Golf cart battery capacity prediction method, device, equipment and storage medium

By combining backpropagation neural network, generalized derivative method and bidirectional long and short-term memory network, combined with metabolic gray model and elastic weight merging framework, an efficient golf cart battery capacity prediction model was constructed, solving the problems of low accuracy and poor adaptability in traditional methods, and achieving more accurate and flexible battery capacity prediction.

CN119247151BActive Publication Date: 2025-03-28SHENZHEN GRENERGY TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202411765232.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-04
Publication Date
2025-03-28
Estimated Expiration
2044-12-04

AI Technical Summary

Technical Problem

Traditional golf cart battery capacity prediction methods are difficult to accurately capture the nonlinear characteristics and long-term dependencies of battery capacity degradation, resulting in low prediction accuracy and inability to effectively deal with battery capacity changes and data noise under different operating conditions.

Method used

The generalized derivative method is used to construct the backpropagation neural network to generate an incremental capacity curve, extract three-dimensional feature vectors, and build an initial capacity prediction model through a bidirectional long and short-term memory network. Then, the initial model is stacked with the metabolic gray model, and the model parameters are dynamically adjusted through the elastic weighting merging framework and the two-layer optimization algorithm to build a target capacity prediction model.

Benefits of technology

It improves the accuracy of battery capacity prediction, enhances the model's learning ability of timing characteristics, improves the adaptability and robustness to different usage conditions, and realizes the full process automation from historical data to real-time capacity prediction.

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Abstract

The present invention relates to the technical field of battery capacity prediction, and discloses a method, device, equipment and storage medium for predicting the battery capacity of a golf cart, wherein the method comprises: collecting and preprocessing historical charge and discharge data of a golf cart battery to obtain a time series feature data set; generating an incremental capacity curve, and inputting it into a bidirectional long short-term memory network, and obtaining an initial capacity prediction model through training; stacking and integrating the initial capacity prediction model as a first-layer model and a metabolic gray model as a second-layer model to obtain a hybrid prediction model; dynamically adjusting model parameters through a double-layer optimization algorithm to obtain a target capacity prediction model; inputting real-time battery operation data into the target capacity prediction model to perform capacity prediction and health status assessment, and outputting a capacity decay trend graph and a remaining service life estimation value, wherein the method improves the accuracy of golf cart battery capacity prediction.
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