Investigation of the Relationship between High and Low Frequency Resistances of Li-ion Batteries: A Machine Learning Approach

Lithium-ion batteries (LiBs) are widely used in diverse applications due to their high energy and power density, efficiency, and long cycle life. However, their performance varies over time due to a combination of reversible effects and irreversible aging mechanisms, driven by factors such as temperature, state of charge (SOC), and current rate. Accurate estimation of key battery parameters—including internal resistance—is essential for assessing the state of health (SOH), SOC, and state of power, which are critical for reliable operation and battery management. This work proposes a machine learning approach for estimating the low-frequency internal resistance of LiBs, leveraging prior estimation of the high-frequency component. The method builds on previous research and aims to provide accurate predictions across varying SOC, temperature, and aging conditions, while avoiding the need for complex hardware or intensive computations. The results show that, although estimating low-frequency resistance is inherently challenging, the use of a neural network—especially when incorporating high-frequency resistance as an input—reduces the estimation error to below 3%. The proposed model represents a promising solution for practical, real-time resistance estimation in advanced battery management systems.