Battery SOC Estimation Using OCV-Range Algorithm Switching
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Solution Overview
Problem
Conventional SOC estimation algorithms for lithium-excess manganese-rich oxide batteries are inaccurate due to changes in the SOC-OCV relationship with degradation, making precise state of charge estimation difficult.
Innovation Solution
An electronic apparatus and method that identify the appropriate estimation algorithm based on OCV ranges, using a first and second SOC-OCV relationship for OCV ranges above and below a specified value (e.g., 3.2 V), employing Extended Kalman Filter operations to estimate SOC accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single SOC estimation algorithm is used for lithium-excess manganese-rich oxide batteries, then the device complexity is reduced, but the measurement precision of SOC estimation deteriorates due to changes in the SOC-OCV relationship with degradation
Solution Approach 1:
The patent divides the SOC estimation system into multiple segments by creating separate estimation algorithms for different OCV ranges (first OCV range and second OCV range). Each algorithm is optimized for specific degradation stages, allowing accurate SOC estimation across the entire battery lifecycle without requiring a single complex algorithm to handle all conditions.
Solution Approach 2:
The patent changes the parameter of OCV range thresholds to switch between different estimation algorithms. By monitoring OCV values and comparing them against predefined thresholds, the system dynamically selects the appropriate estimation algorithm, adapting to battery degradation without increasing overall system complexity.
2Measurement precision
If multiple estimation algorithms are stored for different OCV ranges, then the SOC estimation accuracy is improved, but the memory requirement and algorithm selection complexity increase
Solution Approach 1:
The patent segments the estimation algorithms into distinct groups corresponding to different OCV ranges. Each segment contains estimation parameters optimized for specific battery degradation stages, allowing the system to store multiple algorithms in an organized manner that facilitates efficient retrieval and reduces redundant data storage.
Solution Approach 2:
The patent performs preliminary classification of estimation algorithms based on OCV ranges before actual SOC estimation. By pre-organizing algorithms into first and second groups with defined OCV thresholds, the system eliminates the need for complex real-time analysis when selecting algorithms, reducing both memory overhead and processing complexity.
3Measurement precision
If the SOC-OCV relationship is assumed to be constant, then the estimation algorithm simplicity is maintained, but the SOC estimation accuracy deteriorates due to battery degradation
Solution Approach 1:
The patent introduces dynamics into the SOC estimation system by making the SOC-OCV relationship adaptive to battery degradation. Instead of using a static relationship, the system dynamically selects estimation algorithms based on current OCV ranges, allowing the SOC-OCV relationship to evolve with battery aging while maintaining algorithmic simplicity through structured selection criteria.
Solution Approach 2:
The patent changes the estimation algorithm parameters based on OCV range detection. By monitoring OCV values and switching between different estimation models (first estimation algorithm for first OCV range, second estimation algorithm for second OCV range), the system adapts to degradation without requiring a completely complex dynamic model.
Data Source
AI summary
An electronic apparatus includes: a memory storing a plurality of estimation algorithms for estimating a state of charge (SOC) of a battery cell; and a processor operatively coupled to the memory. The processor acquires open circuit voltage (OCV) data of the battery cell, based on the acquired OCV data of the battery cell, identifies an estimation algorithm from the plurality of estimation algorithms stored in the memory, and based on the identified estimation algorithm, estimates the SOC of the battery cell.