Electric vehicle (EV) battery performance, fast charging, and safety require continuous innovation to meet industry demands. One of the key technologies enhancing these areas is artificial intelligence (AI) — leveraging machine learning (ML) algorithms and advanced data analytics to improve decision-making and predictive capabilities.
AI enables real-time battery management, optimizing fast-charging protocols, mitigating degradation, and improving overall battery safety.
Another important concept in AI-driven battery management is edge computing, which refers to processing data locally within the vehicle or battery management system (BMS) rather than relying on cloud computing. This enables real-time monitoring and adaptive decision-making without the latency associated with remote processing.
AI-driven battery management optimizes fast charging by adapting charging profiles in real time, balancing efficiency and battery lifespan.
Part I of this series explored how AI enhances BMS, optimizing state estimation, thermal management, and fault detection. In Part II, we continue our conversation with Can Kurtulus, CTO of Eatron Technologies, and Christian Weber Senior Product Marketing manager for AURIX with Infineon Technologies, focusing on how AI is affecting fast-charging strategies, predicting battery aging with greater accuracy, and advancing safety measures to improve long-term battery performance.
What are the biggest challenges in EV fast charging, and how does AI help?
Can Kurtulus (CK): The main challenge with fast charging is balancing charging speed with battery longevity. Higher charging currents generate more heat, accelerating battery degradation and increasing the risk of lithium plating. This is a condition where lithium deposits form on the anode, reducing capacity and potentially leading to failure.
Can Kurtulus, CTO of Eatron Technologies
AI, which involves ML and data-driven modeling, helps optimize battery performance by dynamically adjusting the charging profile in real time, considering variables like battery temperature, charge cycles, and cell health. These models ensure that charging remains efficient without compromising battery integrity.
AI also plays a crucial role in thermal management by monitoring temperature levels and optimizing cooling systems to prevent excessive heat buildup. Instead of reacting to high temperatures, AI-driven predictive cooling strategies proactively activate cooling mechanisms (such as liquid cooling or phase change materials) to maintain optimal battery conditions.
Another advantage of AI is its ability to adapt charging strategies based on battery chemistry. Different chemistries, such as lithium nickel manganese cobalt oxide (NMC) and lithium iron phosphate (LFP), respond differently to high charge rates. AI recognizes the specific chemistry of a battery and adjusts charge profiles accordingly to minimize stress on individual cells.
Beyond the vehicle, AI contributes to grid demand response and load balancing at charging stations. It can dynamically distribute energy across multiple chargers, optimizing power usage while minimizing strain on the grid. AI also supports vehicle-to-grid (V2G) strategies, allowing EVs to feed energy back into the grid during peak demand periods, improving overall energy efficiency.
The fast-charging infrastructure itself benefits from AI through predictive maintenance. AI-driven models analyze real-time charger performance data, identifying potential failures before they occur. By predicting maintenance needs, charging networks can reduce downtime and improve reliability, ensuring that chargers remain operational and efficient.
AI has also effectively optimized pulse charging strategies, where charging occurs in controlled bursts rather than as a continuous current. This approach allows heat dissipation between cycles, preventing excessive stress on the battery while maintaining high charge rates. Through these combined strategies, AI enhances the efficiency, longevity, and reliability of EV fast charging, making it a critical tool in advancing charging technology.
How does AI help predict and prevent lithium plating?
Christian Weber (CW): Lithium plating occurs when high current densities push lithium ions too quickly onto the anode, leading to uneven deposition. This can reduce battery capacity, increase internal resistance, and, in severe cases, cause dendrite formation, which increases the risk of short circuits and battery failure.
AI-driven predictive models help mitigate lithium plating by analyzing data from previous charge cycles, temperature variations, and electrochemical behavior. These models identify conditions where lithium plating is likely to occur and adjust charging parameters in real time. The BMS can dynamically modify the charging protocol, reducing current flow at critical moments to prevent damage while maintaining an efficient charge rate.
Christian Weber, Senior Product Marketing Manager for AURIX with Infineon Technologies
AI also helps optimize long-term charging strategies based on individual behaviors. For instance, a driver who frequently charges the battery at high rates (fast or dc charging) may receive a different charging profile than one with less demanding charging behavior (an infrequent high-rate charging and mostly slow charging at home or at work). By adapting charge curves to real-world usage, AI balances charging speed, efficiency, and longevity.
This combination of predictive modeling and adaptive charging makes AI essential in improving battery performance, safety, and lifespan.
What role does AI play in thermal management for EV batteries?
CK: Thermal management is crucial because temperature extremes negatively impact battery performance, efficiency, and longevity. Excessive heat accelerates degradation and increases the risk of thermal runaway, while low temperatures reduce charge acceptance and energy output.
AI enhances thermal management by continuously monitoring temperature fluctuations across individual cells and dynamically adjusting cooling and heating mechanisms. ML models analyze historical and real-time data to anticipate thermal spikes before they occur, allowing the system to modify cooling strategies. This includes optimizing fan speeds, coolant flow rates, and heat dissipation based on real-world conditions rather than relying solely on fixed parameters.
Artificial intelligence also plays a key role in adaptive thermal modeling, where it refines thermal management strategies based on actual driving conditions instead of relying solely on lab-tested scenarios. This ensures more accurate thermal predictions, improving safety, efficiency, and battery lifespan. By integrating AI-driven thermal optimization, EVs can maintain stable operating temperatures, enhance fast-charging performance, and reduce energy loss due to inefficient cooling cycles.
How can AI improve EV battery lifespan and second-life applications?
CW: AI is critical in improving battery longevity and enabling second-life applications by providing precise State of Health (SoH) estimations. Conventional methods rely on static models. However, AI-driven analysis uses real-world data to track degradation trends, charging behaviors, and historical usage patterns, offering a much more accurate prediction of a battery’s remaining useful life.
AI helps manufacturers and vehicle systems optimize charging and discharging strategies for first-life applications to slow down capacity loss and extend battery usability. It can reduce stress on aging battery cells by dynamically adjusting charge limits and discharge depths. For instance, as a battery reaches the later stages of its lifecycle, AI can suggest lower charge limits to minimize deep discharges, which helps preserve overall health.
AI-driven battery management enhances grid demand response, load balancing, and state-of-health estimations, enabling efficient power distribution, proactive maintenance, and second-life applications for EV batteries.
AI is equally valuable in second-life applications, where EV batteries are repurposed for stationary energy storage systems. Since second-life batteries must operate reliably despite prior usage, these models assess individual cell performance and degradation patterns, ensuring efficient reallocation and optimized performance in their new roles. Such predictive insights allow energy storage operators to maximize efficiency, reduce maintenance costs, and extend the functional lifespan of repurposed EV batteries.
What advancements in BMS hardware are necessary to support AI integration?
CK: AI-driven BMSs require high-performance edge computing capabilities to process large volumes of data in real time. Microcontrollers must support low-latency processing, advanced memory architectures, and efficient power management to handle AI inference without excessive energy consumption. Since BMS decisions must be made instantly, particularly during rapid charging or high-demand driving scenarios, the models must be optimized hardware to ensure seamless execution.
Integrated sensor fusion is another critical advancement. AI algorithms depend on high-quality, real-time data, meaning the BMS must incorporate precise temperature, voltage, and current sensors. The accuracy of these sensors directly impacts the effectiveness of AI-driven predictions for state-of-charge (SoC), SoH, and fault detection.
Additionally, self-calibrating sensors powered by AI are becoming increasingly important. Over time, sensor drift can impact accuracy, leading to suboptimal battery performance. AI-powered self-calibration ensures that the BMS continuously adjusts for these variations, maintaining long-term precision in monitoring and control.
These advancements in hardware are essential for enabling predictive maintenance, adaptive charging, and enhanced thermal management, ensuring AI-driven BMS can operate efficiently and reliably in real-world EV applications.
How does AI enhance battery safety and prevent catastrophic failures?
CW: Traditional battery safety systems rely on fixed thresholds for voltage, temperature, and current. While these thresholds provide basic protection, they may not detect anomalies early enough to prevent failures. AI-powered safety monitoring improves upon these methods by using pattern recognition to identify subtle deviations in battery behavior that indicate potential risks, such as thermal runaway conditions, internal short circuits, or cell imbalances.
By continuously analyzing real-time data, AI can detect small fluctuations that might be early failure indicators. This allows the battery management system (BMS) to take preventive action, such as adjusting cooling systems, redistributing charge loads, or limiting power output to mitigate risks before they escalate.
AI has also been instrumental in multi-stage fault detection. Early-stage anomalies may only require minor interventions, such as modifying cooling parameters. At the same time, more critical warnings could trigger immediate power reductions or a complete battery shutdown to prevent hazardous failures. This adaptive response system significantly enhances EV safety by preventing catastrophic battery failures before they occur, ensuring both reliability and longevity in modern electric vehicle applications.
How do AI models adapt to different battery chemistries like LFP?
CK: Battery chemistry significantly influences optimal charging and discharging strategies, making AI-driven adaptation essential for maximizing performance and longevity. Lithium Iron Phosphate (LFP) batteries behave differently from Nickel Manganese Cobalt (NMC) batteries, particularly regarding voltage characteristics, thermal behavior, and degradation patterns.
AI models trained on chemistry-specific datasets can tailor charging profiles and energy management strategies to match the unique properties of each battery type. For example, LFP batteries have a flatter voltage curve than NMC batteries, requiring more sophisticated estimation techniques for State of Charge (SoC) calculations.
Degradation prediction algorithms further enhance battery longevity by analyzing cell expansion, electrolyte composition changes, and resistance buildup over time. By recognizing how different chemistries degrade under various operating conditions, AI ensures predictive maintenance, optimal charge cycles, and customized performance adjustments for each battery type. This level of adaptability is crucial as EV manufacturers explore new chemistries to balance cost, energy density, and safety in future battery technologies.
What does the future hold for AI-driven battery technology in EVs?
CW: AI will continue to drive advancements in efficiency, safety, and battery longevity, enabling EVs to become smarter and more adaptable. One of the most exciting developments might be the evolution of self-learning BMS that continuously refine their models based on real-world driving and charging data.
AI-powered wireless battery management systems eliminate bulky wiring, improving battery pack scalability and flexibility.
Unlike traditional systems that rely on fixed test cycles, AI-driven BMS would adapt to individual driver behavior, climate conditions, and energy demands, optimizing real-time charging strategies to maximize range and lifespan.
Another noteworthy innovation is Wireless BMS (wBMS), which will eliminate bulky wiring and improve battery pack flexibility, scalability, and reliability. This will enable modular battery designs, making replacing and repurposing battery modules easier, extending their usability beyond a vehicle’s first life.
AI will also play a critical role in bidirectional charging, supporting Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) applications. This means EVs will consume energy and restore power to the grid or home systems, improving energy resilience and grid stability.
Looking ahead, AI-driven battery technology will make EVs more autonomous, efficient, and sustainable, reducing energy waste, enhancing predictive maintenance, and pushing the boundaries of how batteries integrate with the broader energy ecosystem.
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AI-driven BMS is changing how EV batteries are charged, managed, and protected. As fast-changing demands increase, artificial intelligence’s ability to predict degradation, optimize thermal management, and enhance safety will be crucial in shaping the next generation of electric vehicles.
Filed Under: Batteries, FAQs