Many automotive manufacturers classify new cars and trucks as software-defined vehicles (SDVs). As SDVs by design, electric vehicles (EVs) optimize vital systems and functions with sophisticated artificial intelligence (AI) and machine learning (ML) capabilities. This article discusses AI’s crucial role in EVs, from smart charging and advanced driver assistance systems (ADAS) to predictive maintenance and personalized in-cabin experiences. It also highlights how automotive manufacturers use AI to design EVs that maximize performance, efficiency, and range.

From SDVs to VSI

In the early 2000s, automotive manufacturers designed cars and trucks with fixed-function hardware blocks. In contrast, modern SDVs (Figure 1) use software to offer flexibility, upgradability, and cloud connectivity. Like many SDVs, EVs incorporate well over 150 million lines of code to efficiently manage various electronic control units (ECUs) and sensors.

SDVs support over-the-air (OTA) updates, facilitating seamless firmware upgrades that optimize key powertrain, drivetrain, and infotainment systems. Automotive manufacturers also apply security patches and offer new features through these updates. For example, Tesla’s 2024 updates implement an auto shift beta feature, a native Audible app for in-car audiobook playback, an upgraded Spotify app with improved synchronization and playback speed control, and adaptive high beams. Tesla’s 2024 updates also introduce a trip progress bar with live traffic conditions, a speed camera notification chime, and a hands-free trunk option while further refining autopilot and full self-driving (FSD) capabilities.

EV manufacturers harness vehicle software intelligence (VSI) to comprehensively design, map, and optimize complex SDV systems. Unlike static code analysis tools, VSI platforms rapidly and accurately analyze software and hardware functions, real-time behavior, and system interdependencies. VSI supports continuous software development (CSD) cycles, simplifying updates with efficient line-of-code modifications, effectively reducing engineering overhead costs and ensuring zero downtime for drivers.

Facilitating smart EV charging

Embedded in EVs and electric vehicle supply equipment (EVSE), AI-enabled smart energy solutions continuously analyze power demand and grid capacity. Many residential and commercial chargers (Figure 2) efficiently reduce grid load and minimize costs by charging EVs during off-peak times. Some of these chargers automatically pause during sudden spikes in grid demand, dynamically adjusting charging rates and speeds based on real-time electricity pricing. AI-enabled smart energy solutions also manage charging schedules based on user preferences, predictive analytics, and the availability of on-site renewable energy sources such as wind or solar.

Beyond charging processes, AI can help streamline vehicle-to-grid (V2G) integration, improving coordination and bi-directional energy transfers between EVs, chargers, and the grid. Lastly, AI supports comprehensive diagnostics and proactive maintenance in EVSE, onboard chargers, and battery management systems (BMS).

Optimizing key EV systems

EVs rely on AI and ML (Figure 3) for optimal performance, efficiency, and range. Specifically, ML algorithms analyze traffic conditions, terrain, and driving patterns, helping drivers adjust acceleration, speed, and regenerative braking in real time. For ADAS, AI facilitates the rapid inferencing of real-time data from radar, lidar, and cameras, enabling safe and reliable object detection, collision avoidance, and adaptive cruise control.

AI-enabled battery management systems (BMS) regulate lithium-ion (Li-ion) battery traction packs, intelligently allocating energy to electric motors (controlling torque) and other key drivetrain and powertrain systems. The BMS also monitors critical battery operational parameters, such as voltage, current, and temperature, alongside the constantly fluctuating state of charge (SoC). Crucially, the BMS prevents battery thermal runaway and physical degradation caused by excessive heat, overcharging, and frequent rapid charges.

Highlighting the capabilities of AI in EVs, Electra’s EVE-AI Range Expert module analyzes data from external sources (traffic, road conditions, and weather), internal battery data (temperature and degradation), and driving patterns. The module accurately estimates total power consumption per trip, alleviating range anxiety by precisely tracking the battery’s SoC, state of health (SoH), and remaining useful life (RUL). Moreover, it proactively detects potential energy-related maintenance issues, extending battery life by up to 40% and increasing reliability and safety.

AI further reduces driver range anxiety by using GPS technology to locate EVSE along specific routes — EV apps filter by charging speed, connector type, and distance. ML algorithms allow these apps, such as rain, flooding, and snow, to efficiently plan and navigate routes while safely avoiding dangerous highways and roads.

Delivering personalized in-cabin experiences

AI-powered sensor networks in many EVs deliver personalized in-cabin experiences. For example, gesture recognition sensors facilitate customized touchless interaction with critical systems for drivers and passengers. Leveraging ML algorithms, EVs proactively adjust in-cabin temperature, select audio inputs and playlists, customize seat settings, and brighten or dim interior lighting. AI-powered sensors also automate windshield wiper speed and headlight intensity based on driver preferences and environmental conditions.

Automotive manufacturers could one day equip EVs with advanced heads-up displays (HUDs), projecting speed, navigation, and alerts directly into the driver’s line of sight (Figure 4). AI and ML will play a major role in HUDs, rapidly analyzing large amounts of data from various ADAS components such as radar, lidar, and computer vision. Lastly, sophisticated natural language processing (NLP) ML algorithms are expected to improve further communication between drivers and their EVs with accurate, near-instant recognition of voice commands.

Exploring AI-powered EV manufacturing

AI tools maximize performance, efficiency, and range during EV design and manufacturing. For example, automotive companies use advanced simulation platforms to optimize key design parameters such as aerodynamics, energy consumption, and motor torque. AI rapidly analyzes vast data, identifying potential improvements for the BMS, drivetrain, powertrain, and vehicle-wide thermal management systems. Additionally, EV engineers harness AI to prototype and test advanced materials, ensuring structural strength and safety for innovative lightweight designs.

Summary

Like many SDVs, EVs incorporate over 150 million lines of code to manage ECUs, ADAS, and sensors efficiently. EVs support routine OTA updates, facilitating firmware upgrades that optimize key powertrain, drivetrain, and infotainment systems. These upgrades also apply security patches and introduce new features. Beyond standard SDV technology, EVs integrate advanced AI and ML capabilities to maximize performance, efficiency, and range further.

References

What Is a Software-Defined Vehicle?, Aptiv
The Definitive Guide to Software Defined Vehicles, Sonatus
The Role of AI in Software-Defined Vehicles, AuroraLabs
AI in EV: Road Towards Greener Transportation, VLink
Leveraging AI and Machine Learning to Optimize EV Energy Efficiency, VHola
Aspects of AI in Future EV Technology for Sustainable Environmental Impact, ScienceDirect
Tesla Adds Speed Camera Alerts, Adaptive High Beams, and More New Features, The Driven
Electra Vehicles to Showcase Revolutionary Ai-Powered Battery Technology at MOVE 2024, Eliminating Range Anxiety, Electra
The Role Of Artificial Intelligence In Optimizing Electric Vehicle Performance, Inc42
How Smart Energy Management is Fueling the Future of EVs, BizBrolly