Research on Fault Diagnosis Method of Lithium Battery

Based on Fuzzy Neural Network

Meng

Chen

, Zongyang Liu

, Jiang Wu

and Kun Ma

School of Electrical Engineering , Xi'an Jiaotong University, Xi'an, China

Lanzhou University of Finance and Economics Accounting School, Lanzhou, China

Keywords: Fuzzy neural network; Lithium battery; Fault diagnosis

Abstract: Aiming at the problem that the battery lithium battery has poor generalization performance for different

health conditions, a fault diagnosis method based on fuzzy neural network is proposed. The neural network

is used to diagnose the battery fault, and then the fuzzy system rules are used to output three fault states of

the lithium battery, namely Corresponding Capacity reduction, Increase of internal resistance, SOC

Reduction, and finally simulation analysis. The effectiveness of this method for fault diagnosis of lithium

battery systems is demonstrated.

1 INTRODUCTION

The domestic power battery technology is not

fully mature, and the battery failure is not easy to

detect at the initial stage. Therefore, it is of great

practical significance to carry out fault diagnosis

research on the battery system to ensure that the

battery is in normal operation.

The paper use fuzzy neural network to combine

fuzzy logic and neural network. The learning

mechanism of neural network is used to

automatically extract rules from input and output

data, and the fuzzy system is easy to express human

knowledge

[1-3]

. The characteristics can improve the

traditional fuzzy controller which must rely on

human thinking to adjust the membership function

repeatedly to reduce the error and improve the

performance. Simulation analysis shows that the

fuzzy neural network can effectively judge the fault

state of the lithium battery.

2 FUZZY NEURAL NETWORK

Design a five-layer feedforward neural network,

see Fig 1.The first layer is the input layer, and the

input value indicates that the first node of the input

layer corresponds to its first component. The input

and output of this layer are:

xI =

（1）

njxIO

jjj

,,2,1,

===

（2）



Fig. 1. Fuzzy neural networks

The second layer is the membership function

layer, and the membership functions

belonging

to the fuzzy sets of the values of the respective

language variables are calculated. The membership

functions are:

( )

 

/exp

ijijj

EUx −−=



（3）

In the formula, the number of fuzzy partitions

= 3,

indicating the mean of the membership

function,

indicating the standard deviation of the

membership function,

and

are both

adjustable parameters. Then the input and output of

the second layer are:

jijjijj

xwOwI

121122

（4）

( )

 

122

2/exp

ijijjijj

EUxwO −−=

（5）

In the formula,

indicates the connection

weight of the first node of the first layer and the

second node of the second layer.

The third layer is the rule front layer. The input

and output of the

jth

node are:

( )

 

 

= =

−−==

ijijjijijjj

wEUxwwOI

122323

2/exp

（ 6 ）

























−+=



ijjj

wOO

2323

exp1/1

（7）

Where,

represents the connection weight of

the second node of the second layer and the

jth

node of the third layer.

The fourth layer is the rule back layer, the input

and output are:

  

= = =

























−==

ijjijijjj

njwOwwOI

1 1 1

232343434

,,2,1,exp/ 

（8）

334



jjj

OOO

（9）

In the formula,

indicates the connection

weight of the

ith

node of the third layer and the

jth

node of the fourth layer; 3 indicates that the

fuzzification level is level 3.

The fifth layer is the output layer, which

represents the output variable. After deblurring, the

network output is obtained:



jijj

OwO

4455

（10）

Where,

represents the connection weight of

the

ith

node of the fourth layer and the

jth

node of

the fifth layer.

3 LEARNING TRAINING

ALGORITHM

Use BP algorithm network to adjust weights

、

Define The network output error function

[4-5]

( )

njbaO

jjp

,,2,1,6.0

=−=



(11）

is the expected output of the network,

the actual output of the network, and

is the

number of output categories.

Suppose

is the error back-propagation signal

the

jth

node of the

ith

layer of the network, then

the layers can be expressed as:

Output layer

G −=



(12)

Fourth floor

444454

























−=



iik

iikij

iikiijjj

OEOEUOEOUGG

(13)

In the formula,

represents the mean value of

the membership function,

represents the standard

deviation of the membership function,

is the

number of output categories.

the third floor

444453

 

























−=

iik

iikij

iikiijjj

OEOEUOEOUGG

(14)

Second floor

( )( )



−

ijijjj

EUGG

(15)

The weight is calculated as:

is 1





j nx

jij

2223



(16)

( )

( )( )

exp1

exp

−+

−

−



(17)

( )



−=

445

exp



(18)

，

are the parameters in the second

layer, find a step:

( ) ( ) ( )

( )















+=+=+

kaakaka

ijijijij



(19)

( ) ( ) ( )

( )















+=+=+

kbbkbkb

ijijijij



(20)

When

less than a given threshold



learning training stops. If the desired output has not

been reached, the weight parameter is adjusted

according to the formula until the predetermined

requirement is reached.

4 SIMULATION

In this paper, the CBP2450 battery pack in

reference[6] is used as the research object. The

charge and discharge experiments are designed by

using the ITECH series of DC electronic load and

DC voltage source. The specific battery parameters

are shown in Tab 1.

Tab. 1. The Battery Parameters

Fuzzy neural network has 3 nodes in the first

layer, and selects voltage, current and temperature as

network input; the second layer has 8 nodes; the

third and fourth layers are fuzzy rules and the post is

also 10 nodes; the fifth layer has 4 nodes. It

corresponds to four states of lithium battery,

including three major fault states and normal states,

such as Corresponding Capacity reduction, Increase

of internal resistance, and SOC Reduction.

Fig. 2 Composite prediction result

Select 180 data to be as training and testing

samples, and predict the actual curve and error curve,

the result is Fig.2.

Fig. 3. Fuzzy neural networks prediction

day

Actual

prediction

curve

Error

curve

Normal

Corresponding

Capacity reduction

SOC Reduction

Increase of

internal resistance

Fig. 3 is a fuzzy neural network prediction, the

function output corresponds to the discharge

capacity. In the sample size, the battery discharge

capacity can reach 100%. In order to facilitate the

observation error, the error curve is drawn by the

difference between the predicted output and the

expected output to predict the lithium battery fault

diagnosis. According to the prediction output , the

lithium battery fault could be diagnosed and the

result is credible.

After constructing the network, 150 training

samples are input for training. In order to avoid

over-learning, the training error precision is set, and

the learning process is stable and convergent. After

75 cycles, the allowable error range has been

reached. The BP network training error curve is

shown in Fig. 4.

−

Performance=0.004227

Goal=0.0015

Training Error

Iterations

Fig. 4 Training error curve.

5 CONCLUSION

Based on the complexity and uncertainty of

lithium battery faults in electric vehicles, this paper

proposes a lithium battery fault diagnosis method

based on fuzzy neural network. The method makes a

preliminary diagnosis of lithium battery fault

through neural network, and then uses the

combination rule to fuse different neural network

outputs, which can successfully diagnose the fault

state of the lithium battery, and the diagnostic

accuracy is higher than the single fault diagnosis

method, and the diagnosis is improved. Sexuality,

the result of the diagnosis is met, and the accurate

judgment of the fault state of the lithium battery of

the electric vehicle is obtained.

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