Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions

TL;DR

This paper introduces a Multivariable Fractional Polynomial regression approach for lithium-ion battery degradation modeling, demonstrating its effectiveness and interpretability compared to neural networks and existing methods under dynamic conditions.

Contribution

It applies MFP regression to battery SoH prediction, highlighting its simplicity, interpretability, and competitive accuracy in dynamic operating scenarios.

Findings

Low prediction errors (1.2% to 7.22%) up to End of Life.

Degradation influenced by historical data and multiple factors.

MFP regression is effective and more interpretable than neural networks.

Abstract

Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and accurate algorithms for State of Health (SoH) monitoring on the Battery Management System. The task is challenging due to the complexity and multitude of the factors contributing to the battery degradation, especially because the different degradation processes occur at various timescales and their interactions play an important role. Data-driven methods bypass this issue by approximating the complex processes with statistical or machine learning models. This paper proposes a data-driven approach which is understudied in the context of battery degradation, despite its simplicity and ease of computation: the Multivariable Fractional Polynomial (MFP) regression. Models are trained from historical data of one exhausted cell and used to predict the SoH of other cells. The data are characterised by varying loads simulating dynamic operating conditions. Two hypothetical scenarios are considered: one assumes that a recent capacity measurement is known, the other is based only on the nominal capacity. It was shown that the degradation behaviour of the batteries under examination is influenced by their historical data, as supported by the low prediction errors achieved (root mean squared errors from 1.2% to 7.22% when considering data up to the battery End of Life). Moreover, we offer a multi-factor perspective where the degree of impact of each different factor is analysed. Finally, we compare with a Long Short-Term Memory Neural Network and other works from the literature on the same dataset. We conclude that the MFP regression is effective and competitive with contemporary works, and provides several additional advantages e.g. in terms of interpretability, generalisability, and implementability.