  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.

Applications;Machine Learning;Systems and Control;Systems and Control