摘要
储能电池组中电池核心温度的实时监控对于防控电池热失控有着重要的意义.为克服工业实际中电池组内部无法布置多温度测点导致的温度数据获取不全面等问题,本文将Gappy POD重构算法引入储能电池核心温度实时监控问题中,通过监测电池组表面温度预测内部核心温度.通过搭建简化的拟储能电池实验台模拟电池温升,测试了Gappy POD算法在工况平稳变化和工况剧烈变化条件下的稳定性和对核心温度的实时重构能力;对比了Gappy POD算法的重构能力和BP神经网络(Back propagation neural network)的预测能力,并探究了Gappy POD算法和BP神经网络在不同大小的数据库训练条件下的重构预测能力.研究表明,Gappy POD重构算法具有高预测精度、稳定性强并且对数据库数据量依赖性低等优势,为算法在储能电池热管理中的实际应用提供了基础.
The reliability and safety of energy-storage battery packs have always been an industry priority.Large energy-storage battery modules are characterized by high power,numerous built-in energy-storage batteries,complex structures,and a heightened risk of thermal runaway.Monitoring the core temperature in energy-storage battery packs in a noncontact,real-time manner is essential for preventing and controlling thermal runaway events.In response to the challenge of incomplete temperature data acquisition,especially in industrial settings where arranging multiple temperature measurement points inside the battery pack may not be feasible,this study introduced the Gappy POD reconstruction algorithm.Gappy POD is a data analysis method based on proper orthogonal decomposition(POD),which is commonly used in inverse heat transfer and fluid mechanics problems.This enables the prediction of the internal core temperature by monitoring the surface temperature of the battery pack.Considering the safety concerns of battery experiments,this study simulated battery temperature changes using a simplified simulated energy-storage battery experimental platform.The platform tests the stability and real-time reconstruction capabilities of the Gappy POD algorithm under stable and drastic changes in operating conditions.Although we did not introduce the equivalent circuit model in the experiment,this preliminary study verified the reconstruction ability of the algorithm under significant fluctuations in working conditions.Neural networks are renowned for their nonlinear solid prediction capabilities and have extensive applications in predicting the temperatures of energy-storage batteries.This study compares the reconstruction ability of the Gappy POD algorithm with the prediction capability of a back-propagation(BP)neural network.This study also explored the reconstruction and prediction capabilities of the Gappy POD and BP neural networks under varying database sizes for training.The research presented in this study indicates that the Ga
作者
苑清扬
薛珂
张博
兰天
YUAN Qingyang;XUE Ke;ZHANG Bo;LAN Tian(School of Energy and Power Engineering,Dalian University of Technology,Dalian 116081,China;Ningbo Institute of Dalian University of Technology,Ningbo 315000,China;Ningbo Ship Group Co.,Ltd,Ningbo 315191,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2024年第1期166-177,共12页
Chinese Journal of Engineering
基金
2023年宁波市重点研发计划资助项目(2023Z150)。
关键词
Gappy
POD
BP神经网络
储能电池
热管理
核心温度预测
Gappy POD
back-propagation neural network
energy-storage battery
thermal management
core temperature prediction
