State of Health Evaluation of Lithium-Ion Batteries Using the Statistical Properties of the Voltage

TL;DR

This paper introduces a new way to assess lithium-ion battery health using voltage statistics, which is more reliable and less affected by noise than traditional methods.

Contribution

The novel contribution is using voltage probability density function divergence as a robust battery health indicator.

Findings

Voltage statistical properties show strong correlation with battery health metrics like capacity.

The proposed health indicator remains effective even at high noise levels (up to 30 dB).

Abstract

Conventional indicators of battery health, such as capacity and energy, are difficult to measure directly and are therefore often estimated. This article proposes assessing lithium-ion battery health using the statistical properties of the voltage across the battery terminals, a measurement already available in battery management systems. The evolution of the voltage probability density function during the cycle is assessed using Kullback–Leibler divergence (KLD) as a health indicator. It is studied for two battery chemistries (Lithium iron Phosphate (LFP) and Nickel Manganese Cobalt (NMC)). The batteries are subjected to cycles with a dynamic current profile derived from globally harmonised test cycles for light vehicles (WLTC). Spearman’s correlation coefficients, above 86% for NMC cells and 74% for LFP cells, also indicate that this new health indicator is strongly correlated with conventional measurements of battery health (capacity or energy). The analysis also shows that the divergence not only closely follows the degradation trend even at high noise levels (SNR = 10 dB) but is also insensitive to noise levels higher than 30 dB.

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