A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model. (1st January 2020)

• Record Type: Journal Article
• Authors: Wang, Shun-Li; Stroe, Daniel-Ioan; Fernandez, Carlos; Xiong, Li-Ying; Fan, Yong-Cun; Cao, Wen
• Is Part Of: Journal of cleaner production. Volume 242(2020)
• Publication Date: 2020-01-01
• Subjects: Data modeling -- Coupling relationship model -- Lithium battery pack -- Multi-parameter optimization -- Power state evaluation; 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.2019.118506
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The power state evaluation plays a decisive influence on the safety implication of the lithium battery packs, and there is no effective online evaluation method so far due to the imbalance phenomenon among the internal connected battery cells, which cannot be abstained by the advancement of the materials and techniques. A novel power state mathematical evaluation method is proposed in this paper by investigating the improved external parameter coupling treatment, in which the mutual relationship description is conducted by the parameter information feature decomposition together with the Bayesian sequential decision algorithm. The complicated power state evaluation model with the coupling relationship decomposition is constructed by investigating the non-convex optimization treatment under complex working conditions for the lithium battery packs. The evidence combination is realized by introducing the information fusion strategy, according to which the multi criteria decision is realized by using the evidence theory. As can be seen from the experimental results, the voltage difference is within 10 mV in both of the first and the second phases, which increases rapidly in the third phase and reaches a maximum of 120 mV. Meanwhile, its power state evaluation accuracy is 95.00% and has a good output voltage tracking effect in the complex working conditions. The power state evaluation can be realized effectively by the proposed model constructing method, which is suitable for the complex battery cell combination structures and environmental influences, protecting the reliable and hierarchical working state monitoring and management of the lithium battery packs. It provides safety protection and energy management basis for the reliable power supply in the cleaner production of the power lithium battery packs. Highlights: A novel power state evaluation method is proposed for the lithium-ion battery packs. The mutual relationship description is conducted by the parameter information feature decomposition. The Bayesian sequential decision is introduced into the iterate calculation process. A complicated power state evaluation model with coupling relationship decomposition is constructed. The non-convex optimization treatment is investigated under complex working conditions.