Deep Gaussian process regression for lithium-ion battery health prognosis and degradation mode diagnosis

Abstract

Accurate state of health estimation and end of life prediction is critical for safe and reliable operation of lithium-ion batteries. This paper proposes a deep Gaussian process algorithm for lithium-ion battery health monitoring. The proposed algorithm uses Gaussian processes to model mapping between the layers, and matrix-variate Gaussian distribution to model correlation between nodes of a given layer. Deep architecture of the proposed algorithm enables capacity estimation using the partial charge-discharge time-series data, in the form of voltage, temperature and current, eliminating need for input feature extraction. The algorithm also estimates statistical correlation between capacity and the elapsed time from the first cycle, which enables end of life prediction. Hidden layer output of the proposed algorithm correlates strongly with the battery internal states, enabling on-board diagnosis of battery degradation mechanisms. The paper demonstrates effectiveness of the proposed algorithm using aging dataset of batteries cycled at different C-rates, temperatures and usage profiles. The proposed algorithm predicts capacity and end of life with coefficient of determinant greater than 0.9 and mean absolute error less than 0.1, demonstrating high prediction accuracy of the proposed approach.