基于深度学习的锂离子电池SOC和SOH联合估算

被引：148

作者：

李超然

肖飞

樊亚翔

唐欣

杨国润

机构：

[1] 舰船综合电力技术国防科技重点实验室(海军工程大学)

来源：

中国电机工程学报 | 2021年 / 41卷 / 02期

关键词：

锂离子电池; 电池荷电状态; 电池健康状态; 深度学习; 门控循环单元循环神经网络; 卷积神经网络;

D O I：

暂无

中图分类号：

TM912 [蓄电池]; TP18 [人工智能理论];

学科分类号：

080802 [电力系统及其自动化]; 140502 [人工智能];

摘要：

锂离子电池常被作为储能元件以实现电能的存储和转化,然而其荷电状态(state of charge,SOC)和健康状态(state of health,SOH)无法被直接测量。为了实现锂离子电池SOC和SOH联合估算,该文分析SOC和SOH之间的关联性,并提出一种基于深度学习的锂离子电池SOC和SOH联合估算方法。该方法能够基于门控循环单元循环神经网络(recurrent neural network with gated recurrent unit,GRU-RNN)和卷积神经网络(convolutional neural network,CNN),利用锂离子电池电压、电流、温度,实现锂离子电池全使用周期内SOC和SOH的同时估算,而且由于将锂离子电池的SOH估算值考虑到SOC估算中,能够消除锂离子电池老化因素对锂离子电池SOC估算造成的负面影响,从而提升SOC估算精度。两个锂离子电池测试数据集上的实验结果表明,提出的估算方法能够在不同温度和不同工况下实现锂离子电池全使用周期SOC和SOH联合估算,且获得较高的精度。

引用

页码：681 / 692

页数：12

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