电子测量与仪器学报

张朝龙,罗来劲,刘惠汉,赵筛筛.基于增量能量法和 BiGRU-Dropout 的锂电池健康状态估计[J].电子测量与仪器学报,2023,37(1):167-176

基于增量能量法和 BiGRU-Dropout 的锂电池健康状态估计

State of health estimation of Lithium-ion batteries based onincremental energy analysis and BiGRU-Dropout

DOI：10.13382/j.issn.1000-7105.2023.01.019

中文关键词: 锂离子电池健康状态增量能量法双向门控循环网络 Dropout 机制

英文关键词:Lithium-ion battery state of health incremental energy analysis bidirectional gated recurrent unit Dropout mechanism

基金项目:国家重点研发计划(2020YFB0905905,2016YFF0102200)、国家自然科学基金重点资助项目(51637004)、金陵科技学院高层次人才科研启动基金(jit-rcyj-202202)、安庆师范大学研究生创新创业项目(2022cxcysj161)资助

作者	单位
张朝龙	1. 金陵科技学院智能科学与控制工程学院，2. 安庆师范大学电子工程与智能制造学院
罗来劲	2. 安庆师范大学电子工程与智能制造学院
刘惠汉	2. 安庆师范大学电子工程与智能制造学院
赵筛筛	2. 安庆师范大学电子工程与智能制造学院

Author	Institution
Zhang Chaolong	1. College of Intelligent Science and Control Engineering, Jinling Institute of Technology，2. School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University
Luo Laijin	2. School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University
Liu Huihan	2. School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University
Zhao Shaishai	2. School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University

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中文摘要:

锂离子电池健康状态(SOH)的精确估计是电池管理系统面临的核心问题之一。针对实际的电池容量很难直接测量和容量再生导致的 SOH 估计误差问题,提出了一种基于增量能量法和双向门控循环网络(BiGRU)-Dropout 的锂离子电池健康状态估计方法。首先分析增量能量曲线随电池老化的衰退规律,提取出最大峰值高度作为电池 SOH 的新健康因子。通过翻转层和门控循环网络层所搭建的 BiGRU 网络得出健康因子与 SOH 的映射关系,同时添加 Dropout 机制网络层防止出现过拟合现象,建立 SOH 估计模型用于电池 SOH 精确估计。实验结果表明,在不同充电倍率条件下,该方法均可快速、准确地估计电池 SOH。

英文摘要:

The accurate state of health (SOH) estimation of Lithium-ion battery is one of the core issues faced by battery management systems. Considering that it is difficult to directly measure the battery capacity in practice, and the capacity regeneration problem always cause SOH estimation errors, a SOH estimation method of Lithium-ion battery is proposed based on incremental energy analysis and bidirectional gate recurrent unit ( BiGRU)-Dropout. The incremental energy curve is used to analyze the battery’ s degeneration characteristic, and the maximum peak height is extracted as a new health factor of battery SOH. Through the BiGRU network built by flip layer and gate recurrent unit layer, the mapping relationship between health factor and SOH is obtained. At the same time, Dropout mechanism network layer is added to prevent overfitting, and a SOH estimation model is established to accurate estimate the battery SOH. The results indicate that the proposed method can estimate battery SOH quickly and accurately under different charging rates.

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