Battery SOH Prediction via Capacity Range Segmentation
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Solution Overview
Problem
Conventional methods for predicting the State of Health (SOH) of Energy Storage Systems (ESS) require extensive data collection over a long period, making it challenging to quickly and accurately determine SOH, which hinders the development and market availability of ESS.
Innovation Solution
The method involves virtually dividing the ESS capacity into multiple ranges, measuring charging/discharging cycle data for each range, and using this data to predict the overall SOH through summing, combination sum, or multiplicative probability methods, thereby reducing the time required for SOH prediction while maintaining reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional SOH prediction methods are used requiring 4000+ charging/discharging cycles, then prediction reliability is maintained, but development time exceeds 30 months
Solution Approach 1:
The patent divides the battery capacity range into multiple sections (e.g., 0-30%, 30-60%, 60-90%, 90-100%) and performs charging/discharging experiments only within each section rather than completing full 4000+ cycle tests across the entire capacity range. This segmentation reduces the number of required cycles while maintaining prediction accuracy through section-specific capacity retention rate measurements.
2Measurement precision
If full capacity charging/discharging experiments are performed, then accurate SOH data is obtained, but productivity of ESS development is reduced
Solution Approach 1:
The patent applies partial action by performing charging/discharging experiments only for specific capacity ranges (sections) rather than complete full-capacity cycles. Each section is tested independently with appropriate charge/discharge cutoffs, obtaining sufficient data for SOH prediction without the time cost of complete cycles, thus improving development productivity while maintaining measurement precision.
3Loss of time
If extrapolation methods are used for SOH prediction, then development time is reduced, but prediction reliability deteriorates due to large errors
Solution Approach 1:
The patent creates section-specific capacity retention rate data as representative copies of full-cycle behavior. By measuring capacity retention in each capacity section (e.g., 0-30%, 30-60%) and combining these section-specific measurements, the method produces reliable SOH predictions without requiring actual full-capacity cycling, thus avoiding extrapolation errors while reducing time.
Data Source
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AI summary
An ESS SOH prediction method includes: a capacity virtual division step of virtually dividing a capacity of a measurement target ESS that is to predict an SOH into two or more; each capacity range-dependent charging/discharging cycle data measurement step of measuring charging/discharging cycle data of each capacity range divided in the capacity virtual division step; and a measurement target ESS SOH prediction step of predicting an SOH of a measurement target ESS based on charging/discharging cycle data of each actual ESS measured in the each capacity range-dependent charging/discharging cycle data measurement step.