Lithium Battery State of Health Prediction Using Partial Charging Curves
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Solution Overview
Problem
Current methods for analyzing and predicting the state of health (SOH) of lithium batteries require special equipment and full-charge or full-discharge tests, which can degrade battery capacity and fail to predict future life, limiting their application and accuracy.
Innovation Solution
A method that collects battery data at regular intervals, determines a target area based on voltage and capacity differences, establishes a relationship between cumulative capacity and SOH, and correlates the number of cycles with SOH to predict future battery health without the need for additional testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-charge or full-discharge tests are performed to accurately analyze SOH, then measurement precision is improved, but battery capacity degradation increases and service life decreases
Solution Approach 1:
The patent applies partial action by using only a segment of the charging curve (the target area between inflection points) rather than requiring complete full-charge or full-discharge cycles. This partial charging approach extracts sufficient information for SOH analysis while avoiding the harmful effects of extreme charging states, thus preserving battery life while maintaining measurement accuracy
Solution Approach 2:
The patent performs preliminary identification of inflection points and determination of the target area before conducting SOH analysis. By pre-processing the charging curve to identify the specific voltage-capacity range for analysis, the method enables accurate SOH prediction without requiring complete charge-discharge cycles, thereby reducing battery stress while maintaining precision
2Measurement precision
If special equipment and complete charge-discharge cycles are used for SOH analysis, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent enables the battery management system to perform self-diagnosis by utilizing its own existing charging data and processing capabilities. The system identifies inflection points and calculates SOH using algorithms implemented within the BMS itself, eliminating the need for external specialized testing equipment while maintaining accurate SOH analysis
Solution Approach 2:
The patent makes the charging process serve multiple functions: it both charges the battery and simultaneously collects data for SOH analysis. The same charging infrastructure and sensors used for normal battery operation are utilized to gather the necessary voltage-capacity data, making the system universally applicable without requiring dedicated testing equipment
3Reliability
If full-charge or full-discharge tests are performed to accurately analyze SOH, then reliability of SOH analysis is improved, but productivity decreases due to time consumption
Solution Approach 1:
The patent achieves reliable SOH analysis by focusing on the critical target area of the charging curve between inflection points, rather than requiring complete charge-discharge cycles. This partial analysis approach extracts the essential degradation information from the most informative portion of the charging process, maintaining reliability while significantly reducing the time required
Solution Approach 2:
The patent performs preliminary identification of inflection points and establishes the target area before conducting the full SOH analysis. This pre-processing step enables the system to focus computational resources on the most relevant data segment, improving both the reliability of the analysis and the efficiency by avoiding unnecessary processing of irrelevant charging data
Data Source
AI summary
Method for analyzing and predicting state of health of a lithium battery includes: collecting battery data within a predetermined time interval, where the battery data comprises at least battery operating time, current, and voltage; determining a target area based on the battery data; establishing a relationship between a cumulative capacity of the target area and the state of health of the battery; correlating the number of cycles in the current state of the battery with the state of health of the battery using the cumulative capacity of the target area and determining a relationship between the number of cycles and the state of health of the battery; and predicting the state of health of the battery in the future according to the relationship between the number of cycles and the state of health of the battery. The method is simple and easy to operate, and practical as well.