Lead-acid Battery Performance Prediction Model Based on Meta Learning and Gated Networks
Authors: 
Yi Lu, Kai Ma, Tingxin Ren, Wei Liu, Qi Wang, Rui WangAuthors Info & Claims
Yi Lu, Kai Ma, Tingxin Ren, Wei Liu, Qi Wang, Rui WangAuthors Info & ClaimsPages 92 - 98
Abstract
Aiming at the problems of low prediction accuracy and poor generalization ability of lead-acid battery performance prediction model in substation, this paper proposes a lead-acid battery performance prediction model based on meta-learning and gating network. Firstly, based on the historical operating data of the battery, the long-term internal resistance change prediction of the battery is realized by introducing meta-learning and gating network. Then, based on the variation trend of battery internal resistance, the battery voltage, internal resistance, temperature and other key factors as well as the data of battery charge and discharge curve are used to build a lead-acid battery performance prediction model, and the prediction accuracy of the model is improved by combining CNN and attention mechanism. The experimental results show that the proposed model can effectively improve the accuracy and generalization ability of battery performance prediction.
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Index Terms
- Lead-acid Battery Performance Prediction Model Based on Meta Learning and Gated Networks
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Published In
January 2024
105 pages
ISBN:9798400716218
DOI:10.1145/3651623
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 May 2024
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- Research-article
- Research
- Refereed limited
Funding Sources
- the Science and Technology Project of State Grid Shandong Electric Power Company: Research on Reliable Power Supply Status Diagnosis Technology for Communication Power Supply System Based on Multi-dimensional System Joint Evaluation
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APIT 2024
APIT 2024: 2024 6th Asia Pacific Information Technology Conference
January 29 - 31, 2024
Bangkok, Thailand
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