Piecewise-linear modelling with feature selection for Li-ion battery end of life prognosis
This work addresses battery end-of-life prognosis for applications requiring fast and flexible models, though it is incremental in combining existing techniques.
The authors tackled the problem of lithium-ion battery health forecasting by proposing a piecewise-linear model with automated feature selection, which performed equally well as Gaussian process regression in trials.
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.