What is it about?
This study presents a new method to evaluate the health condition of lithium iron phosphate (LiFePO₄) batteries, which are commonly used in drones, service robots, and other intelligent systems. As batteries age, their performance and reliability decline, making it critical to detect degradation early to avoid unexpected failures. The researchers applied a physics-based model, originally used to describe magnetic behaviors—the Jiles–Atherton hysteresis model—to analyze the battery’s voltage behavior during charging and discharging. In the study, 30 new and 16 aged LiFePO₄ batteries were tested under controlled charging conditions. By measuring the relationship between open-circuit voltage (OCV) and the battery’s state of charge (SOC), the authors built a voltage hysteresis model and extracted key indicators related to aging. They also analyzed the area enclosed by the hysteresis curve, which proved to be a strong indicator of battery degradation. This modeling approach enables more accurate and early identification of battery wear, helping engineers and system designers determine the optimal time for battery replacement. The proposed method could improve operational safety, extend system lifespan, and enhance performance reliability in applications such as autonomous drones and robots, where battery failure could result in mission interruption or system damage.
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Why is it important?
As drones and service robots become more widespread in daily life and industry, ensuring reliable battery performance is crucial. Traditional methods for evaluating battery health are either invasive, inaccurate, or require long testing cycles. This research introduces a novel, non-destructive approach using a magnetic hysteresis model (Jiles–Atherton model) to assess lithium iron phosphate (LiFePO₄) battery degradation. The work is timely because battery lifespan prediction has become a key challenge in ensuring the safety and longevity of electric-powered systems. By applying a physics-based model originally developed for magnetic materials to voltage hysteresis in batteries, this study bridges two distinct fields—magnetics and electrochemistry—and opens new avenues for predictive battery diagnostics. Furthermore, the method provides clear aging indicators based on model parameters and voltage hysteresis area, allowing engineers to identify when batteries should be replaced, well before performance drops to critical levels. This improves system reliability and reduces unexpected failures, which is especially important for autonomous applications like drones and robots.
Perspectives
As a graduate student in electrical engineering, I have always been fascinated by the intersection of physics and real-world applications. This work allowed me to bridge theoretical modeling with a pressing challenge in battery health management. Applying the Jiles–Atherton hysteresis model—originally developed for magnetic materials—to characterize battery degradation was both intellectually challenging and rewarding. I was particularly motivated by the growing need for safer and longer-lasting batteries in robotics and drone systems. Through this research, I realized the power of combining physical modeling with experimental data to extract meaningful insights about battery behavior. I hope this work inspires further exploration into using magnetic principles for non-invasive diagnostics in energy systems. It has been a valuable learning experience, and I am excited about the potential for this approach to contribute to smarter battery monitoring technologies in the future.
Sin-Yan Chen
Feng Chia University
Read the Original
This page is a summary of: Application of the Jiles–Atherton model to construct the open-circuit voltage hysteresis model and determine the decommissioning of LiFePO4 batteries, AIP Advances, March 2025, American Institute of Physics,
DOI: 10.1063/5.0252031.
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