An Estimation Method of Relative State-of-Health for Lithium-Ion Batteries Using Morlet Wavelet[J]. 2019, 53(12): 97-103+130.
DOI:
An Estimation Method of Relative State-of-Health for Lithium-Ion Batteries Using Morlet Wavelet[J]. 2019, 53(12): 97-103+130.DOI: 10.7652/xjtuxb201912013.
An Estimation Method of Relative State-of-Health for Lithium-Ion Batteries Using Morlet Wavelet
A novel estimation method based on Morlet wavelet for the relative state-of-health(SOH)of lithium-ion batteries is proposed to solve the problem that the existing methods of estimating battery SOH have low estimation accuracy and large computation. Firstly
the correspondence between electrochemical impedance spectroscopy(EIS)and SOH is explored. Then
the data of voltage and current of a battery are obtained under actual working condition
and the Morlet wavelet is used to perform wavelet transform on the data. The EIS of the battery is estimated online by calculating the wavelet coefficient ratio of voltage signal to current signal. Finally
the SOH of the battery is predicted by the online estimated EIS. The proposed method has the advantages of no need for a lot of experimental data
high accuracy and high computational efficiency. Urban dynamometer driving schedule(UDDS)tests are conducted
and results show that the proposed method accurately estimates the relative SOH of the batt
关键词
Keywords
references
XU Jun, CAO Binggang, CHEN Zheng, et al. An online state of charge estimation method with reduced prior battery testing information [J]. International Journal of Electrical Power & Energy Systems, 2014, 63: 178-184.
XU Jun, MI C C, CAO Binggang, et al. The state of charge estimation of lithium-ion batteries based on a proportional-integral observer [J]. IEEE Transactions on Vehicular Technology, 2014, 63(4): 1614-1621.
WANG Xiao, XU Jun, CAO Binggang, et al. A Kalman filter SOC estimation method for lithium-ion batteries based on discrete wavelet transform denoising [J]. Journal of Xi'an Jiaotong University, 2017, 51(10): 71-76.
ANDRE D, NUHIC A, SOCZKA-GUTH T, et al. Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electric-vehicles [J]. Engineering Applications of Artificial Intelligence, 2013, 26(3): 951-961.
LIU Datong, PANG Jingyue, ZHOU Jianbao, et al. Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression [J]. Microelectronics Reliability, 2013, 53(6): 832-839.
NUHIC A, TERZIMEHIC T, SOCZKA-GUTH T, et al. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods [J]. Journal of Power Sources, 2013, 239(1): 680-688.
WENG Caihao, CUI Yujia, SUN Jing, et al. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression [J]. Journal of Power Sources, 2013, 235(1): 36-44.
WANG Shukun, HUANG Miaohua, ZHANG Zhiyun. Prediction of residual capacity of lithium-ion battery based on PSO-SVR optimization [J]. Journal of Wuhan University of Technology, 2016, 40(2): 380-384.
WU Shengjun, YUAN Xiaodong, XU Qingshan, et al. Summary of lithium battery health assessment [J]. Power Technology, 2017, 41(12): 1788-1791.
MOURA S J, CHATURVEDI A N, KRSTIC M, et al. Adaptive partial differential equation observer for battery state-of-charge state-of-health estimation via an electrochemical model [J]. Journal of Dynamic Systems Measurement and Control, 2013, 136(1): 011015-011025.
ZHOU Xin, STEIN J L, ERSAL T, et al. Battery state of health monitoring by estimation of the number of cyclable li-ions [J]. Control Engineering Practice, 2017, 66: 51-63.
XIONG Rui, LI Linlin, LI Zhirun, et al. An electrochemical model based degradation state identification method of lithium-ion battery for all-climate electric vehicles application [J]. Applied Energy, 2018, 219: 264-275.
GALEOTTI M, CINA L, GIAMMANCO C, et al. Performance analysis and SOH(state of health)evaluation of lithium polymer batteries through electrochemical impedance spectroscopy [J]. Energy, 2015, 89: 678-685.
DU Jiani, LIU Zhitao, WANG Youqi, et al. An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles [J]. Control Engineering Practice, 2016, 54: 81-90.
WANG Qiuting, QI Wei, XIAO Duo. Cycle life estimation method for parallel lithium battery pack based on double Kalman filtering [J]. Information and control, 2018, 47(4): 461-467, 472.
XIONG Rui, TIAN Jingpeng, MU Hao, et al. A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries [J]. Applied Energy, 2017, 207: 372-383.
MINGANT R, BERNARD J, MOYNOT V S, et al. Novel state-of-health diagnostic method for li-ion battery in service [J]. Applied Energy, 2016, 183: 390-398.
WANG Xiao, XU Jun, ZHAO Yunfei, et al. Wavelet based denoising for the estimation of the state of charge for lithium-ion batteries [J]. Energies, 2018, 11(5): 1105-1144.
LUNA E G, SILVA D, APONTE G, et al. Obtaining the electrical impedance using wavelet transform from the time response [J]. Electrochimica Acta, 2013, 28: 1242-1244.
ITAGAKI M, UENO M, HOSHI Y, et al. Simultaneous determination of electrochemical impedance of lithium-ion rechargeable batteries with measurement of charge-discharge curves by wavelet transformation [J]. Electrochimica Acta, 2017, 235: 384-389.
HOSHI Y, YAKABE N, LSOBE K, et al. Wavelet transformation to determine impedance spectra of lithium-ion rechargeable battery [J]. Journal of Power Sources, 2016, 315: 351-358.
A Cloud-Edge Collaborative State of Charge Estimation Method for Lithium-Ion Batteries in Mining Environments Integrating Deep Learning and Ampere-Hour Integration
Early Diagnosis of Internal Short Circuit Faults in Lithium Battery Modules Using Impedance Spectroscopy
Analysis of Impedance with Different Discharge Current in Proton Exchange Membrane Fuel Cell
Fractional Model and State-of-Charge of Lithium Battery
A Structure-Semantics-Residual Collaborative Image Compression Framework:Towards Privacy Protection and High-Fidelity Reconstruction
Related Author
ZHANG Xiangfeng
CAO Fengjin
LIU Dong
XIONG Liangqian
WANG Chenglong
ZOU Keyi
YANG Yukang
SHEN Yong
Related Institution
School of Intelligent Manufacturing and Modern Industry, Xinjiang University
State Key Laboratory of Safety Technology of Metal Mines, Changsha Institute of Mining Research Co. Ltd.
School of Mechanical and Automotive Engineering, Xiamen University of Technology
State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University
Electric Power Research Institute of State Grid Shaanxi Electric Power Co., Ltd.