Battery health prognosis with gated recurrent unit neural networks and hidden Markov model considering uncertainty quantification

Abstract

• Battery thermoelectric characteristics in the aging process are depicted by multi-features. • The GRU network is trained to fit the global downward trend. • An HMM is developed to fit the local fluctuations for quantifying the uncertainty. • We conduct experiments on two datasets with fixed and random working conditions. With the widespread use of lithium-ion batteries in various fields, battery failures become the most critical concerns that may lead to enormous economic losses and even serious safety issues. The prognostics and health management of lithium-ion batteries helps to ensure reliable and safe battery operations. Existing studies on the state of health of batteries mainly focus on improving and refining prediction models, while the emerging technologies that address uncertainty issues in the battery degradation process are also receiving more and more attention. In this paper, we propose a new state of health prediction method by using the gated recurrent unit neural networks and the hidden Markov model with considering uncertainty quantification. According to the empirical mode decomposition, the battery capacity is decomposed into the global downward trend and the local fluctuations. We train gated recurrent unit neural networks to fit the long-term global downward trend without gradient vanishing, and a hidden Markov model to fit the local fluctuations for quantifying the uncertainty introduced by the capacity recovery phenomenon in battery degradation. Finally, numerical experiments are conducted on two famous datasets, the experimental results demonstrate that the proposed method outperforms on the accuracy and reliability for battery state of health prediction.