Electric Vehicle Power Battery Reverse Logistics Model Research

Liangyu Pu, Tianyuan Guo, Dongqi Wu, Yicheng Cao, Haiming Wen, Chengran Zhang and Rui Feng

Shanghai Maritime University, China

Keywords: Power Battery, Battery Recycling, Reverse Logistics, Fuzzy Comprehensive Evaluation.

Abstract: The market situation and recycling mode of power batteries at home and abroad are introduced, and the current

situation, relevant policies, and existing problems of China's power battery recycling mode are analyzed.

Combined with market research, an AHP fuzzy comprehensive evaluation mathematical model is established

to explore the most suitable battery reverse logistics model for domestic enterprises, and the results show that

the power battery manufacturer recycling model is the most suitable for Chinese enterprises.

1 INTRODUCTION

Energy and environmental issues are becoming a

global hot topic of research. Traditional fuel vehicles

are facing the problems of fossil energy shortage and

air pollution caused by exhaust emissions, and their

dominance is being challenged by new clean energy

vehicles.

However, the battery state of health (SOH) of

power batteries will gradually decay with the increase

of charge and discharge times, and when the SOH of

power batteries drops below 80% of the original, the

batteries will be forced out according to the existing

national standards. With the rapid increase of electric

vehicle ownership, power battery packs that do not

meet the electric vehicle inspection standards will be

eliminated in large numbers, and from 2018 to 2020,

the first batch of domestic new energy models have

faced retirement, of which the total amount of

batteries to be recycled is about 25 GWh (about

200,000 tons); it is expected that by 2025, the scale

of batteries to be recycled is estimated to grow to 1

million tons (about 110 GWh) (Feng, 2021). At the

same time, with the gradual growth of domestic

demand for power batteries, the supply of raw

materials for batteries has exceeded the demand, and

the prices of raw materials for power batteries, such

as lithium carbonate and lithium hydroxide, have

continued to rise (Jiang, 2013). Therefore, whether

the power battery can be scientifically recycled is

particularly critical, which will be directly related to

the sustainable development of the domestic electric

vehicle industry and the steady promotion of the

"double carbon" policy.

Compared with the mature battery reverse

logistics model in Japan, the United States, and

Germany (Hou, 2015), the current reverse logistics

system of power battery recycling in China has

problems such as a lack of guarantee of recycling

quality, an immature recycling system, and lack of a

perfect regulatory mechanism for power battery

recycling, so it is necessary to choose a suitable

battery reverse logistics model to improve the battery

recycling system in China.

This paper analyzes the current situation of

domestic power battery recycling mode from the

perspective of power battery reverse logistics,

investigates many famous enterprises engaged in

power battery production and recycling in China, and

establishes the AHP fuzzy comprehensive evaluation

mathematical model to study the feasibility and

economy of different reverse logistics modes of

power battery based on the survey results.

2 DOMESTIC POWER BATTERY

RECYCLING REVERSE

LOGISTICS MODEL

To improve China's battery reverse logistics system,

it is necessary to establish a battery recycling

program according to China's national conditions, set

up a producer responsibility system, complete the

functions of the relevant departments and stimulate

the relevant enterprises engaged in battery recycling,

and explore the most efficient power battery

recycling mode, the following is the basic status of

power battery recycling in China today.

Pu, L., Guo, T., Wu, D., Cao, Y., Wen, H., Zhang, C. and Feng, R.

Electric Vehicle Power Battery Reverse Logistics Model Research.

DOI: 10.5220/0012039900003620

In Proceedings of the 4th International Conference on Economic Management and Model Engineering (ICEMME 2022), pages 575-580

ISBN: 978-989-758-636-1

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

575

At present, there are many enterprises engaged in

power battery recycling in China, including not only

battery raw material suppliers, but also midstream

battery manufacturers and third-party waste power

battery recycling enterprises in the industry chain.

Among them, there are four main recycling modes

(Zhu, 2019).

2.1 Producer Recovery Model

In establishing the recycling chain, battery

manufacturers can develop their own reverse

logistics chain to achieve the purpose of recycling, or

they can cooperate with sellers in the industry chain

to convert their forward logistics mode into a reverse

logistics chain. No matter which reverse logistics

model is adopted, all of them are responsible for the

recycling of power batteries through the

manufacturers.

For example, Ningde Times, as the pillar of power

battery production in China, has established a

business model of "production-sales-recycling-

production" by acquiring shares of Bump Group, a

battery recycling company.

2.2 Industry Alliance Recycling Model

In this model, manufacturers and sellers in an

industry form a consortium organization and are

responsible for recycling used products, which is

known as the industry consortium recycling model.

In this model, a type of agreement is reached between

battery manufacturers and consumers to ensure that

consumers will deliver used batteries to the industry

consortium for recycling through reverse logistics.

The industry alliance is large and well-funded and

can build more professional recycling centers. The

battery manufacturers that join the industry alliance

do not have to participate in product recycling, but

instead, pay the industry alliance a commissioning

fee.

In the case of BAIC New Energy, for example,

the company is responsible for the recycling of the

power batteries it sells. The "Optimus Project" of

BAIC New Energy is dedicated to the secondary

utilization of used batteries, promoting the organic

combination of new energy vehicles, lithium-ion

batteries, photovoltaic power storage, and other

industries, and maximizing the residual value of

retired power batteries (Liang, 2022).

2.3 Third-Party Recycling Model

The producer pays the third-party recycling

enterprise according to the type and number of

batteries sold and transfers the responsibility and risk

of handling end-of-life products to the third-party

enterprise. The relationship between the producer and

the third party is a principal-agent relationship, and

because there is inequality in information between

the producer and the third party, this leads to the

reverse selection behavior of the third party that can

cause problems such as a low recycling rate of end-

of-life products and environmental pollution. The

third-party company creates its own reverse logistics

network to recycle the waste products from the

consignee and then transports them to its recycling

center for dismantling to achieve large-scale

processing.

2.4 Small Workshop Recycling Mode

Currently, most of the retired power batteries in

China are sent to small workshops lacking relevant

qualifications for dismantling. These small

companies have rudimentary equipment and mostly

dismantle by hand with low operating costs to

increase the market recovery price and make profits

from it, with high recovery price and low recovery

cost being their biggest competitive advantage.

However, after the recycling of retired power

batteries, only after simple repair and refurbishment,

they will flow back into the market again, thus

causing damage to the normal order of the power

battery market. The end-of-life treatment of batteries

requires money and time, and is more polluting to the

environment, while its reuse has greater economic

value. In addition, the recycling process of these

small enterprises is simple, backward, and not

standardized, and in the process of recycling, a large

amount of solid and liquid waste will be generated,

causing serious pollution to the environment.

3 AHP FUZZY

COMPREHENSIVE

EVALUATION OF WASTE

POWER BATTERY

RECYCLING MODEL

EXPLORATION

3.1 Overview of AHP Fuzzy

Comprehensive Evaluation Method

To explore the most suitable battery recycling reverse

logistics model for domestic enterprises, we

investigated several domestic new energy battery

enterprises such as Aodotion (Shanghai) New

Energy, Ningde Times, Greenmax, Guanghua

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Technology, Fang Yuan Environmental Protection

and Bump Cycle Technology, and established AHP

fuzzy comprehensive evaluation model to calculate

the most suitable battery recycling reverse logistics,

model. Among them, AHP fuzzy comprehensive

evaluation method is a class of comprehensive

evaluation methods based on mathematical

modeling, which transforms qualitative evaluation

into quantitative evaluation according to the principle

of maximum affiliation of fuzzy mathematics.

In the process of the fuzzy comprehensive

evaluation, it is important to determine the evaluation

matrix and weights; in practice, the purpose of the

study can be achieved through hierarchical analysis,

weighted average method, and crowd assessment

method (Liu, 2019). Among them, hierarchical

analysis is a subjective assignment method, but it is a

more commonly used method for establishing the

relative importance of indicators in general and does

not violate basic common sense; it can make a

comprehensive connection between multiple factors

in a complex problem by comparing them two by

two, making it more organized.

To this end, this section combines a battery

manufacturer's characteristics and relevant

theoretical research to identify four high-level

indicators and several bottom-level indicators to

create a screening rubric for the reverse logistics

model of power batteries.

Due to the presence of heavy metal elements

mainly Ni and Co in waste batteries and electrolytes

that can cause environmental pollution, a

comprehensive evaluation index model is created

using the fuzzy comprehensive evaluation method

based on the three existing domestic retired power

battery reverse logistics models, and the reverse

logistics of retired power batteries is evaluated

qualitatively using quantitative forms to establish a

reasonable composite retired power battery reverse

logistics recycling model.

3.2 Build AHP Model

3.2.1 Constructing the Judgment Matrix.

According to the comprehensive evaluation system

constructed above, and combined with the judgment

comparison of senior experts in the industry, the

relative importance of the two factors is evaluated by

the scaling method, and the relative judgment

matrices D, D1, D 2, D 3 and D 4 of the high-level

and low-level indicators are obtained.

Table 1: Scale of 1~9.

Scale Meaning

1 Ai and Aj have the same contribution

to the

oal

3 Ai is slightly more important to the

oal than A

5 Ai is more important to the goal than

7 Ai is significantly more important to

the

oal than A

9 Ai is very important to the target than

2,4,6,8 Between two adjacent importance

rees

Countdown Aij=1/Aij

3.2.2 Calculating the Judgment Matrix

Based on yaahp, MATLAB software, the maximum

eigenvalue λ of each matrix and the eigenvector A

were calculated, then the eigenvectors were

normalized, and finally, the weight magnitude of

each layer evaluation index was calculated.

Table 2: Table of weight values of indicators in each layer.

High-level

indicators

Low-level indicators Weights

Economic

enefits

Profitability 0.0863

(0.1811) Logistics Costs 0.2641

Market share 0.5068

Risk resistance 0.1428

Social benefits Environmental protection 0.2066

(0.4832) Service Quality 0.1481

Policy Support 0.5341

Social Responsibility 0.1113

Development

strategy

(0.0788)

Reverse Logistics Specialization 0.1602

Core Competence 0.5697

Industry Competitiveness 0.2702

Technological Battery Recycling Process 0.4673

Advantages

(0.2569)

Product Development

Technology

0.1537

Reverse logistics infrastructure

system

0.3790

3.2.3 Performing Consistency Tests

To avoid contradictions in the two comparisons of

each indicator, which would lead to obvious errors in

the constructed matrix, we define the consistency

indicator CI = λ-n / n - 1; it can be seen that the CI

changes with n changes, and to measure the value of

CI, the random consistency index RI is introduced.

Electric Vehicle Power Battery Reverse Logistics Model Research

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Table 3: Values of random consistency index RI.

n 1 2 3 456789

RI 0 0 0.58 0.89 1.12 1.24 1.36 1.41 1.45

Define CR = CI / RI, when CI < 0.1, it means that

the matrix has a good consistency and can be

accepted, otherwise the matrix needs to be readjusted.

Table 4: Calculation results of maximum eigenvalue and

consistency ratio.

D D

λ 4.0813 4.0211 4.1028 3.0291 3.0385

CR 0.0304 0.0079 0.0385 0.0279 0.0370

3.3 Constructing the Fuzzy Evaluation

Matrix

3.3.1 Determine the Fuzzy Set.

For example, in the independent recycling model, the

general steps are to create a factor set and a rubric set.

Set the subset of factors as Ui (i = 1,2,3,...,n) as the

top-level factor set, where U ij =｛U ij, U i2,..., U ij}

as the bottom-level factor set, the alternative set T=｛

T 1, T 2, T 3} corresponding to the three recycling

modes, and the set of comments V=(good, good, fair,

poor).

Table 5: Evaluation Levels by Score.

Grade V1 V2 V3 V4

Interval (3-4] (2-3] (1-2] (0-1]

Evaluation

results

Excellent Good Fair Poor

3.3.2 Establishing Fuzzy Matrix.

Using the questionnaire method and combining the

evaluation results of 20 relevant company executives

and professionals in the industry The evaluation

matrix R i = ｛ri 1, ri 2, ri 3, ri 4} (i =1,2, ...,14) was

obtained for every single factor. Thus, a linear

transformation from U to V is induced by judging

every single factor independently.

Table 6: Single-factor evaluation values.

High-level indicators Low-level indicators Excellent

Survey

Goo

Results

Fai

Poor

Economic Profitability 0.10 0.20 0.50 0.20

benefits Logistics Costs 0.40 0.45 0.15 0.00

Market share 0.65 0.30 0.05 0.00

Risk resistance 0.10 0.40 0.40 0.10

Social benefits Environmental protection 0.50 0.40 0.10 0.00

Service Quality 0.40 0.40 0.10 0.10

Policy Support 0.60 0.30 0.10 0.00

Social Responsibility 0.30 0.40 0.20 0.10

Development strategy

Reverse Logistics

Specialization

0.10 0.20 0.50 0.20

Core Competence 0.50 0.30 0.20 0.00

Industry Competitiveness 0.20 0.20 0.40 0.20

Technological

advanta

Battery Recycling Process 0.10 0.20 0.50 0.20

Product Development

Technolo

0.50 0.40 0.10 0.00

Reverse logistics

infrastructure s

ste

0.20 0.20 0.40 0.30

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3.3.3 Calculating the Fuzzy Evaluation

Matrix

The operator M (●, ⊕) is used to fuzzy evaluate the

underlying factor set, and the resulting evaluation

matrix R leads to a fuzzy transformation:

：f(U)





⎯



f(V)；B

·R

This calculation gives the low-level fuzzy

evaluation vector. Then, still using the operator M,

the comprehensive evaluation matrix RD of the high-

level fuzzy vector B can be obtained, and the

calculation results are shown in Table 7.

Table 7: Fuzzy matrix calculation results.

Judgment

matrix

Weight vector Evaluation results

=(0.0863 0.2641 0.5068 0.1428)

=(0.45797 0.34527 0.16522 0.03154)

=(0.2066 0.1481 0.5341 0.1113)

=(0.51639 0.34653 0.11114 0.02594)

=(0.1602 0.5697 0.2702)

=(0.35491 0.25699 0.30212 0.08608)

=(0.4673 0.1537 0.3790)

=(0.12359 0.14067 0.40062 0.13132)

W=(0.1811 0.4832 0.0788 0.2569) B=(0.39220 0.28770 0.21040 0.05870)

Combined with the comprehensive evaluation

result B, according to the principle of maximum

subordination, the maximum value is taken as the

final evaluation, then the company uses independent

recycling of used power batteries mode evaluation

result is "good"; The final score of this model is 3.068

by using the weighted average method. By the same

token, we can obtain the final results of the other two

recycling models.

Table 8: Final evaluation results of the three models.

Evaluation results Evaluation level Total score

Independent mode (0.3922 0.2877

0.2104 0.0587)

Excellent 3.068

Joint Model (0.3684 0.3244

0.2358 0.0715

)

Excellent 2.990

Outsourcing Model (0.2977 0.3066

0.3048 0.0978)

Good 2.800

Using the maximum affiliation and weighted

average for the fuzzy synthesis of the high-level

indicators, it can be found that enterprises such as

Shanghai Aodin New Energy Co., Ltd. have better

results for the independent mode and joint mode of

recycling retired batteries, and the total score of these

three reverse logistics modes can be arranged in

descending order as independent mode > joint mode

> outsourcing mode.

So the same method can be used for the lower-

level indicators to obtain the evaluation rank and total

score contribution ability of the three reverse logistics

recycling models to the higher-level indicators under

different conditions. The affiliation degrees and total

scores of the bottom-level indicators are shown in

Table 9 shows.

Table 9:Bottom fuzzy comprehensive evaluation results.

Indicators

Independent mode Joint Model Outsourcing Model

Comments Score Comments Score Comments Score

Economic benefits Excellent 3.064 Excellent 3.249 Fair 2.742

Social benefits Excellent 3.353 Excellent 3.228 Fair 2.699

Development

Prospects

Excellent 2.881 Good 2.974 Good 2.963

Technological

advantages

Fair 1.865 Fair 2,363 Good 2.972

Electric Vehicle Power Battery Reverse Logistics Model Research

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As can be seen from the information in the table,

in terms of economic and social benefits, both

independent and joint models have a better degree of

suitability, because these two reverse logistics

models will more directly reflect those battery

manufacturers will follow the extended consumer

responsibility system than the third-party outsourcing

model; in terms of technical advantages, the

outsourcing model shows a greater advantage

compared to the first two, because the waste power

battery recycling market is gradually expanding,

more and more companies are beginning to layout

related business, and the ability to outsource

companies to recycle has been in a better technical

position, which better validates the rationality of the

model built.

4 CONCLUSION

In this paper, for the three main domestic retired

power battery recycling modes at present, a fuzzy

comprehensive evaluation method based on AHP is

established to indicate the degree to which the three

reverse logistics modes belong to the fuzzy set V. The

qualitative description is turned into a quantitative

analysis, and the advantages and shortcomings of the

three modes under the influence of several sub-

factors are studied in depth. Therefore, in the field of

decommissioned battery recycling, it is possible to

consider building a type of composite backbone

network that is given to its own company for self-

management at the stage of decommissioned battery

recycling collection and pre-processing and given to

third-party battery recycling companies for

processing at the stage of subsequent battery

dismantling and raw material recycling. Under this

approach, the company can not only bring into play

its advantages in economic and social factors, but

also promote the enthusiasm of third-party battery

recycling enterprises, and the whole battery recycling

chain can thus improve efficiency, reduce costs and

effectively drive the benefit growth of upstream and

downstream enterprises. This paper only investigates

the reverse logistics mode of some enterprises

engaged in power battery production and recycling,

which still has a certain reference value for the

optimization of reverse logistics of other waste

battery-related main enterprises.

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