Lead-acid Battery Performance Prediction Model Based on Meta Learning and Gated Networks-Reference-Cited by-同舟云学术

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Lead-acid Battery Performance Prediction Model Based on Meta Learning and Gated Networks

Published:2024-01-29 Issue: Volume:52 Page:92-98
ISSN:
Container-title:Proceedings of the 2024 6th Asia Pacific Information Technology Conference
language:
Short-container-title:

Author:

Lu Yi¹^ORCID,Ma Kai¹^ORCID,Ren Tingxin¹^ORCID,Liu Wei¹^ORCID,Wang Qi¹^ORCID,Wang Rui¹^ORCID

Affiliation:

1. INFORMATION&TELECOMMUNICATIONS COMPANY, STATE GRID SHANDONG ELECTRIC POWER COMPANY, China

Funder

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

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3651623.3651637

Reference21 articles.

1. Research and design of SOH and SOC detection system for large capacity battery [J];Guilin Zheng;Engineering Journal of Wuhan University,2019

2. Application of improved WNN in battery SOC estimation [J];Siyu Xie;Journal of Power Supply,2020

3. Prediction for state of charge of lead-acid battery by particle swarm optimization with Gaussian process regression [J];Bintai Xu;Journal of Nanjing University of Science and Technology,2018

4. Interactive augmented reality storytelling guided by scene semantics

5. Exertion-aware path generation[J];Li Wanwan;ACM Trans. Graph.

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