Laboratory testing of coin cells, batteries, battery packs and modules is essential in development of durable, reliable and safe battery systems. Battery testing hardware and software have improved significantly over the past decade, but the effectiveness of the laboratory test results from advanced battery testing equipment depends on its accuracy and precision. Battery testing hardware is capable of measuring only primary parameters such as current, voltage and time during testing. Hence any uncertainty or error in measuring these primary parameters will result in loss of accuracy and precision in subsequent analyses and erroneous prediction of battery performance [1, 2].

Analysis of battery systems goes beyond the scope of the primary parameters, however, and depends largely on the derived critical secondary performance parameters such as capacity, energy, power, and Coulombic efficiency. Moreover, for advanced failure mode analysis diagnostic and prognostic tools such as differential capacity and state of charge estimation are used. The battery degradation mechanisms and failure modes can be quantified from differential capacity analysis by evaluating loss of active material, loss of lithium inventory, and change in reaction kinetics [3]. State of charge (SOC) is another critical parameter that defines the performance state of the battery. Uncertainty in SOC estimation limits battery usage during operation [4]. Combined SOC and differential capacity analyses depend on the calculation of parameters such as capacity which in turn depend on accurate and precise primary parameter measurements.

This work addresses an approach to estimate uncertainty in primary measured parameters. Equations to identify uncertainty in derived complex parameters will be discussed. A quantitative analysis of uncertainty in critical analysis and performance parameters will be presented. The results show how errors propagate from the primary measurements to the more advanced diagnostic and prognostic methods as well as the subsequent effects on performance analysis of battery systems.

References:

[1] John L. Morrison, Gary L. Hunt, Donna J. Marts, and Chester G. Motloch, “Uncertainty Study INEEL EST Laboratory Battery Testing Systems, Vol 2: Applications of Results to INEEL Testers,” INEL/EXT-01-00505, Idaho National Laboratory (2003)

[2] Barry N. Taylor and Chris E. Kuyatt, “Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results,” NIST Technical Note, 1297 (1994)

[3] Matthieu Dubarry, Arnaud Devie, and Bor Yann Liaw, “The Value of Battery Diagnostics and Prognostics,” J. Energy Power Sources, 1, 242 (2014).

Zhe Li, Jun Huang, Bor Yann Liaw and Jianbo Zhang, “On State-of-Charge Determination for Lithium-ion Batteries,” J Power Sources, 348, 281, (2017)