Storage Battery Diagnosis Using Point Set Registration
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Solution Overview
Problem
Existing storage battery diagnosis methods face challenges in accurately determining the correspondence between feature points in reference data and acquired data due to changes in feature point positions and heights caused by degradation, measurement errors, and limited charging data, leading to inaccurate degradation diagnosis.
Innovation Solution
A storage battery diagnosis device that generates data point sequences using current and voltage data, performs point set registration between reference and acquired data sequences, and estimates degradation parameters based on the registration results, utilizing techniques like iterative closest point (ICP) for accurate feature point matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If derivative curve analysis method is used for storage battery diagnosis, then degradation diagnosis can be performed non-destructively, but it becomes difficult to accurately determine the correspondence relationship between feature points of reference data and acquired data
Solution Approach 1:
The patent introduces an iterative closest point (ICP) algorithm as an intermediary method to automatically match feature points between reference data and acquired data. The ICP algorithm iteratively finds corresponding points by minimizing the distance between point sets, serving as a mediator that resolves the difficulty of manual feature point correspondence determination while maintaining diagnostic accuracy
Solution Approach 2:
The patent replaces the manual/mechanical process of feature point identification and correspondence determination with an automated computational algorithm. The ICP algorithm automatically identifies and matches feature points through mathematical optimization, substituting the difficult manual measurement process with an automated system that handles the complexity of feature point correspondence
2Loss of information
If feature point positions and heights are used for degradation diagnosis, then degradation information can be obtained, but measurement errors and limited charging data reduce the accuracy of feature point matching
Solution Approach 1:
The ICP algorithm employs an iterative feedback mechanism where each iteration uses the results of the previous iteration to improve feature point matching. The algorithm continuously refines the correspondence between feature points by minimizing the distance between point sets, using feedback from each iteration to progressively reduce matching errors despite measurement noise and limited data
Solution Approach 2:
The patent performs preliminary processing of the derivative curves to identify and extract feature points before attempting correspondence determination. By pre-processing the data to extract meaningful feature points and their characteristics, the system prepares the data in advance for more accurate matching, reducing the impact of measurement errors and limited charging data
Data Source
AI summary
The storage battery diagnosis device includes: a data point sequence generation unit which generates, on the basis of a current of a storage battery detected by a current detection device and a voltage detected by a voltage detection device, a data point sequence including a Zth-order derivative/integral curve expressed with a Zth-order derivative/integral voltage or a Zth-order derivative/integral capacity where Z represents a real number; a reference data provision unit which provides, as a reference, a reference data point sequence of the storage battery or an electrode; a point set registration unit which performs point set registration between the reference data point sequence and the data point sequence generated by the data point sequence generation unit; and a diagnosis unit which estimates a parameter indicating a state of degradation of the storage battery or the electrode on the basis of a result from the point set registration unit.