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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (7): 1470-1478    DOI: 10.3785/j.issn.1008-973X.2023.07.022
电气工程     
基于温度和SOC的锂离子电池特征提取及SOH估计
董浩(),毛玲*(),屈克庆,赵晋斌,李芬
上海电力大学 电气工程学院，上海 200090
Factor extraction and SOH estimation of lithium-ion battery based on temperature and SOC
Hao DONG(),Ling MAO*(),Ke-qing QU,Jin-bin ZHAO,Fen LI
College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
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摘要：

为了解决电池日常使用过程中数据量获取不足和健康因子提取难的问题，通过分析不同温度下锂离子电池的荷电状态(SOC)与充电电压的变化曲线，提出基于温度和SOC的锂离子电池健康因子提取及健康状态(SOH)在线估计的方法. 在电池的实际充电过程中，根据环境温度差异选取电压和电流作为健康因子. 利用遗传-爬山算法优化极限学习机的网络参数，建立健康因子和SOH的映射关系，实现SOH在线估计. 使用9组NASA电池老化数据进行验证，结果表明，本文方法具有估计精度高、环境温度适应性强的优点.
关键词： 日常SOC;  遗传-爬山算法;  极限学习机（ELM）;  健康因子;  SOH在线估计    
Abstract:

The changing curve of the state of charge (SOC) and charging voltage of lithium-ion batteries (LIB) at different temperatures was analyzed in order to solve the problems of insufficient data acquisition and difficulty in extracting health factors (HFs) during the daily use of LIB. A method for LIB HFs extraction and online estimation of state of health (SOH) considering temperature and SOC was proposed. The charging voltage and current were selected as HFs according to the ambient temperature difference during the actual charging process of the battery. Then the network parameters of the extreme learning machine were optimized by the genetic-hill climbing algorithm, and the mapping relationship between the HFs and the SOH was established to realize the online SOH estimation. Nine groups of NASA LIB aging data were used for verification. Results show that the proposed method has the advantages of high estimation accuracy and strong adaptability for ambient temperature.
Key words: daily SOC    genetic-hill climbing algorithm    extreme learning machine (ELM)    health factor    online estimation of SOH
收稿日期: 2022-07-21 出版日期: 2023-07-17
CLC:  TM 912  
基金资助: 国家自然科学基金资助项目（52177184）
通讯作者: 毛玲     E-mail: 1132566511@qq.com;maoling2290@shiep.edu.cn
作者简介: 董浩（1997—），男，硕士生，从事研究锂离子电池状态估计的研究. orcid.org/0000-0001-6477-5372. E-mail： 1132566511@qq.com
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引用本文:

董浩,毛玲,屈克庆,赵晋斌,李芬. 基于温度和SOC的锂离子电池特征提取及SOH估计[J]. 浙江大学学报(工学版), 2023, 57(7): 1470-1478.

Hao DONG,Ling MAO,Ke-qing QU,Jin-bin ZHAO,Fen LI. Factor extraction and SOH estimation of lithium-ion battery based on temperature and SOC. Journal of ZheJiang University (Engineering Science), 2023, 57(7): 1470-1478.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.07.022    或    https://www.zjujournals.com/eng/CN/Y2023/V57/I7/1470

电池编号 Id/A QN/(A·h) te/℃ Ve/V
B0005 2 2 24 2.7
B0006 2 2 24 2.5
B0018 2 2 24 2.5
B0030 4 2 43 2.2
B0031 4 2 43 2.5
B0032 4 2 43 2.7
B0054 2 2 4 2.2
B0055 2 2 4 2.5
B0056 2 2 4 2.7
表 1  锂离子电池参数和运行工况
图 1  不同SOH下电压和电流变化曲线以及不同温度下SOC的变化曲线
图 2  健康因子的提取
图 3  极限学习机的结构
图 4  基于GA-HC的极限学习机框架图
te/℃ 电池编号 Vo/V Δt/s P
24 B0005 4.05 300 0.985 7
24 B0006 4.05 300 0.982 2
24 B0018 4.05 300 0.981 4
43 B0030 4.00 600 0.983 1
43 B0031 4.00 600 0.971 3
43 B0032 4.00 600 0.982 8
表 2  常温和高温下的健康因子相关性分析
te/℃ 电池编号 Io/A Δt/s P
4 B0054 1.4 1000 0.9467
4 B0055 1.4 1000 0.9562
4 B0056 1.4 1000 0.9123
表 3  低温下的健康因子相关性分析
图 5  常温下的SOH估计结果和误差
方法 电池编号 MAE/% MAPE/% RMSE/%
ELM B0005 1.68 1.91 1.95
ELM B0006 21.93 31.68 32.93
ELM B0018 3.15 3.91 3.25
GA-HC-ELM B0005 0.63 0.82 0.86
GA-HC-ELM B0006 0.43 0.64 0.65
GA-HC-ELM B0018 0.75 0.96 0.93
表 4  常温下的SOH估计结果误差
图 6  低温下的SOH估计结果和误差
方法 电池编号 MAE/% MAPE/% RMSE/%
ELM B0054 4.28 5.02 4.84
ELM B0055 1.69 2.15 2.07
ELM B0056 1.79 2.03 2.25
GA-HC-ELM B0054 1.08 1.33 1.41
GA-HC-ELM B0055 1.09 1.35 1.32
GA-HC-ELM B0056 1.36 1.56 1.61
表 5  低温下的SOH估计结果误差
图 7  高温下的SOH估计结果和误差
方法 电池编号 MAE/% MAPE/% RMSE/%
ELM B0030 2.74 2.93 3.07
ELM B0031 1.71 1.79 1.98
ELM B0032 0.93 1.03 1.13
GA-HC-ELM B0030 0.54 0.59 0.71
GA-HC-ELM B0031 0.59 0.63 0.74
GA-HC-ELM B0032 0.48 0.53 0.56
表 6  高温下的SOH估计结果误差
方法 RMSEav/% tav/s
低温 常温 高温
ELM 3.05 2.60 2.06 0.814
LSTM 2.47 1.93 2.14 13.316
GPR 1.86 1.37 1.14 3.276
GA-HC-ELM 1.44 0.81 0.67 3.941
表 7  ELM、LSTM、GPR和GA-HC-ELM模型的对比
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