Real-time monitoring of the SoH of lithium-ion batteries

TL;DR

This paper introduces a novel real-time method for estimating lithium-ion battery health by analyzing voltage responses to discharge pulses, demonstrating high accuracy and potential for integration into battery management systems.

Contribution

The paper presents an innovative approach using discharge pulse analysis and electrical modeling to estimate battery SoH in real-time, suitable for microgrid applications.

Findings

Achieved around 1% mean absolute error in SoH prediction

High explainability score of approximately 0.9

Method successfully predicts capacity degradation during cycling

Abstract

Real-time monitoring of the state of health (SoH) of batteries remains a major challenge, particularly in microgrids where operational constraints limit the use of traditional methods. As part of the 4BLife project, we propose an innovative method based on the analysis of a discharge pulse at the end of the charge phase. The parameters of the equivalent electrical model describing the voltage evolution across the battery terminals during this current pulse are then used to estimate the SoH. Based on the experimental data acquired so far, the initial results demonstrate the relevance of the proposed approach. After training using the parameters of two batteries with a capacity degradation of around 85%, we successfully predicted the degradation of two other batteries, cycled down to approximately 90% SoH, with a mean absolute error of around 1% in the worst case, and an explainability score of the estimator close to 0.9. If these performances are confirmed, this method can be easily integrated into battery management systems (BMS) and paves the way for optimized battery management under continuous operation.