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Lithium-ion batteries typically exhibit a transition to a more rapid capacity fade trend when subjected to extended charge-discharge cycles and storage conditions. The identification of the knee point can be valuable to identify the more severe degradation trend, and to provide guidance when scheduling battery replacements and planning secondary uses of the battery. However, a concise and repeatable determination of a knee point has not been documented. This paper provides a definition of the knee point which can be used as a degradation metric, and develops an algorithm to identify it. The algorithm is implemented on various data cases, and the results indicate that the approach provides repeatable knee point identification.

Abstract:As a critical subsystem in electric vehicles and smart grids, a battery energy storage system plays an essential role in enhancement of reliable operation and system performance. In such applications, a battery energy storage system is required to provide high energy utilization efficiency, as well as reliability. However, capacity inconsistency of batteries affects energy utilization efficiency dramatically; and the situation becomes more severe after hundreds of cycles because battery capacities change randomly due to non-uniform aging. Capacity mismatch can be solved by decomposing a cluster of batteries in series into several low voltage battery packs. This paper introduces a new analysis method to optimize energy utilization efficiency by finding the best number of batteries in a pack, based on capacity distribution, order statistics, central limit theorem, and converter efficiency. Considering both battery energy utilization and power electronics efficiency, it establishes that there is a maximum energy utilization efficiency under a given capacity distribution among a certain number of batteries, which provides a basic analysis for system-level optimization of a battery system throughout its life cycle. Quantitative analysis results based on aging data are illustrated, and a prototype of flexible energy storage systems is built to verify this analysis.

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Based on the actual operating characteristics of series and parallel batteries, the influences of different pack connection methods on the power ability have been analyzed. Meanwhile, the uneven temperature distribution and imbalanced current phenomenon were described. The results of series cycling tests and parallel cycling tests were studied, which indicate the degradation reasons and fading paths of battery pack. Identification and prediction of operating conditions for different connection methods will significantly extend the lifespan of battery pack. The general control principles obtained from evaluations on the influences of series and parallel components can be used to prolong the cycle life and further optimize the grouping management.