking power draw, session status, and grid availability. This typically involves using event streaming platforms, such as Apache Kafka or AWS Kinesis, which are capable of handling large volumes of telemetry with low latency.

Want to learn more about how to use real-time data to influence grid balancing decisions? Read our eBook “From Grid to Charge: A Data-Driven Approach to EV Charging Networks.”

2. Remote EV charging station monitoring

Building a monitoring system enables you to track key performance indicators, including: 

• Charging session success rates

• Power delivery consistency

• Connectors’ health

• Environmental conditions

Developing real-time data pipelines gives you continuous visibility into the operational status of your entire network without needing to visit the location. Behind the scenes, this requires gathering consistent telemetry schemas and building stream processing pipelines that normalize, timestamp, and route event data from each charger.

With structured and normalized charger data, you can then set up automated alerts in case of issues, classify them by severity, and determine an estimated time to resolution (ETR). Otherwise, alerts may be missed or misclassified.

Additionally, accessing real-time EV charging data along with remote troubleshooting capabilities allows operators to:

• Solve software-related issues

• Reset charging sessions

• Update firmware 

• Anticipate future issues (more on this below)

All without the need for on-site technician support. “Most queries and faults can be resolved remotely, with 95% of cases fixed on the phone,” shares Jack Cousens, Head of Roads Policy at The AA. All of these initiatives combined save costs and reduce downtime. 

3. Predictive maintenance

AI-powered real-time data analytics tools gather and analyze EV charging station data to identify which patterns lead to failures. Hence, it can automatically alert you about potential malfunctions based on a combination of factors.

For example, Tesla connectors often degrade after predictable wear patterns depending on usage frequency, environmental exposure, and connection quality. Since all of these metrics can be monitored remotely, it’s easy for Tesla to identify when some of its connectors need maintenance. 

But ML models only work for predictive maintenance after they’re trained on historical data from well-structured sources, such as:

• Time-stamped logs

• Charger diagnostics

• Environmental context

Without reliable and consistent data ingestion pipelines, the predictions will be inaccurate or late.

In addition to predictive maintenance for your commercial EV charger, you can also leverage automation to plan and set condition-based maintenance scheduling (instead of following a calendar). Rather than servicing stations on a fixed schedule, AI-powered systems schedule maintenance when actual wear and tear indicators are present. For example, before predicted heavy rains or snowstorms. 

This approach helps reduce costs by preventing chargers and connectors from breaking, minimizing downtime, and extending equipment lifespan.

4. Customer experience and support automation

This goes beyond having an automated chatbot to answer drivers’ questions. For instance, The Verge shared how Juice, the Face ID for vehicles company, partnered with Revel to install smart cameras at EV charging stations. Juice’s platform can identify each vehicle and automatically start the charging process after an initial sign-up, offering a much smoother customer experience.

A product like Juice’s is complex and requires seamless integration between interconnected systems. For example, combining and streaming data from/to identity management systems, session control APIs, and a cloud-native infrastructure capable of handling real-time authentication events with minimal delay.

Using AI in customer experience and support also involves: 

• Making all stations self-service. Drivers can charge, pay, and get snacks from a machine. 

• Setting up automated troubleshooting guides. Users can solve common issues, such as payment failures, connector problems, or mobile app connectivity, by following procedures.

• Integrating payment platforms with station software. Automated systems can handle payment processing, receipt generation, and basic account management without human intervention. (More on this later!)

• Gathering feedback from the mobile app. Launch in-app surveys for customers to answer after the session and determine areas of improvement in terms of experience, service, and support.

Although many customer-facing processes can be automated, note that 1 in 2 customers still prefer to chat with a human over an AI bot. So, make sure you set up a process for drivers to call, chat, or ask a human in case of unusual issues. 

Using automation to offer good customer support not only helps reduce costs but also allows you to increase revenue. Research shows that customers who have a satisfactory experience with a business are more likely to come back and recommend it to others. In fact, 17% of Americans wouldn’t return after one bad experience. So, stay on top of customer satisfaction metrics to guarantee AI is improving your station experience, not the other way around.

5. Smart dynamic pricing

Just like airlines adjust ticket prices based on high demand, you can also set dynamic pricing algorithms to automatically adjust rates based on real-time factors, including: 

• Grid demand

• Time of day

• Local electricity costs

• Competitive positioning

These systems rely on real-time pricing engines fed by telemetry streams and business logic layers. This makes your event-driven architecture react to demand or energy costs changes and immediately adjust the price.

Leveraging automation in this case empowers you to implement complex pricing strategies that get adjusted in real-time. Dynamic pricing automated tools can monitor competitors’ prices, calculate the current demand, match the time of day with utility bills, and adjust pricing to maximize revenue. 

This can lead to positive (and increased) ROI by increasing revenue, reducing costs, and attracting new customers. 

You can also implement real-time revenue tracking and take action to meet monthly targets in case you’re falling behind. For example, imagine you increase your prices based on demand and notice customers stop visiting the station at that time. This is having a direct impact on your monthly revenue. So, combining smart pricing and real-time revenue tracking systems allows you to make smarter pricing decisions and reach your business goals.

6. Automated billing and payment processing

As we briefly mentioned above, it often takes a team of people to handle billing, payment processing, and bookkeeping tasks. Smart invoicing systems charge users and generate accurate bills using real-time data. This eliminates the room for manual calculation errors and reduces processing times.

But you shouldn’t underestimate this task. For automated invoicing to work correctly, it requires session data to be serialized and mapped in real-time. The invoice and your logs must include:

• Plug-in time

• Energy delivered

• Rate applied

• Time of disconnect 

This allows for auditable, event-based billing with minimal manual correction.

Since the NEVI program requires you to support all payment types, automation enables users to use their preferred payment options while you follow consistent backend processing. Also, these systems can manage subscriptions and membership-based customer accounts, including automatic renewals, payment processing, and service level adjustments. 

Investing in automated billing and payment processing systems reduces operational costs as it reduces error rates, and you only need one person to audit and write the books. 

How to leverage automation to become more scalable

Imagine you need 10 people to manually manage the workload of a single station. If you open a new one, you’ll probably need to hire another 10 to handle the increased tasks. The real risk of manual operations isn’t just hiring more people—it’s duplicating expenses before generating a single dollar.

But as Harry Husted, CTO of BorgWarner, says: “With cloud connectedness come new opportunities.” And along with automated systems, you can easily manage multiple locations with virtually the same team and from the same hub. 

As a CPO, you have three options for leveraging automation to manage your EV charging networks:

• Off-the-shelf software solutions. These are plug-and-play systems that give you immediate access to proven automation features. Plus, these come with support, regular updates, and timely maintenance. However, off-the-shelf platforms are often hard to customize—and they own your data. Meaning that if you want to change solutions in the future, you lose your historical records (or have to move them manually). However, these platforms often limit your access to raw telemetry or historical session logs, making it harder to build custom dashboards or apply ML models later on.

• In-house development. This gives you complete customization as you get to build them around your operational needs. However, developing these solutions requires significant and experienced engineering resources and ongoing maintenance. This approach is best for large networks with unique requirements and dedicated technical teams.

• Third-party development. Working with specialized firms like NaNLABS allows you to build custom solutions by leveraging external senior expertise. This often leads to faster implementation timelines and higher tool performance. Also, this approach tends to be less expensive than hiring developers because they don’t need a long onboarding. A team like NaNLABS can develop cloud-native data engineering, real-time data processing pipelines, and leverage AI & ML models with high levels of excellence.

Automation in EV charging networks: Key tips to activate your business’ self-driving mode

Automation opportunities in EV charging networks help you manage operations remotely while reducing costs by: 

• Improving grid usage 

• Lowering operational and infrastructural expenses

• Increasing uptime

• Optimizing revenue generation

Relying on automation enables you to monitor and grow your business without increasing the overhead or hurting your power infrastructure. To get there, you need to find the right software solutions. 

Unless you have a big internal engineering team with the capacity to develop, test, and iterate on your systems, your best choice is to partner with NaNLABS. Our team can implement intelligent automation solutions for your EV charging business that scale with your growth and deliver measurable ROI from day one. We have proven experience in the EV industry, like the work we did for EV Rechargery, and our clients rate us at 4.9/5 on Clutch.co.

Ready to automate your EV charging networks and reduce operational costs? Let NaNLABS be your tech sidekick, building the real-time data pipelines your EV networks need to scale, without growing your team.

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Sources:

1. 2025 to be the year of electric vehicles: 64% of Consumers Likely to Choose EV as Their Next Vehicle, reveals TCS global Study. TCS, (2025). Found on: https://www.tcs.com/who-we-are/newsroom/press-release/2025-the-year-electric-vehicles-64-percent-consumers-likely-choose-ev-as-their-next-vehicle-reveals-tcs-global-study

2. Trends in electric vehicle charging. IEA, (2024). Found on: https://www.iea.org/reports/global-ev-outlook-2024/trends-in-electric-vehicle-charging