Expanding the usable life of rechargeable Lithium-ion batteries in numerous applications calls for an effective evaluation of probable faults and their diagnosis and control methods. Plenty of work has focused on Lithium-ion battery systems’ fault detection and diagnosis techniques. However, seldom of them are devoted to the comprehensive analysis and comparison of the fault-tolerant control schemes. To fill this gap, a review of the most up-to-date fault-tolerant control methods applied to the Lithium-ion battery systems is conducted in this paper. They are classified as active, passive, and integrated methods. To this end, this paper first presents a detailed description of fault types in Lithium-ion batteries, followed by a proposed classification of fault detection and diagnostic methods. Finally, the paper concludes with a comprehensive discussion of the advantages and disadvantages of the reviewed algorithms.
AbbaspourAMokhtariSSargolzaeiA, et al. (2020) A survey on active fault-tolerant control systems. Electronics9(9): 1513.
2.
AblayG (2013a) An observer-based fault diagnosis in battery systems of hybrid vehicles. In: 2013 8th international conference on electrical and electronics engineering (ELECO), Bursa, Turkey, 28–30 November, pp. 238–242. New York: IEEE.
3.
AblayG (2013b) Online condition monitoring of battery systems with a nonlinear estimator. IEEE Transactions on Energy Conversion29(1): 232–239.
4.
AlaviSMSamadiMFSaifM (2013a) Diagnostics in lithium-ion batteries: Challenging issues and recent achievements. In: FathiM (ed.) Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives. Berlin: Springer, pp. 277–291.
5.
AlaviSMMSamadiMFSaifM (2013b) Plating mechanism detection in lithium-ion batteries, by using a particle-filtering based estimation technique. In: 2013 American control conference, Washington, DC, 17–19 June, pp. 4356–4361. New York: IEEE.
6.
BagheriFKhaloozadedHAbbaszadehK (2007) Stator fault detection in induction machines by parameter estimation, using adaptive Kalman filter. In: 2007 Mediterranean conference on control & automation, Athens, 27–29 June, pp. 1–6. New York: IEEE.
7.
BerdichevskyGKeltyKStraubelJ, et al. (2006) The Tesla roadster battery system. Tesla Motors1(5): 1–5.
8.
ChaoWChunboZJinleiS, et al. (2016) Fault mechanism study on Li-ion battery at over-discharge and its diagnosis approach. IET Electrical Systems in Transportation7(1): 48–54.
9.
ChenMBaiFLinS, et al. (2019) Performance and safety protection of internal short circuit in lithium-ion battery based on a multilayer electro-thermal coupling model. Applied Thermal Engineering146: 775–784.
10.
ChenMBaiFSongW, et al. (2017) A multilayer electro-thermal model of pouch battery during normal discharge and internal short circuit process. Applied Thermal Engineering120: 506–516.
11.
ChenWChenWSaifM, et al. (2014) Simultaneous fault isolation and estimation of lithium-ion batteries via synthesized design of Luenberger and learning observers. IEEE Transactions on Control Systems Technology22(1): 290–298.
12.
ChenZXiongRLuJ, et al. (2018) Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application. Applied Energy213: 375–383.
13.
ChenZXiongRTianJ, et al. (2016) Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles. Applied Energy184: 365–374.
14.
DeySAyalewB (2015) A diagnostic scheme for detection, isolation and estimation of electrochemical faults in lithium-ion cells. In: Dynamic systems and control conference, Columbus, OH, 28–30 October, vol. 57243, p. V001T13A001. New York: American Society of Mechanical Engineers.
15.
DeySBironZATatipamulaS, et al. (2015) On-board thermal fault diagnosis of lithium-ion batteries for hybrid electric vehicle application. IFAC-PapersOnline48(15): 389–394.
16.
DeySBironZATatipamulaS, et al. (2016a) Model-based real-time thermal fault diagnosis of lithium-ion batteries. Control Engineering Practice56: 37–48.
17.
DeySMohonSPisuP, et al. (2016b) Sensor fault detection, isolation, and estimation in lithium-ion batteries. IEEE Transactions on Control Systems Technology24(6): 2141–2149.
18.
DeySPerezHEMouraSJ (2017) Model-based battery thermal fault diagnostics: Algorithms, analysis, and experiments. IEEE Transactions on Control Systems Technology27(2): 576–587.
19.
DijouxESteinerNYBenneM, et al. (2017) A review of fault tolerant control strategies applied to proton exchange membrane fuel cell systems. Journal of Power Sources359: 119–133.
20.
DongTWangYPengP, et al. (2019) Electrical-thermal behaviors of a cylindrical graphite-NCA Li-ion battery responding to external short circuit operation. International Journal of Energy Research43(4): 1444–1459.
21.
DuY (2014) Research on decentralized control and active fault tolerant control for reconfigurable manipulator. PhD Thesis, Department of Communication Engineering, Jilin University, Changchun, China.
22.
DucardGJ (2009) Fault-tolerant Flight Control and Guidance Systems: Practical Methods for Small Unmanned Aerial Vehicles. Berlin: Springer Science & Business Media.
23.
DziekanLWitczakMKorbiczJ (2011) Active fault-tolerant control design for Takagi-Sugeno fuzzy systems. Bulletin of the Polish Academy of Sciences. Technical Sciences59(1): 93–102.
24.
El MejdoubiAChaouiHGualousH, et al. (2018) Lithium-ion batteries health prognosis considering aging conditions. IEEE Transactions on Power Electronics34(7): 6834–6844.
25.
EscobetTBregonAPulidoB, et al. (2019) Fault Diagnosis of Dynamic Systems: Quantitative and Qualitative Approaches. Berlin: Springer.
26.
FanCGrandjeanTRO’ReganK, et al. (2021) Understanding non-linearity in electrochemical systems using multisine-based non-linear characterization. Transactions of the Institute of Measurement and Control. Epub ahead of print 22 September. DOI: 10.1177/01423312211045991.
27.
FengXPanYHeX, et al. (2018) Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm. Journal of Energy Storage18: 26–39.
28.
FengXWengCOuyangM, et al. (2016) Online internal short circuit detection for a large format lithium ion battery. Applied Energy161: 168–180.
29.
FilippettiFMartelliMFranceschiniG, et al. (1992) Development of expert system knowledge base to on-line diagnosis of rotor electrical faults of induction motors. In: Conference record of the 1992 IEEE industry applications society annual meeting, Houston, TX, 4–9 October, pp. 92–99. New York: IEEE.
30.
GalushkinNEYazvinskayaNNGalushkinDN (2018) Mechanism of thermal runaway in lithium-ion cells. Journal of the Electrochemical Society165(7): A1303–A1308.
31.
GaoZJiaJXieJ, et al. (2015) Modelling and simulation of a 12-cell battery power system with fault control for underwater robot. In: 2015 IEEE 7th international conference on cybernetics and intelligent systems (CIS) and IEEE conference on robotics, automation and mechatronics (RAM), Siem Reap, Cambodia, 15–17 July, pp. 261–267. New York: IEEE.
32.
GhaeminezhadNOuyangQHuX, et al. (2021) Active cell equalization topologies analysis for battery packs: A systematic review. IEEE Transactions on Power Electronics36(8): 9119–9135.
33.
GuAZhangX (2016) Fault diagnosis system for power battery based on RBF neural network. Chinese Journal of Power Sources40(10): 1943–1945.
34.
GuoHWenJJiangJ, et al. (2011) New method of insulation detection for electrical vehicle. Journal of Electronic Measurement and Instrument25(3): 253–257.
35.
GuoRLuLOuyangM, et al. (2016) Mechanism of the entire overdischarge process and overdischarge-induced internal short circuit in lithium-ion batteries. Scientific Reports6(1): 1–9.
36.
HashemiSREsmaeeliRNazariA, et al. (2020) A fast diagnosis methodology for typical faults of a lithium-ion battery in electric and hybrid electric aircraft. Journal of Electrochemical Energy Conversion and Storage17(1): 1–20.
37.
HashemiSRNazariAEsmaeeliR, et al. (2018) Fast fault diagnosis of a lithium-ion battery for hybrid electric aircraft. In: ASME 2018 power conference collocated with the ASME 2018 12th international conference on energy sustainability and the ASME 2018 nuclear forum, Lake Buena Vista, FL, 24–28 June, p. V001T06A026. New York: American Society of Mechanical Engineers Digital Collection.
38.
HeldMBrönnimannR (2016) Safe cell, safe battery fire investigation using FMEA, FTA and practical experiments. Microelectronics Reliability64: 705–710.
39.
HinzH (2019) Comparison of lithium-ion battery models for simulating storage systems in distributed power generation. Inventions4(3): 41.
40.
HongJWangZLiuP (2017) Big-data-based thermal runaway prognosis of battery systems for electric vehicles. Energies10(7): 919.
41.
HongbinLZhaoQYangZ (2007) Reliability modeling of fault tolerant control systems. International Journal of Applied Mathematics and Computer Science17(4): 491–504.
42.
HuXZhangKFengF, et al. (2019) Battery pack multi fault comprehensive diagnosis method based on combination of model and signal processing. China Patent CN 110441695 A.
43.
HuXZhangKLiuK, et al. (2020) Advanced fault diagnosis for lithium-ion battery systems: A review of fault mechanisms, fault features, and diagnosis procedures. IEEE Industrial Electronics Magazine14(3): 65–91.
44.
HuangGLiZFukushimaEF, et al. (2020) An equivalent-input-disturbance approach for current sensor faults estimation and rejection in permanent magnet synchronous motor drives. Transactions of the Institute of Measurement and Control42(3): 365–373.
45.
IsermannR (2005) Model-based fault-detection and diagnosis–status and applications. Annual Reviews in Control29(1): 71–85.
JiNLiuJ (2020) Vibration control for a flexible satellite with adaptive actuator fault-tolerant and input quantization. Transactions of the Institute of Measurement and Control42(3): 451–460.
48.
KangYDuanBShangY, et al. (2017) Multi-fault online detection method for series-connected battery packs. In: 2017 Chinese automation congress (CAC), Jinan, China, 20–22 October, pp. 235–240. New York: IEEE.
49.
KangYDuanBZhouZ, et al. (2019) A multi-fault diagnostic method based on an interleaved voltage measurement topology for series connected battery packs. Journal of Power Sources417: 132–144.
50.
KangYYangXZhouZ, et al. (2021) A comparative study of fault diagnostic methods for lithium-ion batteries based on a standardized fault feature comparison method. Journal of Cleaner Production278: 123424.
51.
KimGHSmithKIrelandJ, et al. (2012) Fail-safe design for large capacity lithium-ion battery systems. Journal of Power Sources210: 243–253.
52.
KinnaertM (1996) Design of redundancy relations for failure detection and isolation by constrained optimization. International Journal of Control63(3): 609–622.
53.
KorbiczJKoscielnyJM (2010) Modeling, Diagnostics and Process Control: Implementation in the Diaster System. Berlin: Springer Science & Business Media.
54.
KorbiczJKoscielnyJMKowalczukZ, et al. (2012) Fault Diagnosis: Models, Artificial Intelligence, Applications. Berlin: Springer Science & Business Media.
LebretonCBenneMDamourC, et al. (2015) Fault tolerant control strategy applied to PEMFC water management. International Journal of Hydrogen Energy40(33): 10636–10646.
57.
LebretonCDamourCBenneM, et al. (2016) Passive fault tolerant control of PEMFC air feeding system. International Journal of Hydrogen Energy41(34): 15615–15621.
58.
LeeJLeeILeeM, et al. (2017) Fault tolerant wireless battery area network for a smart battery management system. US Patent 9,564,762.
59.
LelieMBraunTKnipsM, et al. (2018) Battery management system hardware concepts: An overview. Applied Sciences8(4): 534.
60.
LiLLiuJ (2020) Neural-network-based adaptive fault-tolerant vibration control of single-link flexible manipulator. Transactions of the Institute of Measurement and Control42(3): 430–438.
61.
LiXWangZ (2018) A novel fault diagnosis method for lithium-ion battery packs of electric vehicles. Measurement116: 402–411.
62.
LiYLiKLiuX, et al. (2021) Lithium-ion battery capacity estimation—A pruned convolutional neural network approach assisted with transfer learning. Applied Energy285: 116410.
63.
LiYWangCGongJ (2016) A combination Kalman filter approach for state of charge estimation of lithium-ion battery considering model uncertainty. Energy109: 933–946.
64.
LiangYWLiawDCLeeTC (2000) Reliable control of nonlinear systems. IEEE Transactions on Automatic Control45(4): 706–710.
65.
LiaoFWangJLYangGH (2002) Reliable robust flight tracking control: An LMI approach. IEEE Transactions on Control Systems Technology10(1): 76–89.
66.
LinMWuDZhengG, et al. (2021) A novel long short-term memory network for lithium-ion battery health diagnosis using charging curve. Transactions of the Institute of Measurement and Control. Epub ahead of print 30 August. DOI: 10.1177/01423312211040901.
67.
LishuoLMingxuanZLanguangL, et al. (2018) Recent progress on mechanism and detection of internal short circuit in lithium-ion batteries. Energy Storage Science and Technology7(6): 1003.
68.
LiuKGaoYZhuC, et al. (2022a) Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries. Control Engineering Practice124: 105176.
69.
LiuKLiKPengQ, et al. (2019) A brief review on key technologies in the battery management system of electric vehicles. Frontiers of Mechanical Engineering14(1): 47–64.
70.
LiuKWeiZZhangC, et al. (2022b) Towards long lifetime battery: Ai-based manufacturing and management. IEEE/CAA Journal of Automatica Sinica9: 1139–1165.
71.
LiuXQZhangHYLiuJ, et al. (2000) Fault detection and diagnosis of permanent-magnet dc motor based on parameter estimation and neural network. IEEE Transactions on Industrial Electronics47(5): 1021–1030.
72.
LiuZAhmedQRizzoniG, et al. (2014) Fault detection and isolation for lithium-ion battery system using structural analysis and sequential residual generation. In: ASME 2014 dynamic systems and control conference, San Antonio, TX, 22–24 October, p. V002T36A005. New York: American Society of Mechanical Engineers Digital Collection.
73.
LiuZAhmedQZhangJ, et al. (2016) Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications. Control Engineering Practice52: 46–58.
74.
LiuZHeH (2015) Model-based sensor fault diagnosis of a lithium-ion battery in electric vehicles. Energies8(7): 6509–6527.
75.
LiuZHeH (2017) Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter. Applied Energy185: 2033–2044.
76.
LiuZHeHAhmedQ, et al. (2015) Structural analysis based fault detection and isolation applied for a lithium-ion battery pack. IFAC-PapersOnline48(21): 1465–1470.
77.
LombardiWZarudnievMLesecqS, et al. (2014) Sensors fault diagnosis for a BMS. In: 2014 European control conference (ECC), Strasbourg, 24–27 June, pp. 952–957. New York: IEEE.
78.
MaMWangYDuanQ, et al. (2018) Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis. Energy164: 745–756.
79.
MaharjanLYamagishiTAkagiH, et al. (2010) Fault-tolerant operation of a battery-energy-storage system based on a multilevel cascade PWM converter with star configuration. IEEE Transactions on Power Electronics25(9): 2386–2396.
80.
MahmoudMJiangJZhangY (2003) Active Fault Tolerant Control Systems: Stochastic Analysis and Synthesis, vol. 287. Berlin: Springer Science & Business Media.
81.
MajdzikPAkielaszek-WitczakASeyboldL (2016) A fault-tolerant approach to the control of a battery assembly system. In: 2016 21st international conference on methods and models in automation and robotics (MMAR), Miedzyzdroje, 28 August–1 September, pp. 601–606. New York: IEEE.
82.
MalyszPGuRYeJ, et al. (2016) State-of-charge and state-of-health estimation with state constraints and current sensor bias correction for electrified powertrain vehicle batteries. IET Electrical Systems in Transportation6(2): 136–144.
83.
MaoBChenHCuiZ, et al. (2018) Failure mechanism of the lithium ion battery during nail penetration. International Journal of Heat and Mass Transfer122: 1103–1115.
84.
MarcickiJOnoriSRizzoniG (2010) Nonlinear fault detection and isolation for a lithium-ion battery management system. Dynamic Systems and Control Conference44175: 607–614.
85.
MuddappaVKAnwarS (2014) Electrochemical model based fault diagnosis of Li-ion battery using fuzzy logic. In: ASME 2014 international mechanical engineering congress and exposition, Montreal, QC, Canada, 14–20 November, vol. 46483, p. V04BT04A048. New York: American Society of Mechanical Engineers Digital Collection.
86.
NahaAKhandelwalAAgarwalS, et al. (2020) Internal short circuit detection in Li-ion batteries using supervised machine learning. Scientific Reports10(1): 1–10.
87.
NofalMAl-HallajSPanY (2020) Thermal management of lithium-ion battery cells using 3D printed phase change composites. Applied Thermal Engineering171: 115–126.
88.
NouraHTheilliolDPonsartJC, et al. (2009) Fault-tolerant Control Systems: Design and Practical Applications. Berlin: Springer.
89.
NozariHACastaldiPBanadakiHD, et al. (2019) Novel non-model-based fault detection and isolation of satellite reaction wheels based on a mixed-learning fusion framework. Ifac-papersonline52(12): 194–199.
90.
NozariHANazeriSBanadakiHD, et al. (2018) Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques—A comparative study. Control Engineering Practice73: 134–148.
91.
OfferGJYufitVHoweyDA, et al. (2012) Module design and fault diagnosis in electric vehicle batteries. Journal of Power Sources206: 383–392.
OuyangDChenMLiuJ, et al. (2018) Investigation of a commercial lithium-ion battery under overcharge/over-discharge failure conditions. RSC Advances8(58): 33414–33424.
94.
OuyangQChenJZhengJ (2020a) State-of-charge observer design for batteries with online model parameter identification: A robust approach. IEEE Transactions on Power Electronics35(6): 5820–5831.
95.
OuyangQHanWZouC, et al. (2020b) Cell balancing control for lithium-ion battery packs: A hierarchical optimal approach. IEEE Transactions on Industrial Informatics16(8): 5065–5075.
96.
OuyangQWangZLiuK, et al. (2020c) Optimal charging control for lithium-ion battery packs: A distributed average tracking approach. IEEE Transactions on Industrial Informatics16(5): 3430–3438.
97.
OuyangQXuGLiuK, et al. (2019) Wireless battery charging control for electric vehicles: A user-involved approach. IET Power Electronics12(10): 2688–2696.
98.
PanchalSMcgroryJKongJ, et al. (2017) Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions. International Journal of Energy Research41(15): 2565–2575.
99.
ParkSHimmelblauDM (1983) Fault detection and diagnosis via parameter estimation in lumped dynamic systems. Industrial & Engineering Chemistry Process Design and Development22(3): 482–487.
100.
Paté-CornellME (1984) Fault trees vs. event trees in reliability analysis. Risk Analysis4(3): 177–186.
101.
QuZIhlefeldCMJinY, et al. (2003) Robust fault-tolerant self-recovering control of nonlinear uncertain systems. Automatica39(10): 1763–1771.
102.
RheinfeldANoelAWilhelmJ, et al. (2018) Quasi-isothermal external short circuit tests applied to lithium-ion cells: Part I. Measurements. Journal of the Electrochemical Society165(14): A3427.
103.
SaderMWangFLiuZ, et al. (2021) Fuzzy fault-tolerant containment control for multi-agent systems with unknown nonlinear dynamics. Transactions of the Institute of Measurement and Control43: 3663–3671.
104.
SaidaniFHutterFXScurtuRG, et al. (2017) Lithium-ion battery models: A comparative study and a model-based powerline communication. Advances in Radio Science15: 83–91.
105.
SamantaBAl-BalushiK (2003) Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing17(2): 317–328.
106.
SeoMGohTParkM, et al. (2017) Detection of internal short circuit in lithium ion battery using model-based switching model method. Energies10(1): 76.
107.
SeyboldLWitczakMMajdzikP, et al. (2015a) Predictive fault-tolerant control for a battery assembly system. IFAC-Papersonline48(21): 476–483.
108.
SeyboldLWitczakMMajdzikP, et al. (2015b) Towards robust predictive fault–tolerant control for a battery assembly system. International Journal of Applied Mathematics and Computer Science25(4): 849–862.
109.
ShangHQiHLiuX, et al. (2019) Structural optimization of lithium-ion battery for improving thermal performance based on a liquid cooling system. International Journal of Heat and Mass Transfer130: 33–41.
110.
ShangYLuGKangY, et al. (2020) A multi-fault diagnosis method based on modified sample entropy for lithium-ion battery strings. Journal of Power Sources446: 227275.
111.
SidhuAIzadianAAnwarS (2015) Adaptive nonlinear model-based fault diagnosis of Li-ion batteries. IEEE Transactions on Industrial Electronics62(2): 1002–1011.
112.
SinghAIzadianAAnwarS (2013) Fault diagnosis of Li-ion batteries using multiple-model adaptive estimation. In: IECON 2013 - 39th annual conference of the IEEE industrial electronics society, Vienna, 10–13 November, pp. 3524–3529. New York: IEEE.
113.
SinghAIzadianAAnwarS (2014) Model based condition monitoring in lithium-ion batteries. Journal of Power Sources268: 459–468.
114.
SitterlyMYinGGWangC, et al. (2011) Noise estimation and bias correction in identification of battery models. In: 2011 IEEE power and energy society general meeting, Detroit, MI, 24–28 July, pp. 1–4. New York: IEEE.
115.
StetterR (2020) Fault-Tolerant Design and Control of Automated Vehicles and Processes. Berlin: Springer.
116.
SuiXHeSVilsenSB, et al. (2021) A review of non-probabilistic machine learning-based state of health estimation techniques for lithium-ion battery. Applied Energy300: 117346.
117.
TangXGaoFLaiX (2022a) Compressing and reconstructing the voltage data for lithium-ion batteries using model migration and un-eq4 sampling techniques. eTransportation13: 100186.
118.
TangXGaoFLiuK, et al. (2022b) A balancing current ratio based state-of-health estimation solution for lithium-ion battery pack. IEEE Transactions on Industrial Electronics69(8): 8055–8065.
119.
TangXLiuKLiK, et al. (2021a) Recovering large-scale battery aging dataset with machine learning. Patterns2(8): 100302.
120.
TangXWangYLiuQ, et al. (2021b) Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries. Iscience24(10): 103103.
121.
TranMKFowlerM (2020a) A review of lithium-ion battery fault diagnostic algorithms: Current progress and future challenges. Algorithms13(3): 62.
122.
TranMKFowlerM (2020b) Sensor fault detection and isolation for degrading lithium-ion batteries in electric vehicles using parameter estimation with recursive least squares. Batteries6(1): 1.
123.
WangSZhangXChenW, et al. (2021) State of health prediction based on multi-kernel relevance vector machine and whale optimization algorithm for lithium-ion battery. Transactions of the Institute of Measurement and Control. Epub ahead of print 6 September. DOI: 0.1177/01423312211042009.
124.
WangWZhangXXinC, et al. (2018) An experimental study on thermal management of lithium ion battery packs using an improved passive method. Applied Thermal Engineering134: 163–170.
125.
WangYTianJChenZ, et al. (2019a) Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles. Measurement131: 443–451.
126.
WangYHJiangCH (2015) Fuzzy neural network expert system for fault diagnosis in power lithium battery application. Electrical Measure Instrument52: 118–123.
127.
WangYWJiangJ-MChungY-H, et al. (2019b) Forced-air cooling system for large-scale lithium-ion battery modules during charge and discharge processes. Journal of Thermal Analysis and Calorimetry135(5): 2891–2901.
128.
WangZHongJLiuP, et al. (2017) Voltage fault diagnosis and prognosis of battery systems based on entropy and z-score for electric vehicles. Applied Energy196: 289–302.
129.
WeiYAgelin-ChaabM (2020) Experimental study of a thermal cooling technique for cylindrical batteries. Journal of Electrochemical Energy Conversion and Storage17(2): 021102.
130.
WeiZZhaoJHeH, et al. (2021) Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement. Journal of Power Sources489: 229462.
131.
WidodoAYangBS (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing21(6): 2560–2574.
132.
WitczakM (2006) Advances in model-based fault diagnosis with evolutionary algorithms and neural networks. International Journal of Applied Mathematics and Computer Science16(1): 85.
133.
WitczakM (2007) Modelling and Estimation Strategies for Fault Diagnosis of Non-linear Systems: From Analytical to Soft Computing Approaches, vol. 354. Berlin: Springer Science & Business Media.
134.
WitczakM (2014) Fault diagnosis and fault-tolerant control strategies for non-linear systems. Lecture Notes in Electrical Engineering266: 375–392.
135.
WuBYufitVMarinescuM, et al. (2013) Coupled thermal–electrochemical modelling of uneven heat generation in lithium-ion battery packs. Journal of Power Sources243: 544–554.
136.
WuCZhuCGeY (2017) A new fault diagnosis and prognosis technology for high-power lithium-ion battery. IEEE Transactions on Plasma Science45(7): 1533–1538.
137.
WuCZhuCGeY, et al. (2015) A review on fault mechanism and diagnosis approach for Li-ion batteries. Journal of Nanomaterials2015: 1–9.
138.
WuCZhuCGeY, et al. (2016) A diagnosis approach for typical faults of lithium-ion battery based on extended Kalman filter. International Journal of Electrochemical Science11: 5289–5301.
139.
XiaBMiC (2016) A fault-tolerant voltage measurement method for series connected battery packs. Journal of Power Sources308: 83–96.
140.
XiaBChenZMiC, et al. (2014) External short circuit fault diagnosis for lithium-ion batteries. In: 2014 IEEE transportation electrification conference and expo (ITEC), Dearborn, MI, 15–18 June, pp. 1–7. New York: IEEE.
141.
XiaBMiCChenZ, et al. (2015) Multiple cell lithium-ion battery system electric fault online diagnostics. In: 2015 IEEE transportation electrification conference and expo (ITEC), Dearborn, MI, 14–17 June, pp. 1–7. New York: IEEE.
142.
XiaBNguyenTYangJ, et al. (2016) The improved interleaved voltage measurement method for series connected battery packs. Journal of Power Sources334: 12–22.
143.
XiaGCaoLBiG (2017) A review on battery thermal management in electric vehicle application. Journal of Power Sources367: 90–105.
144.
XiongJBanvaitHLiL, et al. (2012) Failure detection for over-discharged Li-ion batteries. In: 2012 IEEE international electric vehicle conference, Greenville, SC, 4–8 March, pp. 1–5. New York: IEEE.
145.
XiongRMaSLiH, et al. (2020a) Toward a safer battery management system: A critical review on diagnosis and prognosis of battery short circuit. Iscience23(4): 101010.
146.
XiongRSunWYuQ, et al. (2020b) Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles. Applied Energy279: 115855.
147.
XiongRYangRChenZ, et al. (2019a) Online fault diagnosis of external short circuit for lithium-ion battery pack. IEEE Transactions on Industrial Electronics67(2): 1081–1091.
148.
XiongRYuQShenW (2018) Review on sensors fault diagnosis and fault-tolerant techniques for lithium ion batteries in electric vehicles. In: 2018 13th IEEE conference on industrial electronics and applications (ICIEA), Wuhan, China, 31 May–2 June, pp. 406–410. New York: IEEE.
149.
XiongRYuQShenW, et al. (2019b) A sensor fault diagnosis method for a lithium-ion battery pack in electric vehicles. IEEE Transactions on Power Electronics34(10): 9709–9718.
150.
XuDLiuGShiK (2017) Structure of fault-tolerant lithium-ion battery pack and fault detection method. China Patent CN 104578307 B.
151.
XuJWangJLiS, et al. (2016) A method to simultaneously detect the current sensor fault and estimate the state of energy for batteries in electric vehicles. Sensors16(8): 1328.
152.
XuLLiJOuyangM, et al. (2010) Active fault tolerance control system of fuel cell hybrid city bus. International Journal of Hydrogen Energy35(22): 12510–12520.
153.
YanJXuGXuY, et al. (2008) Battery state-of-charge estimation based on h-infinity filter for hybrid electric vehicle. International Conference on Control, Automation, Robotics and Vision2008: 464–469.
154.
YangFXiaoD (2006) Model and fault inference with the framework of probabilistic SDG. In: 2006 9th international conference on control, automation, robotics and vision, Singapore, 5–8 December.
155.
YangRXiongRHeH, et al. (2018) A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application. Journal of Cleaner Production187: 950–959.
156.
YangRXiongRMaS, et al. (2020) Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks. Applied Energy260: 114253.
157.
YaoLWangZMaJ (2015) Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles. Journal of Power Sources293: 548–561.
158.
YountLJBlevinsWAVermaIJ (2013) Fault-tolerant battery management system, circuits and methods. US Patent 8,598,840.
159.
YulinLBZ (1999) Parity space method for fault diagnosis of liquid rocket engine. Journal of Propulsion Technology6: 6–9.
160.
ZavalisTGBehmMLindberghG (2012) Investigation of short-circuit scenarios in a lithium-ion battery cell. Journal of the Electrochemical Society159(6): A848.
161.
ZhangHPeiLSunJ, et al. (2016) Online diagnosis for the capacity fade fault of a parallel-connected lithium ion battery group. Energies9(5): 387.
162.
ZhangYJiangJ (2001) Integrated active fault-tolerant control using IMM approach. IEEE Transactions on Aerospace and Electronic Systems37(4): 1221–1235.
163.
ZhangYJiangJ (2002) Active fault-tolerant control system against partial actuator failures. IEE Proceedings-control Theory and Applications149(1): 95–104.
164.
ZhangYJiangJ (2003) Bibliographical review on reconfigurable fault-tolerant control systems. IFAC Proceedings Volumes36(5): 257–268.
165.
ZhangYJiangJ (2008) Bibliographical review on reconfigurable fault-tolerant control systems. Annual Reviews in Control32(2): 229–252.
166.
ZhaoBLiCLiuD, et al. (2015a) Decentralized sliding mode observer based dual closed-loop fault tolerant control for reconfigurable manipulator against actuator failure. PLoS ONE10(7): e0129315.
167.
ZhaoRLiuJGuJ (2015b) The effects of electrode thickness on the electrochemical and thermal characteristics of lithium ion battery. Applied Energy139: 220–229.
168.
ZhaoRLiuJGuJ (2016a) Simulation and experimental study on lithium ion battery short circuit. Applied Energy173: 29–39.
169.
ZhaoSDuncanSRHoweyDA (2016b) Observability analysis and state estimation of lithium-ion batteries in the presence of sensor biases. IEEE Transactions on Control Systems Technology25(1): 326–333.
170.
ZhaoYLiuPWangZ, et al. (2017) Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods. Applied Energy207: 354–362.
171.
ZhengCChenZHuangD (2020) Fault diagnosis of voltage sensor and current sensor for lithium-ion battery pack using hybrid system modeling and unscented particle filter. Energy191: 116504.
172.
ZhengCGeYChenZ, et al. (2017) Diagnosis method for Li-ion battery fault based on an adaptive unscented Kalman filter. Energies10(11): 1810.
173.
ZhengYHanXLuL, et al. (2013) Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles. Journal of Power Sources223: 136–146.
174.
ZhongHLiuJWangL, et al. (2021) Bearing fault diagnosis based on kernel independent component analysis and antlion optimization. Transactions of the Institute of Measurement and Control43: 3573–3587.
175.
ZhouXZouLBriggsR (2009) Prognostic and diagnostic technology for dc actuated contactors and motor starters. IEICE Transactions on Electronics92(8): 1045–1051.
