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To access ground truth degradation information, we simulatedcharge and discharge cycles of automotive lithium ion batteriesin their healthy and degrading states and used this informationto determine performance of an autoencoder-basedanomaly detector. The simulated degradation mechanism wasan abrupt increase in the battery’s rate of time-dependent capacityfade. The neural network topology was based on onedimensionalconvolutional layers. The decision-support system,based on the sequential probability ratio test, interpretedthe anomaly generated by the autoencoder. Detection timeand time to failure were the metrics used for performanceevaluation. Anomaly detection was evaluated on five differentsimulated progressions of damage to examine the effectsof driving profile randomness on performance of the anomalydetector.

The economic value of high-capacity battery systems, being used in a wide variety of automotive and energy storage applications, is strongly affected by the duration of their service lifetime. Because many battery systems now feature a very large number of individual cells, it is necessary to understand how cell-to-cell interactions can affect durability, and how to best replace poorly performing cells to extend the lifetime of the entire battery pack. This paper first examines the baseline results of aging individual cells, then aging of cells in a representative 3S3P battery pack, and compares them to the results of repaired packs. The baseline results indicate nearly the same rate of capacity fade for single cells and those aged in a pack; however, the capacity variation due to a few degrees changes in room temperature (≃±3 ∘ C) is significant (≃±1.5% of capacity of new cell) compared to the percent change of capacity over the battery life cycle in primary applications (≃20–30%). The cell replacement strategies investigation considers two scenarios: early life failure, where one cell in a pack fails prematurely, and building a pack from used cells for less demanding applications. Early life failure replacement found that, despite mismatches in impedance and capacity, a new cell can perform adequately within a pack of moderately aged cells. The second scenario for reuse of lithium ion battery packs examines the problem of assembling a pack for less-demanding applications from a set of aged cells, which exhibit more variation in capacity and impedance than their new counterparts. The cells used in the aging comparison part of the study were deeply discharged, recovered, assembled in a new pack, and cycled. We discuss the criteria for selecting the aged cells for building a secondary pack and compare the performance and coulombic efficiency of the secondary pack to the pack built from new cells and the repaired pack. The pack that employed aged cells performed well, but its efficiency was reduced.

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.

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