What Is the Data That Drives Your Electric Car? Explained Simply

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Electric vehicles (EVs) run on motors powered by electricity stored in batteries, and they require a vast amount of data to operate efficiently and safely.

There may be some obvious ones you can guess, such as the battery temperature and state of charge, but what about user interaction and environmental impact?

Your electric car is collecting a vast amount of data, and artificial intelligence (AI) is now being used in places to tweak your vehicle’s performance and send data back home.

Your car is becoming a “service on wheels.” Techopedia explores the data that drives your electric vehicle.

Key Takeaways

  • Electric vehicles rely on massive datasets to monitor performance, manage batteries, and improve the driver experience.
  • AI is used to analyze battery data, predict failures, and personalize driving settings.
  • EVs rely on vehicle-to-everything (V2X) communication to exchange real-time data for autonomous features.
  • Networks like Ethernet and 5G are essential as the data demands in EVs grow.
  • Cybersecurity measures and standardized data formats are also key to protecting EV systems and drivers’ privacy.

Data in Electric Vehicles: More Than a Gas Gauge

The latest EVs incorporate millions of lines of code into their systems. Influenced by the advanced features popular with drivers, vehicles with traditional engines are increasingly becoming software-defined — sending data around the car rather than relying on mechanical hardware-based systems.

All of this data requires in-vehicle networks that can handle exponentially growing data volumes as manufacturers add more in-car features and autonomous driving functions — it is known as vehicle-to-everything (V2X) communication.

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Richard Herbert, Director of Business Development and Marketing at Ethernovia, told Techopedia:

“As cars become more of a service on wheels and offer new user features, their data demands increase. OEMs are constantly searching for the next answer to meet these growing demands and ways to optimize the computing power and networks needed.”

What Types of Data Do EVs Generate?

An infographic displaying five types of electric vehicle data for analysis and comparison

EVs typically have large touchscreens on the dashboard that display data on battery status, estimated range, and charger locations to help drivers manage their battery levels.

But there is plenty more behind the scenes, not least location information, driver habits, and user preferences. This informs the infotainment and environmental systems and provides manufacturers with regular updates via application programming interfaces (APIs) for diagnostics and development.

This includes battery management data, such as “state of charge” (SOC), which indicates the vehicle’s current battery level and how much charge is remaining, and “state of health” (SOH), which assesses the battery’s rate of degradation and lifespan.

Temperature data is also crucial to monitoring the battery’s thermal condition and preventing overheating. Meanwhile, charging cycles track how often the battery has been charged and discharged.

Telematics and navigation data include GPS data to provide location tracking for navigation, combined with traffic data for route optimization. Navigation systems also incorporate weather data to adjust routes and other driving dynamics based on real-time conditions.

Performance and diagnostic data measure the performance of the motor and drivetrain wear and tear, while error codes alert drivers to potential issues with the vehicle’s systems.

Modern vehicles incorporate an increasing number of sensors to power Advanced Driver Assistance Systems (ADAS) and autonomous features. These include ground-monitoring sensors (Light Detection and Ranging), cameras, and ultrasonic sensors.

Meanwhile, User interaction data records driving habits such as acceleration, braking, and speed to enhance ADAS systems. And in-cabin settings designed to improve driver and passenger comfort store their preferences for seat positioning, climate control, and infotainment systems.

Battery electricity usage and efficiency data not only track the battery’s health but also inform the vehicle’s environmental impact data and enable carbon footprint estimates of emissions saved compared to driving a traditional internal combustion engine (ICE) vehicle.

This data can be used to develop new manufacturer-owned or independent apps for EV drivers. These apps use two-way communication to locate the vehicle, remotely lock or unlock doors, open and close windows, adjust climate control settings, and start or end charging sessions.

For instance, an app can use APIs to collate location information, energy usage, odometer values, state of charge, and the cost of charging sessions to display detailed trip and charging history. Drivers can then use this information to decide where to charge and manage their energy consumption.

Fleet managers can use APIs to compile location, SoC, and odometer data from all their vehicles to a central hub automatically rather than rely on dongles installed in each vehicle’s onboard diagnostics port.

The Strain of EV Bandwidth Demand

Handling these types of data requires in-vehicle networks using higher bandwidth high-speed communication protocols like Ethernet rather than traditional controller area network (CAN) systems.

Time-sensitive networking (TSN) is being developed as an extension to the Ethernet standard to ensure real-time data transmission of critical control data and high-bandwidth data with strict requirements for timing and delivery, which is essential to support autonomous driving.

Using 5G network connectivity is also key for ultra-fast data exchange between vehicles and infrastructure, enabling V2X communication, including smarter traffic management and safety features.

Advanced driving systems require coordination between sensors inside vehicles and external cloud systems. Manufacturers increasingly deploy edge computing within EVs to reduce latency and use bandwidth more efficiently to process data locally.

More data processing in-vehicle can also incorporate solid-state drives (SSDs), advanced memory, and data compression to store and process large datasets.

The growing importance of in-vehicle networks raises the threat of cyberattacks on individual vehicles, fleets, or power grids. Encryption and intrusion detection systems are needed to protect the wealth of data being transmitted into and out of the vehicle.

As manufacturers scale up EV production, it is essential that they ensure the vehicles comply with various international cybersecurity standards.

Eurelectric, an industry association representing European utilities, has launched an initiative to improve the sharing of EV data between vehicles, utilities, grid operators, urban planners, and other relevant players to support better infrastructure planning while recognizing drivers’ privacy concerns.

The Role of AI in Optimizing EV Data

How can AI help to manage and make the most out of the complex datasets that EVs generate?

Ethernovia’s Herbert told Techopedia:

“As of right now, AI has entered the industry as a driver of data bandwidth.

Its adoption either in the cloud, as a local autonomy agent, or locally through the next generation of SoCs supporting large language models increases the bandwidth to meet demands and enables new features, such as voice commands, safety, and driver assistance measures, including driver monitoring and braking.

“All of these together are establishing a new standard across the industry for the networks that automobiles must have.”

As applications develop, AI algorithms can analyze battery data to enhance battery management and optimization. AI-powered analytics tools can precisely predict remaining battery life, optimize charging schedules, facilitate dynamic energy management, and balance power usage between the motor and auxiliary systems.

AI can also minimize repair costs by supporting predictive maintenance. Machine learning (ML) models can analyze vehicle performance and diagnostic data to detect problems, predict potential failures, proactively schedule maintenance, and reduce downtime.

Incorporating AI into EV systems can enhance the user experience by using telematics, GPS, and traffic data to suggest the most efficient routes based on real-time conditions and integrate with smart charging infrastructure to help drivers find the nearest working charging station.

AI can also personalize the in-cabin experience by learning driver preferences to automatically adjust settings, suggest eco-friendly driving habits based on the driver’s behavior, and adapt driving dynamics to maximize battery range.

Manufacturers are also increasingly incorporating intelligent AI-based voice assistants to enable hands-free control of cabin settings, such as seating and climate control, and infotainment systems.

As EVs advance toward autonomous driving, AI algorithms can process real-time data from cameras, LiDAR, and sensors to recognize objects, pedestrians, and other vehicles. They use this information to make split-second decisions to ensure vehicles operate safely.

Edge computing and over-the-air cloud updates ensure AI-based vehicle systems continuously learn and improve driving algorithms.

The Bottom Line

If batteries are the heart of EVs, data is the blood circulating throughout, feeding the systems that allow them to function.

Data is essential to battery management, ADAS, advanced infotainment features, and in-cabin settings that enhance the driving experience. Given the synergy between data and AI, algorithms can analyze and optimize this data to realize the full potential of EVs.

Advances in AI, ML, and LLM are poised to make EVs smarter. Advanced network communications technologies like 5G could enhance real-time data processing capabilities, eventually enabling full vehicle autonomy.

Despite the advantages, using data and AI in electric vehicle systems presents challenges. Standardization that harmonizes data formats across manufacturers will help implement advanced data security systems that ensure drivers’ privacy and protect against cyber threats.

FAQs

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Nicole Willing
Technology Journalist
Nicole Willing
Technology Journalist

Nicole is a professional journalist with 20 years of experience in writing and editing. Her expertise spans both the tech and financial industries. She has developed expertise in covering commodity, equity, and cryptocurrency markets, as well as the latest trends across the technology sector, from semiconductors to electric vehicles. She holds a degree in Journalism from City University, London. Having embraced the digital nomad lifestyle, she can usually be found on the beach brushing sand out of her keyboard in between snorkeling trips.