Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries

This paper proposes a novel, informed deep-learning-based prognostics framework for on-board state of health and remaining useful life estimations of lithium-ion batteries, which are critical components for strategizing energy and power used in electric vehicles. The framework comprises three phases. First, reliable and online accessible impedance-related features are collected from discharge curves. Second, these features are inputted into the proposed knowledge-infused recurrent neural network, a hybrid model that combines an empirical model with a deep neural network. Third, Monte Carlo dropout, a deep learning method for obtaining a probabilistic prediction of a neural network, is addressed to secure robustness in estimating the state of health and remaining useful life. Layer-wise relevance propagation, a deep learning technique for tracking the evolution of feature importance and offering scientific reasoning of the output, confirms that impedance-related features significantly contribute to the estimation accuracy compared to other features investigated in previous studies. Moreover, the hybrid model improves the estimation accuracy and robustness, whereas Monte Carlo dropout ensures robustness and reliability. Specifically, the estimation results for the public degradation data reveal that the proposed model can output significantly more accurate state of health and remaining useful life estimations than the baseline deep neural networks. The findings of this study provide insight into the explicable and uncertainty-based pipeline of deep neural networks with respect to battery health monitoring, which are highly recommendable features for decision-making and corrective planning of power and energy used in lithium-ion battery cells and packs.