  This paper presents the development of machine learning-enabled data-driven
models for effective capacity predictions for lithium-ion batteries under
different cyclic conditions. To achieve this, a model structure is first
proposed with the considerations of battery ageing tendency and the
corresponding operational temperature and depth-of-discharge. Then based on a
systematic understanding of covariance functions within the Gaussian process
regression, two related data-driven models are developed. Specifically, by
modifying the isotropic squared exponential kernel with an automatic relevance
determination structure, 'Model A' could extract the highly relevant input
features for capacity predictions. Through coupling the Arrhenius law and a
polynomial equation into a compositional kernel, 'Model B' is capable of
considering the electrochemical and empirical knowledge of battery degradation.
The developed models are validated and compared on the Nickel Manganese Cobalt
Oxide (NMC) lithium-ion batteries with various cycling patterns. Experimental
results demonstrate that the modified Gaussian process regression model
considering the battery electrochemical and empirical ageing signature
outperforms other counterparts and is able to achieve satisfactory results for
both one-step and multi-step predictions. The proposed technique is promising
for battery capacity predictions under various cycling cases.
