Neural Network Nyquist Diagram Battery Capacity Estimation
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Solution Overview
Problem
Current methods for estimating battery capacity are not accurate and efficient, particularly in utilizing alternating current impedance measurement methods.
Innovation Solution
A battery capacity estimation method and device that involves obtaining image data of a Nyquist diagram from AC-IR measurements and inputting it into a pre-trained neural network model to estimate battery capacity, with the neural network model including an input layer, intermediate layer, and output layer to compute and output the capacity estimate value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alternating current impedance measurement method is used to estimate battery capacity, then the measurement process can be completed, but the estimation accuracy is insufficient
Solution Approach 1:
The patent creates image data representations of Nyquist diagrams from impedance measurement data, effectively creating a visual copy of the electrical characteristics. This image data is then processed by neural networks to extract capacity information, transforming the measurement approach from direct numerical analysis to image-based pattern recognition, thereby improving both accuracy and efficiency
Solution Approach 2:
The patent replaces traditional mechanical/mathematical analysis methods with a neural network-based computational system. The neural network automatically processes the Nyquist diagram image data and extracts battery capacity information, substituting manual or algorithmic analysis with intelligent computational processing that achieves higher accuracy and efficiency
2Measurement precision
If traditional battery capacity estimation methods are used, then the process can be completed, but both accuracy and efficiency are insufficient
Solution Approach 1:
The patent pre-trains neural networks using大量 Nyquist diagram image data and corresponding battery capacity labels before actual measurement. This preliminary training phase enables the neural network to quickly and accurately estimate capacity from new measurements without requiring complex real-time calculations, significantly reducing estimation time while maintaining high accuracy
Data Source
AI summary
A battery capacity estimation method includes a first step and a second step. The first step is a step of obtaining image data of a Nyquist diagram drawn by a predetermined method, based on a Nyquist plot obtained by a predetermined AC-IR measurement. The second step is a step of obtaining a battery capacity estimate value of a battery to be measured by inputting the image data of the Nyquist diagram obtained in the first step into an input layer of a pre-trained neural network model.