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Abstract Monitoring the health condition as well as predicting the performance of Lithium-ion batteries are crucial to the reliability and safety of electrical systems such as electric vehicles. However, estimating the discharge capacity and end-of-discharge (EOD) of a battery in real-time remains a challenge. Few works have been reported on the relationship between the capacity degradation of a battery and EOD. We introduce a new data-driven method that combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) models to predict the discharge capacity and the EOD using online condition monitoring data. The CNN model extracts long-term correlations among voltage, current, and temperature measurements and then estimates the discharge capacity. The BiLSTM model extracts short-term dependencies in condition monitoring data and predicts the EOD for each discharge cycle while utilizing the capacity predicted by CNN as an additional input. By considering the discharge capacity, the BiLSTM model is able to use the long-term health condition of a battery to improve the prediction accuracy of its short-term performance. We demonstrated that the proposed method can achieve online discharge capacity estimation and EOD prediction efficiently and accurately. 

Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful life (RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions.