A Data-Driven Method for Predicting Capacity Degradation of Rechargeable Batteries

• Published in: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Vol. 1; pp. 1259 - 1265
• Main Authors: Pajovic, Milutin, Orlik, Philip V., Wada, Toshihiro, Takegami, Tomoki
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: battery prognosis; data-driven method; degradation prediction; rechargeable battery; state of health
• ISSN: 2378-363X
• DOI: 10.1109/INDIN41052.2019.8972129

Rechargeable batteries supply numerous devices with electric power and are critical part in a variety of applications. An accurate monitoring and prediction of capacity degradation is directly related to making timely decision as to when a battery should be replaced, so that power disruption of the system it supplies power to is avoided. We propose a methodology for predicting capacity of a battery over future time horizon. The proposed method is based on training data consisting of occasional measurements, taken under the same conditions, of capacity and charge/discharge voltage/current of a certain number of batteries sharing the same chemistry and manufacturer, that otherwise undergo different usage patterns. In the operational/online stage, capacity degradation over future time horizon of a test battery cell of unknown state of health and previous usage pattern is predicted based on its capacity and voltage/current measurements over one charge/discharge cycle and the training dataset. The experimental validation reveals that the proposed method predicts capacity of a test battery cell over prediction time horizon of few hundred days of battery's operation with relative prediction error below 1%.