Battery Cell SOH Deviation Analysis for Defect Diagnosis
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Solution Overview
Problem
Existing methods for detecting electrode bridge defects in batteries are inaccurate due to noise in State of Health (SOH) estimation, and they fail to identify defective batteries that develop during use.
Innovation Solution
A battery diagnosis apparatus calculates SOH deviations, first and second difference values, and error bounds for each battery cell to accurately diagnose defects, using a processor to analyze data from sensors and communicate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If module-level SOH comparison is used to detect electrode bridge defects, then detection coverage is improved, but measurement precision deteriorates due to noise in SOH estimation
Solution Approach 1:
The patent segments the battery system into individual battery cells for independent diagnosis. Instead of treating the battery as a module-level unit, the system calculates SOH deviation and error bounds for each individual cell, enabling precise identification of defective cells while filtering out noise through statistical analysis of multiple parameters.
Solution Approach 2:
The patent introduces multiple new parameters (SOH deviation dSOH, first difference values ΔdSOH, second difference values SOHdt, and error bounds) to transform the single SOH value into a multi-dimensional diagnostic framework. This parameter expansion allows the system to distinguish between normal variations and actual defects by analyzing relationships between multiple derived parameters.
2Measurement precision
If individual battery cell diagnosis is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal diagnostic framework that can be applied to any battery cell within a battery management system. The same set of calculations (SOH deviation, difference values, error bounds) is universally applied to all cells, providing a standardized multi-functional approach that simplifies implementation despite the individualized diagnosis.
Solution Approach 2:
The system uses the battery's own operational data (voltage, current, temperature measurements during charging/discharging) to perform self-diagnosis. Each cell's defect status is determined by analyzing its own SOH trajectory and comparing it with statistical thresholds, enabling autonomous cell-level monitoring without requiring additional external diagnostic equipment.
Data Source
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AI summary
battery diagnosis apparatus for diagnosing whether a battery is defective includes a processor and a memory storing a plurality of instructions, in which the instructions cause, when executed by the processor, the battery diagnosis apparatus, to calculate an SOH deviation dSOH between an SOH in a designated cycle and an average SOH in the designated cycle, for each of a plurality of battery cells, calculate first difference values ΔdSOH between SOH deviations dSOH in different cycles for each of the plurality of battery cells, calculate second difference values SOHdt between SOH values in the different cycles for each of the plurality of battery cells, calculate an error bound of each of the plurality of battery cells based on the first difference values and the second difference values, and diagnose whether each of the plurality of battery cells is defective, based on the error bound.