Evaluating Lithium-Ion Cells with DRT Method | #Sciencefather #Researcherawards

 



Introduction

Lithium-ion batteries are widely used in portable electronics, electric vehicles, and renewable energy systems, but their performance degrades over time due to complex aging mechanisms. Reliable estimation of the State of Health (SOH) is critical for ensuring safety, efficiency, and longevity of these systems. Traditional methods often require extensive historical data, making them less practical in real-world applications. To address this, Distribution of Relaxation Time (DRT) analysis has emerged as a powerful technique to evaluate electrochemical processes, providing deeper insight into battery degradation and enabling more accurate SOH estimation.

Distribution of Relaxation Time (DRT) Analysis

DRT analysis transforms electrochemical impedance spectroscopy (EIS) data into relaxation time distributions, allowing separation of overlapping electrochemical processes. This approach reveals clear insights into charge transfer resistance, diffusion processes, and electrode kinetics, which are not easily distinguished in Nyquist plots. By mapping relaxation peaks across frequencies, DRT provides a robust tool to track degradation trends in lithium-ion batteries, offering researchers and engineers a more comprehensive view of electrochemical dynamics.

Characteristic Relaxation Time Parameter

A novel contribution of this research is the introduction of the characteristic relaxation time as a key parameter for SOH estimation. Specifically, the ratio of central relaxation time (τ) between the charge transfer and diffusion peaks was found to be a reliable indicator of battery degradation. This parameter captures the evolving electrochemical responses during aging, enabling SOH estimation without relying on historical battery performance data, thus simplifying predictive modeling.

Experimental Validation on Lithium-Ion Cells

The study evaluated the proposed method on different battery formats, including 18650 cells and LR2032 coin cells, across their entire lifespan. DRT and Nyquist plots consistently revealed aging signatures, particularly in the shifting and merging of charge transfer and diffusion peaks. These experimental results confirmed that the τ ratio effectively tracks degradation trends, making it a universally applicable tool across various lithium-ion cell designs and chemistries.

Polynomial Modeling for Battery Lifespan Prediction

A polynomial equation was fitted to the τ ratio curve, achieving an impressive adjusted R² value of 0.9994. This strong correlation demonstrates the method’s predictive accuracy in estimating battery lifespan. Unlike conventional approaches requiring long-term cycling data, this model predicts remaining battery life based solely on DRT analysis, providing a rapid and non-intrusive solution for battery management systems.

Application to Commercial Smartphone Batteries

To demonstrate real-world applicability, the method was tested on a Samsung Galaxy S9+ battery. The model successfully predicted a total lifespan of approximately 2100 cycles, compared to the 1000 cycles already completed. This highlights the potential of DRT-based SOH estimation for consumer electronics and other applications, enabling accurate, data-independent predictions of battery health and longevity, and paving the way for smarter energy storage systems.

Global Particle Physics Excellence Awards


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Hashtags

#Sciencefather, #Reseacherawards, #LithiumIonBatteries, #BatterySOH, #DRTAnalysis, #ElectrochemicalImpedance, #BatteryAging, #EnergyStorage, #BatteryDegradation, #BatteryResearch, #RelaxationTime, #BatteryHealth, #Electrochemistry, #BatteryLifePrediction, #BatteryManagement, #BatteryTesting, #BatteryScience, #BatteryDurability, #BatteryDiagnostics, #AdvancedBatteries, #BatteryLifespan, #NextGenBatteries,

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