Power Battery Aging Screening Using Incremental Capacity Curves
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Solution Overview
Problem
Current methods for evaluating power battery aging and screening retirement are time-consuming, costly, and lack precision, especially for lithium ion batteries like LiNCM and LiFePO4, with long test times and poor adaptability.
Innovation Solution
A method involving multi-level screening using appearance, voltage data, and direct current internal resistance, combined with incremental capacity and capacity-voltage curves, utilizing a random forest model for battery categorization and consistency determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement method with voltage data and charging-discharging tests is used, then measurement precision is improved, but loss of time and device complexity increase
Solution Approach 1:
The screening process is divided into three levels: first-level screening using appearance and voltage data, second-level screening using derived capacity-voltage curve indices, and third-level screening using direct current internal resistance. This segmentation allows progressive filtering of batteries, reducing the time spent on detailed testing by eliminating obviously unsuitable batteries earlier in the process.
Solution Approach 2:
The method performs preliminary screening actions before comprehensive testing. By first evaluating appearance features and voltage data, then deriving capacity-voltage curve indices, the system identifies and separates batteries that can be discarded or need detailed testing, thereby reducing the overall testing time for the entire battery population.
2Measurement precision
If multi-parameter feature acquisition method is used, then measurement precision is improved, but loss of time and device complexity increase
Solution Approach 1:
The complex multi-parameter screening is segmented into three manageable levels with increasing detail. The first level uses simple appearance and voltage checks, the second level uses derived indices from capacity-voltage curves, and the third level uses internal resistance measurements. This segmentation reduces the complexity burden at any single stage while maintaining overall screening accuracy.
Solution Approach 2:
The method applies partial action by not requiring all possible battery parameters to be measured for every battery. Instead, it measures a subset of parameters at each screening level, with the understanding that not all batteries will undergo all levels of testing, thereby reducing the overall complexity and time required.
3Measurement precision
If complete charging process is required for capacity index acquisition, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The method extracts key information from the capacity-voltage curve without requiring complete charging processes. By deriving indices from the curve's characteristics (such as slope changes or inflection points), the system obtains sufficient capacity information faster than waiting for complete charge-discharge cycles, thereby reducing testing time while maintaining precision.
Solution Approach 2:
The method uses partial action by performing charging tests only to the extent necessary to extract meaningful indices from the capacity-voltage curve, rather than completing full charging cycles. This partial testing approach provides sufficient data for aging assessment while significantly reducing the time required.
4Measurement precision
If temperature parameter acquisition is included, then measurement precision is improved, but loss of energy and device complexity increase
Solution Approach 1:
Temperature measurement is integrated into the existing three-level screening framework as part of the second or third level, rather than being a separate comprehensive testing phase. This segmentation allows temperature data to be collected only when and where it is most relevant, reducing the overall energy consumption of the screening process while maintaining evaluation accuracy.
Data Source
AI summary
A method and system for evaluating power battery aging state and screening retirement, wherein the method includes: performing first-level screening according to appearance and voltage data of a power battery; obtaining charging test data in a set time of the power battery subjected to the first-level screening, performing derivation and secondary derivation based on a capacity-voltage curve of the battery, respectively extracting a first index and a second index of a set peak of the derivated curve, and respectively using the first index and the second index to determine a battery category and consistency, thereby realizing second-level screening; and performing third-level screening on the battery subjected to the second-level screening based on a direct current internal resistance of the battery. By using the method and system, the detection time can be effectively reduced, the screening test cost can be reduced and the evaluation and screening precision can be improved.