STATE OF HEALTH ASSESSMENT FOR ECHELON UTILIZATION BATTERIES BASED ON DEEP NEURAL NETWORK

Wei Zixuan; Han Xiaojuan; Li Xuan

doi:10.19912/j.0254-0096.tynxb.2022-0550

PDF(68948 KB)

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (5) : 518-524. DOI: 10.19912/j.0254-0096.tynxb.2022-0550

STATE OF HEALTH ASSESSMENT FOR ECHELON UTILIZATION BATTERIES BASED ON DEEP NEURAL NETWORK

Wei Zixuan, Han Xiaojuan, Li Xuan

Author information +

History +

Abstract

With a large number of retired batteries being used for secondary use, an accurate estimation of the health status of retired power batteries is a prerequisite for ensuring safe and efficient operation of battery secondary use. To address the above problem, a deep neural network learning-based method is proposed to evaluate the health status of the retired batteries. According to the difference of the charge and discharge performance of the battery under different cycle times, the main parameters affecting the aging characteristics of the battery are excavated from the perspective of the physical characteristics of the battery, and the correlation coefficient between the aging characteristics of the battery and the state of health of the battery is calculated by the Pearson method, select the high correlation feature as the input of the deep neural network, and establish a battery health state evaluation model based on the learning of the deep neural network. The validity of the method was verified by simulating the lithium-ion battery test data from the NASA Ames Prediction Center of Excellence. The simulation results show that compared with traditional neural networks, the deep neural network learning can significantly improve the prediction accuracy of the health status of the battery and provide a theoretical basis for the evaluation of the health status of retired power batteries.

Key words

secondary batteries / neural network models / battery modelling / health state assessment / correlation analysis / battery

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Wei Zixuan, Han Xiaojuan, Li Xuan. STATE OF HEALTH ASSESSMENT FOR ECHELON UTILIZATION BATTERIES BASED ON DEEP NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2022, 43(5): 518-524 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0550

References

[1] 孙舟, 田贺平, 王伟贤, 等. 梯次利用电池储能系统参与用户侧削峰填谷的经济性研究[J]. 太阳能学报, 2021, 42(4): 95-100.
SUN Z, TIAN H P, WANG W X, et al.Research on economy of echelon utilization battery energy storage system for user-side peak load shifting[J]. Acta energiae solaris sinica, 2021, 42(4): 95-100.
[2] 孙冬, 许爽. 梯次利用锂电池健康状态预测[J]. 电工技术学报, 2018, 33(9): 2121-2129.
SUN D, XU S.State of health prediction of second-use lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2018, 33(9): 2121-2129.
[3] 马玲, 魏成伟, 谢丽蓉, 等. 基于退役动力电池的风储有功功率协调控制策略[J]. 太阳能学报, 2021, 42(10): 437-443.
MA L, WEI C W, XIE L R, et al.Coordinated control strategy for wind storage active power of decommissioned power battery[J]. Acta energiae solaris sinica, 2021, 42(10): 437-443.
[4] 谢英豪, 余海军, 李长东. 车用动力电池梯次利用回收风险与安全性研究[J]. 电源技术, 2018, 42(5): 638-640.
XIE Y H, YU H J, LI C D.Research on recycling risk and security of EV battery echelon use[J]. Chinese journal of power sources, 2018, 42(5): 638-640.
[5] LIU W, XU Y.Data-driven online health estimation of li-ion batteries using a novel energy-based health indicator[J]. IEEE transactions on energy conversion, 2020, 35(3): 1715-1718.
[6] 李文华, 邵方旭, 暴二平, 等. 六自由度振动老化条件下锂离子电池的衰退机理诊断与SOH预测[J]. 仪器仪表学报, 2021, 42(8): 62-69.
LI W H, SHAO F X, BAO E P, et al.Diagnosis of degradation mechanism and SOH prediction of lithium-ion batteries under 6-DOF vibration and aging conditions[J]. Chinese journal of scientific instrument, 2021, 42(8): 62-69.
[7] 刘熹, 李琳, 刘海龙. 动力型锂电池SOC与SOH协同估计[J]. 太赫兹科学与电子信息学报, 2020, 18(4): 750-755.
LIU X, LI L, LIU H L.Cooperative estimation of SOC and SOH for power lithium-ion batteries[J]. Journal of terahertz science and electronic information technology, 2020, 18(4): 750-755.
[8] 陈翌, 白云飞, 何瑛. 数据驱动的锂电池健康状态估算方法比较[J]. 储能科学与技术, 2019, 8(6): 1204-1210.
CHEN Y, BAI Y F, HE Y.Comparison of data-driven lithium battery state of health estimation methods[J]. Energy storage science and technology, 2019, 8(6): 1204-1210.
[9] 王萍, 弓清瑞, 张吉昂, 等. 一种基于数据驱动与经验模型组合的锂电池在线健康状态预测方法[J]. 电工技术学报, 2021, 36(24): 5201-5212.
WANG P, GONG Q R, ZHANG J A, et al.An online state of health prediction method for lithium batteries based on combination of data-driven and empirical model[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5201-5212.
[10] 赵显赫, 耿光超, 林达, 等. 基于数据驱动的锂离子电池健康状态评估综述[J]. 浙江电力, 2021, 40(7): 65-73.
ZHAO X H, GENG G C, LIN D, et al.Review of data-driven state of health estimation for lithium-ion battery[J]. Zhejiang electric power, 2021, 40(7): 65-73.
[11] 郑永飞, 文怀兴, 韩昉, 等. 基于电池外特征的粒子群神经网络电池健康状态预测[J]. 科学技术与工程, 2019, 19(36): 184-189.
ZHENG Y F, WEN H X, HAN F, et al.Prediction of state of health based on particle swarm neural network with battery external characteristics[J]. Science technology and engineering, 2019, 19(36): 184-189.
[12] 陈峥, 顾青峰, 沈世全, 等. 基于健康特征提取和PSO-RBF神经网络的锂离子电池健康状态预测[J]. 昆明理工大学学报(自然科学版), 2020, 45(6): 92-103.
CHEN Z, GU Q F, SHEN S Q, et al.State of health predictionfor lithium-ion batteries based on health feature extraction and PSO-RBF neural network[J]. Journal of Kunming University of Science and Technology(natural science edition), 2020, 45(6): 92-103.
[13] MAWONOU K R, EDDAHECH A, DUMUR D, et al.State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking[J]. Journal of power sources, 2020, 25: 229154.
[14] 宋哲, 高建平, 潘龙帅, 等. 基于主成分分析和改进支持向量机的锂离子电池健康状态预测[J]. 汽车技术, 2020(11): 21-27.
SONG Z, GAO J P, PAN L S, et al.Prediction for the state of health of lithium-ion batteries based on PCA and Improved SVR[J]. Automobile technology, 2020(11): 21-27.
[15] STROE DL-I, SCHALTZ E.Lithium-ion battery state-of-health estimation using the incremental capacity analysis technique[J]. IEEE transactions on industry applications, 2020, 56(1): 678-685.
[16] 王育飞, 叶俊斌, 薛花, 等. 基于改进雨流计数法的梯次利用电池储能系统优化控制[J]. 电力系统自动化, 2022, 46(8): 39-49.
WANG Y F, YE J B, XUE H, et al.Optimal control for energy storage system with echelon utilization batteries based on improved rain-flow counting method[J]. Automation of electric power systems, 2022, 46(8): 39-49.
[17] BOLE B, KULKARNI C S, DAIGLE M.Adaptation of an electrochemistry-based li-ion battery model to account for deterioration observed under randomized use[C]// Conference of the prognostics & health management Society, Fort Worth, TX, USA, 2011.
[18] 魏学哲, 陆天怡, 房乔华. 基于电压弛豫模型的锂离子电池老化状态估计[J]. 同济大学学报(自然科学版), 2018, 46(1): 171-177.
WEI X Z, LU T Y, FANG Q H.Aging estimation of li-ion battery based on voltage relaxation model[J]. Journal of Tongji University(natural science), 2018, 46(1): 171-177.
[19] JIANG B, DAI H F, WEI X Z.Incremental capacity analysis based adaptive capacity estimation forlithium-ion battery considering charging condition[J]. Applied energy, 2020, 269: 115074.
[20] 丁镇涛, 邓涛, 李志飞, 等. 基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究[J]. 中国机械工程, 2020, 31(15): 1823-1830.
DING Z T, DENG T, LI Z F, et al.SOC estimation of lithium-ion battery based on ampere hour integral and unscented Kalman filter[J]. China mechanical engineering, 2020, 31(15): 1823-1830.
[21] 宫彦双,谷坛,安超,等. 基于Pearson相关系数的集输管道流动腐蚀主控因素分析[J]. 石油与天然气化工, 2021, 50(2): 93-99.
GONG Y S, GU T, AN C, et al.Analysis of main control factors for flow corrosion of gathering and transportation pipeline based on Pearson correlation coefficient[J]. Chemical engineering of oil and gas, 2021, 50(2): 93-99.
[22] 徐岩, 程姝, 薛艳静. 基于故障暂态电流Pearson相关系数的直流配电网保护[J]. 华北电力大学学报(自然科学版), 2021, 48(4): 11-19.
XU Y, CHENG S, XUE Y J.Protection of DC distribution network based on pearson correlation coefficient of fault transient current[J]. Journal of North China Electric Power University, 2021, 48(4): 11-19.
[23] 党建, 罗燚, 田录林, 等. 基于优化的VMD融合信息熵和FA_PNN的风电机组齿轮箱故障诊断[J]. 太阳能学报, 2021, 42(1): 198-204.
DANG J, LUO Y, TIAN L L, et al.Fault diagnosis of wind turbine gearbox based on optimized VMD fusion information entropy and FA_PNN[J]. Acta energiae solaris sinica, 2021, 42(1): 198-204.
[24] IBRAHIMY, WANGH B, LIU J Y, et al.Soft errors in DNN accelerators: A comprehensive review[J]. Microelectronics reliability, 2020, 115: 113969.
[25] HAN H, XU L, CUI X Y, et al.Novel chiller fault diagnosis using deep neural network (DNN) with simulated annealing (SA)[J]. International journal of refrigeration, 2021, 121: 269-278.
[26] HAN X, ZHANG LH, ZHOU K, et al.ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework[J]. Computer and chemical engineering, 2019, 131: 106533.
[27] 张强, 魏儒义, 严强强, 等. 深度神经网络在红外光谱定量分析VOCs中的应用[J]. 光谱学与光谱分析, 2020, 40(4): 1099-1106.
ZHANG Q, WEI R Y, YAN Q Q, et al.Application of deep neural network in quantitative analysis of VOCs by infrared spectroscopy[J]. Spectroscopy and spectral analysis, 2020, 40(4): 1099-1106.
[28] HAN X J, WEI Z X, HONG Z P, et al.Ordered charge control considering the uncertainty of charging load of electric vehicles based on Markov chain[J]. Renewable energy, 2020, 161: 419-434.