Model-Based Health Diagnosis for Lithium-Ion Battery Pack in Space Applications

• Published in: IEEE transactions on industrial electronics (1982) Vol. 68; no. 12; pp. 12375 - 12384
• Main Authors: Song, Yuchen, Peng, Yu, Liu, Datong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Degradation; Diagnosis; Discharge; Discharge process modeling; Discharges (electric); Electronic countermeasures; Equivalent circuits; Estimation; improved equivalent circuit model (ECM); Lithium; Lithium-ion batteries; lithium-ion battery pack; Lookup tables; model-based health diagnosis; Rechargeable batteries; Service life; Space applications; space operating conditions; Space vehicles; Spacecraft; Spacecraft reliability
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2020.3045745

Lithium-ion battery packs are critical to ensure the operational reliability and service life of spacecraft. The battery pack health diagnosis is meaningful for reasonable mission plan and proper control actions of spacecraft. However, the characters of space application scenarios include periodic charge and discharge, nearly constant discharge current, low electric current rate, and fixed depth of discharge, which bring challenges to battery pack state-of-health estimation. Considering these characters, this article proposes a health diagnosis approach for lithium-ion battery pack based on an improved equivalent circuit model. First, an empirical model is introduced into the standard first-order equivalent circuit model to improve the accuracy of discharge process modeling under constant current. Furthermore, a parameter lookup table is developed to simulate the complete discharge based on the in-orbit partial discharge. Finally, the battery pack complete discharge process is virtually conducted to realize battery pack capacity estimation. The proposed method just satisfies the special operating conditions in space applications. The proposed method is validated based on a real tested lithium-ion battery pack. And the results indicate that the proposed method can realize the high-accurate health diagnosis for both battery pack and its inner cells under space operating conditions, which illustrate the adaptability and applicability of the proposed method in space applications.