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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (7): 1470-1478    DOI: 10.3785/j.issn.1008-973X.2023.07.022
    
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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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     
Received: 21 July 2022      Published: 17 July 2023
CLC:  TM 912  
Fund:  国家自然科学基金资助项目（52177184）
Corresponding Authors: Ling MAO     E-mail: 1132566511@qq.com;maoling2290@shiep.edu.cn
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Hao DONG
Ling MAO
Ke-qing QU
Jin-bin ZHAO
Fen LI
Cite this article:

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.

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


基于温度和SOC的锂离子电池特征提取及SOH估计

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 
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
Tab.1 Li-ion batteries parameters and operating conditions
Fig.1 Voltage and current change curves at different SOH and SOC change curves at different temperatures
Fig.2 Extraction of health factor
Fig.3 Structure of extreme learning machine
Fig.4 Frame diagram of extreme learning machine based on GA-HC
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
Tab.2 Correlation analysis of health factors at room temperature and high temperature
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
Tab.3 Correlation analysis of health factors at low temperature
Fig.5 SOH estimation results and errors at room temperature
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
Tab.4 Error in SOH estimation results at room temperature
Fig.6 Estimation result and error of SOH at low temperature
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
Tab.5 Error in SOH estimation results at low temperature
Fig.7 Estimation result and error of SOH at high temperature
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
Tab.6 Errors of SOH estimation results at high temperature
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
Tab.7 Comparison of ELM, LSTM, GPR, and GA-HC-ELM models
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