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A reliability study of electric vehicle battery from the perspective of power supply system
Journal of Power Sources 451 (2020) 227805
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A reliability study of electric vehicle battery from the perspective of power supply system Xiong Shu a, b, Wenxian Yang c, Yingfu Guo a, Kexiang Wei b, c, *, Bo Qin b, Guanghui Zhu d a
School of Mechanical Engineering, Hunan University of Science and Technology, Hunan, Xiangtan, 411201, China Hunan Provincial Key Laboratory of Vehicle Power and Transmission System, Hunan Institute of Engineering, Xiangtan, 411104, China c School of Engineering, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK d Technology Center of Xiangtan Electric Manufacturing Group, Hunan, Xiangtan, 411201, China b
H I G H L I G H T S
� The reliability of EV’s entire power supply system is assessed. � BMS controller is most vulnerable at subsystem level. � Battery cell and SMCs are most unreliable at component level. � The entire system degrades more rapidly than individual power supply components do. A R T I C L E I N F O
A B S T R A C T
Keywords: Reliability Electric vehicle Battery system Fault tree analysis
Battery cells and modules are critical to the reliability of the power supply system of an EV. Therefore, their reliability is mainly concern before. However, EV’s power supply system is a complex system. Besides battery cells and modules, it also includes many other components, the failure of which can cause a breakdown of the system as well. So, the reliability of the battery should be evaluated from the perspective of the entire power supply system rather than only considering the reliability of the battery cells and modules. This motivates the research of this paper. In the present study, the failure rates of all major components are predicted first. Then, based on the prediction results, the reliability of the entire battery system is assessed. From the assessment re sults, it is found that the reliability of the entire battery system is indeed much lower than those of individual components including battery modules. Moreover, as compared to the degradation of individual components, the entire system degrades more rapidly over time. Additionally, it is interestingly found that the Battery Manage ment System (BMS) controller is not as reliable as the battery modules in the battery system considered in this paper.
1. Introduction Nowadays, the transportation sector has been recognised as one of the major sources of pollutant emissions [1]. Particularly in the light of the fact that traditional fossil fuels are depleting [2] and the world is facing pressure to mitigate climate change [3], electric vehicles (EVs) have attracted more interest than ever before and the global EV market is booming today as a consequence. Taking the Chinese market as an example, there are 984,000 pure electric vehicles sold in China in 2018, which is an increase of 50.8% over the same period of the previous year
[4]. It is expected that pure EVs will replace traditional diesel and petrol-fuelled vehicles in the near future. This implies that more EVs will be on the road in the coming years. In an EV, the battery is the only system to power the vehicle. So, the reliability of the battery system has a significant impact on the safety, reliability, and availability of the EV. In 2018, more than 40 reported EV failures were related to batteries [5]. Therefore, how to improve the reliability of the EV’s battery has long been concerned by both the public and the EV industry. In the past years, much effort has been made to address this issue by using various approaches. For example, much
* Corresponding author. Hunan Provincial Key Laboratory of Vehicle Power and Transmission System, Hunan Institute of Engineering, Xiangtan, 411104, China. E-mail addresses: [email protected] (X. Shu), [email protected] (W. Yang), [email protected] (Y. Guo), [email protected] (K. Wei), qinbo@ hnie.edu.cn (B. Qin), [email protected] (G. Zhu). https://doi.org/10.1016/j.jpowsour.2020.227805 Received 25 December 2019; Received in revised form 21 January 2020; Accepted 24 January 2020 Available online 31 January 2020 0378-7753/© 2020 Elsevier B.V. All rights reserved.
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Fig. 1. Schematic diagram of the battery system in a pure electric van.
research was conducted to improve EV’s lithium battery from the point of view of battery materials. To name a few, a novel electrolyte syn thesized through environmentally friendly procedures and tailored for the use in polymer-based lithium batteries is proposed in Ref. [6], and the experimental results have shown that the electrolyte has a high ionic conductivity in a temperature range of 40 � C–100 � C; similar studies were also conducted in Refs. [7,8] in order to achieve composite poly mer electrolyte for high-rate, ambient temperature lithium batteries; a preparation procedure of crosslinked polymer electrolyte for application in sodium-ion batteries was presented in Ref. [9]. In Ref. [10], the electrochemical response of electrodes based on gallium oxide nanorods in lithium and sodium cells was evaluated for the first time; efficient hybrid solid polymer/ceramic electrolytes were presented in Ref. [11], and it is said that the achieved electrolytes represent a step further to ward truly all-solid-state batteries conceived for high energy/power technologies; the nickel–rich layered positive electrode materials of lithium-ion batteries was investigated in Ref. [12], and the problems, modification and future research directions of these materials were discussed, respectively; the perspectives of lithium metal negative electrodes of lithium-ion batteries were systematically analysed in Ref. [13], and so on. Since electrolytes and electrodes are important parts of battery cells, all these available studies are great contributions to improving the reliability of the EV’s battery system. Besides these, the effort was also made to increase the reliability of the battery pack system [14,15]. For example, a reliability-based design concept for Li-ion bat tery pack was proposed in Ref. [16], which provided a way to improve the reliability of battery pack by optimising the configuration of redundant cells. In Ref. [14], the reliability of the battery packs that have different numbers and configurations of redundant cells were compared and it was found that the reliability did not monotonically increase with the increase of the number of redundant cells due to the thermal disequilibrium effects. A modified reliability model for lithium-ion battery packs based on the stochastic capacity degradation and dynamic response impedance was proposed in Ref. [17], and it was found that Multiphysics-based system reliability model enabled more scientific and accurate reliability evaluation of the battery packs. In Refs. [18,19], a multi-fault diagnosing method was proposed to improve the reliability of the operation of the battery system, and the method was further improved in Ref. [20] by adding a function to estimate the charging state of the battery pack. The Monte Carlo simulation approach
was adopted in Refs. [21,22] and statistical analysis and cluster analysis were used in Ref. [23] to predict the reliability of the battery. In Ref. [24], the performance and the reliability of the battery subjected to varying operational conditions were investigated. In addition, the in fluence of the interaction between different battery cells during the charging and discharging processes and the influence of the degradation of individual cells on the degradation of a battery pack were also investigated by scholars. For example, it was found that the interaction between battery cells could affect the performance and lifetime of a battery pack in Ref. [25,26]; the wiener process was used to analyze the reliability of lithium-ion battery packs based on a lithium-ion battery degradation test in Ref. [25]. Besides these, the impact of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) strategies was also considered in the EV battery reliability assessment [27]. All these studies have greatly improved the reliability of EV batteries. However, the battery system in an EV is a complex system. In addition to battery cells and battery modules, it also has many other components, such as battery system connectors, the controller of the battery management system (BMS), power electronic components, signal detection components, etc. They are integrated together and are requested to work synchronously as one system. This means that the failure of any of them can cause the breakdown of the entire battery system and thus the breakdown of the vehicle. So, the reliability assessment of EV batteries should not be based solely on reviewing the reliability of battery cells and battery modules. Instead, the assessment should be performed from the perspective of the entire power supply system. However, this has never been done before. The purpose of this paper is to fill this knowledge gap by investigating the reliability of the entire battery system and understanding its degradation over time. The study will be conducted based on fault tree analysis (FTA), due to the FTA has been successfully applied before to achieve various goals of reliability and safety assessment [28–31]. Herein, it is worth noting that the battery systems in different types of EVs may be more or less different from each other. In order to facilitate the research, the battery system in a pure electric van is considered in this paper as an example to demonstrate the proposed idea. The remaining parts of the paper are organized below. In Section 2, the battery system in a pure electric van will be briefly explained first; in Section 3, the fault trees of the battery system will be established, and following which the mathematical methods for esti mating the failure rates of basic events will be developed; the reliability 2
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Fig. 2. Reliability block diagram and fault tree model of the battery system. (a) Reliability block diagram of the battery system. (b) Fault tree model of the bat tery system.
of the battery system in a pure electric van is investigated in Section 4, in which the failure rate and reliability of both individual battery com ponents and the entire battery system are evaluated; finally, the paper will be completed in Section 5 with a few key conclusions.
battery cells, and analyses the measured data. Then, based on the analysis results it takes an appropriate control strategy to prevent the presence of abnormal incidents, such as excess discharge, overcharge, and excess temperature of the battery system. As shown in Fig. 1, the BMS controller is composed of two parts, i.e. a master controller and a slave controller. The former is responsible for communicating with the vehicle control unit (VCU) and developing control strategies; the latter is responsible for collecting the measurement results of current, voltage and temperature from individual battery cells and implementing energy balancing management of the cells [34]. Power electronic components comprise fuse and relay. The function of the fuse is to protect the battery cells from being damaged by the excessive current in the presence of a short-circuit fault; the function of a relay is to control the power-on and power-off operation of the electric system. In case of emergency, the relay can be used to cut off the power supply to the electric system to guarantee the safety of the battery system. Signal detection components of the battery system include current sensors, voltage sensors, and temperature sensors. Based on the signals measured by these sensors, the BMS controller can assess the operational status of all battery cells and send orders to the system in real-time. From the above description, it is found that the failure of any of these components may result in the failure of the entire battery system. However, so far, it is still unknown how significant these components can affect the reliability of the entire power supply system and how the
2. The battery system in a pure electric van As opposed to traditional diesel and petrol-fuelled vehicles, the power supply of an EV is via a specifically designed battery system. As shown in Fig. 1, the battery system in a pure electric van is composed of multiple components. Besides the battery cells and battery modules, the battery system also has the controller of signal detection devices, power electronic components and so on. These components are integrated together and are requested to work synchronously as one system in the normal operation of an EV. In Fig. 1, the battery module is an energy storage component in the battery system, which is composed of multiple battery cells that are connected either in series or in parallel. When any of these battery cells fail or are aged, other cells will have to share more load to supply the required power [32]. This is harmful to the health of the battery cells as it can accelerate the aging of the battery cells as well. Therefore, the quality of the battery cells in battery modules has a profound impact on the reliability of the entire battery system [33]. The BMS controller is responsible to monitor and manage the battery system. It measures the voltage, current, and temperature of individual 3
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3.2. Estimation of failure rate
Table 1 Intermediate events failure of battery system components. Intermediate event
Code
Failure rate
Intermediate event
Code
Failure rate
Failure of battery system connectors and battery module Failure of BMS controller Failure of power electronic components Failure of signal detection components
gb1
λgb1
Failure of connectors for battery system
gb5
λgb5
gb2
λgb2
gb6
λgb6
gb3
λgb3
Failure of battery module Failure of master controller
gb7
λgb7
gb4
λgb4
Failure of slave controller
gb8
λgb8
From Fig. 2(b), it is seen that the failure of the ‘battery system con nectors and battery module’ is due to the fault occurring in either the battery system connectors or in the battery module. Battery system connectors include power output connectors (i.e. battery bus positive connector, battery bus negative connector) and signal output connector. As the failure of the battery module can happen due to the failure of battery cells, signal connectors for battery cells, and the failure of fastening screws, the failure of the ‘battery system connectors and bat tery module’ can be expressed as
where λgb5 and λgb6 have been explained in Table 1. To ease under standing, the assembling process of the battery system is shown in Fig. 3. From Fig. 3, it is seen that the battery system is composed of a number of battery modules that are connected in series, whereas every battery module consists of a number of battery cells that are connected in parallel. The number of battery modules and the number of battery cells in each module is dependent on the requirements of the voltage, current, energy storage capacity, and the size of the EV, as shown in Fig. 4. As shown in Fig. 4(d), a single battery cell module comprises mul tiple battery cells that are connected in parallel. In this study, it is assumed that the cells in a battery module are independent of each other [35,36]. Then, for a battery system that has y battery modules connected in series and each battery module has x battery cells connected in par allel, the failure rate of the battery module can be expressed as:
Table 2 Basic events failure of battery system components or parts. Basic event
Code
Failure rate
Basic event
Code
Failure rate
Failure of signal connector for battery system Failure of power connector for battery system Failure of battery cells module Failure of signal connectors for battery cells Failure of fastening screw for battery module Failure of PCB for master controller Failure of SMCs for master controller Failure of master chip
eb1
λeb1
Failure of PCB for slave controller
eb9
λeb9
eb2
λeb2
Failure of SMCs for slave controller
eb10
λeb10
eb3
λeb3
eb11
λeb11
eb4
λeb4
Failure of communication chip for slave controller Failure of fuse for main circuit
eb12
λeb12
eb5
λeb5
Failure of relay for main circuit
eb13
λeb13
eb6
λeb6
Failure of current sensor
eb14
λeb14
eb7
λeb7
Failure of voltage sensor
eb15
λeb15
eb8
λeb8
Failure of temperature sensor
eb16
λeb16
(2)
λgb1 ¼ λgb5 þ λgb6
1 λgb6 ¼ �y � λeb3s þ y � λeb4s þ ðy þ 1Þλeb5s x
(3)
where λeb3s is the failure rate of a single battery cell, λeb4s is the failure rate of a single battery cell signal connector, and λeb5s is the failure rate of a single basic battery module fastening screw. As mentioned in Section 2, the BMS controller is designed mainly to perform the following functions: voltage equalization to reduce the cellto-cell imbalance in voltage or state-of-charge (SOC), over-current pro tection, under-/over-voltage protections to prevent the battery from over-charge or over-discharge, parameters and SOC estimation, diag nosis and prognosis. The BMS controller consists of two parts (namely a master controller and a slave controller). The hardware of both the master and slave controllers are integrated circuit boards [37]. Ac cording to the fault tree shown in Fig. 2(b), the failure rate of the BMS controller can be estimated by 8 < λgb2 ¼ λgb7 þ λgb8 λgb7 ¼ λeb6 þ λeb7 þ λeb8 (4) : λgb8 ¼ λeb9 þ λeb10 þ λeb11
Note: PCB – Printed circuit board; SMC – Surface-mounted component.
reliability of the battery system will evolve over time. To answer these questions, a detailed reliability study of the battery system is conducted in the following. 3. Reliability study of the battery system
where the physical means of λgb1 λgb8 and λeb6 λeb11 have been explained in Tables 1 and 2 Likewise, the failure rates of power electronic components and signal detection components can be expressed as: � λgb3 ¼ λeb12 þ λeb13 (5) λgb4 ¼ λeb14 þ λeb15 þ λeb16
3.1. Reliability analysis and fault tree modeling First of all, to facilitate the research a reliability block diagram of the battery system is shown in Fig. 2(a). Based on the block diagram shown in Fig. 2(a), the reliability of the battery system can be estimated by: Y4 R ¼ Rðbx1 ÞRðbx2 Þ…Rðbx4 Þ ¼ i¼1 Rðbxi Þ (1)
where λgb3 and 2
where Rðbxi Þ refers to the reliability of the i-th component. In order to investigate the reliability of individual components in the battery system, the fault tree of the battery system is developed, see Fig. 2(b). In the figure, ‘Battery System Failure’ is defined as the top event, and the basic events em1 to em16 are the failure events of battery system components or parts; gb1 to gb8 refer to intermediate events (or logic gate events), which are the logical combination of relevant basic events. All of these events have been explained in Tables 1 and 2.
λgb4 and λeb12
λeb16 are already explained in Tables 1
3.3. Failure rate of the battery system According to the US Navy Mechanical Equipment Reliability Esti mation Procedures Manual, IEC TR 62380:2004, and MIL-HDBH [38–40], the failure rate of battery system connectors and battery module can be estimated by 4
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Fig. 3. Assembling process of the battery system.
Fig. 4. Different types of battery modules.
5
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Table 3 Other parameters in formula (6) to (9).
Table 4 Performance parameters of the battery system of interest.
Symbol
Meaning
Symbol
Meaning
Symbol
Meaning
Item
Parameters
Item
Parameters
λb;3
Base failure rate of primary battery Number of active battery cells Connector type
neb5
Number of active fasteners
λb;14
Nominal voltage of cell (V)
3.6
Failure of PCB
λS14
3–4.2
λB
Failure of all components on PCB
Operating voltage range of cell (V) Cell weight (kg)
λT14
Cell energy density (Wh/kg) The number of total battery cells connected in series in the battery system
221
The number of total battery cells connected in parallel in the battery system Nominal voltage of battery packs (V) Total energy of battery pack (kWh) Continuous discharge current Continuous charging current
6
λA
Base failure rate of current sensor Failure rate of sensing element Failure rates of current sensor transmission line Failure rate of the current sensor power source Failure rate of the current sensor other components Number of active current sensor Base failure rate of voltage sensor Failure rate of sensing element Failure rates of voltage sensor transmission line Failure rate of the voltage sensor other components Number of active voltages sensor Base failure rate of temperature sensor
neb3
λb;1;2;4
πt1;2;4
Ambient temperature
λSbx
Failure of SMCs
λP14
πC1;2;4
Number of active contacts
λfbx
λX14
πM1;2;4
Contact area material
λb;x
Failure rates of through hole component Failure of master chip
πi1;2;4
Nominal current
λb;12
Base failure of fuse
λb;15
neb1;2;4
Number of active connectors Base failure rates of fasteners
πI12
Fuse stress coefficient
λS15
neb12
Number of active fuses
λT15
Endurance or fatigue limit under S–N test specimen Multiplying factor of size deviation
λEOS
λX15
Multiplying factor of different loading application Elevated temperature multiplying factor Multiplying factor of inservice cyclic shock loading
πT13
Failure rate of overcurrent stress Temperature stress coefficient of relay Relay type
πP13
Environment pollution factor
λS16
Failure rate of sensing element
πY13
Operating cycles per hours
λT16
Csc5
Surface coatings multiplying factor
πC13
Number of active contacts
λX16
Ck
Stress concentration multiplying factor for fastener threads
neb13
Number of active relays
neb16
Failure rate of temperature sensor transmission line Failure rate of the temperature sensor other components Number of temperatures sensor
λF;B
σe;S
CSZ
CL
CT9
CI
N
πt13
neb14
neb15
λb;16
0.54
96
Fig. 5. The battery system of interest.
6
345.6 44.85 1C 0.8C
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Table 5 Parts of the battery system of interest. Part
Model/Specification
Number
Part
Model/Specification
Number
Positive connector of main circuit Negative connector for main circuit Signal connector for battery system Current sensor Temperature sensor Voltage sensor Fastening screw for battery module
EVH1-F1ZK-M8A EVH1-F1ZK-M8B Amphenol12492 PL-2/75mV400A NTC10K R34-7 M6
1 1 1 1 10 1 97
Negative relay for main circuit Positive relay for main circuit Fuse for main circuit Signal connectors for battery cells Master controller of BMS Slave controller of BMS Battery Cell
EV200 HFZ16V-50–900/B-SHSL5P-1 MSD Amphenol -TP1 BCU0V3 BMU1V1 11217127
1 1 1 96 1 1 96S6P -(576)
Table 6 Parameters for calculating the failure rates of battery system components. Name
symbol
units
value
Name
symbol
units
value
Connector type coefficient of battery pack
λb;1
FPMH
0.003
Ck
NA
3.000
Ambient temperature coefficient
πt1
NA
1.500
Stress concentration multiplying factor for fastener threads Base failure rate of fuses
0.001
πC1
NA
1.000
Electric stress coefficient
λb;12
FPMH
Active contacts number factor of power delivery connector
NA
0.000
πM1
NA
1.414
Temperature stress coefficient of relay
πI12 πt13
NA
1.500
πi1
NA
2.000
Relay type coefficient
πT13
NA
1.000
λb;2
FPMH
0.002
Environment factor
NA
3.000
NA
1.500
Operating cycles per hours
πP13
NA
1.000
πC2
NA
5.670
Number of active contacts coefficient
πC13
NA
1.500
πM2
NA
1.740
Base failure rate of current sensor
FPMH
0.600
NA
1.000
Failure rate of sensing element
λb;14
πi2
FPMH
1.200
NA
0.150
Failure rate of current sensor transmission line
FPMH
0.015
FPMH
0.004
Failure rate of the current sensor power source
λP14
FPMH
1.210
λX14
FPMH
0.020
λb;15
FPMH
1.000
Contact area material factor of battery pack power delivery connector Nominal current factor of battery pack power delivery connector Connector type factor of signal transmission for battery pack Ambient temperature coefficient Active contacts number factor of signal transmission connector for battery pack Contact area material factor of signal transmission connector Nominal current factor of signal transmission connector Base failure rate of primary battery Connector type factor of signal transmission for battery module
πt2
λb;3 λb;4
πY13
λS14 λT14
Ambient temperature coefficient of signal transmission connector for battery module Active contacts number factor of signal transmission connector for battery module Contact area material factor of signal transmission connector for battery module Nominal current factor of signal transmission connector for battery module Base failure rate of fasteners
πt4
NA
1.700
πC4
NA
2.240
Failure rate of the current sensor other components Base failure rate of voltage sensor
πM4
NA
8.000
Failure rate of sensing element
λS15
FPMH
0.600
πi4
NA
1.000
Failure rate of voltage sensor transmission line
λT15
FPMH
0.015
λF;B
FPMH
0.0001
λX15
FPMH
0.020
Multiplying factor of size deviation
CSZ
NA
1.000
Failure rate of the voltage sensor other components Base failure rate of temperature sensor
1.000
1.000
Failure rate of sensing element
λb;16
FPMH
NA
FPMH
0.390
CT5
NA
1.000
λT16
FPMH
0.022
CI
NA
1.250
λX16
FPMH
0.013
Csc5
NA
1.160
Failure rates of temperature sensor transmission line Failure rate of the temperature sensor other components
λS16
Multiplying factor of different loading application
CL
Elevated temperature multiplying factor Multiplying factor considering for the severity of in-service cyclic shock loading Surface coatings multiplying factor
Note:FPMH - failures per million hours.
Table 7 Components on board and their failure rate calculation results (BMS master controller).
Table 8 Components on board and their failure rate calculation results (BMS slave controller).
Component
Type of package
Number
Single device failure rate/FPMH
Component
Type of package
Number
Single device failure rate/FPMH
Ceramic capacitor Capacitance X2 Diode Integrated chip Inductance Optocoupler Relay MOSFET Resistance of direct plug Resistance of Surface mounted Master chip
0603-C MPX-X2-GMF SMA TSSOP MSS SO-8 HF46F SOT-223 AXIAL 0603-R
206 2 10 2 3 4 4 8 4 110
0.00049 0.00069 0.04010 0.08100 0.07800 0.08800 0.02500 0.07100 0.00685 0.00320
Ceramic capacitor
126
0.00049
9 2 4 3 5 120
0.04010 0.00069 0.08100 0.07800 0.07100 0.00310
LQFP144
1
0.15000
1206-C/0603C SMA MPX-X2-330 TSSOP MSS SOB 0603-R/2512/ 1206 SSOP-48 JQC-32F SOP14
4 2 1
0.10100 0.02510 0.05200
Capacitance X2 Diode Op-amp chip Inductance MOSFET Resistance of surface mounted LTC6811 Relay Communication chip
7
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Table 9 Parameters used for calculating the failure rates of equipped printed circuit board. Name
codes
Value/ FPMH
Name
codes
Value/ FPMH
PCB layer coefficient of master controller PCB layer coefficient of slave controller Track width factor of master controller Track width factor of slave controller Tracks number of master controller Tracks number of slave controller
πcA5
1.4
Number of SMCs on PCB of master controller
NSB7
609
πcA9
1.4
NSB10
463
πLA5
2
NfA5
10
πLA9
2
NfA9
4
NpA5
415
SA5
142
NpA9
386
Number of SMCs on PCB of slave controller Number of THCs on PCB of master controller Number of THCs on PCB of slave controller Surface area of PCB for master control Surface area of PCB for slave control
SA9
124
8 > > λeb1;2;4s ¼ > > > <
The failure rate of the fuse and relay can be estimated by λeb12 ¼ ðλb;12 þ πI12 λEOS Þneb12 λeb13 ¼ 1:5πt13 πT13 πP13 πY13 πC13 � j � hX i� 3 > ðπn13 Þi ðΔTi Þ0:68 > : 1 þ 2:7 � 10 8 > > <
Failure rate/ FPMH
Codes
Failure rate/ FPMH
Codes
Failure rate/ FPMH
λeb1
0.1757
λeb9
0.2786
λgb1
3.0762
λeb3 λeb4 λeb5 λeb6 λeb7 λeb8
2.4001 0.4389 0.0084 0.3363 2.2965 0.1500
λeb11 λeb12 λeb13 λeb14 λeb15 λeb16
2.1720 0.0520 0.0010 0.1613 0.6450 0.0362 0.3300
λgb2 λgb3 λgb4 λgb5 λgb6 λgb7 λgb8
(7)
i¼1
Codes
λeb10
(6)
λeb5s ¼ ðλF;B CSZ CL CT5 CI CSC5 CK Þneb5
�neb13 The failure rate of the current, voltage and temperature sensor can be estimated by 8 � < λeb14 ¼ λb;14 þ λS14 þ λT14 þ λp14 þ λX14 neb14 (8) λ ¼ ðλb;15 þ λS15 þ λT15 þ λX15 Þneb15 : eb15 λeb16 ¼ ðλb;16 þ λS16 þ λT16 þ λX16 Þneb16 The failure rates of the printed circuit board and the surfacemounted components (SMCs) can be estimated by " # rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffi Ntbx 1 þ 0:1 Sbx 3 πLbx þ Npbx 8 λA ¼ 5 � 10 πtbx πcbx Ntbx 1 þ S 3 > > > > > > j hX i� � > > > < 1 þ 3 � 10 3 ðπn ÞðΔTi Þ0:68 (9) i¼1 > > > > j i h > X X X � > > > λsbx þ λfbx þ 1 þ 3 � 10 3 ðπn ÞiðΔTi Þ0:68 > λB ¼ :
Table 10 Failure rates of battery system parts.
0.0533
i¼1
� > > λeb3 ¼ λb;3 � 10 9 neb3 > > > : neb3 ¼ x*y
Note: THCs – Through hole components.
λeb2
! λb;1;2;4 πt1;2;4 πC1;2;4 πM1;2;4 πi j � hX i� neb1;2;4 1 þ 2:7 � 10 3 ðπn Þi � ð△Ti Þ0:68
5.2854 0.1623
i¼1
1.0112
λIx ¼ λb;x � 10
0.2290
�
2.8472
1740
2.7828
1 303
6
� 1 273þtA
where πtbx ¼ e is a factor indicating the influence of temperature, tA represents ambient temperature, πcbx indicates the in fluence of the number of layers, Ntbx refers to the total number of holes, S is the surface area of the board, NPbx is the number of tracks. Its default P P Ns þ Nf value is NPbx ¼ , in which Ns is the number of lines that 2 connect to every component, Nf is the number of components that connect to every hole, πLbx is the coefficient of track width. ðπn Þi ¼ n0:76 i according to Refs. [34,39], ΔTi indicates the variation of temperature,
2.5026
Fig. 6. Calculation results of the failure rates of different parts in the battery system. 8
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Fig. 7. The reliability indices of the battery system and its components over time.
λdbx is the failure rate of interconnections. Other parameters in formula (6) to (9) are further explained in Table 3.
well as PCB of the slave controller are also prone to develop a fault in operation. However, they are comparatively more reliable than the battery cells module. Their failure rates vary in the range of 0.200–0.439. By contrast, the socket of the battery pack, fuse for main circuit and master chip are relatively more reliable. The fastening screws and fuse are the most reliable components in the battery system. They are almost free of fault. It is believed that the understanding of such a figure is of great significance to the reliability design and maintenance of the battery system in pure EVs.
4. Reliability of the battery system in a pure electric van Based on the aforementioned failure rate estimation methods, the reliability of the battery system in a pure electric van is quantitatively assessed in this section. The performance parameters of the battery system of interest are listed in Table 4.
4.2. Reliability index of the battery system
4.1. Failure rates of battery system components
Although the calculation results presented above have indicated the reliability of individual battery system components, the information provided by them remains the ‘snapshots’ of the reliability of the battery system. For this reason, the reliability indices of the entire battery sys tem and its connecting components are evaluated based on the above calculation results in order to obtain a more comprehensive under standing of the reliability of the battery system. With the aid of equation (1) and the fault tree model of the battery system shown in Fig. 2(b), the corresponding estimation results of the reliability indices when the service life of the battery system is respectively, 2,000, 5,000, 10,000, 15,000 and 20,000 h, are shown in Fig. 7. From Fig. 7, it is clearly seen that the BMS controller, battery system connectors and battery module are much less reliable than the others. This is in agreement with the research conclusion obtained from the failure rate calculation results in Section 4.1. Additionally, all battery system components will become more and more unreliable with the increase of their service time, i.e. the longer the service time, the lower their reliability indices will tend to be. For example, after the battery system continuously run for 2,000, 5,000, 10,000, 15,000 and 20,000 h, the reliability index of the battery system is 98.1%, 95.3%, 90.9%, 86.7% and 82.6%, respectively. The most important is that, despite the service time, the calculation result of the reliability index of the entire battery system is much lower than the corresponding value of the reli ability index of battery cells module or other individual components. This further proves that all components in the battery system should be taken into account when the reliability of the battery system is assessed. This is because the failure of any of them can, to different extents, affect the reliability of the entire system. In addition, from the further obser vation of the varying tendencies of the reliability index curves in Fig. 7 it is found that with the continual increase of the service time, the reli ability index curve of the BMS controller is closer to the curve of the entire battery system than the other curves do. This suggests that the BMS system rather than battery modules is the most vulnerable sub system in the battery system considered in this paper. Moreover, with
To ease understanding, the battery system of interest is shown in Fig. 5 and its parts are listed in Table 5. As mentioned earlier, the failure of the battery system is mainly caused by the faults developed in battery system connectors and battery module, BMS controller, power electronic components, and other asso ciated components. Therefore, according to the Handbook of Reliability Prediction Procedures for Mechanical Equipment NSWC-09 [38] and IEC TR 62380-2004 [39], all parameters used for calculating the failure rates of these battery system components are listed in Table 6. As shown in Fig. 5c and 5d, the BMS controller is composed of a master controller and a slave controller. From the design documents and the bill of material provided by the BMS controller manufacturer, the types, quantities, and specifications of electronic components in the BMS controllers are listed in Tables 7 and 8. According to IEC TR62308-2004, NSWC-09, MIL-HDBH-217E and the technical standards for the Chinese electric vehicle industry [38–42], the failure rates of all components are calculated. The relevant parameters used in the calculations are listed in Tables 7–9. Based on the data listed in Tables 6–9, the failure rates of individual parts of the battery system are calculated using equations (2)–(9). The calculation results are listed in Table 10. In parallel, the calculation results are also illustrated in Fig. 6 so that the interested reader can have an intuitive understanding of the reliability of the different parts of the battery system. From Table 10, it is clearly seen that the failure rate of BMS con trollers (i.e. BMS master controller and BMS slave controller) is the highest (i.e. 5.284), followed by the failure rates of battery cells module, signal detection components and power electronic components. From Fig. 6, it is found that, among the components in the battery system, battery cells module, SMCs for master controller and SMCs for slave controller have higher failure rates than others, and their failure rates are 2.4001, 2.2965, and 2.1720, respectively. The current sensor, signal connectors, temperature sensor and PCB of master controller as 9
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the continual increase of the service time, the BMS system will have a more and more negative impact on the reliability of the entire battery system.
was also funded by the National Study Abroad Fund.
5. Conclusions
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[14] Quan Xia, Zili Wang, Yi Ren, BoSun, DezhenYang, QiangFeng, A reliability design method for a lithium-ion battery pack considering the thermal disequilibrium in electric vehicles, J. Power Sources 386 (2018) 10–20, https://doi.org/10.1016/j. jpowsour.2018.03.036. [15] Foad H. Gandoman, Abdollah Ahmadi, Peter Van den Bossche, et al., Status and future perspectives of reliability assessment for electric vehicles, Reliab. Eng. Syst. Saf. 183 (2019) 1–16, https://doi.org/10.1016/j.ress.2018.11.013. [16] Zhitao Liu, CherMing Tan, Feng Leng, A reliability-based design concept for lithium-ion battery pack in electric vehicles, Reliab. Eng. Syst. Saf. 134 (2015) 169–177, https://doi.org/10.1016/j.ress.2014.10.010. [17] Quan Xia, Zili Wang, Yi Ren, Laifa Tao, Lua Chen, et al., A modified reliability model for lithium-ion battery packs based on the stochastic capacity degradation and dynamic response impedance, J. Power Sources 423 (2019) 40–51, https:// doi.org/10.1016/j.jpowsour.2019.03.042. [18] Yongzhe Kang, Bin Duan, Zhongkai Zhou, Yunlong Shang, Chenghui Zhang, A multi-fault diagnostic method based on an interleaved voltage measurement topology for series connected battery packs, J. Power Sources 417 (2019) 132–144, https://doi.org/10.1016/j.jpowsour.2019.01.058. [19] Bing Xia, Chris Mi, A fault-tolerant voltage measurement method for series connected battery packs, J. Power Sources 308 (2016) 83–96, https://doi.org/ 10.1016/j.jpowsour.2016.01.057. [20] Xu Zhang, Yujie Wang, Duo Yang, Zonghai Chen, An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model, Energy 115 (2016) 219–229, https://doi.org/10.1016/j.energy.2016.08.109. [21] X. Wang, Rajesh Karki, Exploiting PHEV to augment power system reliability, IEEE Transactions on Smart Grid 8 (2017) 2100–2108, https://doi.org/10.1109/ TSG.2016.2515989.
References
In order to provide a more reliable prediction to the reliability of the entire battery system in pure EVs, a detailed study of the reliability of almost all components in the battery system (including battery system connectors and battery module, BMS controller, signal detection de vices, power electronic components) are investigated in this paper by the approach of fault tree analysis. From the research reported above, the following conclusions can be drawn. � Due to aging, the reliability of the EV’s battery system will decrease gradually with the increase of their service time. For example, after the battery system continuously run for 2,000, 5,000, 10,000, 15,000 and 20,000 h, the reliability index of the battery system is 98.1%, 95.3%, 90.9%, 86.7% and 82.6%, respectively. Moreover, it is interestingly found that the degradation is dominated mainly by the BMS controller rather than by battery cells and battery modules; � Despite the service time, at the level of subsystems BMS controller is more unreliable than other subsystems in the battery system, its failure rate is more than the sum of the failure rates of the other subsystems (i.e. battery system connectors and battery module, entire power electronic components and entire signal detection components). At the level of parts or components, battery cell module, SMCs for master controller and SMCs for slave controller are the three most vulnerable components in the battery system, and their failure rates are 2.4001, 2.2965, and 2.1720, respectively; the sum of the failure rates of these three components (i.e. battery cell module, SMCs for master controller and SMCs for slave controller) is 2.5 times of the sum of the failure rates of the other components or parts in the entire battery system (see Table .10). This further proves that improving the reliability of battery cell module and BMS controller is critical to guarantee the reliability of the entire battery system. � The estimation of the reliability of the battery system should be based on the consideration of the reliability of all battery system components rather than only considering the reliability of battery cells and battery modules. Otherwise, the reliability of the entire battery system would be overestimated. This is because these com ponents are integrated together and are requested to work synchro nously as one system in the normal operation of an EV. The failure of any of them can, to different extents, affect the reliability of the entire battery system. � The research of this paper also suggests that more attention should be paid to the reliability of the BMS controller in the future because it is not as reliable as the battery modules in the battery system consid ered in the paper. Declaration of competing interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative in terest that represents a conflict of interest in connection with the work submitted. Acknowledgment The authors gratefully acknowledge the support from the National Natural Science Foundation of China (Grant No. 11772126), the Hunan Province Science and Technology Innovation Program (Grant Nos. 2017XK2303 and 2019JJ50099), the Research Foundation of Education Department of Hunan Province, China (Grant No. 19A115). This study 10
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A reliability study of electric vehicle battery from the perspective of power supply system Xiong Shu a, b, Wenxian Yang c, Yingfu Guo a, Kexiang Wei b, c, *, Bo Qin b, Guanghui Zhu d a
School of Mechanical Engineering, Hunan University of Science and Technology, Hunan, Xiangtan, 411201, China Hunan Provincial Key Laboratory of Vehicle Power and Transmission System, Hunan Institute of Engineering, Xiangtan, 411104, China c School of Engineering, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK d Technology Center of Xiangtan Electric Manufacturing Group, Hunan, Xiangtan, 411201, China b
H I G H L I G H T S
� The reliability of EV’s entire power supply system is assessed. � BMS controller is most vulnerable at subsystem level. � Battery cell and SMCs are most unreliable at component level. � The entire system degrades more rapidly than individual power supply components do. A R T I C L E I N F O
A B S T R A C T
Keywords: Reliability Electric vehicle Battery system Fault tree analysis
Battery cells and modules are critical to the reliability of the power supply system of an EV. Therefore, their reliability is mainly concern before. However, EV’s power supply system is a complex system. Besides battery cells and modules, it also includes many other components, the failure of which can cause a breakdown of the system as well. So, the reliability of the battery should be evaluated from the perspective of the entire power supply system rather than only considering the reliability of the battery cells and modules. This motivates the research of this paper. In the present study, the failure rates of all major components are predicted first. Then, based on the prediction results, the reliability of the entire battery system is assessed. From the assessment re sults, it is found that the reliability of the entire battery system is indeed much lower than those of individual components including battery modules. Moreover, as compared to the degradation of individual components, the entire system degrades more rapidly over time. Additionally, it is interestingly found that the Battery Manage ment System (BMS) controller is not as reliable as the battery modules in the battery system considered in this paper.
1. Introduction Nowadays, the transportation sector has been recognised as one of the major sources of pollutant emissions [1]. Particularly in the light of the fact that traditional fossil fuels are depleting [2] and the world is facing pressure to mitigate climate change [3], electric vehicles (EVs) have attracted more interest than ever before and the global EV market is booming today as a consequence. Taking the Chinese market as an example, there are 984,000 pure electric vehicles sold in China in 2018, which is an increase of 50.8% over the same period of the previous year
[4]. It is expected that pure EVs will replace traditional diesel and petrol-fuelled vehicles in the near future. This implies that more EVs will be on the road in the coming years. In an EV, the battery is the only system to power the vehicle. So, the reliability of the battery system has a significant impact on the safety, reliability, and availability of the EV. In 2018, more than 40 reported EV failures were related to batteries [5]. Therefore, how to improve the reliability of the EV’s battery has long been concerned by both the public and the EV industry. In the past years, much effort has been made to address this issue by using various approaches. For example, much
* Corresponding author. Hunan Provincial Key Laboratory of Vehicle Power and Transmission System, Hunan Institute of Engineering, Xiangtan, 411104, China. E-mail addresses: [email protected] (X. Shu), [email protected] (W. Yang), [email protected] (Y. Guo), [email protected] (K. Wei), qinbo@ hnie.edu.cn (B. Qin), [email protected] (G. Zhu). https://doi.org/10.1016/j.jpowsour.2020.227805 Received 25 December 2019; Received in revised form 21 January 2020; Accepted 24 January 2020 Available online 31 January 2020 0378-7753/© 2020 Elsevier B.V. All rights reserved.
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Fig. 1. Schematic diagram of the battery system in a pure electric van.
research was conducted to improve EV’s lithium battery from the point of view of battery materials. To name a few, a novel electrolyte syn thesized through environmentally friendly procedures and tailored for the use in polymer-based lithium batteries is proposed in Ref. [6], and the experimental results have shown that the electrolyte has a high ionic conductivity in a temperature range of 40 � C–100 � C; similar studies were also conducted in Refs. [7,8] in order to achieve composite poly mer electrolyte for high-rate, ambient temperature lithium batteries; a preparation procedure of crosslinked polymer electrolyte for application in sodium-ion batteries was presented in Ref. [9]. In Ref. [10], the electrochemical response of electrodes based on gallium oxide nanorods in lithium and sodium cells was evaluated for the first time; efficient hybrid solid polymer/ceramic electrolytes were presented in Ref. [11], and it is said that the achieved electrolytes represent a step further to ward truly all-solid-state batteries conceived for high energy/power technologies; the nickel–rich layered positive electrode materials of lithium-ion batteries was investigated in Ref. [12], and the problems, modification and future research directions of these materials were discussed, respectively; the perspectives of lithium metal negative electrodes of lithium-ion batteries were systematically analysed in Ref. [13], and so on. Since electrolytes and electrodes are important parts of battery cells, all these available studies are great contributions to improving the reliability of the EV’s battery system. Besides these, the effort was also made to increase the reliability of the battery pack system [14,15]. For example, a reliability-based design concept for Li-ion bat tery pack was proposed in Ref. [16], which provided a way to improve the reliability of battery pack by optimising the configuration of redundant cells. In Ref. [14], the reliability of the battery packs that have different numbers and configurations of redundant cells were compared and it was found that the reliability did not monotonically increase with the increase of the number of redundant cells due to the thermal disequilibrium effects. A modified reliability model for lithium-ion battery packs based on the stochastic capacity degradation and dynamic response impedance was proposed in Ref. [17], and it was found that Multiphysics-based system reliability model enabled more scientific and accurate reliability evaluation of the battery packs. In Refs. [18,19], a multi-fault diagnosing method was proposed to improve the reliability of the operation of the battery system, and the method was further improved in Ref. [20] by adding a function to estimate the charging state of the battery pack. The Monte Carlo simulation approach
was adopted in Refs. [21,22] and statistical analysis and cluster analysis were used in Ref. [23] to predict the reliability of the battery. In Ref. [24], the performance and the reliability of the battery subjected to varying operational conditions were investigated. In addition, the in fluence of the interaction between different battery cells during the charging and discharging processes and the influence of the degradation of individual cells on the degradation of a battery pack were also investigated by scholars. For example, it was found that the interaction between battery cells could affect the performance and lifetime of a battery pack in Ref. [25,26]; the wiener process was used to analyze the reliability of lithium-ion battery packs based on a lithium-ion battery degradation test in Ref. [25]. Besides these, the impact of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) strategies was also considered in the EV battery reliability assessment [27]. All these studies have greatly improved the reliability of EV batteries. However, the battery system in an EV is a complex system. In addition to battery cells and battery modules, it also has many other components, such as battery system connectors, the controller of the battery management system (BMS), power electronic components, signal detection components, etc. They are integrated together and are requested to work synchronously as one system. This means that the failure of any of them can cause the breakdown of the entire battery system and thus the breakdown of the vehicle. So, the reliability assessment of EV batteries should not be based solely on reviewing the reliability of battery cells and battery modules. Instead, the assessment should be performed from the perspective of the entire power supply system. However, this has never been done before. The purpose of this paper is to fill this knowledge gap by investigating the reliability of the entire battery system and understanding its degradation over time. The study will be conducted based on fault tree analysis (FTA), due to the FTA has been successfully applied before to achieve various goals of reliability and safety assessment [28–31]. Herein, it is worth noting that the battery systems in different types of EVs may be more or less different from each other. In order to facilitate the research, the battery system in a pure electric van is considered in this paper as an example to demonstrate the proposed idea. The remaining parts of the paper are organized below. In Section 2, the battery system in a pure electric van will be briefly explained first; in Section 3, the fault trees of the battery system will be established, and following which the mathematical methods for esti mating the failure rates of basic events will be developed; the reliability 2
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Fig. 2. Reliability block diagram and fault tree model of the battery system. (a) Reliability block diagram of the battery system. (b) Fault tree model of the bat tery system.
of the battery system in a pure electric van is investigated in Section 4, in which the failure rate and reliability of both individual battery com ponents and the entire battery system are evaluated; finally, the paper will be completed in Section 5 with a few key conclusions.
battery cells, and analyses the measured data. Then, based on the analysis results it takes an appropriate control strategy to prevent the presence of abnormal incidents, such as excess discharge, overcharge, and excess temperature of the battery system. As shown in Fig. 1, the BMS controller is composed of two parts, i.e. a master controller and a slave controller. The former is responsible for communicating with the vehicle control unit (VCU) and developing control strategies; the latter is responsible for collecting the measurement results of current, voltage and temperature from individual battery cells and implementing energy balancing management of the cells [34]. Power electronic components comprise fuse and relay. The function of the fuse is to protect the battery cells from being damaged by the excessive current in the presence of a short-circuit fault; the function of a relay is to control the power-on and power-off operation of the electric system. In case of emergency, the relay can be used to cut off the power supply to the electric system to guarantee the safety of the battery system. Signal detection components of the battery system include current sensors, voltage sensors, and temperature sensors. Based on the signals measured by these sensors, the BMS controller can assess the operational status of all battery cells and send orders to the system in real-time. From the above description, it is found that the failure of any of these components may result in the failure of the entire battery system. However, so far, it is still unknown how significant these components can affect the reliability of the entire power supply system and how the
2. The battery system in a pure electric van As opposed to traditional diesel and petrol-fuelled vehicles, the power supply of an EV is via a specifically designed battery system. As shown in Fig. 1, the battery system in a pure electric van is composed of multiple components. Besides the battery cells and battery modules, the battery system also has the controller of signal detection devices, power electronic components and so on. These components are integrated together and are requested to work synchronously as one system in the normal operation of an EV. In Fig. 1, the battery module is an energy storage component in the battery system, which is composed of multiple battery cells that are connected either in series or in parallel. When any of these battery cells fail or are aged, other cells will have to share more load to supply the required power [32]. This is harmful to the health of the battery cells as it can accelerate the aging of the battery cells as well. Therefore, the quality of the battery cells in battery modules has a profound impact on the reliability of the entire battery system [33]. The BMS controller is responsible to monitor and manage the battery system. It measures the voltage, current, and temperature of individual 3
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3.2. Estimation of failure rate
Table 1 Intermediate events failure of battery system components. Intermediate event
Code
Failure rate
Intermediate event
Code
Failure rate
Failure of battery system connectors and battery module Failure of BMS controller Failure of power electronic components Failure of signal detection components
gb1
λgb1
Failure of connectors for battery system
gb5
λgb5
gb2
λgb2
gb6
λgb6
gb3
λgb3
Failure of battery module Failure of master controller
gb7
λgb7
gb4
λgb4
Failure of slave controller
gb8
λgb8
From Fig. 2(b), it is seen that the failure of the ‘battery system con nectors and battery module’ is due to the fault occurring in either the battery system connectors or in the battery module. Battery system connectors include power output connectors (i.e. battery bus positive connector, battery bus negative connector) and signal output connector. As the failure of the battery module can happen due to the failure of battery cells, signal connectors for battery cells, and the failure of fastening screws, the failure of the ‘battery system connectors and bat tery module’ can be expressed as
where λgb5 and λgb6 have been explained in Table 1. To ease under standing, the assembling process of the battery system is shown in Fig. 3. From Fig. 3, it is seen that the battery system is composed of a number of battery modules that are connected in series, whereas every battery module consists of a number of battery cells that are connected in parallel. The number of battery modules and the number of battery cells in each module is dependent on the requirements of the voltage, current, energy storage capacity, and the size of the EV, as shown in Fig. 4. As shown in Fig. 4(d), a single battery cell module comprises mul tiple battery cells that are connected in parallel. In this study, it is assumed that the cells in a battery module are independent of each other [35,36]. Then, for a battery system that has y battery modules connected in series and each battery module has x battery cells connected in par allel, the failure rate of the battery module can be expressed as:
Table 2 Basic events failure of battery system components or parts. Basic event
Code
Failure rate
Basic event
Code
Failure rate
Failure of signal connector for battery system Failure of power connector for battery system Failure of battery cells module Failure of signal connectors for battery cells Failure of fastening screw for battery module Failure of PCB for master controller Failure of SMCs for master controller Failure of master chip
eb1
λeb1
Failure of PCB for slave controller
eb9
λeb9
eb2
λeb2
Failure of SMCs for slave controller
eb10
λeb10
eb3
λeb3
eb11
λeb11
eb4
λeb4
Failure of communication chip for slave controller Failure of fuse for main circuit
eb12
λeb12
eb5
λeb5
Failure of relay for main circuit
eb13
λeb13
eb6
λeb6
Failure of current sensor
eb14
λeb14
eb7
λeb7
Failure of voltage sensor
eb15
λeb15
eb8
λeb8
Failure of temperature sensor
eb16
λeb16
(2)
λgb1 ¼ λgb5 þ λgb6
1 λgb6 ¼ �y � λeb3s þ y � λeb4s þ ðy þ 1Þλeb5s x
(3)
where λeb3s is the failure rate of a single battery cell, λeb4s is the failure rate of a single battery cell signal connector, and λeb5s is the failure rate of a single basic battery module fastening screw. As mentioned in Section 2, the BMS controller is designed mainly to perform the following functions: voltage equalization to reduce the cellto-cell imbalance in voltage or state-of-charge (SOC), over-current pro tection, under-/over-voltage protections to prevent the battery from over-charge or over-discharge, parameters and SOC estimation, diag nosis and prognosis. The BMS controller consists of two parts (namely a master controller and a slave controller). The hardware of both the master and slave controllers are integrated circuit boards [37]. Ac cording to the fault tree shown in Fig. 2(b), the failure rate of the BMS controller can be estimated by 8 < λgb2 ¼ λgb7 þ λgb8 λgb7 ¼ λeb6 þ λeb7 þ λeb8 (4) : λgb8 ¼ λeb9 þ λeb10 þ λeb11
Note: PCB – Printed circuit board; SMC – Surface-mounted component.
reliability of the battery system will evolve over time. To answer these questions, a detailed reliability study of the battery system is conducted in the following. 3. Reliability study of the battery system
where the physical means of λgb1 λgb8 and λeb6 λeb11 have been explained in Tables 1 and 2 Likewise, the failure rates of power electronic components and signal detection components can be expressed as: � λgb3 ¼ λeb12 þ λeb13 (5) λgb4 ¼ λeb14 þ λeb15 þ λeb16
3.1. Reliability analysis and fault tree modeling First of all, to facilitate the research a reliability block diagram of the battery system is shown in Fig. 2(a). Based on the block diagram shown in Fig. 2(a), the reliability of the battery system can be estimated by: Y4 R ¼ Rðbx1 ÞRðbx2 Þ…Rðbx4 Þ ¼ i¼1 Rðbxi Þ (1)
where λgb3 and 2
where Rðbxi Þ refers to the reliability of the i-th component. In order to investigate the reliability of individual components in the battery system, the fault tree of the battery system is developed, see Fig. 2(b). In the figure, ‘Battery System Failure’ is defined as the top event, and the basic events em1 to em16 are the failure events of battery system components or parts; gb1 to gb8 refer to intermediate events (or logic gate events), which are the logical combination of relevant basic events. All of these events have been explained in Tables 1 and 2.
λgb4 and λeb12
λeb16 are already explained in Tables 1
3.3. Failure rate of the battery system According to the US Navy Mechanical Equipment Reliability Esti mation Procedures Manual, IEC TR 62380:2004, and MIL-HDBH [38–40], the failure rate of battery system connectors and battery module can be estimated by 4
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Journal of Power Sources 451 (2020) 227805
Fig. 3. Assembling process of the battery system.
Fig. 4. Different types of battery modules.
5
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Journal of Power Sources 451 (2020) 227805
Table 3 Other parameters in formula (6) to (9).
Table 4 Performance parameters of the battery system of interest.
Symbol
Meaning
Symbol
Meaning
Symbol
Meaning
Item
Parameters
Item
Parameters
λb;3
Base failure rate of primary battery Number of active battery cells Connector type
neb5
Number of active fasteners
λb;14
Nominal voltage of cell (V)
3.6
Failure of PCB
λS14
3–4.2
λB
Failure of all components on PCB
Operating voltage range of cell (V) Cell weight (kg)
λT14
Cell energy density (Wh/kg) The number of total battery cells connected in series in the battery system
221
The number of total battery cells connected in parallel in the battery system Nominal voltage of battery packs (V) Total energy of battery pack (kWh) Continuous discharge current Continuous charging current
6
λA
Base failure rate of current sensor Failure rate of sensing element Failure rates of current sensor transmission line Failure rate of the current sensor power source Failure rate of the current sensor other components Number of active current sensor Base failure rate of voltage sensor Failure rate of sensing element Failure rates of voltage sensor transmission line Failure rate of the voltage sensor other components Number of active voltages sensor Base failure rate of temperature sensor
neb3
λb;1;2;4
πt1;2;4
Ambient temperature
λSbx
Failure of SMCs
λP14
πC1;2;4
Number of active contacts
λfbx
λX14
πM1;2;4
Contact area material
λb;x
Failure rates of through hole component Failure of master chip
πi1;2;4
Nominal current
λb;12
Base failure of fuse
λb;15
neb1;2;4
Number of active connectors Base failure rates of fasteners
πI12
Fuse stress coefficient
λS15
neb12
Number of active fuses
λT15
Endurance or fatigue limit under S–N test specimen Multiplying factor of size deviation
λEOS
λX15
Multiplying factor of different loading application Elevated temperature multiplying factor Multiplying factor of inservice cyclic shock loading
πT13
Failure rate of overcurrent stress Temperature stress coefficient of relay Relay type
πP13
Environment pollution factor
λS16
Failure rate of sensing element
πY13
Operating cycles per hours
λT16
Csc5
Surface coatings multiplying factor
πC13
Number of active contacts
λX16
Ck
Stress concentration multiplying factor for fastener threads
neb13
Number of active relays
neb16
Failure rate of temperature sensor transmission line Failure rate of the temperature sensor other components Number of temperatures sensor
λF;B
σe;S
CSZ
CL
CT9
CI
N
πt13
neb14
neb15
λb;16
0.54
96
Fig. 5. The battery system of interest.
6
345.6 44.85 1C 0.8C
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Journal of Power Sources 451 (2020) 227805
Table 5 Parts of the battery system of interest. Part
Model/Specification
Number
Part
Model/Specification
Number
Positive connector of main circuit Negative connector for main circuit Signal connector for battery system Current sensor Temperature sensor Voltage sensor Fastening screw for battery module
EVH1-F1ZK-M8A EVH1-F1ZK-M8B Amphenol12492 PL-2/75mV400A NTC10K R34-7 M6
1 1 1 1 10 1 97
Negative relay for main circuit Positive relay for main circuit Fuse for main circuit Signal connectors for battery cells Master controller of BMS Slave controller of BMS Battery Cell
EV200 HFZ16V-50–900/B-SHSL5P-1 MSD Amphenol -TP1 BCU0V3 BMU1V1 11217127
1 1 1 96 1 1 96S6P -(576)
Table 6 Parameters for calculating the failure rates of battery system components. Name
symbol
units
value
Name
symbol
units
value
Connector type coefficient of battery pack
λb;1
FPMH
0.003
Ck
NA
3.000
Ambient temperature coefficient
πt1
NA
1.500
Stress concentration multiplying factor for fastener threads Base failure rate of fuses
0.001
πC1
NA
1.000
Electric stress coefficient
λb;12
FPMH
Active contacts number factor of power delivery connector
NA
0.000
πM1
NA
1.414
Temperature stress coefficient of relay
πI12 πt13
NA
1.500
πi1
NA
2.000
Relay type coefficient
πT13
NA
1.000
λb;2
FPMH
0.002
Environment factor
NA
3.000
NA
1.500
Operating cycles per hours
πP13
NA
1.000
πC2
NA
5.670
Number of active contacts coefficient
πC13
NA
1.500
πM2
NA
1.740
Base failure rate of current sensor
FPMH
0.600
NA
1.000
Failure rate of sensing element
λb;14
πi2
FPMH
1.200
NA
0.150
Failure rate of current sensor transmission line
FPMH
0.015
FPMH
0.004
Failure rate of the current sensor power source
λP14
FPMH
1.210
λX14
FPMH
0.020
λb;15
FPMH
1.000
Contact area material factor of battery pack power delivery connector Nominal current factor of battery pack power delivery connector Connector type factor of signal transmission for battery pack Ambient temperature coefficient Active contacts number factor of signal transmission connector for battery pack Contact area material factor of signal transmission connector Nominal current factor of signal transmission connector Base failure rate of primary battery Connector type factor of signal transmission for battery module
πt2
λb;3 λb;4
πY13
λS14 λT14
Ambient temperature coefficient of signal transmission connector for battery module Active contacts number factor of signal transmission connector for battery module Contact area material factor of signal transmission connector for battery module Nominal current factor of signal transmission connector for battery module Base failure rate of fasteners
πt4
NA
1.700
πC4
NA
2.240
Failure rate of the current sensor other components Base failure rate of voltage sensor
πM4
NA
8.000
Failure rate of sensing element
λS15
FPMH
0.600
πi4
NA
1.000
Failure rate of voltage sensor transmission line
λT15
FPMH
0.015
λF;B
FPMH
0.0001
λX15
FPMH
0.020
Multiplying factor of size deviation
CSZ
NA
1.000
Failure rate of the voltage sensor other components Base failure rate of temperature sensor
1.000
1.000
Failure rate of sensing element
λb;16
FPMH
NA
FPMH
0.390
CT5
NA
1.000
λT16
FPMH
0.022
CI
NA
1.250
λX16
FPMH
0.013
Csc5
NA
1.160
Failure rates of temperature sensor transmission line Failure rate of the temperature sensor other components
λS16
Multiplying factor of different loading application
CL
Elevated temperature multiplying factor Multiplying factor considering for the severity of in-service cyclic shock loading Surface coatings multiplying factor
Note:FPMH - failures per million hours.
Table 7 Components on board and their failure rate calculation results (BMS master controller).
Table 8 Components on board and their failure rate calculation results (BMS slave controller).
Component
Type of package
Number
Single device failure rate/FPMH
Component
Type of package
Number
Single device failure rate/FPMH
Ceramic capacitor Capacitance X2 Diode Integrated chip Inductance Optocoupler Relay MOSFET Resistance of direct plug Resistance of Surface mounted Master chip
0603-C MPX-X2-GMF SMA TSSOP MSS SO-8 HF46F SOT-223 AXIAL 0603-R
206 2 10 2 3 4 4 8 4 110
0.00049 0.00069 0.04010 0.08100 0.07800 0.08800 0.02500 0.07100 0.00685 0.00320
Ceramic capacitor
126
0.00049
9 2 4 3 5 120
0.04010 0.00069 0.08100 0.07800 0.07100 0.00310
LQFP144
1
0.15000
1206-C/0603C SMA MPX-X2-330 TSSOP MSS SOB 0603-R/2512/ 1206 SSOP-48 JQC-32F SOP14
4 2 1
0.10100 0.02510 0.05200
Capacitance X2 Diode Op-amp chip Inductance MOSFET Resistance of surface mounted LTC6811 Relay Communication chip
7
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Journal of Power Sources 451 (2020) 227805
Table 9 Parameters used for calculating the failure rates of equipped printed circuit board. Name
codes
Value/ FPMH
Name
codes
Value/ FPMH
PCB layer coefficient of master controller PCB layer coefficient of slave controller Track width factor of master controller Track width factor of slave controller Tracks number of master controller Tracks number of slave controller
πcA5
1.4
Number of SMCs on PCB of master controller
NSB7
609
πcA9
1.4
NSB10
463
πLA5
2
NfA5
10
πLA9
2
NfA9
4
NpA5
415
SA5
142
NpA9
386
Number of SMCs on PCB of slave controller Number of THCs on PCB of master controller Number of THCs on PCB of slave controller Surface area of PCB for master control Surface area of PCB for slave control
SA9
124
8 > > λeb1;2;4s ¼ > > > <
The failure rate of the fuse and relay can be estimated by λeb12 ¼ ðλb;12 þ πI12 λEOS Þneb12 λeb13 ¼ 1:5πt13 πT13 πP13 πY13 πC13 � j � hX i� 3 > ðπn13 Þi ðΔTi Þ0:68 > : 1 þ 2:7 � 10 8 > > <
Failure rate/ FPMH
Codes
Failure rate/ FPMH
Codes
Failure rate/ FPMH
λeb1
0.1757
λeb9
0.2786
λgb1
3.0762
λeb3 λeb4 λeb5 λeb6 λeb7 λeb8
2.4001 0.4389 0.0084 0.3363 2.2965 0.1500
λeb11 λeb12 λeb13 λeb14 λeb15 λeb16
2.1720 0.0520 0.0010 0.1613 0.6450 0.0362 0.3300
λgb2 λgb3 λgb4 λgb5 λgb6 λgb7 λgb8
(7)
i¼1
Codes
λeb10
(6)
λeb5s ¼ ðλF;B CSZ CL CT5 CI CSC5 CK Þneb5
�neb13 The failure rate of the current, voltage and temperature sensor can be estimated by 8 � < λeb14 ¼ λb;14 þ λS14 þ λT14 þ λp14 þ λX14 neb14 (8) λ ¼ ðλb;15 þ λS15 þ λT15 þ λX15 Þneb15 : eb15 λeb16 ¼ ðλb;16 þ λS16 þ λT16 þ λX16 Þneb16 The failure rates of the printed circuit board and the surfacemounted components (SMCs) can be estimated by " # rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffi Ntbx 1 þ 0:1 Sbx 3 πLbx þ Npbx 8 λA ¼ 5 � 10 πtbx πcbx Ntbx 1 þ S 3 > > > > > > j hX i� � > > > < 1 þ 3 � 10 3 ðπn ÞðΔTi Þ0:68 (9) i¼1 > > > > j i h > X X X � > > > λsbx þ λfbx þ 1 þ 3 � 10 3 ðπn ÞiðΔTi Þ0:68 > λB ¼ :
Table 10 Failure rates of battery system parts.
0.0533
i¼1
� > > λeb3 ¼ λb;3 � 10 9 neb3 > > > : neb3 ¼ x*y
Note: THCs – Through hole components.
λeb2
! λb;1;2;4 πt1;2;4 πC1;2;4 πM1;2;4 πi j � hX i� neb1;2;4 1 þ 2:7 � 10 3 ðπn Þi � ð△Ti Þ0:68
5.2854 0.1623
i¼1
1.0112
λIx ¼ λb;x � 10
0.2290
�
2.8472
1740
2.7828
1 303
6
� 1 273þtA
where πtbx ¼ e is a factor indicating the influence of temperature, tA represents ambient temperature, πcbx indicates the in fluence of the number of layers, Ntbx refers to the total number of holes, S is the surface area of the board, NPbx is the number of tracks. Its default P P Ns þ Nf value is NPbx ¼ , in which Ns is the number of lines that 2 connect to every component, Nf is the number of components that connect to every hole, πLbx is the coefficient of track width. ðπn Þi ¼ n0:76 i according to Refs. [34,39], ΔTi indicates the variation of temperature,
2.5026
Fig. 6. Calculation results of the failure rates of different parts in the battery system. 8
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Journal of Power Sources 451 (2020) 227805
Fig. 7. The reliability indices of the battery system and its components over time.
λdbx is the failure rate of interconnections. Other parameters in formula (6) to (9) are further explained in Table 3.
well as PCB of the slave controller are also prone to develop a fault in operation. However, they are comparatively more reliable than the battery cells module. Their failure rates vary in the range of 0.200–0.439. By contrast, the socket of the battery pack, fuse for main circuit and master chip are relatively more reliable. The fastening screws and fuse are the most reliable components in the battery system. They are almost free of fault. It is believed that the understanding of such a figure is of great significance to the reliability design and maintenance of the battery system in pure EVs.
4. Reliability of the battery system in a pure electric van Based on the aforementioned failure rate estimation methods, the reliability of the battery system in a pure electric van is quantitatively assessed in this section. The performance parameters of the battery system of interest are listed in Table 4.
4.2. Reliability index of the battery system
4.1. Failure rates of battery system components
Although the calculation results presented above have indicated the reliability of individual battery system components, the information provided by them remains the ‘snapshots’ of the reliability of the battery system. For this reason, the reliability indices of the entire battery sys tem and its connecting components are evaluated based on the above calculation results in order to obtain a more comprehensive under standing of the reliability of the battery system. With the aid of equation (1) and the fault tree model of the battery system shown in Fig. 2(b), the corresponding estimation results of the reliability indices when the service life of the battery system is respectively, 2,000, 5,000, 10,000, 15,000 and 20,000 h, are shown in Fig. 7. From Fig. 7, it is clearly seen that the BMS controller, battery system connectors and battery module are much less reliable than the others. This is in agreement with the research conclusion obtained from the failure rate calculation results in Section 4.1. Additionally, all battery system components will become more and more unreliable with the increase of their service time, i.e. the longer the service time, the lower their reliability indices will tend to be. For example, after the battery system continuously run for 2,000, 5,000, 10,000, 15,000 and 20,000 h, the reliability index of the battery system is 98.1%, 95.3%, 90.9%, 86.7% and 82.6%, respectively. The most important is that, despite the service time, the calculation result of the reliability index of the entire battery system is much lower than the corresponding value of the reli ability index of battery cells module or other individual components. This further proves that all components in the battery system should be taken into account when the reliability of the battery system is assessed. This is because the failure of any of them can, to different extents, affect the reliability of the entire system. In addition, from the further obser vation of the varying tendencies of the reliability index curves in Fig. 7 it is found that with the continual increase of the service time, the reli ability index curve of the BMS controller is closer to the curve of the entire battery system than the other curves do. This suggests that the BMS system rather than battery modules is the most vulnerable sub system in the battery system considered in this paper. Moreover, with
To ease understanding, the battery system of interest is shown in Fig. 5 and its parts are listed in Table 5. As mentioned earlier, the failure of the battery system is mainly caused by the faults developed in battery system connectors and battery module, BMS controller, power electronic components, and other asso ciated components. Therefore, according to the Handbook of Reliability Prediction Procedures for Mechanical Equipment NSWC-09 [38] and IEC TR 62380-2004 [39], all parameters used for calculating the failure rates of these battery system components are listed in Table 6. As shown in Fig. 5c and 5d, the BMS controller is composed of a master controller and a slave controller. From the design documents and the bill of material provided by the BMS controller manufacturer, the types, quantities, and specifications of electronic components in the BMS controllers are listed in Tables 7 and 8. According to IEC TR62308-2004, NSWC-09, MIL-HDBH-217E and the technical standards for the Chinese electric vehicle industry [38–42], the failure rates of all components are calculated. The relevant parameters used in the calculations are listed in Tables 7–9. Based on the data listed in Tables 6–9, the failure rates of individual parts of the battery system are calculated using equations (2)–(9). The calculation results are listed in Table 10. In parallel, the calculation results are also illustrated in Fig. 6 so that the interested reader can have an intuitive understanding of the reliability of the different parts of the battery system. From Table 10, it is clearly seen that the failure rate of BMS con trollers (i.e. BMS master controller and BMS slave controller) is the highest (i.e. 5.284), followed by the failure rates of battery cells module, signal detection components and power electronic components. From Fig. 6, it is found that, among the components in the battery system, battery cells module, SMCs for master controller and SMCs for slave controller have higher failure rates than others, and their failure rates are 2.4001, 2.2965, and 2.1720, respectively. The current sensor, signal connectors, temperature sensor and PCB of master controller as 9
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Journal of Power Sources 451 (2020) 227805
the continual increase of the service time, the BMS system will have a more and more negative impact on the reliability of the entire battery system.
was also funded by the National Study Abroad Fund.
5. Conclusions
[1] Sainan Shi, Haoran Zhang, Yang Wen, Qianru Zhang, Xuejun Wang, A life-cycle assessment of battery electric and internal combustion engine vehicles: a case in Hebei Province, China, J. Clean. Prod. 228 (2019) 606–618, https://doi.org/ 10.1016/j.jclepro.2019.04.301. [2] S. Suganya, S. Charles Raja, P. Venkatesh, Simultaneous coordination of distinct plug-in hybrid electric vehicle charging stations: a modified particle swarm optimization approach, Energy 138 (2017) 92–102, https://doi.org/10.1016/j. energy.2017.07.036. [3] Raquel Figueiredo, Pedro Nunes, C. Miguel, Brito, The feasibility of solar parking lots for electric vehicles, Energy 140 (2017) 1182–1197, https://doi.org/10.1016/ j.energy.2017.09.024. [4] Passenger Car Market Information Association. Electric vehicle sales.
References
In order to provide a more reliable prediction to the reliability of the entire battery system in pure EVs, a detailed study of the reliability of almost all components in the battery system (including battery system connectors and battery module, BMS controller, signal detection de vices, power electronic components) are investigated in this paper by the approach of fault tree analysis. From the research reported above, the following conclusions can be drawn. � Due to aging, the reliability of the EV’s battery system will decrease gradually with the increase of their service time. For example, after the battery system continuously run for 2,000, 5,000, 10,000, 15,000 and 20,000 h, the reliability index of the battery system is 98.1%, 95.3%, 90.9%, 86.7% and 82.6%, respectively. Moreover, it is interestingly found that the degradation is dominated mainly by the BMS controller rather than by battery cells and battery modules; � Despite the service time, at the level of subsystems BMS controller is more unreliable than other subsystems in the battery system, its failure rate is more than the sum of the failure rates of the other subsystems (i.e. battery system connectors and battery module, entire power electronic components and entire signal detection components). At the level of parts or components, battery cell module, SMCs for master controller and SMCs for slave controller are the three most vulnerable components in the battery system, and their failure rates are 2.4001, 2.2965, and 2.1720, respectively; the sum of the failure rates of these three components (i.e. battery cell module, SMCs for master controller and SMCs for slave controller) is 2.5 times of the sum of the failure rates of the other components or parts in the entire battery system (see Table .10). This further proves that improving the reliability of battery cell module and BMS controller is critical to guarantee the reliability of the entire battery system. � The estimation of the reliability of the battery system should be based on the consideration of the reliability of all battery system components rather than only considering the reliability of battery cells and battery modules. Otherwise, the reliability of the entire battery system would be overestimated. This is because these com ponents are integrated together and are requested to work synchro nously as one system in the normal operation of an EV. The failure of any of them can, to different extents, affect the reliability of the entire battery system. � The research of this paper also suggests that more attention should be paid to the reliability of the BMS controller in the future because it is not as reliable as the battery modules in the battery system consid ered in the paper. Declaration of competing interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative in terest that represents a conflict of interest in connection with the work submitted. Acknowledgment The authors gratefully acknowledge the support from the National Natural Science Foundation of China (Grant No. 11772126), the Hunan Province Science and Technology Innovation Program (Grant Nos. 2017XK2303 and 2019JJ50099), the Research Foundation of Education Department of Hunan Province, China (Grant No. 19A115). This study 10
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