Stats reveal that electric vehicle (EV) adoption has surged in recent years, with EVs accounting for over 17 million sales globally in 2024, representing more than 20% of new passenger car sales. Considering that, it is not surprising that business owners often regard data as an untraditional asset due to its objective value, and EV data analytics has gained adoption.
Jumping ahead, let’s recap several pain points that EV charging optimization aims to address: Suboptimal charging station locations, inefficient operations and maintenance, and inability to scale. But there’s more to it.
In this post, you’ll explore the role of data in the EV charging business and review physical devices that can be used in such a business model. Besides, you’ll find out how to optimize charging operations through data analytics step-by-step, find out more about common challenges and solutions, and have a look at the potential future of EV charging data analytics.
The role of data in EV charging site selection
The best way to reveal the usefulness of having the right amount of high-quality data is to delve into its application on real examples. Within this topic, for instance, we can freely explore the factors taken into account in EV charging site selection. The list goes as follows:
- Traffic flow. Such data shows where vehicles most frequently travel or stop. High-traffic corridors (e.g., highways, urban intersections) indicate strong potential for station usage.
- Population density. This factor identifies areas with enough people — and therefore vehicles — to generate consistent demand. Dense urban zones may need more charging spots per square mile.
- EV adoption rates. Such information reveals where EV users are most concentrated. Regions with fast-growing EV ownership offer immediate customer bases and higher ROI potential.
- Dwell time alignment. This metric considers how long people stay at a location. Sites near shopping centers, restaurants, or offices allow vehicles to charge during natural pauses in activity.
- Land availability and zoning. This factor helps to determine whether a site can legally and physically accommodate charging infrastructure. Permissive zoning laws and open space speed up development.
- Charger network gaps. This data shows underserved areas lacking nearby stations. Charging sites located in such areas may cover the demand successfully.
The big idea here, as you might note, is that having the necessary data can help EV charging projects. Take a look at the exact benefits of EV data analytics in the infographics below:
Discover tips to streamline EV fleet management in business in another of our blog posts.
Devices and software solutions used to collect data for EV charging data analytics
Here’s the scope of devices you may need to implement EV charging data and analytics, along with examples of real instruments available on the market:
#1 Remote sensing instruments:
They are used for capturing aerial and satellite imagery to assess environmental and infrastructure conditions.
- DJI Phantom 4 RTK: A drone equipped with a high-precision RTK module for aerial mapping.
- MicaSense RedEdge-MX: A multispectral camera designed for agricultural and environmental remote sensing.
#2 Surveying equipment:
It measures precise distances, angles, and positions for terrain mapping and infrastructure planning.
- Leica Total Stations: High-accuracy instruments for surveying and construction measurements.
- Topcon GT Series: Robotic total stations offering advanced surveying capabilities.
#3 Data loggers:
They record environmental or operational data over time for analysis and reporting.
- Campbell Scientific CR1000X: A versatile data logger for environmental and industrial applications.
- Onset HOBO Data Loggers: Devices for monitoring environmental conditions like temperature and humidity.
For a better understanding of how data loggers work and how to use their input in mapping EV charging station accessibility, take a look at the video below:
#4 Sensors:
Such devices detect and transmit physical parameters like temperature, humidity, motion, and pressure in real-time.
- Bosch BME280: An integrated environmental sensor measuring temperature, humidity, and pressure.
- Libelium Waspmote: A sensor platform for wireless sensor networks and IoT applications.
#5 Geospatial Analytics Software:
These instruments process spatial data to reveal patterns, optimize infrastructure, and support decision-making.
- CARTO: A cloud-native location intelligence platform for spatial analysis.
- ArcGIS by Esri: A comprehensive suite for mapping and spatial analytics.
How to optimize charging station operations through data analytics: Step by step
Let’s get down to the very implementation of data analytics for EV charging:
Step 1: Evaluate existing infrastructure, IoT, and data analytics needs
Conduct a comprehensive review of your current EV charging infrastructure, identify technical limitations, and define the business and operational goals that data analytics should support. This step sets the foundation for all subsequent efforts.
Action points:
- Audit hardware and software infrastructure
- Review current data sources and flow
- Identify missing sensors or data points
- Define business goals for analytics
- Map regulatory and security requirements
Expertise needed: Infrastructure engineer, IoT specialist, data analyst, compliance officer
Step outcome: A clear understanding of what needs to be upgraded or added to enable data-driven optimization.
Step 2: Prepare charging infrastructure by reinforcing it with IoT solutions
Upgrade your charging stations with IoT devices to enable real-time data collection, remote diagnostics, and dynamic control. This step ensures your infrastructure is digitally connected and ready for intelligent monitoring.
Action points:
- Install IoT devices (sensors, meters, controllers)
- Ensure secure connectivity (e.g., 4G/5G, Wi-Fi, LoRa)
- Enable data logging and timestamping
- Integrate with charging station software
- Test IoT data transmission and reliability
Expertise needed: IoT engineer, hardware technician, network engineer
Step outcome: A connected charging infrastructure capable of real-time data capture.
Step 3: Establish means to collect, clean, and centralize data
Implement a data architecture that consolidates data from all stations and sensors into a centralized, structured system. Cleaning and validation ensure the accuracy and reliability of data before it’s used for analytics.
Action points:
- Set up a cloud-based data warehouse
- Develop ETL (extract, transform, load) processes
- Implement data validation and error detection
- Automate regular data collection
- Comply with data protection standards
Expertise needed: Data engineer, cloud architect, cybersecurity specialist
Step outcome: Reliable, structured, and secure access to unified operational data.
Step 4: Choose and integrate business intelligence, data analytics, and data visualization software
Select and configure the right tools to analyze trends, measure performance, and visualize operations in real time. Integration ensures that insights can flow seamlessly into decision-making processes.
Action points:
- Evaluate BI and analytics platforms (e.g., Power BI, Tableau)
- Set up dashboards and KPIs
- Connect tools to the data warehouse
- Configure access for stakeholders
- Ensure scalability for growing datasets
Expertise needed: BI developer, data analyst, software integrator
Step outcome: An operational analytics suite that transforms raw data into actionable insights.
Step 5: Train team on working with IoT-reinforced charging infrastructure and data analytics solutions
Equip your team with the necessary skills to operate IoT-powered stations and interpret analytics tools. Training ensures adoption, minimizes errors, and empowers staff to make data-informed decisions.
Action points:
- Conduct training sessions for hardware operation
- Provide data interpretation workshops
- Create documentation and SOPs
- Appoint internal analytics champions
- Set up a feedback and support channel
Expertise needed: Technical trainer, operations manager, analytics consultant
Step outcome: A knowledgeable team empowered to use data and IoT tools in daily operations.
Step 6: Launch real-time monitoring and data analytics processes, improve, and iterate
Deploy your live monitoring and analytics systems, start generating insights, and establish a culture of continuous improvement by analyzing results and refining strategies based on what the data reveals.
Action points:
- Deploy real-time dashboards
- Monitor for anomalies or inefficiencies
- Use predictive analytics for maintenance and demand
- Gather feedback and analyze trends
- Adjust algorithms and processes regularly
Expertise needed: Data scientist, operations analyst, DevOps engineer
Step outcome:
An adaptive, intelligent system that continuously improves station efficiency and user satisfaction.
Explore EV big data services by Intelliarts at our respective service page, so you can both explore how our offering stands out and reach out to us in a few clicks!
Here, we would like to reveal our case on exactly the usage of data analytics for EV charging infrastructure.
Challenge:
EV Connect needed to prevent unexpected charger downtimes and improve service reliability but lacked structured data for predictive maintenance.
Solution:
Intelliarts analyzed historical EV charger data, cleaned and restructured it for ML use, and applied anomaly detection algorithms to identify failure patterns. We provided detailed recommendations for improving data quality, collection pipelines, and storage.
Results:
EV Connect gained strategic insights into charger behavior, optimized maintenance planning, and prepared data infrastructure for a future predictive maintenance system. This resulted in enhanced uptime, reduced service interruptions, and smarter, data-driven EV charging operations.
Challenges in EV charging data analytics and solutions to them
As with any innovation and initiative, there are difficulties related to adopting EV charging and data analytics and applying it to real business processes. Here are the main concerns, and ways to address them:
Challenge 1: Collecting high-quality, standardized data across multiple stations
EV charging infrastructure often involves various hardware vendors and network operators, leading to inconsistent data formats and metrics. This fragmentation limits the effectiveness of EV charging station data analysis and hinders network-wide insights. In the meanwhile, according to the Garbage-In-Garbage-Out (GIGO) rule, the quality of output largely depends on input data quality.
Solution: Implementing a sufficient big data analytics pipeline and having a unified data model can ensure compatibility across systems. Data-driven EV charging solutions that normalize incoming data streams enable effective EV charging analytics and network-wide benchmarking.
Take a look at our data collection guide for a complete perspective.
Challenge 2: Privacy and security issues related to sensitive user data
User-level information collected through EV charging analytics — like location, preferences, and payment details — poses risks if not properly protected. Data breaches can compromise customer trust and violate compliance laws.
Solution: Leverage encrypted data storage and role-based access within EV data analytics systems. It can also be recommended to implement privacy-by-design into smart EV charging analytics platforms.
Challenge 3: Difficulty in forecasting demand and optimizing station locations
Without having predictive capabilities, it’s challenging to know where to expand or how to balance charger loads, leading to overuse or underuse of assets. This impacts ROI and customer experience and basically mitigates the entire purpose of integrating smart EV charging with IoT.
Solution: Apply predictive data analytics in electric vehicles using machine learning models rather than through conventional data analytics and visualization means. This would lead to having better forecasting capabilities and is basically an intended way of implementing smart EV charging reinforced with data analytics.
Explore the application of machine learning for EV charging.
Challenge 4: Privacy and security issues related to sensitive user data
Many charging network operators and infrastructure providers lack in-house expertise in handling advanced EV charging data analytics. This limits their ability to extract value from data, model demand accurately, or optimize performance effectively.
Solution: It’s recommended to collaborate with external data science partners to cover every tech aspect. Besides, we recommend using custom or out-of-box EV charging software solutions to overcome the skill gap of employees intending to work with EV charging and data analytics.
Discover more about big data trends in another blog post by Intelliarts.
It’s often that difficulties boil down to three main constraints: expertise availability, budget, and time. Luckily, partnering with a trusted tech partner is a solution to all three pain points, as it’s more cost-effective for non-development companies to utilize other vendors’ expertise rather than grow their own.
The future of predictive & autonomous networks with Intelliarts’ insights
And now, let’s have a glance at what the future of EV charging networks reinforced with data analytics may look like:
#1 Reducing wait times through charging pattern insights
Modern EV charging analytics tracks when, where, and how long drivers charge their vehicles. With this data, providers can apply data-driven EV charging optimization to:
- Analyze charging frequency and dwell time
- Predict congestion and recommend nearby available stations
- Implement smart EV charging analytics with dynamic queue management and pricing models
The result? Reduced wait times, higher utilization, and a smoother charging experience.
#2 Big data enables autonomous load balancing
EV charging infrastructure is now powered by data analytics in electric vehicles to dynamically manage energy across multiple sites. By leveraging big data:
- Load is distributed intelligently to avoid grid overload
- Charging sessions are scheduled based on grid conditions and pricing
- Predictive models trigger automated fault detection and maintenance
This form of EV charging station data analysis does not only improve efficiency but enables autonomous systems to run without constant human oversight.
#3 Integrated insights to reduce costs and waste
EV charging data and analytics become even more powerful when layered with other datasets, such as:
- Weather forecasts (e.g., solar output predictions)
- Real-time energy pricing and availability
- Charger usage data across locations
You may be interested in exploring renewable energy solutions development services by Intelliarts.
This comprehensive approach to optimizing EV charging networks can shift high-energy sessions to off-peak times, align with renewable energy output, and cut operational costs — all through intelligent automation.
What Tesla did for cars, data analytics is doing for infrastructure. We already could see a reduction in operational costs upon the integration of real-time demand data and local grid inputs in actual cases. — Alexander Barinov, Managing Partner at Intelliarts
Take a look at a sample use case on EV charging data and analytics optimization:
Our customers used to manage chargers based on a reactive approach to complaints or failures. With EV charging data analytics, we can now offer to anticipate issues, balance grid loads, and serve more vehicles without adding new stations. It’s not just optimization; it’s transformation. — Yuliia Zabudska, EV expert at Intelliarts
Final take
EV charging data analytics plays a critical role in optimizing site selection, station performance, and user experience. By integrating IoT devices, big data tools, and predictive models, businesses can improve decision-making and enable proactive maintenance.
Though challenges like data quality, privacy, and expertise gaps remain, a structured data-driven approach positions EV infrastructure for smarter, scalable, and more efficient operations in the rapidly growing electric mobility market.
The Intelliarts team has substantial expertise in creating data analytics solutions for renewable energy and EV markets. With more than 24 years’ of experience in software engineering, 80+ large projects delivered, and a 90% customer retention rate, we can deliver a top-notch software solution for specific EV charging data needs, so don’t hesitate to reach out to us with your request.
FAQ
1. How can data analytics help improve my EV charging station operations?
Data analytics enables EV charging station optimization by identifying peak usage, predicting maintenance needs, and improving energy distribution. With data-driven EV charging optimization, operators can reduce costs, increase uptime, and enhance user experience through informed decision-making.
2. What data sources are essential for effective EV charging analytics?
Key sources include charger usage logs, grid interaction data, payment records, and real-time vehicle demand. These form the foundation of EV charging data and analytics, enabling accurate forecasting, dynamic load management, and efficient resource allocation.
3. What’s your solution to reduce EV charging station downtime?
We use EV charging station data analysis to monitor hardware performance, detect faults early, and trigger predictive maintenance. This data-driven EV charging solution minimizes outages, cuts repair times, and ensures operational reliability at scale.
