Battery Life Estimation Using Early-Cycle Slippage Data
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Solution Overview
Problem
Existing methods for estimating battery life are time-consuming and cumbersome, making it difficult to predict battery failure at an early stage.
Innovation Solution
A system and method that collect life estimation data from the manufacturing process, calculate slippage-related data, and use linear regression to predict battery life based on cumulative capacity profiles, allowing for quick and accurate estimation of battery health and potential defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery life is estimated through repeated charging and discharging under actual usage conditions, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by measuring battery performance during the initial charging and discharging cycles (first 0-250 cycles) and using these early-stage data to predict long-term battery life. The system calculates cumulative capacity profiles and slippage values during this preliminary phase, then extrapolates the results to estimate the entire battery lifecycle, avoiding the need to wait for the battery to naturally deteriorate through repeated cycling.
Solution Approach 2:
The patent employs parameter changes by transforming raw charging and discharging data into derived parameters such as cumulative capacity profiles and slippage values. By converting the voltage-capacity data into these transformed parameters, the system can establish correlation relationships that enable rapid prediction of battery life without performing extensive repeated testing.
2Reliability
If battery life is estimated through repeated charging and discharging, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent applies the extraction principle by isolating and analyzing specific key parameters from the charging and discharging data, namely the cumulative capacity profile and slippage value. Instead of processing the entire complex dataset of repeated charging-discharging cycles, the system extracts these critical parameters that capture the essential degradation characteristics, simplifying the analysis while maintaining prediction reliability.
Solution Approach 2:
The patent uses an intermediary approach by introducing correlation relationships as a mediator between the initial charging/discharging data and the final life prediction. The system establishes correlation models that link the calculated slippage values to battery life outcomes, serving as an intermediary step that transforms raw measurement data into reliable predictions without requiring direct complex simulation of entire battery lifecycles.
3Measurement precision
If battery life is estimated through repeated charging and discharging, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent applies preliminary action by completing the essential measurement and calculation work during the initial charging and discharging phase (first 0-250 cycles). By performing these critical measurements early and using them to predict long-term life, the system eliminates the need for extended testing periods, thereby accelerating the productivity of battery development and manufacturing processes while maintaining measurement precision.
Data Source
AI summary
The present disclosure relates to a method of estimating a life of a battery including collecting life estimation data of a battery cell from a manufacturing process device of the battery cell, calculating slippage-related data based on the life estimation data, predicting a life of the battery cell based on the calculated slippage-related data, and estimating a life quality of the battery cell based on the predicted life.