  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 capacity
degradation, especially because the different degradation processes occur at
various timescales and their interactions play an important role. This paper
proposes and compares two data-driven approaches: a Long Short-Term Memory
neural network, from the field of deep learning, and a Multivariable Fractional
Polynomial regression, from classical statistics. Models from both classes are
trained from historical data of one exhausted cell and used to predict the SoH
of other cells. This work uses data provided by the NASA Ames Prognostics
Center of Excellence, characterised by varying loads which simulate dynamic
operating conditions. Two hypothetical scenarios are considered: one assumes
that a recent true capacity measurement is known, the other relies solely on
the cell nominal capacity. Both methods are effective, with low prediction
errors, and the advantages of one over the other in terms of interpretability
and complexity are discussed in a critical way.
