Lithium-Ion Battery Health Estimation From Partial Charging Data
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Solution Overview
Problem
Existing methods for estimating the state of health of lithium-ion batteries are inaccurate due to the need for complete charging or discharging processes, which are not feasible in practical use, and the variability of user charging behaviors, leading to insufficient feature extraction.
Innovation Solution
A method involving data collection during actual vehicle charging, analysis of charging data, extraction of health features from the IC curve, and establishment of a state of health estimation model using a TCN-BiGRU network, incorporating user charging behavior data to predict battery health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete charging or discharging process data are required for health feature extraction, then the estimation accuracy can be improved, but the ease of operation deteriorates due to impracticality in real-world usage
Solution Approach 1:
The patent applies partial action by extracting health features from partial charging processes rather than requiring complete charging or discharging cycles. The method identifies characteristic voltage points and extracts features from incremental capacity curves during arbitrary charging segments, enabling SOH estimation without full cycle data while maintaining acceptable accuracy.
2Device complexity
If a fixed voltage is used to extract health features, then the device complexity is reduced, but the productivity decreases due to insufficient feature extraction frequency
Solution Approach 1:
The patent implements dynamics by making the voltage selection adaptive rather than fixed. The system dynamically identifies characteristic voltage points based on the actual charging curve morphology and user charging behavior patterns. This allows the extraction of health features at multiple voltage points throughout the charging process, increasing feature extraction frequency without significantly increasing system complexity.
Solution Approach 2:
The patent changes the parameter approach by transitioning from a single fixed voltage point to multiple variable voltage points determined by the charging curve characteristics. The method identifies different characteristic voltage points (such as inflection points, plateau regions) and extracts features at each point, thereby increasing the frequency and reliability of SOH estimation.
3Device complexity
If user charging behavior variability is not considered, then the device complexity is reduced, but the measurement precision deteriorates due to inability to capture random charging patterns
Solution Approach 1:
The patent applies universality by creating a charging feature extraction method that works across diverse charging scenarios. The system identifies universal characteristic voltage points and feature extraction patterns that remain valid regardless of user charging behavior variations. This allows the same methodology to handle partial charging, fast charging, and various charging interruptions while maintaining estimation accuracy.
Data Source
AI summary
A method for estimating the state of health of lithium-ion batteries considering user charging behavior is provided. Firstly, data during the cycle charging process of the actual vehicles are collected, and it is analyzed. Then, the charging process data is applied to extract health features. The obtained health features are used to establish a state of health estimation model, and the established model is used to estimate the state of health of the battery. It involves reading the charging data, analyzing the frequency and occurrence of the start charging voltage and stop charging voltage, integrating them into a heat map, and drawing the IC curve of the charging and discharging process. This enables any charging behavior to extract the health features of battery aging, and predicts the health values of the battery, thus completing the estimation of the state of health for lithium-ion batteries.