Abstract
We describe an approach to estimate state-of-charge and faded capacity of cobalt-based lithium-ion cell based on timedomain analysis of a short-term transient. This approach requires a relatively short-duration test and is suitable for repurposing cells for less demanding applications. The successful estimation requires previous characterization of the cells for the given family because lithium ion chemistries differ significantly. Two algorithms were considered for estimation of unknown state-of-charge and capacity: Bayesian inference and boosted regression trees. The achieved accuracy was 95 % of capacity estimations; estimations were within 2 % of the nominal cell capacity from the true value.
Highlights
Lithium-ion (Li-ion) batteries in high-end applications, such as communications systems, electric vehicles, and plug-in hybrid electric vehicles, are typically replaced at an early point in their lives, while they still have considerable capacity left
prognostics health monitoring (PHM) systems are concerned with the battery system as a whole and they often do not monitor individual cells
Because the time domain approach yields a more accurate model, within our experimental and analytical setting, parameters extracted from the time-domain analysis are used for subsequent estimations of the hidden parameters
Summary
Lithium-ion (Li-ion) batteries in high-end applications, such as communications systems, electric vehicles, and plug-in hybrid electric vehicles, are typically replaced at an early point in their lives, while they still have considerable capacity left. This percentage is ∼80 % of new because, by design , in the demanding applications, the remaining capacity determines an important system parameter, with low degradation tolerance. In order to make sustainable decisions about pack maintenance, methods are needed to understand the relative health of each cell In both applications (reuse and maintenance), it is crucial to be able to efficiently assess Li-ion state-of-charge SOC and remaining capacity Qc. While the most reliable estimation is obtained by cycling a cell from its fully charged state to its fully discharged state, this process is expensive and timeconsuming.
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