The increasing adoption of electric vehicles (EVs) has made battery management systems (BMS) more critical than ever. These systems monitor, protect, and optimize battery packs, ensuring safety, longevity, and efficiency.
However, as EV batteries incorporate more advanced chemistries and higher energy densities, managing them effectively becomes increasingly complex. Accurate state estimation, predictive maintenance, and real-time monitoring are essential for ensuring performance and safety. Artificial intelligence (AI) enhances these capabilities by improving the precision of battery monitoring and optimizing energy usage.
Another important concept in AI-driven battery management is computing “at the edge,” which refers to processing data locally within the vehicle or battery management system (BMS) instead of relying on cloud computing. This allows for real-time monitoring and decision-making without latency issues.
Advancements in AI-driven battery management systems (BMS) are enhancing EV safety, optimizing charge cycles, and improving battery longevity through real-time monitoring and predictive analytics.
To explore these innovations, we spoke with Can Kurtulus, CTO of Eatron Technologies, and Christian Weber, Senior Product Marketing Manager for AURIX with Infineon Technologies, to discuss the role of AI in BMS, battery aging mitigation, and the key advancements shaping the future of EV battery technology.
This discussion is divided into two parts: Part 1 covers AI’s role in battery monitoring, predictive maintenance, and microcontroller selection, while Part 2 will explore AI’s impact on fast charging, battery longevity, and safety advancements.
What is a battery management system (BMS), and why is it critical for EVs?
Can Kurtulus (CK): A BMS is essential for monitoring, protecting, and optimizing battery packs in EVs. It ensures safety by detecting conditions like overvoltage, under-voltage, and short circuits while also managing thermal conditions to keep the battery operating efficiently.
Can Kurtulus, CTO of Eatron Technologies
Additionally, the BMS estimates the State of Charge (SoC), State of Health (SoH), and State of Power (SoP), all of which are critical for battery longevity and reliability. SoC refers to the remaining energy in a battery as a percentage of its total capacity. SoH measures the battery’s overall condition and degradation over time, and SoP estimates the available power output under current conditions.
A well-optimized BMS enhances battery lifespan, efficiency, and reliability by managing charging cycles, preventing over-discharge, and mitigating extreme temperature effects. Without an effective BMS, an EV battery’s safety, performance, and long-term durability would be significantly compromised.
What are the biggest challenges in EV battery management today?
Christian Weber (CW): Safety remains the primary concern. While EV battery fires are rare, they receive significant attention and require OEMs to implement stringent safety measures. Effective battery monitoring and fault detection systems are essential to identify thermal runaway risks early and implement preventative actions.
Another challenge is performance reliability, as drivers expect precise range estimates and fast charging without compromising battery longevity. However, factors such as temperature fluctuations, charging cycles, and inconsistent energy distribution can cause unexpected performance drops if not properly managed.
Christian Weber, Senior Product Marketing Manager for AURIX with Infineon Technologies
Battery lifespan and second-life applications are also growing concerns. OEMs must ensure their batteries meet warranty commitments while maintaining resale value, especially as EV adoption scales. AI plays a key role in predictive maintenance, allowing manufacturers to detect early signs of degradation, optimize charge-discharge cycles, and enhance long-term battery efficiency.
Additionally, AI-driven automated anomaly detection can identify subtle performance deviations that may indicate hidden faults or premature wear, helping prevent costly failures and improving battery reliability across its lifecycle.
How does AI improve the accuracy of SoC and SoH predictions?
CK: Traditional SoC and SoH estimation models rely on simplified algorithms that struggle with nonlinear battery behavior, particularly for newer chemistries like lithium-iron-phosphate (LFP). AI enhances accuracy by analyzing vast datasets from real-world battery usage, allowing for more precise estimations.
AI leverages machine learning (ML) — a subset of AI that enables systems to recognize patterns, make decisions, and continuously improve from experience without explicit programming. ML models can analyze voltage, temperature, and current fluctuations to identify patterns that impact battery performance. Unlike traditional lookup-table-based methods, ML-driven SoC and SoH estimations adapt dynamically to battery aging, temperature variations, and different usage conditions.
AI further enables predictive analytics, helping OEMs improve range accuracy by forecasting a battery’s behavior under specific conditions. Adaptive AI models adjust SoC and SoH predictions as the battery ages and degrades, ensuring more reliable and consistent performance over its lifecycle.
In some cases, hybrid AI models combine physics-based and ML learning approaches to enhance SoC and SoH accuracy, providing greater robustness in real-world EV applications.
What factors influence battery aging, and how does AI help mitigate degradation?
CW: Battery aging is influenced by factors such as charging cycles, depth of discharge, temperature fluctuations, and high C-rates (charge/discharge rates). Repeated exposure to high temperatures and deep discharges accelerates degradation, reducing overall capacity. AI-driven models help mitigate these effects by identifying optimal charging patterns, adjusting power delivery dynamically, and predicting when maintenance is required.
By leveraging AI for predictive battery health monitoring, manufacturers can extend battery lifespan and ensure reliable performance over time. AI has also been instrumental in estimating cycle life based on real-world usage patterns, allowing engineers to tailor battery management strategies to different driving behaviors.
Why is edge computing significant for AI-driven BMS?
CW: Edge computing allows real-time battery monitoring and decision-making without relying on cloud connectivity. This is crucial for applications requiring low latency, such as dynamic power estimation during acceleration or overtaking maneuvers. Additionally, processing AI models on the edge reduces dependence on cloud storage and lowers operating costs for OEMs.
In BMS applications, edge computing enables faster response times by directly processing critical battery data — such as temperature changes, current fluctuations, and voltage stability — within the vehicle. This is particularly beneficial when instantaneous power adjustments are required, such as during regenerative braking or rapid acceleration. Reducing reliance on cloud computing also enhances data security, as sensitive battery and vehicle diagnostics remain within the embedded system.
When evaluating microcontrollers for AI-driven BMS, engineers should look for low-latency processing, power efficiency, high data throughput, and robust AI acceleration capabilities. AI-enabled edge computing also supports self-learning BMS models, allowing the system to refine battery predictions based on real-world driving conditions. Furthermore, advanced diagnostics and anomaly detection improve battery longevity by identifying early signs of degradation and adapting energy distribution strategies accordingly.
What should engineers look for in microcontrollers when implementing AI in BMS?
CK: The primary requirements for microcontrollers in AI-driven BMS include high-performance digital signal processing (DSP) capabilities, parallel processing support, and advanced memory architectures to handle inference efficiently. In addition, integrated security features are critical to protecting against cyber threats in connected EV systems. Engineers should also consider low-power operation to enhance energy efficiency, particularly for embedded BMS applications.
Microcontrollers enable real-time processing in AI-driven battery management systems (BMS), supporting edge computing for faster decision-making, enhanced safety, and optimized energy distribution in EV batteries.
Selecting a microcontroller that supports hardware-accelerated AI processing can significantly improve the responsiveness and accuracy of real-time battery monitoring. Multi-core architectures allow AI-driven BMS to process multiple sensor data streams simultaneously, improving fault detection and predictive analysis.
Dedicated AI acceleration engines also help optimize complex machine learning workloads while reducing computational overhead. For enhanced efficiency, engineers should evaluate microcontrollers with specialized AI instructions or neural processing units (NPUs), which can improve power efficiency while executing AI inference tasks.
Another key consideration is real-time operating system (RTOS) support, which enables the deterministic execution of AI tasks in safety-critical applications. In high-performance BMS, integrated functional safety features (such as ISO 26262 compliance) can help meet automotive industry standards while maintaining system integrity. By selecting microcontrollers with these capabilities, engineers can optimize AI-driven battery management while ensuring performance, security, and long-term reliability.
How does AI enhance fault detection in battery cells?
CW: AI enables more sophisticated fault detection by analyzing voltage, current, and temperature variations across individual battery cells. Conventional fault detection methods rely on pre-defined thresholds, which may miss early-stage issues, particularly in high-energy-density battery chemistries where minor deviations can rapidly escalate into critical failures.
AI models can identify subtle anomalies that indicate potential failures before they become critical. This allows pre-emptive action, reducing the risk of catastrophic failures, overvoltage conditions, or thermal imbalances, thereby improving overall battery reliability. Machine learning algorithms trained on large datasets can distinguish between typical aging patterns and emerging faults, providing more accurate diagnostics than traditional monitoring systems.
AI-driven BMS can also predict thermal runaway conditions by detecting deviations in heat distribution and internal resistance across cells. This ensures proactive safety measures, such as triggering cooling adjustments or modifying charge rates to prevent hazardous conditions.
In some cases, these systems can implement early-stage mitigation measures, such as redistributing charge loads across the pack or isolating affected cells, extending operational safety before a fault escalates. For example, AI-enhanced cell-balancing algorithms help ensure uniform charge distribution, reducing stress on individual cells and improving long-term battery health.
Can AI help optimize EV battery charging strategies?
CK: Absolutely! AI can adapt charging strategies based on battery chemistry, environmental conditions, and real-time usage patterns. By dynamically adjusting charging rates, AI helps prevent issues such as lithium plating, excessive heat generation, and accelerated aging, which can compromise battery lifespan. AI-driven predictive modeling enables BMS to adjust voltage and current levels in real time to optimize energy flow without exceeding thermal or electrochemical limits.
Adaptive charging algorithms also enhance fast-charging capabilities while minimizing long-term degradation. Instead of applying fixed charge rates, AI models can predict optimal charging curves based on historical charge/discharge cycles, ensuring a balance between efficiency and battery health.
AI models are now being designed to customize charging profiles for individual vehicles. This means that two EVs with the same battery chemistry may receive different charge rates based on their historical usage patterns, ambient temperature conditions, and thermal behavior. Some next-generation BMS systems incorporate reinforcement learning-based AI, which continuously refines its charging strategy based on real-world performance data.
These advancements are essential for improving EV charging efficiency, reducing charge times, and extending overall battery life.
Stay tuned for Part II, where we discuss AI’s role in EV fast charging, battery longevity, and safety advancements.
Filed Under: BMS, FAQs