Storage battery pack performance evaluation method and system based on big data

Through big data analysis and machine learning technology, the charging and discharging data characteristics of the battery under different operating conditions are extracted, and the prediction model is constructed, which solves the accuracy and applicability of battery performance evaluation in the existing technology, and achieves more accurate and reliable performance evaluation and health status prediction.

CN119667484AActive Publication Date: 2025-03-21CHINA YANGTZE POWER
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Patent Information

Application Number
CN202411441635.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-16
Publication Date
2025-03-21
Estimated Expiration
2044-10-16

AI Technical Summary

Technical Problem

The existing battery performance evaluation methods are difficult to adapt to complex working conditions, and based on limited sample data and empirical models, it is difficult to make full use of massive operation monitoring data, resulting in limited accuracy and applicability of evaluation results.

Method used

Using a big data-based method, the charging and discharging data of the battery under different operating conditions is obtained, and the denoising and normalization process is performed in combination with wavelet transformation and gradient descent algorithms, the time domain and frequency domain features are extracted, and the high-dimensional feature vectors are combined to obtain. Then, a prediction model is constructed through manifold learning and a bidirectional gating recurrent unit network, and hyperparameter optimization and health status prediction are combined with automatic machine learning and Bayesian learning algorithms.

Benefits of technology

It improves the accuracy and reliability of battery performance evaluation, can better adapt to complex working conditions, dynamically update model parameters, adapt to the changing trends of battery performance, provide comprehensive performance evaluation results, and provide data support for health management, fault diagnosis and life prediction.

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Abstract

The invention provides a storage battery pack performance evaluation method and system based on big data, and relates to the technical field of storage battery performance evaluation, and the method comprises the steps: obtaining the charging and discharging data of a to-be-evaluated storage battery under different working conditions, carrying out the denoising processing of the charging and discharging data, carrying out the adaptive normalization processing, and obtaining the standard charging and discharging data, equalization processing is carried out, time domain features and frequency domain features are extracted respectively, and high-dimensional feature vectors are obtained through combination; performing dimension reduction operation on the high-dimensional feature vector to obtain low-dimensional embedded representation, generating an initial data set, dividing the initial data set into a training data set and a test data set, constructing an initial prediction model and performing hyper-parameter optimization to obtain a first prediction model; and acquiring real-time charging and discharging data, constructing a real-time feature vector, adding the real-time feature vector into the first prediction model to generate a real-time performance parameter, filtering the real-time performance parameter, constructing a health state prediction model for prediction, and obtaining a comprehensive performance evaluation result.
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