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Researchers Develop Magnetic Sensor System to Detect Counterfeit Lithium-Ion Batteries

Researchers have discovered a new method to identify individual lithium-ion batteries (LIBs) using magnetic field measurements. Published in Green Energy and Intelligent Transportation, the study explores how magnetic sensors can analyze battery-specific magnetic fields, laying the foundation for a more secure and efficient battery identification system for electric vehicles (EVs).

Aerial view electric car recharging battery from EV charging station.
Study: Evaluation of lithium-ion batteries with different structures using magnetic field measurement for onboard battery identification. Image Credit: Owlie Productions/Shutterstock.com

Background on LIBs

LIBs have become the preferred choice for EVs due to their high energy density and efficiency. However, as the market grows, concerns over counterfeit batteries and quality inconsistencies have intensified—especially when OEM batteries are replaced with lower-quality alternatives. These substitutions pose serious safety risks, including potential battery-related fires.

Current identification methods, such as barcodes and integrated circuit (IC) chips, are vulnerable to counterfeiting, underscoring the need for more reliable authentication technologies. While past research has employed non-destructive techniques like magnetic resonance imaging (MRI) to assess battery conditions, existing methods for evaluating current distributions are often too complex for real-time vehicle applications.

The Study

In this study, the research team examined prismatic lithium-ion cells—labeled as samples A, B, and degraded B—to explore how internal structural differences impact magnetic field emissions. Although these cells appeared identical in size and shape, they were sourced from different manufacturers and contained distinct internal configurations.

To capture the magnetic fields generated by internal current flow, the researchers attached magnetic sensors to the battery surfaces. Two key approaches were used: experimental measurements and simulations. The experiments were carefully controlled to mimic real-world conditions, and the collected data was compared with theoretical models to validate accuracy.

To test their findings further, the researchers analyzed two cells connected in series, simulating a battery module. This setup allowed them to observe how adjacent cells influence overall magnetic field characteristics. The use of advanced magnetic field measurement systems enabled the precise detection of variations in magnetic signatures, forming the basis of the proposed identification method. Additionally, simulations helped assess how different cell arrangements or positions might affect detection, offering insights into potential real-world optimizations.

Results and Discussion

The study revealed that each battery sample exhibited a unique magnetic field pattern, enabling the successful identification of individual LIBs. Specifically, differences in the shape and size of the current collectors significantly impacted the measured magnetic fields. Even within a battery module, the sensors could distinguish between cells based on their structural design.

One key finding was that only two strategically placed sensors were needed to identify the unique characteristics of each cell. This efficiency suggests that the method could be integrated into existing battery management systems with minimal modifications. Additionally, the consistency between magnetic field measurements and output current further reinforced the reliability of the approach.

The study also showed that the magnetic properties of neighboring cells affected overall measurements. Simulations indicated that altering the distance between cells or changing their arrangement produced different magnetic signatures. These insights suggest that the system could be valuable not only for identifying individual batteries but also for monitoring interconnected battery modules in practical applications.

Conclusion

This research marks a significant advancement in battery identification technology. By leveraging magnetic field measurements to distinguish LIBs based on their internal structures, the study highlights the feasibility of real-time battery identification in electric vehicles. Even minor variations in design and assembly produced distinct magnetic signatures, providing a reliable method for differentiating OEM from non-OEM batteries.

The proposed system has substantial potential for improving safety and performance in EV applications by offering a secure and efficient authentication method. Future research could focus on refining sensor placement, optimizing detection algorithms, and expanding the approach to various battery types. With further development, integrating magnetic field analysis into battery management systems could enhance the safety and reliability of modern electric vehicles, contributing to broader sustainability efforts.

Journal Reference

Eto A., Akimoto Y., et al. (2025). Evaluation of lithium-ion batteries with different structures using magnetic field measurement for onboard battery identification. Green Energy and Intelligent Transportation. DOI: 10.1016/j.geits.2025.100257, https://www.sciencedirect.com/science/article/pii/S2773153725000076?via%3Dihub

Dr. Noopur Jain

Written by

Dr. Noopur Jain

Dr. Noopur Jain is an accomplished Scientific Writer based in the city of New Delhi, India. With a Ph.D. in Materials Science, she brings a depth of knowledge and experience in electron microscopy, catalysis, and soft materials. Her scientific publishing record is a testament to her dedication and expertise in the field. Additionally, she has hands-on experience in the field of chemical formulations, microscopy technique development and statistical analysis.    

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