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

Piecewise-linear modelling with feature selection for Li-ion battery end of life prognosis