Abstract
Battery sorting is an important process in the production of lithium battery module and battery pack for electric vehicles (EVs). Accurate battery sorting can ensure good consistency of batteries for grouping. This study investigates the mechanism of inconsistency of battery packs and process of battery sorting on the lithium-ion battery module production line. Combined with the static and dynamic characteristics of lithium-ion batteries, the battery parameters on the production line that can be used as a sorting basis are analyzed, and the parameters of battery mass, volume, resistance, voltage, charge/discharge capacity and impedance characteristics are measured. The data of batteries are processed by the principal component analysis (PCA) method in statistics, and after analysis, the parameters of batteries are obtained. Principal components are used as sorting variables, and the self-organizing map (SOM) neural network is carried out to cluster the batteries. Group experiments are carried out on the separated batteries, and state of charge (SOC) consistency of the batteries is achieved to verify that the sorting algorithm and sorting result is accurate.
Highlights
The initial differences among batteries which lead to inconsistency after charging and discharging are the main reasons for the shortened life and low safety in the use of battery packs
Cell sorting in lithium-ion battery industry is an indispensable process to assure the reliability and safety of cells that are assembled into strings, blocks, modules and packs [3]
In the current lithium-ion power battery pack production line, cell sorting refers to the selection of qualified cells from raw ones according to quantitative criterions in terms of accessible descriptors such as battery resistance, open circuit voltage (OCV), charging/discharging capacity, etc
Summary
The initial differences among batteries which lead to inconsistency after charging and discharging are the main reasons for the shortened life and low safety in the use of battery packs. In the current lithium-ion power battery pack production line, cell sorting refers to the selection of qualified cells from raw ones according to quantitative criterions in terms of accessible descriptors such as battery resistance, open circuit voltage (OCV), charging/discharging capacity, etc. Resistance sorting, voltage sorting and capacity sorting are the main single parameter sorting methods used in battery pack production lines at present [4,5,6,7]. The self-organizing map (SOM) neural network is an unsupervised learning clustering algorithm which realizes high-dimension visualization. It is an artificial neural network developed by simulating the characteristics of human brain to signal. With self-organizing and unsupervised learning, it can be applied to situations where the characteristics of input data are not fully understood
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