Journal of Electrical Engineering ›› 2022, Vol. 17 ›› Issue (3): 40-57.doi: 10.11985/2022.03.006
Previous Articles Next Articles
XU Maoshu1,2(), SHEN Yi3,4, WANG Sheng1,2, ZHANG E1,2, LI Haomiao1,2, ZHOU Min1,2, WANG Wei3,4, WANG Kangli1,2, JIANG Kai1,2,5(
)
Received:
2022-04-30
Revised:
2022-08-12
Online:
2022-09-25
Published:
2022-10-28
Contact:
JIANG Kai
E-mail:msxu@hust.edu.cn;kjiang@hust.edu.cn
CLC Number:
XU Maoshu, SHEN Yi, WANG Sheng, ZHANG E, LI Haomiao, ZHOU Min, WANG Wei, WANG Kangli, JIANG Kai. Application and Enlightenment of Advanced Sensing Technology in Battery State Estimation[J]. Journal of Electrical Engineering, 2022, 17(3): 40-57.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
[1] | 彭鹏, 胡振恺, 李毓烜, 等. 储能参与电网辅助调频的协调控制策略研究[J]. 电气工程学报, 2021, 16(3):106-114. |
PENG Peng, HU Zhenkai, LI Yuxuan, et al. Research on coordination control strategy of frequency regulation in grid with energy storage[J]. Journal of Electrical Engineering, 2021, 16(3):106-114. | |
[2] | 刘鑫, 原熙博, 韩东旭, 等. 一种基于矩阵变换器的海上风力机中压电能变换系统[J]. 电气工程学报, 2021, 16(1):127-133. |
LIU Xin, YUAN Xibo, HAN Dongxu, et al. A medium voltage power conversion system for offshore wind turbine based on matrix converter[J]. Journal of Electrical Engineering, 2021, 16(1):127-133. | |
[3] | 符杨, 魏钰柠, 贾锋, 等. 分布式风电机组电压波动特性分析及平抑控制策略[J]. 中国电机工程学报, 2020, 40(14):4496-4505. |
FU Yang, WEI Yuning, JIA Feng, et al. The voltage fluctuation characteristic of distributed wind generators and its smoothing control strategy[J]. Proceedings of the CSEE, 2020, 40(14):4496-4505. | |
[4] |
SUKUMAR S, MARSADEK M, AGILESWARI K R, et al. Ramp-rate control smoothing methods to control output power fluctuations from solar photovoltaic(PV) sources:A review[J]. Journal of Energy Storage, 2018, 20:218-229.
doi: 10.1016/j.est.2018.09.013 |
[5] |
CHEN T, JIN Y, LV H, et al. Applications of lithium-ion batteries in grid-scale energy storage systems[J]. Transactions of Tianjin University, 2020, 26(3):208-217.
doi: 10.1007/s12209-020-00236-w |
[6] |
FAN X, LIU B, LIU J, et al. Battery technologies for grid-level large-scale electrical energy storage[J]. Transactions of Tianjin University, 2020, 26(2):92-103.
doi: 10.1007/s12209-019-00231-w |
[7] | 杨瑞鑫, 熊瑞, 孙逢春. 锂离子动力电池外部短路测试平台开发与试验分析[J]. 电气工程学报, 2021, 16(1):103-118. |
YANG Ruixin, XIONG Rui, SUN Fengchun, et al. Experimental platform development and characteristics analysis of external short circuit in lithium-ion batteries[J]. Journal of Electrical Engineering, 2021, 16(1):103-118. | |
[8] |
FINEGAN D P, ZHU J, FENG X, et al. The application of data-driven methods and physics-based learning for improving battery safety[J]. Joule, 2021, 5(2):316-329.
doi: 10.1016/j.joule.2020.11.018 |
[9] | DAI H, JIANG B, HU X, et al. Advanced battery management strategies for a sustainable energy future:Multilayer design concepts and research trends[J]. Renewable and Sustainable Energy Reviews, 2021, 138:110480. |
[10] | HAN G, YAN J, GUO Z, et al. A review on various optical fibre sensing methods for batteries[J]. Renewable and Sustainable Energy Reviews, 2021, 150:111514. |
[11] | MEYER J, NEDJALKOV A, DOERING A, et al. Fiber optical sensors for enhanced battery safety[C]//Fiber Optic Sensors and Applications XII. Baltimore,MD,USA: International Society for Optics and Photonics, 2015, 9480:94800Z. |
[12] |
CAO-PAZ A M, MARCOS-ACEVEDO J, RÍO-VÁZQUEZ D, et al. A multi-point sensor based on optical fiber for the measurement of electrolyte density in lead-acid batteries[J]. Sensors, 2010, 10(4):2587-2608.
doi: 10.3390/s100402587 |
[13] |
LI H, WEI F, LI Y, et al. Optical fiber sensor based on upconversion nanoparticles for internal temperature monitoring of Li-ion batteries[J]. Journal of Materials Chemistry C, 2021, 9(41):14757-14765.
doi: 10.1039/D1TC03701C |
[14] |
EDDAHECH A, BRIAT O, BERTRAND N, et al. Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks[J]. International Journal of Electrical Power & Energy Systems, 2012, 42(1):487-494.
doi: 10.1016/j.ijepes.2012.04.050 |
[15] |
DENG Z, ZHANG Z, LAI Y, et al. Electrochemical impedance spectroscopy study of a lithium/sulfur battery:Modeling and analysis of capacity fading[J]. Journal of The Electrochemical Society, 2013, 160(4):A553.
doi: 10.1149/2.026304jes |
[16] |
CHOI W, SHIN H C, KIM J M, et al. Modeling and applications of electrochemical impedance spectroscopy (EIS) for lithium-ion batteries[J]. Journal of Electrochemical Science and Technology, 2020, 11(1):1-13.
doi: 10.33961/jecst.2019.00528 |
[17] | BARAI P, MUKHERJEE P P. Mechano-electrochemical model for acoustic emission characterization in intercalation electrodes[J]. Journal of The Electrochemical Society, 2014, 161(11):F3123. |
[18] | KOMAGATA S, KUWATA N, BASKARAN R, et al. Detection of degradation of lithium-ion batteries with acoustic emission technique[J]. ECS Transactions, 2010, 25(33):163. |
[19] |
GALOS J, KHATIBI A A, MOURITZ A P. Vibration and acoustic properties of composites with embedded lithium-ion polymer batteries[J]. Composite Structures, 2019, 220:677-686.
doi: 10.1016/j.compstruct.2019.04.013 |
[20] |
BAUER M, WACHTLER M, STÖWE H, et al. Understanding the dilation and dilation relaxation behavior of graphite-based lithium-ion cells[J]. Journal of Power Sources, 2016, 317:93-102.
doi: 10.1016/j.jpowsour.2016.03.078 |
[21] |
BITZER B, GRUHLE A. A new method for detecting lithium plating by measuring the cell thickness[J]. Journal of Power Sources, 2014, 262:297-302.
doi: 10.1016/j.jpowsour.2014.03.142 |
[22] |
FU R, XIAO M, CHOE S Y. Modeling,validation and analysis of mechanical stress generation and dimension changes of a pouch type high power Li-ion battery[J]. Journal of Power Sources, 2013, 224:211-224.
doi: 10.1016/j.jpowsour.2012.09.096 |
[23] |
KOMSIYSKA L, BUCHBERGER T, DIEHL S, et al. Critical review of intelligent battery systems:Challenges,implementation,and potential for electric vehicles[J]. Energies, 2021, 14(18):5989.
doi: 10.3390/en14185989 |
[24] |
XING Y, HE W, PECHT M, et al. State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures[J]. Applied Energy, 2014, 113:106-115.
doi: 10.1016/j.apenergy.2013.07.008 |
[25] |
BAO Y, DONG W, WANG D. Online internal resistance measurement application in lithium ion battery capacity and state of charge estimation[J]. Energies, 2018, 11(5):1073.
doi: 10.3390/en11051073 |
[26] | POP V, BERGVELD H J, HET VELD J H G O, et al. Modeling battery behavior for accurate state-of-charge indication[J]. Journal of The Electrochemical Society, 2006, 153(11): A2013. |
[27] |
ZHENG L, ZHANG L, ZHU J, et al. Co-estimation of state-of-charge,capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model[J]. Applied Energy, 2016, 180:424-434.
doi: 10.1016/j.apenergy.2016.08.016 |
[28] |
ALI M U, ZAFAR A, NENGROO S H, et al. Towards a smarter battery management system for electric vehicle applications:A critical review of lithium-ion battery state of charge estimation[J]. Energies, 2019, 12(3):446.
doi: 10.3390/en12030446 |
[29] |
SNIHIR I, REY W, VERBITSKIY E, et al. Battery open-circuit voltage estimation by a method of statistical analysis[J]. Journal of Power Sources, 2006, 159(2):1484-1487.
doi: 10.1016/j.jpowsour.2005.11.090 |
[30] |
JOKAR A, RAJABLOO B, DÉSILETS M, et al. Review of simplified Pseudo-two-dimensional models of lithium-ion batteries[J]. Journal of Power Sources, 2016, 327:44-55.
doi: 10.1016/j.jpowsour.2016.07.036 |
[31] |
ZHANG D, POPOV B N, WHITE R E. Modeling lithium intercalation of a single spinel particle under potentiodynamic control[J]. Journal of The Electrochemical Society, 2000, 147(3):831.
doi: 10.1149/1.1393279 |
[32] |
LI W, FAN Y, RINGBECK F, et al. Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter[J]. Journal of Power Sources, 2020, 476:228534.
doi: 10.1016/j.jpowsour.2020.228534 |
[33] |
CEN Z, KUBIAK P. Lithium-ion battery SOC/SOH adaptive estimation via simplified single particle model[J]. International Journal of Energy Research, 2020, 44(15):12444-12459.
doi: 10.1002/er.5374 |
[34] | 高铭琨, 徐海亮, 吴明铂. 基于等效电路模型的动力电池SOC估计方法综述[J]. 电气工程学报, 2021, 16(1):90-102. |
GAO Mingkun, XU Hailiang, WU Mingbo. Review of SOC estimation methods for power battery based on equivalent circuit model[J]. Journal of Electrical Engineering, 2021, 16(1):90-102. | |
[35] |
MENG J, LUO G, RICCO M, et al. Overview of lithium-ion battery modeling methods for state-of-charge estimation in electrical vehicles[J]. Applied Sciences, 2018, 8(5):659.
doi: 10.3390/app8050659 |
[36] |
YUAN S, WU H, YIN C. State of charge estimation using the extended Kalman filter for battery management systems based on the ARX battery model[J]. Energies, 2013, 6(1):444-470.
doi: 10.3390/en6010444 |
[37] |
CHEN Z, SUN H, DONG G, et al. Particle filter-based state-of-charge estimation and remaining-dischargeable-time prediction method for lithium-ion batteries[J]. Journal of Power Sources, 2019, 414:158-166.
doi: 10.1016/j.jpowsour.2019.01.012 |
[38] | HOW D N T, HANNAN M A, LIPU M S H, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods:A review[J]. IEEE Access, 2019, 7:136116-136136. |
[39] |
CHEMALI E, KOLLMEYER P J, PREINDL M, et al. Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2017, 65(8):6730-6739.
doi: 10.1109/TIE.2017.2787586 |
[40] |
YANG F, SONG X, XU F, et al. State-of-charge estimation of lithium-ion batteries via long short-term memory network[J]. IEEE Access, 2019, 7:53792-53799.
doi: 10.1109/ACCESS.2019.2912803 |
[41] |
DANG X, YAN L, XU K, et al. Open-circuit voltage-based state of charge estimation of lithium-ion battery using dual neural network fusion battery model[J]. Electrochimica Acta, 2016, 188:356-366.
doi: 10.1016/j.electacta.2015.12.001 |
[42] | HOSSAIN L M S, HANNAN M A, HUSSAIN A, et al. Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection[J]. Journal of Renewable and Sustainable Energy, 2017, 9(6):064102. |
[43] |
LI I H, WANG W Y, SU S F, et al. A merged fuzzy neural network and its applications in battery state-of-charge estimation[J]. IEEE Transactions on Energy Conversion, 2007, 22(3):697-708.
doi: 10.1109/TEC.2007.895457 |
[44] |
DAI H, ZHAO G, LIN M, et al. A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain[J]. IEEE Transactions on Industrial Electronics, 2018, 66(10):7706-7716.
doi: 10.1109/TIE.2018.2880703 |
[45] |
TANG X, LIU K, WANG X, et al. Model migration neural network for predicting battery aging trajectories[J]. IEEE Transactions on Transportation Electrification, 2020, 6(2):363-374.
doi: 10.1109/TTE.2020.2979547 |
[46] |
HU J N, HU J J, LIN H B, et al. State-of-charge estimation for battery management system using optimized support vector machine for regression[J]. Journal of Power Sources, 2014, 269:682-693.
doi: 10.1016/j.jpowsour.2014.07.016 |
[47] |
MENG J, LUO G, GAO F. Lithium polymer battery state-of-charge estimation based on adaptive unscented Kalman filter and support vector machine[J]. IEEE Transactions on Power Electronics, 2015, 31(3):2226-2238.
doi: 10.1109/TPEL.2015.2439578 |
[48] | TING T O, MAN K L, LIM E G, et al. Tuning of Kalman filter parameters via genetic algorithm for state-of-charge estimation in battery management system[J]. The Scientific World Journal, 2014, 2014:176052. |
[49] |
CHEN L, WANG Z, LÜ Z, et al. A novel state-of-charge estimation method of lithium-ion batteries combining the grey model and genetic algorithms[J]. IEEE Transactions on Power Electronics, 2017, 33(10):8797-8807.
doi: 10.1109/TPEL.2017.2782721 |
[50] | 曹李华. 光纤倏逝波谱传感系统及应用研究[D]. 重庆: 重庆理工大学, 2014. |
CAO Lihua. Reserch on fiber optical evanescent wave spectrum sensing systems and applications[D]. Chongqing: Chongqing University of Technology, 2014. | |
[51] |
SOMMER L W, RAGHAVAN A, KIESEL P, et al. Monitoring of intercalation stages in lithium-ion cells over charge-discharge cycles with fiber optic sensors[J]. Journal of The Electrochemical Society, 2015, 162(14):A2664.
doi: 10.1149/2.0361514jes |
[52] |
RENTE B, FABIAN M, VIDAKOVIC M, et al. Lithium-ion battery state-of-charge estimator based on FBG-based strain sensor and employing machine learning[J]. IEEE Sensors Journal, 2020, 21(2):1453-1460.
doi: 10.1109/JSEN.2020.3016080 |
[53] |
BANDHAUER T M, GARIMELLA S, FULLER T F. A critical review of thermal issues in lithium-ion batteries[J]. Journal of The Electrochemical Society, 2011, 158(3):R1-R25.
doi: 10.1149/1.3515880 |
[54] |
NOVAIS S, NASCIMENTO M, GRANDE L, et al. Internal and external temperature monitoring of a Li-ion battery with fiber Bragg grating sensors[J]. Sensors, 2016, 16(9):1394.
doi: 10.3390/s16091394 |
[55] |
RAGHAVAN A, KIESEL P, SOMMER L W, et al. Embedded fiber-optic sensing for accurate internal monitoring of cell state in advanced battery management systems part 1:Cell embedding method and performance[J]. Journal of Power Sources, 2017, 341:466-473.
doi: 10.1016/j.jpowsour.2016.11.104 |
[56] |
GANGULI A, SAHA B, RAGHAVAN A, et al. Embedded fiber-optic sensing for accurate internal monitoring of cell state in advanced battery management systems part 2:Internal cell signals and utility for state estimation[J]. Journal of Power Sources, 2017, 341:474-482.
doi: 10.1016/j.jpowsour.2016.11.103 |
[57] | GHANNOUM A R, NORRIS R C, IYER K, et al. Optical characterization of commercial lithiated graphite battery electrodes and in situ fiber optic evanescent wave spectroscopy[J]. ACS Applied Materials & Interfaces, 2016, 8(29):18763-18769. |
[58] | GHANNOUM A R, NIEVA P, YU A, et al. Development of embedded fiber-optic evanescent wave sensors for optical characterization of graphite anodes in lithium-ion batteries[J]. ACS Applied Materials & Interfaces, 2017, 9(47):41284-41290. |
[59] | GHANNOUM A R, NIEVA P. Graphite lithiation and capacity fade monitoring of lithium ion batteries using optical fibers[J]. Journal of Energy Storage, 2020, 28:101233. |
[60] | MODRZYNSKI C, ROSCHER V, RITTWEGER F, et al. Integrated optical fibers for simultaneous monitoring of the anode and the cathode in lithium ion batteries[C]//2019 IEEE SENSORS. Montreal,Canada: IEEE, 2019:1-4. |
[61] |
ANDRE D, MEILER M, STEINER K, et al. Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. I. Experimental investigation[J]. Journal of Power Sources, 2011, 196(12):5334-5341.
doi: 10.1016/j.jpowsour.2010.12.102 |
[62] | CARTHY K M, GULLAPALLI H, RYAN K M, et al. Review-use of impedance spectroscopy for the estimation of Li-ion battery state of charge,state of health and internal temperature[J]. Journal of The Electrochemical Society, 2021, 168(8):080517. |
[63] |
WAAG W, KÄBITZ S, SAUER D U. Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application[J]. Applied Energy, 2013, 102:885-897.
doi: 10.1016/j.apenergy.2012.09.030 |
[64] | WANG X, WEI X, DAI H, et al. State estimation of lithium ion battery based on electrochemical impedance spectroscopy with on-board impedance measurement system[C]//2015 IEEE Vehicle Power and Propulsion Conference (VPPC). Montreal,Canada: IEEE, 2015:1-5. |
[65] |
GALEOTTI M, CINÀ L, GIAMMANCO C, et al. Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy[J]. Energy, 2015, 89:678-686.
doi: 10.1016/j.energy.2015.05.148 |
[66] |
XIONG R, TIAN J, MU H, et al. A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries[J]. Applied Energy, 2017, 207:372-383.
doi: 10.1016/j.apenergy.2017.05.124 |
[67] |
XU J, MI C C, CAO B, et al. A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model[J]. Journal of Power Sources, 2013, 233:277-284.
doi: 10.1016/j.jpowsour.2013.01.094 |
[68] |
ITAGAKI M, HONDA K, HOSHI Y, et al. In-situ EIS to determine impedance spectra of lithium-ion rechargeable batteries during charge and discharge cycle[J]. Journal of Electroanalytical Chemistry, 2015, 737:78-84.
doi: 10.1016/j.jelechem.2014.06.004 |
[69] | DENSMORE A, HANIF M. Determining battery SOC using electrochemical impedance spectroscopy and the extreme learning machine[C]//2015 IEEE 2nd International Future Energy Electronics Conference (IFEEC),Taipei,China. IEEE, 2015:1-7. |
[70] | MIDDLEMISS L A, RENNIE A J R, SAYERS R, et al. Characterisation of batteries by electrochemical impedance spectroscopy[J]. Energy Reports, 2020, 6:232-241. |
[71] |
李伟恒, 黄秋安, 杨维明, 等. 基于伪随机二进制序列的阻抗谱快速重构及其在电化学能源领域的应用[J]. 电化学, 2020, 26(3):370-388.
doi: 10.13208/j.electrochem.190309 |
LI Weiheng, HUANG Qiuan, YANG Weiming, et al. Recent advancement in pseudo-random binary sequence signals based fast reconstruction of impedance spectrum and its applications in electrochemical energy sources[J]. Journal of Electrochemistry, 2020, 26(3):370-388.
doi: 10.13208/j.electrochem.190309 |
|
[72] | CARTHY K M, GULLAPALLI H, KENNEDY T. Real-time internal temperature estimation of commercial Li-ion batteries using online impedance measurements[J]. Journal of Power Sources, 2022, 519:230786. |
[73] | QAHOUQ J A A. Online battery impedance spectrum measurement method[C]//2016 31th Annual IEEE Applied Power Electronics Conference and Exposition (APEC). Long Beach,CA: IEEE, 2016:3611-3615. |
[74] |
ZAPPEN H, RINGBECK F, SAUER D U. Application of time-resolved multi-sine impedance spectroscopy for lithium-ion battery characterization[J]. Batteries, 2018, 4(4):64.
doi: 10.3390/batteries4040064 |
[75] | CRESCENTINI M, ANGELIS D A, RAMILLI R, et al. Online EIS and diagnostics on lithium-ion batteries by means of low-power integrated sensing and parametric modeling[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70:1-11. |
[76] |
HUANG W, QAHOUQ J A A. An online battery impedance measurement method using DC-DC power converter control[J]. IEEE Transactions on Industrial Electronics, 2014, 61(11):5987-5995.
doi: 10.1109/TIE.2014.2311389 |
[77] |
WANG Q K, HE Y J, SHEN J N, et al. State of charge-dependent polynomial equivalent circuit modeling for electrochemical impedance spectroscopy of lithium-ion batteries[J]. IEEE Transactions on Power Electronics, 2017, 33(10):8449-8460.
doi: 10.1109/TPEL.2017.2780184 |
[78] | HICKEY R, JAHNS T M. Measuring individual battery dimensional changes for state-of-charge estimation using strain gauge sensors[C]//2019 IEEE Energy Conversion Congress and Exposition (ECCE). Baltimore,MD: IEEE, 2019:2460-2465. |
[79] | LEE J H, LEE H M, AHN S. Battery dimensional changes occurring during charge/discharge cycles—thin rectangular lithium ion and polymer cells[J]. Journal of Power Sources, 2003, 119:833-837. |
[80] | POPP H, KOLLER M, JAHN M, et al. Mechanical methods for state determination of lithium-ion secondary batteries:A review[J]. Journal of Energy Storage, 2020, 32:101859. |
[81] |
SAMAD N A, KIM Y, SIEGEL J B, et al. Battery capacity fading estimation using a force-based incremental capacity analysis[J]. Journal of The Electrochemical Society, 2016, 163(8):A1584.
doi: 10.1149/2.0511608jes |
[82] |
OH K Y, SIEGEL J B, SECONDO L, et al. Rate dependence of swelling in lithium-ion cells[J]. Journal of Power Sources, 2014, 267:197-202.
doi: 10.1016/j.jpowsour.2014.05.039 |
[83] | KIM Y, SAMAD N A, OH K Y, et al. Estimating state-of-charge imbalance of batteries using force measurements[C]//2016 American Control Conference (ACC). Boston,MA: IEEE, 2016:1500-1505. |
[84] |
OH K Y, EPUREANU B I. A novel thermal swelling model for a rechargeable lithium-ion battery cell[J]. Journal of Power Sources, 2016, 303:86-96.
doi: 10.1016/j.jpowsour.2015.10.085 |
[85] |
DAI H, YU C, WEI X, et al. State of charge estimation for lithium-ion pouch batteries based on stress measurement[J]. Energy, 2017, 129:16-27.
doi: 10.1016/j.energy.2017.04.099 |
[86] | MAJASAN J, ROBINSON J, OWEN R, et al. Recent advances in acoustic diagnostics for electrochemical power systems[J]. Journal of Physics:Energy, 2021, 3(3):032011. |
[87] |
FUKUSHIMA T, KATO S, KUWATA N, et al. In-situ acoustic emission study of Sn anode in Li ion battery[J]. ECS Transactions, 2014, 62(1):215.
doi: 10.1149/06201.0215ecst |
[88] | 张闯, 孙博, 金亮, 等. 基于声波时域特征的锂离子电池荷电状态表征[J]. 电工技术学报, 2021, 36(22):4666-4676. |
ZHANG Chuang, SUN Bo, JIN Liang, et al. Characterization of the state of charge of lithium-ion batteries based on the time-domain characteristics of acoustic waves[J]. Transactions of China Electrotechnical Society, 2021, 36(22):4666-4676. | |
[89] |
ZHANG K, YIN J, HE Y. Acoustic emission detection and analysis method for health status of lithium ion batteries[J]. Sensors, 2021, 21(3):712.
doi: 10.3390/s21030712 |
[90] | BEGANOVIC N, SÖFFKER D. Estimation of remaining useful lifetime of lithium-ion battery based on acoustic emission measurements[J]. Journal of Energy Resources Technology, 2019, 141(4):041901. |
[91] |
ZAPPEN H, FUCHS G, GITIS A, et al. In-operando impedance spectroscopy and ultrasonic measurements during high-temperature abuse experiments on lithium-ion batteries[J]. Batteries, 2020, 6(2):25.
doi: 10.3390/batteries6020025 |
[92] | ROBINSON J B, OWEN R E, KOK M D R, et al. Identifying defects in Li-ion cells using ultrasound acoustic measurements[J]. Journal of The Electrochemical Society, 2020, 167(12):120530. |
[93] |
WU Y, WANG Y, YUNG W K C, et al. Ultrasonic health monitoring of lithium-ion batteries[J]. Electronics, 2019, 8(7):751.
doi: 10.3390/electronics8070751 |
[94] | SOOD B, OSTERMAN M, PECHT M. Health monitoring of lithium-ion batteries[C]//2013 10th Annual IEEE Symposium on Product Compliance Engineering (ISPCE). Austin,TX: IEEE, 2013:1-6. |
[95] | HSIEH A G, BHADRA S, HERTZBERG B J, et al. Electrochemical-acoustic time of flight:In operando correlation of physical dynamics with battery charge and health[J]. Energy & Environmental Science, 2015, 8(5):1569-1577. |
[96] |
CHOU Y S, HSU N Y, JENG K T, et al. A novel ultrasonic velocity sensing approach to monitoring state of charge of vanadium redox flow battery[J]. Applied Energy, 2016, 182:253-259.
doi: 10.1016/j.apenergy.2016.08.125 |
[97] |
GOLD L, BACH T, VIRSIK W, et al. Probing lithium-ion batteries’ state-of-charge using ultrasonic transmission- Concept and laboratory testing[J]. Journal of Power Sources, 2017, 343:536-544.
doi: 10.1016/j.jpowsour.2017.01.090 |
[98] | WANG X, LYU Y, SONG G, et al. Theoretical analysis of ultrasonic reflection/trasmission characteristics of lithium-ion battery[C]//Proceedings of the 2020 15th Symposium on Piezoelectrcity,Acoustic Waves and Device Applications (SPAWDA). Henan Polytechn Univ.,Zhengzhou,China:IEEE, 2021:292-296. |
[99] |
DAVIES G, KNEHR K W, VAN TASSELL B, et al. State of charge and state of health estimation using electrochemical acoustic time of flight analysis[J]. Journal of The Electrochemical Society, 2017, 164(12):A2746.
doi: 10.1149/2.1411712jes |
[100] | KIM J Y, JO J H, BYEON J W. Ultrasonic monitoring performance degradation of lithium ion battery[J]. Microelectronics Reliability, 2020, 114:113859. |
[101] | POPP H, KOLLER M, KELLER S, et al. State estimation approach of lithium-ion batteries by simplified ultrasonic time-of-flight measurement[J]. IEEE Access, 2019, 7:170992-171000. |
[102] | LADPLI P, KOPSAFTOPOULOS F, NARDARI R, et al. Battery charge and health state monitoring via ultrasonic guided-wave-based methods using built-in piezoelectric transducers[C]// Smart Materials and Nondestructive Evaluation for Energy Systems 2017. Portland,OR: International Society for Optics and Photonics, 2017, 10171:1017108. |
[103] |
LADPLI P, KOPSAFTOPOULOS F, CHANG F K. Estimating state of charge and health of lithium-ion batteries with guided waves using built-in piezoelectric sensors/actuators[J]. Journal of Power Sources, 2018, 384:342-354.
doi: 10.1016/j.jpowsour.2018.02.056 |
[104] | BOMBIK A, HA S Y S, HAIDER M F, et al. Li-ion battery health estimation using ultrasonic guided wave data and an extended Kalman filter[C]//2021 Thirty-sixth Annual IEEE Applied Power Electronics Conference and Exposition (APEC). Electr Network: IEEE, 2021:962-966. |
[105] | 赵振喜, 李洪丰, 侯伟, 等. 国网吉林电力基建电力物联网研究与实践[J]. 电气工程学报, 2021, 16(2):166-173. |
ZHAO Zhenxi, LI Hongfeng, HOU Wei, et al. Research and practice of power iot in infrastructure construction in Jilin Branch of State Grid[J]. Journal of Electrical Engineering, 2021, 16(2):166-173. |
[1] | LI Tianliang, GUO Jinxiu, WU Dongjian, TAN Yuegang, ZHOU Zude. Recent Advances and Tendency of Optical Fiber Sensing Technology for Equipment Manufacturing and Operating States Monitoring in Extreme Environments [J]. Journal of Mechanical Engineering, 2022, 58(8): 27-53. |
[2] | YIN Guolu, JIANG Rui, XU Zhou, ZHOU Lei, NIU Yangyang, LÜ Lei, ZHU Tao. Fast and High Spatial Resolution Distributed Optical Fiber Sensing Technology and Its Application in Mechanical Deformation and Temperature Monitoring [J]. Journal of Mechanical Engineering, 2022, 58(8): 96-104. |
[3] | DANG Yuemao, ZHANG Xuechun, XU Chuyi, JIANG Quanyuan. Lithium-ion Battery State of Health Assessment Algorithm Based on DT Curve [J]. Journal of Electrical Engineering, 2022, 17(3): 58-65. |
[4] | WU Chunling, CHENG Yanqing, XU Xianfeng, MENG Jinhao, XIE Meimei. SOC Estimation of Lithium Battery Based on Monte Carlo and SH-AUKF Algorithm [J]. Journal of Electrical Engineering, 2022, 17(3): 66-75. |
[5] | ZHANG Bozhao, GOU Bin, XU Yanzhang. Effect Analysis of Recycling and Storage Conditions on Graphite/LiCoO2 Battery Life [J]. Journal of Electrical Engineering, 2022, 17(2): 38-48. |
[6] | ZHANG Chaolong, ZHAO Shaishai, HE Yigang. State-of-health Estimate for Lithium-ion Battery Using Information Entropy and PSO-LSTM [J]. Journal of Mechanical Engineering, 2022, 58(10): 180-190. |
[7] | CHEN Cheng, PI Zhiyong, ZHAO Yinglong, LIAO Xuan, ZHANG Mingmin, LI Yong. State of Charge Estimation with Adaptive Cataclysm Genetic Algorithm-recurrent Neural Network for Li-ion Batteries [J]. Journal of Electrical Engineering, 2022, 17(1): 86-94. |
[8] | FANG Hongyan, MA Ping. Adaptive Droop Control Strategy Considering Frequency Modulation Dead Band of Hybrid Energy Storage [J]. Journal of Electrical Engineering, 2021, 16(4): 213-222. |
[9] | PENG Peng, HU Zhenkai, LI Yuxuan, LIU Zejian, FAN Ziwei. Research on Coordination Control Strategy of Frequency Regulation in Grid with Energy Storage [J]. Journal of Electrical Engineering, 2021, 16(3): 106-114. |
[10] | JIA Jun, HU Xiaosong, DENG Zhongwei, XU Huachi, XIAO Wei, HAN Feng. Data-driven Comprehensive Evaluation of Lithium-ion Battery State of Health and Abnormal Battery Screening [J]. Journal of Mechanical Engineering, 2021, 57(14): 141-149,159. |
[11] | WU Shujie, WANG Jun, XIONG Rui, LI Xinggang. Calculation and Verification of Thermal Safety Boundary for Dynamic Equalization of Battery Pack [J]. Journal of Mechanical Engineering, 2021, 57(14): 160-167. |
[12] | GAO Mingkun, XU Hailiang, WU Mingbo. Review of SOC Estimation Methods for Power Battery Based on Equivalent Circuit Model [J]. Journal of Electrical Engineering, 2021, 16(1): 90-102. |
[13] | HAN Dongxu,ZHAO Kai,LIU Xin,YOU Rui. Energy Management Strategy of Hybrid Energy Storage System Considering the State of Charge of the Supercapacitor [J]. Journal of Electrical Engineering, 2020, 15(3): 31-37. |
[14] | WANG Yixiu, WEI Xuezhe, FANG Qiaohua, ZHU Jiangong, DAI Haifeng. Consistency Variation Law and Equalization Strategy of Electric Vehicle Battery for Maximizing the Life of the Battery Pack [J]. Journal of Mechanical Engineering, 2020, 56(22): 176-183. |
[15] | Guo Lizhi,Zhang Xing,Hu Chao,Liu Fang. State of Charge Control Strategy of VSG in Microgrid [J]. Journal of Electrical Engineering, 2016, 11(4): 29-34. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||