Analysis and prediction of the discharge characteristics of the lithium–ion battery based on the Grey system theory. Issue 12 (1st December 2015)

• Record Type: Journal Article
• Authors: Chen, Lin; Tian, Binbin; Lin, Weilong; Ji, Bing; Li, Junzi; Pan, Haihong
• Is Part Of: IET power electronics. Volume 8:Issue 12(2015)
• Page Start: 2361
• Page End: 2369
• Publication Date: 2015-12-01
• Subjects: secondary cells -- grey systems -- battery powered vehicles -- condition monitoring -- prediction theory; discharge characteristics prediction -- lithium–ion battery -- grey system theory -- electric vehicles -- condition monitoring techniques -- discharge cycle -- Grey relation analysis -- SoC -- state of charge -- Grey prediction model -- Grey theory model -- GRA -- EV batteries; Power electronics -- Periodicals; 621.31705
• DOI: 10.1049/iet-pel.2015.0182
• Languages: English
• ISSNs: 1755-4535

Abstract

Abstract : The capacity/state‐of‐charge (SoC) and voltage of lithium–ion batteries are of prime importance in electric vehicles (EVs), so their condition‐monitoring techniques are extensively studied. This study focuses on the application of the grey system theory to the parameters analysing and predicting behaviour during the discharge/charge cycles of the battery. First, Grey relation analysis is applied to study and analyse the relationship between capacity/SoC and various influencing factors. Second, the segment Grey prediction model is proposed in order to test and improve the accuracy of the capacity/SoC prediction. Finally, based on the ageing data from the National Aeronautics and Space Administration Prognostics Data Repository, the effects of different Grey theory models, such as the GM(1, 1), the Verhulst model and the segment Grey prediction model, are investigated. The results show that: (i) the GRA is efficient in figuring out the relationship between the capacity/SoC and various influencing factors; (ii) the segment Grey prediction model is an effective mode of prediction for EV batteries, because its accuracy is more reliable than other two Grey models; and (iii) the segment Grey prediction model is suitable for predicting the capacity/SoC of batteries under various loading conditions.