On-line life cycle health assessment for lithium-ion battery in electric vehicles. (20th October 2018)

• Record Type: Journal Article
• Authors: Liu, Datong; Song, Yuchen; Li, Lyu; Liao, Haitao; Peng, Yu
• Is Part Of: Journal of cleaner production. Volume 199(2018)
• Page Start: 1050
• Page End: 1065
• Publication Date: 2018-10-20
• Subjects: capacity estimation -- On-line self-adaptive degradation feature -- Lithium-ion battery -- Electric vehicles; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2018.06.182
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Lithium-ion battery is a critical part in various industrial applications. In practice, the performance of such batteries degrades over time. To maintain the battery performance and ensure their reliability, it is important to implement on-line life cycle health state assessment in a battery management system. However, two big challenges in on-line battery actual capacity estimation must be overcome. The first one is the on-line extraction of measurable degradation features. The other one is the on-line mapping from the degradation feature space to the battery capacity space. This paper proposes a self-adaptive life-cycle health state assessment method based on the on-line measurable parameters of lithium-ion battery. Ten different degradation features are extracted from the voltage, electric current and critical time during operation. These degradation features are fused to achieve a higher adaptability to complex operating conditions. The lithium-ion battery health state is assessed with a mapping model that links the feature space to the capacity space. The model is trained by the least squares support vector machine method for less computational complexity. The experimental results based on the real battery testing data show that the correlation between the degradation feature and the battery capacity is higher than 0.7 and the mean error of capacity estimation is less than 0.05. For the dynamic operation conditions, the mean error of capacity estimation is less than 11 mAh. This study illustrates the adaptability and applicability of the proposed on-line life-cycle health state assessment approach in various electric vehicle applications.