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 . 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 . 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.
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