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

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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Publication Details

Journal
Entropy
Published
2026-02-14
DOI
https://doi.org/10.3390/e28020221
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
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article

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

Abdelilah Hammou, Claude Delpha, Raffaele Petrone, Hamid Gualous et al.
Entropy
Advanced Battery Technologies Research
article

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

Abdelilah Hammou, Claude Delpha, Raffaele Petrone, Hamid Gualous, Demba Diallo
article en

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.

EntropyVol. 28(2)
Centre National de la Recherche Scientifique (FR), Université Paris-Saclay (FR), CentraleSupélec (FR), Centre Hospitalier Public du Cotentin (FR), Groupe d’Etude des Cétacés du Cotentin (FR)
Affordable and clean energy
Openalex Percentile: Top 57%
Advanced Battery Technologies Research
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