What are the battery cabinet grouping technologies
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What is battery grouping?
Essentially, battery grouping aims to categorize battery cells according to their diversities in various characteristics. These characteristics mainly comprise static capacity, voltage, internal resistance (Li, 2014) and thermal behavior (Fang et al., 2013). Battery grouping can be achieved via a similarity analysis of any characteristic above.
How can battery grouping be achieved?
Battery grouping can be achieved via clustering techniques based on characteristics like static capacity, internal resistance etc. The dynamic characteristics-based method considers the battery performance during the entire charging-discharging process and has become one of the most promising grouping method.
Why is grouping important for lithium-ion power battery packs?
The service life, safety, and capacity of lithium-ion power battery packs relies heavily on the consistency among battery cells. Grouping is an effective procedure to improve consistency by screening cells with similar performance and assembling them into an identical group.
How does a lithium-ion battery grouping process work?
In a typical lithium-ion battery grouping process, the charging and discharging data are collected by formation cabinets and sent to host computers for temporary storage. Each host computer manages a formation cabinet group and controls the behaviors of all cabinets in the group.
What is distributed battery grouping?
A two-stage distributed battery grouping scheme that splits the original centralized clustering approach into local clustering and global merging is proposed for consistency and efficiency improvement. These two stages are implemented on edge computing devices and cloud data center respectively.
Does a self-organizing map improve battery grouping based on cell temperature and capacity?
In (He et al., 2014, Yun et al., 2019), the self-organizing map (SOM) is adopted for battery grouping based on cell temperature and capacity. The demonstrated grouping experiments prove its validity in reducing the variation of the above two parameters.
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