An insight into the errors and uncertainty of the lithium-ion battery characterisation experiments. (August 2019)

• Record Type: Journal Article
• Authors: Taylor, James; Barai, Anup; Ashwin, T.R.; Guo, Yue; Amor-Segan, Mark; Marco, James
• Is Part Of: Journal of energy storage. Volume 24(2019)
• Publication Date: 2019-08
• Subjects: Battery testing -- Experimental error -- Standard error -- Test setup; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2019.100761
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: Errors need to be incorporated or at least their long-term implications understood. A rigorous analysis of source of error in battery testing results presented. Environmental errors can be identified and treated beforehand. Careful evaluation of process and test data is required for procedural error. Understanding sources of errors can result in significant improvements. Abstract: Errors and uncertainty within the experimental results have long-term implications in lithium-ion battery research. Experimental directly feed into the development of different battery models, thus having a direct impact on the accuracy of the models, which are commonly employed to forecast short to long term battery performance. The estimations made by such models underpin the design of key functions within the BMS, such as state of charge and state of health estimation. Therefore, erroneous experimental results could evolve into a much larger issue such as the early retirement of a battery pack from the end-use application. For original equipment manufacturers (OEM), such as automotive OEMs this may have a significant impact, e.g. high warranty returns and damage to the brand. Although occasionally reported in published results, currently, little research exists within the literature to systematically define the error and uncertainty of battery experimental results. This article focuses on the fundamental sources of error and uncertainty from experimental setup and procedure and suggests control measures to remove or minimize the contributions from the sources identified. Our research shows that by implementing the control measures proposed, the error and uncertainty can be reduced to around 0.6%, from the figure of around 4.0%.