基于径向基神经网络退役锂电池分选研究

被引：7

作者：

何忠霖

周萍

机构：

[1] 上海理工大学机械工程学院

来源：

电子科技 | 2021年 / 34卷 / 02期

关键词：

退役锂离子电池; 快速分选; 并联均衡; 恒流充电; 径向基神经网络; 容量估计;

D O I：

暂无

中图分类号：

TM912 [蓄电池]; TP183 [人工神经网络与计算]; X705 [固体废物的处理与利用];

学科分类号：

080802 [电力系统及其自动化]; 083001 [环境科学]; 140502 [人工智能];

摘要：

随着电动汽车关键零部件锂电池的寿命逐步到期,对退役锂电池梯次利用的研究愈发重要,其中对退役锂电池的分选技术是该领域的一大难点。传统的分选方法需要对单个电池进行逐个测试从而完成分选,但此方法不适于大批量电池快速分选。为了提高对退役动力电池的分选速度,文中采用了均衡-充电分选法。该方法对待分选的电池进行并联均衡,待电压一致后进行串联恒流充电;然后根据老化程度不同电池具有不同电压曲线的特点,结合径向基神经网络的非线性函数逼近能力;通过模型训练,实现电池容量估计,从而完成电池分选。仿真验证显示新方法容量误差不超过±5%,容量误差不超过±3%,表明文中所提方法可以实现对退役锂电池的分选。

引用

页码：38 / 44

页数：7

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