A novel fault diagnosis method for lithium-Ion battery packs of electric vehicles

Measurement 116 (2018) 402–411

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A novel fault diagnosis method for lithium-Ion battery packs of electric vehicles

T

⁎

Xiaoyu Li, Zhenpo Wang

National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Electric vehicles Fault diagnosis Lithium-ion batteries Interclass correlation coefficient Service and management center for electric

This paper focuses on fault detection based on interclass correlation coefficient (ICC) method for guaranteeing safe and reliable of electric vehicles (EVs). The proposed method calculates ICC values by capturing the off-trend voltage drop and the voltages are extracted from Service and Management Center of electric vehicles. The ICC value is employed to analyze battery fault by ICC principle. The ICC value not only has advanced fault resolution by amplifying the voltage difference, but also can prolong the fault memory by setting moving windows. Moreover, a loop joints the first and last voltages is designed to locate faults in battery pack. In addition, simulation and experiment are employed to validate and analyze the voltage faults. Based on the simulation verification, the appropriate size of moving windows is set to ensuring sensitivity of fault detection method. The experiment results indicate the method can appropriately detect fault signals for EVs.

1. Introduction Electric vehicles (EVs) have reigned supreme as the most popular transportation applications for their capabilities to protect environment including high performance, and non-pollution [1–3]. Battery pack is the core component in EVs, which can not only provide driving force but absorb braking energy. The battery pack typically consists of numerous battery cells in various series-parallel patterns due to limitations of voltage and capacity for each cell [4,5]. During the EV operation, it is vitally important to ensure the safety and reliability by monitoring states of cells. Thus, a so-called battery management system (BMS) has been employed to collect the identification data and to estimate states for cells [6–8]. In addition, some applications of the BMS such as equalization and energy management are designed for the sake of prolong the battery lifetime [9–11]. Generally, the identification data refers to temperature, current and voltage, and the cell state includes state of charge (SOC) [12,13], state of health (SOH) [14–16], state of energy (SOE) [17] and remaining useful lifetime (RUL) [18,19]. However, the general BMS cannot detect the battery faults, which contribute much to fire or explosion accidents of EVs [20–22]. Nowadays, the electrical accidents can be classified into four types, i.e., over charge [23], over-discharge [24], external short circuit and internal short circuit [25,26]. The first two types mentioned above are voltage abnormity, which can usually be avoided by means of battery SOC estimation rather than providing valuable information to customers

⁎

[4,27,28]. The battery external/internal short circuit is more hazardous compared with over charge/discharge. The short circuit fault is more likely to lead to high heat generation and induce thermal runaway [25,29,30]. The thermal runaway is defined as a case that the solid electrolyte interface (SEI) begins to decompose when the temperature close to 90 °C. Then the negative material, positive material and electrolyte start to interact [31–33]. Thus, it is necessary to detect the battery fault during the EV operation process. At present, based on features like temperature and voltage, many researchers are dedicated to detecting battery faults and to predicting battery system faults [30,32,34,35]. The battery pack is a largely sealed space, in which the temperature of cells should be same or slightly different. Generally, the temperature sensors are disturbed evenly in pack to collect internal temperature and investigate temperature distribution of lithium-ion batteries. According to the collected temperatures, two popular methods have been proposed to detect battery short circuit faults: threshold and model methods. The temperature threshold method will be classified into two parts. Firstly, the difference between two temperature values should be calculated and then compared with the threshold, when the difference value exceeds the preset value, the fault-detection mechanism will give an alarm. Based on threshold method, the temperature difference is usually set as less than 10 °C [36–38]. The model method consists of two steps. The key step is to establish temperature model for simulating the temperature distribution in pack. Afterwards, the normal operation temperature condition is

Corresponding author. E-mail addresses: [email protected] (X. Li), [email protected] (Z. Wang).

https://doi.org/10.1016/j.measurement.2017.11.034 Received 24 July 2017; Received in revised form 18 October 2017; Accepted 13 November 2017 Available online 14 November 2017 0263-2241/ © 2017 Elsevier Ltd. All rights reserved.

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vehicles system (SMCEVS) and the fault detection process on computer center, which not only reduces the computing load for BMS but also improves safety and reliability. The reminder of the paper is organized as follows: Section 2 describes a general presentation of the big data platform and the main functions of SMCEVS. Section 3 gives a specific fault detection method. The proposed method is applied to simulate and analyze in Section 4, followed by the voltage fault analysis based on SMCEVS in Section 5. Finally, Section 6 presents a general conclusion of this work.

compared with the simulated temperature and when the difference value exceeds the preset value, the alert mechanism will be triggered [39–41]. Obviously, these two methods based on the temperature feature show three defects for detection battery fault. First, it will take a relatively longer time to show the differences between model and actual temperature value, therefore, bringing certain latency. Specifically, it is time-consuming process to obtain the corresponding output temperature value by simulation model for the current computation ability of BMS. Second, since the temperature sensor is evenly distributed among the lithium-ion batteries instead of equipping each cell with a sensor, it cannot locate the specific faulted battery. Third, the methods are rather vulnerable to signal disturbance during the operation. Typically, the method applies the differences between the simulation result and actual sampling data to detect faults, however, the actual sampling data easily suffer from the environment interference and contribute to the differences over the preset threshold. Voltage-based fault detection methods are often employed to diagnose and locate faults by collecting certain voltage signals [35,42–44]. Many researchers have proposed considerable approaches to detect battery short circuit faults, which can be divided into two groups, i.e., the thresholdbased method and model-based method. When there exists an external short circuit accident in the battery, the increasing resistance is consistent with temperature rise and the terminal voltage goes up rapidly. The threshold method captures and analyses the terminal voltages of batteries whether or not go beyond the preset voltage values [45–48]. Although some vehicle original equipment manufacturers (OEMs) and BMS manufacturers set different failure levels, the limitation of the method is that the voltage of short circuit battery may not trigger any preset value. To address this issue, an improved threshold method has been proposed [34,43]. The method needs to calculate the voltage difference between any two terminal voltages and choose the maximum difference value within battery pack that is then compared with threshold [43,45]. For this method, the most difficult place is to say an appropriate threshold. If the threshold is too high, the maximum difference is hard to touch the threshold and the fault may not be detected timely, however, if the threshold is too small, the alarm mechanism can be triggered easily and maybe contribute to the mistake fault information. The model-based method, basically, needs extensive experiments to be conducted, which typically contains indentation [49], nail penetration [50], fabrication with defect structures [51] and extreme high temperatures [31] four parts. Based on the data of aforementioned experiments, the models of short circuit was built. During the EV operation process, the model output voltages are compared with the actual output voltages of batteries. If the absolute value of the difference between actual output voltage and model output voltage goes beyond the threshold, the alarm system will be triggered [52,53]. In order to overcome defects above, numerous articles and publications have built multi-model structure, which comprises battery parameter estimation, temperature and voltage model, for the sake of improving the reliability and stability for fault detection [54,55]. In this method, the result of fault detection is affected by batteries inconsistences and has a high computing cost. Thus, there also exist some barriers to diagnose and detect battery fault for battery pack. Nowadays, however, there exists few publication that focuses on detecting and diagnosing battery fault for the EV applications. In order to deal with high computing costs, the influence of unbalance among batteries and improving safety and stability for battery system, this paper employs an interclass correlation coefficient (ICC) method to detect the battery system accident in battery packs. This method captures the terminal voltages and calculates the ICC values among cells. The ICC values indicate how strong a cell is compared with other cells in the same group. The ICC method, which eliminates the voltage sensors noise and inconsistency in batteries, can amplify the fluctuations in group, and the method can avoid false diagnosis with the battery at different states. In addition, this work collects the real-time voltage data from the service and management center for electric

2. Big data platform Currently, some accidents of EVs have been worldwide reported such as fire incidents and explosion hazards, which indicates there are many potential safe problems in EVs. The problems of safety will weaken the confidence of users and retard the market-penetration and large-scale expansion of EVs. Therefore, it is imperative to develop an effective system for safe operation. The system, which is the so-called big data platform, not only includes monitoring and analysis of the realtime data such as temperature, current, voltage, state of charge (SOC) and so on, but also diagnose and predict the operation conditions of EVs for manufactures and consumers by employing big data techniques. The detailed information of SMCEVS is based on big data platform shown in Fig. 1. In general, the SMCEVS mainly consists of two parts: the realtime monitoring data and estimation data. The real-time monitored data reflects the external features of EV and the estimation data can explore deep conditions of batteries and so on. The big data platform provides all-weather service for various vehicles, including passenger cars, commercial vehicles, logistics vehicles and buses. The monitoring information about vehicles is further processed using corresponding strategy or/and algorithm. In practical application, the battery pack is comprised of various series-parallels structures. The battery module is composed of many battery cells. Generally, the BMS collects the terminal voltages from the first battery module to the last and the detailed process is shown in Fig. 2. 3. Detection method description 3.1. Battery fault problem statement Currently, the detection methods of battery faults are divided into two aspects: the hardware detection and model detection. The hardware detection for battery fault needs a number of electric elements, which not only leads to the complexity of hardware system but increases the system cost. Hence, it is an impractical method for detecting battery faults. And the model detection usually establish a consistent model by means of various algorithms. Then the actual output value is compared with the model output value. It is the difficulty of this method to construct an accurate model, however, most systems are nonlinear in practical problems. To sum up, the aforementioned methods focus on detection of special single battery fault or individual problem. For the battery pack, it is worthwhile to note that the cells forming battery modules in parallels can generate internal circuit equilibration. The battery pack comprises many modules in series and the modules share the same input/output current. In normal condition, the battery modules should have the same voltage. The battery fault detection method needs to diagnose and locate the battery faults in a short time. Thus, a new fault detection method should be proposed to deal with battery fault problems. Based on the detailed investigation above, the battery pack construction system should be simple and the ICC detection method is described in Fig. 3. 3.2. The ICC method description The ICC conception was firstly proposed by Ronald Fisher [56]. Afterwards, Bartko used the ICC algorithm to measure and evaluate the 403

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Fig. 1. The main functions of SMCEVS.

Speed Collect

Motor Control

Battery Monitoring

Vehicle Safety

Battery Mangement

Voltage Voltage

Mismatch OverCharge

OverDischarge

Battery Safety and Short Protection Circuit

Park Design

Operation service and management center for electric vehicle

Current

Cell Monitorin g

Temper ature

Heat Dissipation SOC Estimation

SOE Estimation

Extreme

SOH Estimation

Battery Status

SOP Estimation

where the IC(x1,x2) is the ICC value between x1 and x2 , which is an unitless value and ranges from 0 to 1. The denominator S 2 can be calculated as

reliability [57]. It described the proportion of individual variability to the total variability that was less than 1. Generally, the value less than 0.4 suggests the test data is incredible, while the value more than 0.75 illustrates that the data is basically credible [58,59]. Considering a data set consists of N paired data values (x i,1,x i,2) , for n = 1,2…N , the ICC calculation formula can be described as,

n

S2 =

n

IC(x1,x2) =

Energy

n

∑ i=1

⎤ (x i,2−x )2⎥ ⎦

(2)

where the x is mean value of data x i,1 and x i,2 . The specific calculating process is as follows,

∑i = 1 (x i,1−x )(x i,2−x ) (1)

nS 2

1 ⎡ ∑ (xi,1−x )2 + 2n ⎢ i = 1 ⎣

Fig. 2. The battery pack construction.

==

==

==

==

==

==

==

==

Battery cell Battery module

==

==

==

==

==

==

==

==

404

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IC(V1,V2) n

=

(

n

1

2 ∑i = 1 Vi,1− 2n ∑i = 1 (Vi,1 + Vi,2) n ∑i = 1

(

1 Vi,1− 2n

n ∑i = 1

(Vi,1 + Vi,2)

)

2

+

)(V

1 i,2− 2n

n ∑i = 1

(

n

∑i = 1 (Vi,1 + Vi,2)

1 Vi,2− 2n

n ∑i = 1

)

(Vi,1 + Vi,2)

)

2

(7) The ICC calculating process can be easily applied for online application. Meanwhile, the data needs to accumulate during the initial stage until the operation time i is more than the size of moving windows (n > m) . Hence, the final recursive formulas after the transformation can be expressed as n 1

Ri,1 = Vi,1− 2m

∑

(Vi,1 + Vi,2)

i=n−m n 1

Ri,2 = Vi,2− 2m

∑

(Vi,1 + Vi,2)

i=n−m

Generally, the terminal voltages of two adjacent cells are the same under the various driving cycles. The ICC value ranges from 0.75 to 1 for the normal operation condition. And in this range, the voltage sampling error and noise factors can be filtered. When the ICC value is less than 0.75, there is likely to be potential fault in the cell. Hence, the ICC value can reflect the battery fault when the EV is operating under abnormal condition. The specific schematic diagram is presented to explain the ICC method for battery pack fault detection as shown in Fig. 4. The overall process is divided into three parts including Extraction Data, Fault Detection and Fault Location. The three parts will be depth analysis in next sections.

Fig. 3. The ICC calculation for a particular vehicle.

x =

1 2n

n

∑

(x i,1 + x i,2)

(3)

i=1

(8)

Based on (2) and (3), the ICC calculation formulas is redefined as follows,

IC(x1,x2) n

=

(

n

1

2 ∑i = 1 xi,1− 2n ∑i = 1 (xi,1 + x i,2) n

(

n

1

∑i = 1 xi,1− 2n ∑i = 1 (xi,1 + x i,2)

)

2

)(x n

1 i,2− 2n

(

n

∑i = 1 (xi,1 + x i,2) 1

n

4. Simulation analysis

)

+ ∑i = 1 xi,2− 2n ∑i = 1 (xi,1 + x i,2)

)

2

4.1. Normal condition

(4)

In order to verify the stability and feasibility, two cells connecting in series are chosen to acquire test data and the cell parameters are listed in Table 1. Based on specific current changes in driving cycles, the cells charging/discharging experiment is carried out in Arbin tester, BT-2000 under ambient temperature. It is worth mentioning that the current load of driving cycles is extracted from SMCEVS, then the specific current load is transformed based on corresponding vehicle type (the number of series and parallel of battery system) for single cell. The current load consists of acceleration, deceleration and idling three parts. The corresponding two voltage response curves are shown in Fig. 5(a). When the cells have strong consistency, differences between two terminal voltages under the same output current are small. In depth analysis, the small differences may result from measurement noise and/ or small resistance diversity. During this condition, the availability of proposed method is verified by calculating the ICC value and ICC value is shown in Fig. 5(b). From the Fig. 5(b), the ICC value is more than 0.75 in total operation time. This value demonstrates the batteries have perfect consistency. Compared with the Fig. 5(a), although the voltage response curves have extreme fluctuations that contain high charging/ discharging rate, the ICC values are showing a tendency to stabilization and close to 1. Additionally, during the idling process, the two voltage curves have a gradually rising tendency. However, the ICC values are still remaining unchanged and only relate to the difference between two voltages. The results indicate the ICC fault detection method has better stability and strong robustness. It is also worth pointing out the ICC value remains almost constant at first thirty seconds because the size of moving window equals 30. During this process, the ICC value keeps fixed value that is calculated at first step. This approach is designed to form the initial group of the ICC method.

For the sake of iterative regression calculation, the numerator and denominator can be expressed by two symbols as 1 ⎛ Ri,1 = ⎜xi,1− 2n ⎝

Ri,2

1 ⎛ = ⎜xi,2− 2n ⎝

n

∑ i=1 n

∑ i=1

⎞ (xi,1 + x i,2) ⎟ ⎠ ⎞ (xi,1 + x i,2)⎟ ⎠

(5)

Thus, Eq. (4) can be simplified as n

2 ∑ Ri,1 Ri,2 IC(X1,X2) =

i=1 n

∑ i=1

.

n

Ri2,1 +

∑ i=1

Ri2,1

(6)

For the online battery fault detection, the ICC value should be calculated in a sequential manner. However, it is difficult to calculate all data during the sampling process. A forgetting mechanism is employed to deal with the problem, which designs a moving window to update the history data. Generally speaking, the cells in battery packs are not in complete uniformity because they contain many influencing factors such as production process, manufacturing techniques, utilization process and environment. When short circuit fault occurs in the cell, the voltage signs are able to change immediately. Based on the ICC method, N is set as the total operation time in a driving cycle and m is the moving windows for simplifying calculation. The data set (x i,1,x i,2) expresses the cell voltages of V1 and V2 in sampling time i . To sum up, the ICC method is applied to battery fault detection and formula (4) can be redefined as follows, 405

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X. Li, Z. Wang

Extraction Data

Fault Detection

Fault Location

Extraction Data from SMCEVS

Calculation ICC value

BMS Fault Diagnosis

Data Cleaning

Capture Fault Signal

Confirmation Vehicle Type

Terminal voltage signals

If ICC >0.75

Fig. 4. The schematic diagram of fault detection process.

Salve system Fault location

No

Yes Normal condition

Fault Alarm

whole process maintains 30 sampling points which always equals to the size of moving window. Afterwards, the ICC rapidly recovers to the normal value. For the signals transmission between BMS and the big data platform, the fault retention time is much longer than signals transmission and computation time. Thus, the battery fault detection method can capture the fault signal during the actual operation process. When short circuit accident occurs, the battery terminal voltage suddenly drops in less than five seconds, however, the battery detection method can extend the fault time and memorize the fault phenomenon. During the fault detection process, the detection speed depends on the sampling speed to a certain extent. From another perspective, the result of battery fault detection is also influenced by the size of moving windows. In this study, three different sizes of moving windows are employed to verify the fault detection speed. Hence, three ICC values are calculated based on different window sizes, as shown in Fig. 7. The battery fault memory time increases with increasing window sizes. When the window size is set as 50, the fault memory time is extended to 150 s. Although the fault memory time is extended to some extent, the sensitivity of fault detection decreases and the ICC value becomes smaller. When the moving window sizes are set as 30 or 40, the ICC values are under 0.75 at 100th sampling point. However, the ICC value

Table 1 Battery parameters. Parameter type

Cylindrical 26650

Rated voltage Rated capacity Charge cut-off voltage Discharge cut-off voltage Max discharge rate

3.3 V 5 Ah 3.65 V 2.0 V 5C

4.2. Abnormal condition During the abnormal condition, the battery fault detection method should appropriately and rapidly inform of the battery fault. Based on the battery fault principle, the different voltage signals are built by reducing one cell voltage in a short time. The fault signals between two batteries are constructed by a sudden voltage drop at the one hundredth sampling points and the max voltage drop is close to 100 mV. The specific voltage faults signals are plotted in Fig. 6(a). The ICC value for the fault voltage signal is calculated in Fig. 6(b). Obviously, the ICC value suddenly drops below 0.75 at the 100th sampling point and the

1.005

3.36

V1 V2

1

ICC Coefficient

Voltage(V)

3.34 3.32 3.3 3.28

0.99 0.985

3.26 3.24

0.995

0

100

200

300

400

500

600

700

800

900

0.98

0

100

200

300

400

500

Time(s)

Time(s)

(a)

(b)

Fig. 5. Two cells voltage curves and corresponding ICC value.

406

600

700

800

900

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X. Li, Z. Wang 1

V1

3.32

0.95

FV2

3.3

0.9

ICC Coefficient

Voltage(V)

3.28 3.26 3.24 3.22

3.3

3.2 3.2

3.18 3.16 100

200

0.8 0.75 0.7 0.65

70

0

0.85

80

300

90

400

100

500

110

600

120

700

800

900

0

100

200

300

400

500

Time(s)

Time(s)

(a)

(b)

600

700

800

900

Fig. 6. The fault voltage signals and corresponding ICC value. 1

Current(A)

0.9 0.85 0.8

0.9 0.8 0.7

0.75 0.7 0.65

100

0

100

200

300

0 -200 -400 0

100

200

300

100

200

300

400

500

600

700

800

400

500

600

700

800

205

Voltage(V)

ICC Coefficient

200

N=30 N=40 N=50

0.95

150

400

500

600

700

800

900

Time(s)

200 195 0

Time(10s)

Fig. 7. The results based on different window sizes. Fig. 10. The current and voltage of EV practical operation. 3.4

V1 FV2 V3

Voltage(V)

3.35

moving window is set as 30, which has obvious advantages including not only a sensitive ICC value but also a faster fault detection speed. 4.3. Fault location

3.3

700

800

900

The fault location is another important part for battery fault diagnosis. When the battery fault happens, the ICC value suddenly drop and the location of battery fault should be identified immediately. Three terminal voltages data are captured from series cells under the same charge/discharge current. A voltage drop around 100 mV is set as 100th second of the second cell. The specific voltage profiles are plotted in Fig. 8. The first and third cells are normal with good consistency, while the second cell has a maximum difference with those two cells at the 100th second. Based on the ICC fault diagnosis method, the ICC values are calculated depending on number of voltages or cells. The number of ICC values can be summarized as,

{

3.25 3.2

0

100

200

300

400

500

600

Time(s)

ICC(1,2)

Fig. 8. The battery voltage signals.

0.9 0.8 0.7

ICC(1,3)

ICC(2,3)

0

100

200

300

400

500

600

700

800

900

0 1

100

200

300

400

500

600

700

800

900

0.95 0

100

200

300

400

500

600

700

800

900

0.9 0.8 0.7

n, n > 2 1, n = 2

(9)

where n is the number of voltages. Therefore, three ICC values are calculated for location the battery fault and the values are described in Fig. 9. The ICC values of ICC(1,2) and ICC(2,3) are less than 0.75 after the 100th second. Those two values indicate that at least one cell happens fault. In addition, the ICC value of ICC(1,3) is more than 0.75 during overall testing procedure. The value manifests the first and third cells are not occurring short circuit fault. In conclusion, the fault cell can be located in second cell at 100th second.

Time(s) Fig. 9. The ICC values between two battery voltages.

5. Voltage fault analysis based on SMCEVS is over 0.75 at 100th sampling point when the moving window size equals 50. After 10 s, the ICC value gradually falls to less than 0.75. From the overall operation stage, the last ICC value is more than those of the first two types. To sum up, the size of moving window should be considered to fault detection speed. Compared with the cases when the sizes of moving windows are set as 40 and 50, in this study, the size of

The cell datasets are extracted from big data platform and the specific fault vehicle: No. GA0083X. The data period was two hours with a data acquisition frequency of 0.1 Hz. It is well known that an electric vehicle consists of hundreds of battery modules. Therefore, a large number of voltages are collected by salve systems. Then, the 407

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X. Li, Z. Wang 1.05

3360

ICC1

V1

3340

ICC2

V2

1

3320

ICC4

V5

3300

V6 V7

3280

V8

ICC5

0.95

ICC6 ICC7 ICC8

0.9

ICC9

V9

3260

ICC10

V10

ICC11

0.85

V11

3240 3220

ICC3

V4

ICC Value

Voltage(mV)

V3

ICC12

V12

0

100

200

300

400

500

600

700

0.8

800

0

100

200

300

400

500

600

700

800

Time(10s)

Time(10s)

(a)

(b)

Fig. 11. The voltages and ICC values of the first salve system. 3360

1.05 V1

3340

V4 V5

3300

V6 V7

3280

V8 V9

3260

V10 V11

3240 3220

V12

0

100

200

300

400

500

600

700

ICC2 ICC3

0.95

ICC Value

Voltage(mV)

V3

3320

ICC1

1

V2

800

ICC4 ICC5

0.9

ICC6

0.85

ICC7 ICC8

0.8

ICC9

0.75

ICC10

0.7

ICC12

0.65

ICC11

0

100

200

300

400

500

Time(10s)

Time(10s)

(a)

(b)

600

700

800

Fig. 12. The voltages and ICC values of the second salve system. 3360

1.05 V1

3340

ICC3

V4 V5

3300

V6 V7

3280

V8

ICC4

ICC Value

Voltage(mV)

ICC2

1

V3

3320

ICC5

0.95

ICC6 ICC7

0.9

ICC8

V9

3260

ICC9

V10

ICC10

0.85

V11

3240 3220

ICC1

V2

ICC11

V12

0

100

200

300

400

500

600

700

ICC12

0.8

800

0

100

200

300

400

500

Time(10s)

Time(10s)

(a)

(b)

600

700

800

Fig. 13. The voltages and ICC values of the third salve system.

two voltages. The specific fault detection analysis method is described in Figs. 11–15. Fig. 11(a) shows the voltage curves of the first salve system, obviously, the voltage curves are inconsistent under practical operation situation. It is evident that the seventh voltage in black varies greatly when comparing with others. Based on the proposed fault detection method, the ICC values can demonstrate this difference clearly. In Fig. 11(b), the ICC values of ICC(6,7) and ICC(7,8) have less values compared with other values. According to the fault detection method, the seventh voltage of first salve system has fault potential. Meanwhile, the voltages of the second salve system are extracted to analyze, and the voltages and ICC values are plotted in Fig. 12. Obviously, Fig. 12(a) shows the tenth voltage of the second salve system is higher (lower) than other voltages under charge (discharge) condition. Similarly, the ICC values of ICC(9,10) and ICC(10,11) are between 0.95 and 0.85 on the whole. In the initial stage, the ICC value of ICC(9,10) is less

voltage data of salve systems is uploaded to BMS. In this study, the salve system can collect twelve voltages each time. Based on the big data platform, sixty groups voltages are extracted from different five salve systems. The total voltage of sixty groups voltages and charge/discharge current of EV practical operation are described in Fig. 10. The overall driving cycle comprises of acceleration, deceleration and idle three parts through analysis the current changes. The maximum absolute value of charge/discharge current is around 200 A. The total voltage changes with the current. The voltage decreases (increases) under discharge (charge). The data can reflect the daily operation condition. The sampling voltages of five salve systems are verified based on the proposed ICC method. Twelve voltages of each salve system are set as an accuracy to three decimal places, for the sake of manifesting the voltages changes clearly. The data of each salve system is flagged for locating the voltages. And twelve ICC values can be calculated between 408

Measurement 116 (2018) 402–411

X. Li, Z. Wang 3500

1.4 ICC1

V1

3000

1.2

V2

ICC2

2500

ICC3

1

V4 V5

2000

V6 V7

1500

V8 V9

1000

V11

100

200

300

400

500

600

700

ICC5

0.8

ICC6

0.6

0

800

0.75

ICC7

0.5

ICC8 ICC9

0.25 0 430

0.2

V12

0

ICC4

0.4

V10

500 0

ICC Value

Voltage(mV)

V3

ICC10 440

450

460

470

ICC11

480

ICC12

0

100

200

300

400

500

600

700

800

Time(10s)

Time(10s)

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(b)

Fig. 14. The voltages and ICC values of the fourth salve system.

1.05

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3320 3300 3280 3260 3240 3220 0

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0.85

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200

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400

500

600

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Fig. 15. The voltages and ICC values of the fifth salve system.

In the fifth salve system, the voltage curves fluctuate in normal scope besides the tenth voltage curve. The specific voltages change are shown in Fig. 15(a). The ICC values can explain the voltages condition of the fifth salve system. Fig. 15(b) shows all ICC values are more than 0.75 and the values range from 0.9 to 1 on the whole. By special considerations of the ICC values, the tenth voltage maybe has a fault potential due to the lesser ICC values between ICC(9,10) and ICC(10,11) values. In addition, the primary reasons are measure noise, production technology and grouping technique for ICC values are less than 1. However, those problems can be neglected during the voltage fault detection process.

than 0.75, which is the preset threshold of normal condition. Therefore, the two-level alarm mechanism can be triggered and the process maybe keep 300 s. Because the fault occurs at the initial stage and ICC value is updated after the moving windows work. Compared with other ICC values at the initial stage, the values are less than the values of practical operation. To sum up, the ICC value are below 0.75 at the initial stage should be neglected and this phenomenon may be contributed by the measure noises. Based on the proposed fault detection method and Eq. (9), the ICC value is calculated in a loop from the first to last voltages for sake of detecting the fault of first and last battery modules. It is a quite smart method for locating battery fault detection. Fig. 13(a) shows the last voltage of the fourth salve system has great fluctuation. However, it is difficult to determine the fault degree of the voltage and locate the fault just through voltage fluctuation condition. The ICC values of ICC(11,12) and ICC(12,1) can easily find the voltage location and the specific ICC value can reflect fault degree on some extent. The ICC values of the third salve system are showing in Fig. 13(b). The short circuit of the battery is collected by the fourth salve system and the fault voltage signal appears and recovers in few sampling points. In addition, the fault voltage signals may be ignored due to the data transfers frequency. Thus, it is important for the fault voltage signals to be retention. The moving windows of this proposed fault detection method have significant effects on extension voltage signals. The fault voltage signals are shown in Fig. 14(a), the signals just keep few sampling point, specifically, the fault signals only last 20 s. However, the ICC values of ICC(2,3) and ICC(3,4) can capture the fault signals and prolong the signals, which are specifically described in Fig. 14(b). The fault signals are retention 30 sampling points that value equals to the window size and the fault signals are extended to 300 s. Thus, the fault can be detected and captured timely.

6. Conclusion In this paper, a novel voltage fault detection method is proposed based on the service and management center for electric vehicles system of electric vehicles. The concept of the interclass correlation coefficient is first introduced and then the interclass correlation coefficient is applied to analyze battery short circuit fault by capturing the off-trend voltage drop. The coefficient value not only has advanced fault resolution by amplifying the voltage difference, but also can prolong the fault memory by setting moving windows. In addition, the proposed method has a feasible calculation processing, which does not need to build model and redundant hardware design. And a smart fault location method is completed by designing a loop joints the first and last voltages. Moreover, simulation and experiment are employed to validate and analyze the voltage faults. First, simulation is applied to verify the method availability by collecting three group voltages from Service and Management Center of electric vehicles. Meanwhile, the sensitivity of fault signals is analyzed by setting three different sizes of moving 409

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windows. The experiment is proposed to verify the method for battery fault of electric vehicles and the voltage data is extracted from service and management center of electric vehicles. To sum up, the proposed method not only detects and located voltage faults, but also can reflect fault potential. In addition, the method can neglect the inevitable effects such as measure noise, production technology and grouping technique. Further work will focus on battery current and temperature abnormality and those phenomena are detected by this proposed method. In addition, the method can be applied to big data platform for other fault detection fields.

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