Lithium-ion battery performance degradation evaluation in dynamic operating conditions based on a digital twin model

• Published in: Microelectronics and reliability Vol. 114; p. 113857
• Main Authors: Qu, X., Song, Y., Liu, D., Cui, X., Peng, Y.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2020
• DOI: 10.1016/j.microrel.2020.113857
• ISSN: 0026-2714

The performance of lithium-ion batteries degrades over time. Evaluating the performance degradation for lithium-ion batteries is essential to ensure the operational reliability and reduces the risk of host-system downtime. The battery capacity that is obtained by completely charging and discharging a battery cell, directly reflects the performance of a lithium-ion battery. But in practical applications, the battery is dynamically charged and discharged. This makes it difficult to measure the actual capacity and further evaluate battery performance degradation to ensure the battery operating safety. To address this challenging issue, this paper proposes a performance degradation evaluation model by estimating the battery actual capacity in dynamic operating conditions. A health indicator (HI) is extracted from the measurable parameters to reflect the battery performance degradation. A battery digital twin model that describes the relationship between the cell voltage and the cell state-of-charge (SOC) are modelled by the long short-term memory (LSTM) algorithm, which takes the HI as a temporal measurement. The battery actual capacity can be obtained by virtually completely discharging this digital twin model. The experimental results illustrate the potential of the proposed method applying in dynamic operating conditions. •A deep learning-based lithium-ion battery digital twin model is proposed.•The battery capacity test can be validated virtually in this digital twin model.•Battery degradation is evaluated based on partially discharge process.