  The complex nature of lithium-ion battery degradation has led to many machine
learning based approaches to health forecasting being proposed in literature.
However, machine learning can be computationally intensive. Linear approaches
are faster but have previously been too inflexible for successful prognosis.
For both techniques, the choice and quality of the inputs is a limiting factor
of performance. Piecewise-linear models, combined with automated feature
selection, offer a fast and flexible alternative without being as
computationally intensive as machine learning. Here, a piecewise-linear
approach to battery health forecasting was compared to a Gaussian process
regression tool and found to perform equally well. The input feature selection
process demonstrated the benefit of limiting the correlation between inputs.
Further trials found that the piecewise-linear approach was robust to changing
input size and availability of training data.
