Data-Driven Online Prediction of Discharge Capacity and End-of-Discharge of Lithium-Ion Batteries

Publication Date

9-1-2024

Document Type

Article

Publication Title

Journal of Computing and Information Science in Engineering

Volume

24

Issue

9

DOI

10.1115/1.4063985

Abstract

Monitoring the health condition as well as predicting the performance of lithium-ion batteries is 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 the 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.

Funding Number

ECCS-2131619

Funding Sponsor

National Science Foundation

Keywords

battery aging, deep learning, discharge capacity, end-of-discharge

Department

Industrial and Systems Engineering

Link to Full Text

Share

COinS