Storage Battery Diagnostic Device Using OCV Model Functions
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Solution Overview
Problem
Current storage battery diagnostic technologies require preliminary information on OCV curves and dV/dQ curves for accurate degradation analysis, which is challenging to obtain, especially for various batteries manufactured by different companies, and often necessitates specialized equipment and expertise.
Innovation Solution
A storage battery diagnostic device that uses time-series data on currents and voltages to generate OCV model functions for positive and negative electrodes, allowing for degradation diagnosis without prior information on these curves, by fitting OCV element functions to the data and estimating model parameters to minimize evaluation functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If preliminary information on OCV curves and dV/dQ curves is obtained through decomposition or reference electrodes, then degradation diagnosis accuracy is improved, but device complexity and measurement difficulty increase
Solution Approach 1:
The patent creates virtual copies of OCV and dV/dQ curves through mathematical modeling and data processing. By constructing equivalent circuit models and using optimization algorithms to fit model parameters to measured voltage-current data, the system generates synthetic OCV and dV/dQ curves that replicate the characteristics of actual battery electrodes without requiring physical decomposition or reference electrodes.
Solution Approach 2:
The patent replaces the mechanical/physical approach of battery decomposition and reference electrode insertion with an electrical/mathematical approach. Instead of physically separating electrodes to measure their individual OCV curves, the system uses electrical measurements (voltage and current) combined with mathematical optimization to extract electrode-specific information, thereby eliminating the need for complex physical modification of the battery.
2Measurement precision
If battery decomposition is performed to acquire electrode-specific data, then degradation analysis accuracy is improved, but ease of operation and accessibility deteriorate
Solution Approach 1:
The patent enables the battery itself to provide the information needed for diagnosis through its normal operational characteristics. By analyzing voltage and current data obtained during standard charging and discharging cycles, the system extracts OCV and dV/dQ curve information without requiring external intervention such as decomposition or specialized measurement equipment. The battery's own operational data serves as the source for diagnosis.
Solution Approach 2:
The patent creates a universal diagnostic method that can be applied to various battery types without requiring type-specific preparation or decomposition procedures. The mathematical modeling and optimization approach works across different battery chemistries and configurations, making the diagnosis accessible and applicable broadly without specialized expertise or equipment for each battery type.
3Measurement precision
If specialized equipment and expertise are used to obtain OCV and dV/dQ curve information, then measurement accuracy is improved, but ease of manufacture and scalability worsen
Solution Approach 1:
The patent transforms the measurement approach from requiring precise physical parameters (electrode potentials measured with reference electrodes) to using easily measurable electrical parameters (terminal voltage and current). By changing the measurement parameters from difficult-to-obtain electrode-specific values to simple terminal measurements, the system achieves accurate curve information extraction while greatly improving ease of manufacture and scalability.
Solution Approach 2:
The patent generates synthetic copies of complex electrode characteristics through mathematical modeling. Instead of directly measuring difficult-to-obtain OCV and dV/dQ curves, the system creates accurate replicas of these curves by fitting model parameters to easily measurable voltage-current data, thereby achieving high measurement precision without specialized equipment.
Data Source
AI summary
Provided is a storage battery diagnostic device including: a positive/negative electrode OCV model function generation unit (106) configured to generate a positive electrode OCV model function and a negative electrode OCV model function for N storage batteries by a sum of OCV element functions; and a storage battery model function parameter group estimation unit (107), which is configured to generate a storage battery model function based on the positive electrode OCV model function and the negative electrode OCV model function, and to generate an evaluation function L indicating an error between the time-series data stored in the data storage unit (104) and time-series data on estimation data calculated by using the storage battery model function, to thereby calculate such an optimal group of estimation parameters of the storage battery model functions for the respective storage batteries as to minimize a value of the evaluation function L.