Online Fault Diagnosis of External Short Circuit for Lithium-Ion Battery Pack

Battery safety is one of the most crucial issues in the utilization of lithium-ion batteries (LiBs) for all-climate electric vehicles. Short circuit, overcharge, and overheat are three common field failures of LiBs. In this paper, online fault diagnosis for external short circuit (ESC) of LiB packs is investigated. The experiments are carried out to obtain and compare ESC characteristics of 18650-type NMC battery pack and single cell. Based on the analysis of experimental results, a two-step equivalent circuit model is established to describe the ESC process and an online model-based scheme is proposed to diagnose ESC faults of battery packs. The proposed scheme is evaluated by experimental data. The results show that it can effectively diagnose ESC faults in 3.5 s after their occurrences with the terminal voltage error less than 25 mV. The proposed scheme has shown great generalization ability. ESC faults of battery packs under different number of cells connected in series and unavailable current information can also be diagnosed at the terminal voltage error less than 48 and 60 mV, respectively.

[1]  Afshin Izadian,et al.  Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries , 2015, IEEE Transactions on Industrial Electronics.

[2]  Kuan-Cheng Chiu,et al.  An electrochemical modeling of lithium-ion battery nail penetration , 2014 .

[3]  Mingyi Chen,et al.  Experimental Study on the Combustion Characteristics of Primary Lithium Batteries Fire , 2014, Fire Technology.

[4]  Doron Aurbach,et al.  Performances and safety behaviour of rechargeable AA-size Li/LixMnO2 cell , 1995 .

[5]  Zeyu Chen,et al.  Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application , 2018 .

[6]  S. Chakraborty,et al.  Thermal runaway inhibitors for lithium battery electrolytes , 2006 .

[7]  Caisheng Wang,et al.  Active Diagnosability of Discrete Event Systems and its Application to Battery Fault Diagnosis , 2014, IEEE Transactions on Control Systems Technology.

[8]  A. Sahu,et al.  Online monitoring of fuel starvation and water management in an operating polymer electrolyte membrane fuel cell by a novel diagnostic tool based on total harmonic distortion analysis , 2018, Journal of Power Sources.

[9]  Bing Xia,et al.  External short circuit fault diagnosis for lithium-ion batteries , 2014, 2014 IEEE Transportation Electrification Conference and Expo (ITEC).

[10]  Shunichi Higuchi,et al.  Predicted and observed initial short circuit current for lead-acid batteries , 1986 .

[11]  Cheng Siong Chin,et al.  Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles , 2017 .

[12]  Mao-Sung Wu,et al.  Correlation between electrochemical characteristics and thermal stability of advanced lithium-ion batteries in abuse tests—short-circuit tests , 2004 .

[13]  Jinpeng Tian,et al.  Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles , 2016 .

[14]  Hongwen He,et al.  A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application , 2018, Journal of Cleaner Production.

[15]  Pierluigi Pisu,et al.  Model-based real-time thermal fault diagnosis of Lithium-ion batteries , 2016 .

[16]  Ilias Belharouak,et al.  High-energy cathode material for long-life and safe lithium batteries. , 2009, Nature materials.

[17]  Dongmin Im,et al.  LiFeO2-Incorporated Li2MoO3 as a Cathode Additive for Lithium-Ion Battery Safety , 2012 .

[18]  Michel Benne,et al.  Polymer electrolyte membrane fuel cell fault diagnosis based on empirical mode decomposition , 2015 .

[19]  Kevin L. Gering,et al.  Fluorinated phosphazene co-solvents for improved thermal and safety performance in lithium-ion battery electrolytes , 2014 .

[20]  Hitoshi Maruyama,et al.  Improving battery safety by reducing the formation of Li dendrites with the use of amorphous silicon polymer anodes , 2015, Scientific Reports.

[21]  Xiaosong Hu,et al.  A comparative study of equivalent circuit models for Li-ion batteries , 2012 .

[22]  P. Ramadass,et al.  Study of internal short in a Li-ion cell-II. Numerical investigation using a 3D electrochemical-thermal model , 2014 .

[23]  R. Spotnitz,et al.  Abuse behavior of high-power, lithium-ion cells , 2003 .

[24]  Lei Mao,et al.  Effectiveness of a Novel Sensor Selection Algorithm in PEM Fuel Cell On-Line Diagnosis , 2018, IEEE Transactions on Industrial Electronics.

[25]  P. Van den Bossche,et al.  A review of international abuse testing standards and regulations for lithium ion batteries in electric and hybrid electric vehicles , 2018 .

[26]  Yongdong Li,et al.  Fault detection and isolation for Polymer Electrolyte Membrane Fuel Cell systems by analyzing cell voltage generated space , 2015 .

[27]  Jonghyeon Kim,et al.  On-line and real-time diagnosis method for proton membrane fuel cell (PEMFC) stack by the superposition principle , 2016 .

[28]  E. Takeuchi,et al.  Abuse Testing of Lithium-Ion Batteries: Characterization of the Overcharge Reaction of LiCoO2/Graphite Cells , 2001 .

[29]  G. Offer,et al.  Lithium sulfur battery nail penetration test under load , 2015 .

[30]  Tung-Kuan Liu,et al.  Optimal design of digital IIR filters by using hybrid taguchi genetic algorithm , 2006, IEEE Trans. Ind. Electron..

[31]  Rudolf Scitovski,et al.  Solving the parameter identification problem of mathematical models using genetic algorithms , 2004, Appl. Math. Comput..

[32]  M. Behm,et al.  Investigation of Short-Circuit Scenarios in a Lithium-Ion Battery Cell , 2012 .