电池2024，Vol.54Issue(4)：497-502,6.DOI:10.19535/j.1001-1579.2024.04.012
基于时空建模的锂离子电池温度预测
Temperature prediction of Li-ion battery based on spatiotemporal modeling
摘要
Abstract
The temperature of Li-ion battery has the characteristics of spatiotemporal coupling,strong nonlinear and time-varying,it is difficult to establish an accurate prediction model.A temperature distribution prediction method for Li-ion battery based on spatiotemporal modeling is proposed.The battery temperature is separated into orthogonal space basis functions and time coefficients by orthogonal locality preserving projections (OLPP).With the current and voltage as input,time coefficient as output,a low-order time sequence model based on online sequential extreme learning machine with forgetting factors (FFOS-ELM) is established.The original temperature distribution is reconstructed according to the spatiotemporal synthesis.Results of the temperature prediction of a ternary pouch Li-ion battery show that compared with the online space-time modeling method based on Laplacian eigenmaps and online sequential extreme learning machine (LE-OS-ELM),the proposed method has higher prediction accuracy.Under galvanostatic discharge and urban dynamometer driving schedule (UDDS) conditions,the temporal normalized absolute errors are within the range of (0.030,0.155) and (0.095,0.110),the root-mean-square errors are 0.0972 and 0.1084,respectively.关键词
锂离子电池温度/在线时空建模/正交局部保持投影(OLPP)/带遗忘因子的在线顺序超限学习机(FFOS-ELM)Key words
Li-ion battery temperature/online spatiotemporal modeling/orthogonal locality preserving projection (OLPP)/online sequential extreme learning machine with forgetting factor(FFOS-ELM)分类
信息技术与安全科学引用本文复制引用
吕洲,何波,宋连..基于时空建模的锂离子电池温度预测[J].电池,2024,54(4):497-502,6.基金项目
国家自然科学基金面上项目(52373306),重庆市自然科学基金(2023NSCQ-MSX2249) （52373306）
