LFP Battery SOC-OCV Curve Correction for Mid-Range Charge Estimation
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Solution Overview
Problem
Existing methods for estimating the state of charge (SOC) of lithium iron phosphate batteries, particularly in the 30%-80% range, suffer from low accuracy, leading to potential vehicle breakdowns or safety risks due to falsely high or low SOC readings.
Innovation Solution
Determine correction points on the SOC-OCV curve based on temperature and current combinations, apply filtering and noise reduction techniques, and update the SOC-OCV curve to improve estimation precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SOC estimation methods are used for lithium iron phosphate batteries, then the estimation process is simple, but the estimation accuracy is low especially in the 30%-80% SOC range
Solution Approach 1:
The patent segments the SOC estimation process into multiple distinct modules: OCV-SOC curve generation module, correction point determination module, SOC correction module, and filtering module. Each module handles a specific aspect of the estimation process, allowing for improved accuracy through specialized processing while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent performs preliminary actions by pre-generating OCV-SOC curves under different temperature and current conditions, and pre-identifying correction points on these curves before actual SOC estimation is needed. This preparation work enables faster and more accurate real-time SOC estimation without requiring complex real-time calculations.
2Measurement precision
If the SOC-OCV curve is corrected frequently to improve accuracy, then the estimation precision improves, but the computational burden and system complexity increases
Solution Approach 1:
The patent applies partial correction action by selectively correcting the SOC-OCV curve only at specific correction points rather than continuously or uniformly across the entire curve. This approach improves accuracy at critical regions while minimizing unnecessary computational effort in other regions.
Solution Approach 2:
The system uses previously stored OCV-SOC curve data and correction point information to perform self-correction without requiring extensive real-time computational resources. The filtering module also uses historical data to smooth corrections, reducing the need for frequent high-computation operations.
3Measurement precision
If filtering and noise reduction are applied to current and voltage data, then the data accuracy improves, but the processing complexity increases
Solution Approach 1:
The filtering module uses feedback mechanisms where filtered current and voltage data are continuously compared with raw data, and the filtering parameters are adjusted based on the observed noise characteristics. This adaptive feedback approach improves data accuracy while avoiding excessive complexity by using simple comparison and adjustment logic.
Data Source
AI summary
A method and apparatus for estimating a state of charge of a lithium iron phosphate battery are described. The implementation solution of the method for estimating a state of charge of a lithium iron phosphate battery is: acquiring the temperature of the current time period and an SOC-OCV curve under the current of the current time period; determining, on the basis of the state of charge of the SOC-OCV curve, a correction point of the SOC-OCV curve; and correcting, on the basis of a combination of at least one temperature and at least one current, the SOC-OCV curve at the correction point, and updating the SOC-OCV curve.