  Predicting the end-of-life or remaining useful life of batteries in electric
vehicles is a critical and challenging problem, predominantly approached in
recent years using machine learning to predict the evolution of the
state-of-health during repeated cycling. To improve the accuracy of predictive
estimates, especially early in the battery lifetime, a number of algorithms
have incorporated features that are available from data collected by battery
management systems. Unless multiple battery data sets are used for a direct
prediction of the end-of-life, which is useful for ball-park estimates, such an
approach is infeasible since the features are not known for future cycles. In
this paper, we develop a highly-accurate method that can overcome this
limitation, by using a modified Gaussian process dynamical model (GPDM). We
introduce a kernelised version of GPDM for a more expressive covariance
structure between both the observable and latent coordinates. We combine the
approach with transfer learning to track the future state-of-health up to
end-of-life. The method can incorporate features as different physical
observables, without requiring their values beyond the time up to which data is
available. Transfer learning is used to improve learning of the hyperparameters
using data from similar batteries. The accuracy and superiority of the approach
over modern benchmarks algorithms including a Gaussian process model and deep
convolutional and recurrent networks are demonstrated on three data sets,
particularly at the early stages of the battery lifetime.
