Data Mining and Analysis of New Energy Vehicles Based on Cluster
Analysis Technology
Zhengmi Wang
Yunnan College of Business Management, KunMing, 650000, China
Keywords: Data Mining, Cluster Analysis Techniques, New Energy Vehicles, New Energy.
Abstract: Under the trend of social energy gradually moving towards clean energy, the scale of new energy vehicles
has gradually increased. The use and production of new energy vehicles generates a large amount of data,
such as the status of batteries and motors. Therefore, the role of data mining in the data collection and in-
depth analysis of new energy vehicles is very important. However, there is a problem of inaccurate data
collection, and the improvement of the battery and power output are unreasonable. Therefore, this paper
proposes a cluster analysis technique to perform extensive data mining analysis. Firstly, the data preprocessing
and cluster analysis in data mining are used to collect and sort out the data with poor data integrity, and the
unified data with strong integrity is obtained for comprehensive analysis. Under the condition that the data
evaluation criteria are fixed, the data mining accuracy and response speed of cluster analysis technology to
new energy vehicle data Optimal traditional analytical techniques.
1 INTRODUCTION
The development of new energy vehicles is one of
the important manifestations of social progress and is
of great significance to the automotive field (Chen,
You, et al. 2023). However, in the process of data
mining, there is a problem of poor accuracy in data
mining and analysis (Chen, Liu, et al. 2023), which
brings poor experience to customers who use cars
(Fu, Lan, et al. 2023). Some scholars believe that the
application of cluster analysis technology to new
energy vehicle data can effectively analyze new
energy vehicle data information and provide
corresponding support for data mining and analysis
(Guo, You, et al. 2023). On this basis, this paper
proposes a cluster analysis technique to optimize the
data mining analysis and verify the effectiveness of
the technique (Guo, Sun, et al. 2023).
With the continuous enhancement of
environmental awareness, new energy vehicles have
become an important direction for the development of
future automobiles (Hong, Liang, et al. 2023). In the
research and development process of new energy
vehicles, the application of cluster analysis
technology can provide important support and help
for the production and sales of new energy vehicles.
This paper will explain the application of cluster
analysis technology in the field of new energy
vehicles, the mining of relevant data and the analysis
of advantages (Li, Zhou, et al. 2023).
1.1 Application of Cluster Analysis
Technology in the Field of New
Energy Vehicles
1.1.1 User Segmentation
Through cluster analysis technology, consumers of
new energy vehicles can be grouped, and then the
needs and psychology of different user groups can be
deeply understood, and the market competitiveness
and brand image of new energy vehicles can be
improved (Li, and Zhang, 2023). For example,
cluster analysis technology can be used to understand
which users pay more attention to the range of the
vehicle and which users value the safety performance
of the vehicle, so as to launch different products and
services for different user groups.
1.1.2 Failure Analysis and Resolution
Through cluster analysis technology, the fault data of
new energy vehicles can be classified and analyzed,
the commonality and law of faults can be found, and
corresponding solutions can be proposed (Li, Ma, et
300
Wang, Z.
Data Mining and Analysis of New Energy Vehicles Based on Cluster Analysis Technology.
DOI: 10.5220/0013540300004664
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 300-306
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

al. 2023). For example, through cluster analysis
technology, different engine faults can be classified
into different categories, and corresponding solutions
can be proposed according to the characteristics of
each category to improve the reliability and safety
performance of vehicles.
1.1.3 Product Design and Optimization
Through cluster analysis technology, the design and
performance of new energy vehicles can be analyzed
and evaluated, the characteristics and commonalities
of different models and components can be found,
and the product design and optimization of new
energy vehicles can be provided (Li, Liu, et al. 2023).
For example, the data of different models can be
classified into different categories through cluster
analysis technology, so as to provide a scientific basis
for the product design and optimization of new
energy vehicles.
1.2 Mining of Relevant Data
The research and production of new energy vehicles
involves a large amount of data, and cluster analysis
technology can mine and analyze the relevant data of
new energy vehicles (Li, Peng, et al. 2023.
Specifically, it includes the following two aspects:
1.2.1 Mining of User Behavior Data
The user behavior data of new energy vehicles is of
great significance for the research and development
of new energy vehicles (Li, Zhang, et al. 2023).
Through cluster analysis technology, the behavior
data of new energy vehicle users can be mined and
analyzed, including car purchase time, vehicle usage,
charging habits, etc., so as to better understand the use
of new energy vehicles and user needs (Liu, Wang,
Kim, 2023).
1.2.2 Mining of Vehicle Performance Data
The performance data of new energy vehicles is also
of great significance for the research and
development of new energy vehicles. Through cluster
analysis technology, the performance data of new
energy vehicles can be mined and analyzed, including
energy consumption, power output, braking effect,
etc., so as to better understand the performance and
optimization direction of new energy vehicles (Hou,
Liu, et al. 2023)
1.3 Advantage analysis
The application of cluster analysis technology in the
field of new energy vehicles has the following
advantages:
1.3.1 Improve Product Competitiveness
Through cluster analysis technology, we can deeply
understand the needs and psychology of different user
groups, and improve the market competitiveness and
brand image of new energy vehicles (Pang, Ye, et al.
2023).
1.3.2 Optimize Vehicle Maintenance
Through cluster analysis technology, the fault data of
new energy vehicles can be classified and analyzed,
and corresponding solutions can be proposed to
improve the reliability and safety performance of
vehicles (Shao, Jiang, et al. 2023).
1.3.3 Improve Product Design Efficiency
Through cluster analysis technology, the design and
performance of new energy vehicles can be analyzed
and evaluated, so as to provide a scientific basis for
the product design and optimization of new energy
vehicles and improve product design efficiency
(Song, and Jiang, 2023).
1.3.4 Optimize Vehicle Performance
Through cluster analysis technology, the performance
data of new energy vehicles can be mined and
analyzed, so as to better understand the performance
and optimization direction of new energy vehicles,
and improve the performance and efficiency of
vehicles.
This paper elaborates from three aspects: the
application of cluster analysis technology in the field
of new energy vehicles, the mining of related data and
the analysis of advantages, aiming to reveal the
importance and application prospect of cluster
analysis technology in the field of new energy
vehicles (Sun, Zhang, et al. 2023). The application of
cluster analysis technology can optimize the product
design and performance of new energy vehicles,
improve the operational efficiency and market
competitiveness of new energy vehicles, and have
greater social value and economic benefits (Tan,
Wang, et al. 2023).
Data Mining and Analysis of New Energy Vehicles Based on Cluster Analysis Technology
301

1.4 Research Status of New Energy
Vehicles
1.4.1 Electric Vehicles
Electric vehicle is a kind of new energy vehicle, which
has the characteristics of zero emission, no noise, low
carbon and environmental protection compared with
traditional internal combustion engine vehicles. At
present, domestic and foreign automakers are
researching and developing electric vehicles, and
cities around the world have set stricter emission
standards to promote the development of electric
vehicles. At the same time, the cruising range,
charging time, battery life, etc. of electric vehicles are
still problems that electric vehicle researchers need to
solve.
1.4.2 Hybrid Vehicles
Hybrid vehicles are vehicles with both internal
combustion engine and electric motor powertrain, and
the energy consumption and emissions of the vehicle
are reduced by working together with each other. At
present, domestic and foreign automakers are
researching and developing hybrid vehicles, and
different hybrid systems are constantly emerging.
1.4.3 Fuel Cell Vehicles
A fuel cell vehicle is a vehicle that uses hydrogen and
oxygen as fuel and generates electricity through the
fuel cell to drive the car. Compared to electric and
hybrid vehicles, fuel cell vehicles have longer range,
shorter charging times and zero emissions. At present,
domestic and foreign automobile manufacturers are
also researching and producing fuel cell vehicles, and
the research and development of fuel cell technology
is also accelerating.
1.4.4 Smart Cars
Intelligent vehicles refer to vehicles that achieve more
intelligence and automation through digital,
networked and intelligent means, including automatic
driving, intelligent traffic management, and vehicle-
mounted intelligent terminals. With the continuous
development of artificial intelligence and Internet of
Things technology, the research and development in
the field of intelligent vehicles has also received more
and more attention.
1.4.5 Internet of Vehicles
Internet of Vehicles refers to the combination of cars
and the Internet to form a new Internet application
ecosystem, through the information interaction
between vehicles and between vehicles and the
Internet, to achieve more intelligent, efficient and safe
car driving. At present, the research and application of
Internet of Vehicles technology is also accelerating.
The research status of new energy vehicles
involves electric vehicles, hybrid vehicles, fuel cell
vehicles, intelligent vehicles and Internet of Vehicles.
Automakers and scientific research institutions in
various countries and regions around the world are
accelerating the research and development of new
energy vehicles. In the future, the research and
application of new energy vehicles will face more
challenges and opportunities.
2 RELATED CONCEPTS
2.1 Mathematical Description of
Clustering Techniques
Cluster analysis technology is based on massive
database information, and according to the data
information analyzed by data mining, the internal
laws of information are classified, and the data with
great similarity is analyzed to sort out and form a data
set. Then, the data mining analysis is used to find the
most optimal data node parameter values in the data,
and finally judge the feasibility of the development of
new energy vehicles, to optimize the data mining and
analysis of new energy vehicles (Wang, Nie, et al.
2023).
Suppose I. The data set is represented as a matrix
of is k, and the data mining analysis is
i
set , the
vector represents data is n, and the vector property is
X, As shown in Equation (1).
11 12 1
2
21 22
12
k
k
nk
nn nk
aa a
a
aa
x
aa a



=





 

(1
)
2.2 Selection of New Energy Vehicle
Development Plan
Hypothesis II The first attribute value of the first
i
scheme is g, and the weight coefficient is
j
, then,
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302

after the scheme is standardized, the data value is
i
w
, as shown in Equation is
ij
g
, (2):
ij j
ij i
j
gg
gbac
s
−
=⋅−
(1
)
2.3 Data Mining Analysis
Before performing clustering techniques, perform
two-dimensional analysis of data mining analysis and
map the data for data mining analysis to the data
database A large amount of historical data and
indirectly generated data are collected to generate
datasets (Wang, Chen, et al. 2023). Then, the data
with high similarity and different similarity are
divided into hierarchies, stored, and managed, and the
sorted data is easier to be used by data mining and
analysis. Finally, the use of data mining analysis to
clean up the wrong data, the accurate data to integrate,
transform to improve the accuracy of cluster analysis
technology, improve the level of data mining, to
select the data of data mining analysis, the specific
selection method is shown in Figure 1.
Response
speed
Development
of new energy
vehicles
Battery
energy
consumptio
n
Automobile
power
Energy
improvemen
t
Energy
output
Data mining
Figure 1: Results of the selection of new energy vehicle
development plans
After data mining and analysis, it is shown that the
development data of new energy vehicles has the
characteristics of large amount of information, high
dimension, and complex structure, which is easy to
cause the loss and abnormality of data information
(Wang, Yi, et al. 2023). The data of new energy
vehicles also includes battery energy consumption
data, motor power data, and motor response speed
data. According to the cluster analysis technology, the
feature constraint adjustment of the new energy
vehicle development data is carried out, the duplicate
and irrelevant data is removed, and the default data is
supplemented, so that the integrity of the new energy
vehicle development scheme is strong (Yu, Han, et
al. 2023).
3 OPTIMIZATION STRATEGY
FOR NEW ENERGY VEHICLES
The optimization strategy of cluster analysis
technology for new energy vehicles, including battery
energy consumption, motor power, and motor
response speed. The power of new energy vehicles
comes from batteries and motors, so accurate fault
warning can effectively reduce the economic losses
and accidents caused by failures. Cluster analysis
technology divides the data of new energy vehicles
into different levels and randomly selects different
data for analysis. In the iterative process, data of
different levels of data mining analysis is optimized
and analyzed. After the optimization analysis is
completed, the best parameter values of different data
are compared to record the best new energy vehicle
data.
4 PRACTICAL EXAMPLES OF
NEW ENERGY VEHICLE
OPTIMIZATION
4.1 Introduction to Data Mining
To facilitate data mining and analysis, this paper takes
new energy vehicles as the research object in actual
situations, with 9 paths and a test time of 24h The data
mining analysis of specific new energy vehicles is
shown in Table 1.
Table 1: New energy vehicle data mining analysis
Scope of
application
Battery energy
consumption
Car
p
owe
r
Speed of
response
Pure electric
Oil mixe
d
86.57 85.64 83.20
87.86 83.37 82.90
Domestic
brands
Foreign
b
rands
85.74 82.79 83.51
87.12 87.18 87.29
joint venture
import
88.01 87.13 92.20
87.49 81.35 82.43
Data Mining and Analysis of New Energy Vehicles Based on Cluster Analysis Technology
303

The data mining analysis in Table 1 is shown in
Figure 2.
Cluster analysis
Data set
Similarity
classification
Hierarchical
clustering
Clustering
results
Mean
clustering
Data mining
Figure 2: Analysis process of data mining of new energy
vehicles
Compared with traditional technical analysis, the
data mining analysis of cluster analysis technology is
closer to the actual user needs. In terms of the
rationality and fluctuation range of the development
of new energy vehicles, cluster analysis technology is
better than traditional technical analysis. Through the
changes of data mining analysis in Figure 2, the
stability of cluster analysis technology is better, and
the response speed is faster. Therefore, the new
energy vehicle development scheme analyzed by data
mining and analysis of cluster analysis technology
has better stability.
4.2 Development of New Energy
Vehicles
The data mining analysis of the development of new
energy vehicles includes battery improvement,
energy efficiency output, and data mining depth.
After the preselection of cluster analysis technology
and the preprocessing of data mining analysis, the
preliminary new energy vehicle development plan
and the new energy vehicle development plan are
obtained the feasibility is analyzed. To verify the
improvement effect of new energy vehicle
development more accurately, the new energy vehicle
development scheme with different data mining and
analysis levels is selected, as shown in Table 2.
Table 2: Improvement of the new energy vehicle
development plan
category Structural
ad
j
ustment
Distribution
ad
j
ustment
Battery
improvements
84.58 81.48
Battery output
reasonableness
84.82 80.25
Data mining
depth
86.75 84.98
mean 86.54 89.58
4.3 Development and Stability of New
Energy Vehicles for Data Mining
To verify the accuracy of clustering techniques,
compared with traditional technical analysis and data
mining analysis, the data mining analysis is shown in
Figure 3.
Figure 3: Development of new energy vehicles with
different methods
It can be seen from Figure 3 that the development
of new energy vehicles by cluster analysis technology
is better than that of traditional technical analysis, and
the improvement effect is obvious, indicating that the
data mining of cluster analysis technology is obvious
the analysis is relatively stable, while the data mining
analysis of traditional analysis techniques has a single
nature. The average data mining analysis for the
above two methods is shown in Table 3.
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304

Table 3: Comparison of data mining accuracy of different
methods
Algorithm Development
of new
energy
vehicles
Magnitude
of change
Error
Clusteranalysis
techniques
92.64 93.82 1.20
Traditional
technical
anal
y
sis
79.36 83.80 4.52
P 55.21 53.28 54.62
By Table 3 It can be seen that in the development
of new energy vehicles, traditional technical analysis
has inaccurate data information and stability
deficiencies in new energy vehicle data, and the data
and information of new energy vehicle development
have changed significantly, the error rate is high. The
general results of cluster analysis technology have
higher data information on the development of new
energy vehicles, which is better than traditional
technical analysis. At the same time, the new energy
vehicle development data information of cluster
analysis technology is greater than 2%, and the
accuracy has not changed significantly. In order to
further verify the superiority of clustering techniques
and the effectiveness of the methods, different
methods are used to perform general analysis of
clustering techniques, as shown in Figure 4.
Figure 4: Cluster analysis technology: the development of
new energy vehicles for data mining and analysis
By Figure 4, the development of new energy
vehicles by cluster analysis technology is
significantly better than traditional technical analysis,
and the reason is that cluster analysis technology
combines deep mining analysis It makes up for the
singleness of deep mining analysis, optimizes deep
mining analysis from multiple dimensions, and
makes the development plan of new energy vehicles
better.
5 CONCLUSIONS
With the progress and development of society, the
development speed of new energy vehicles has
increased in recent years, aiming at the problem that
the battery energy consumption information, vehicle
power information, and response speed information
of new energy vehicles are not ideal, which is easy to
cause errors in vehicle energy output data, response
speed data, and mileage data. Based on this, this paper
proposes a cluster analysis technology and combines
data mining to optimize the development of new
energy vehicles. At the same time, the depth, breadth
and threshold innovation of data mining are analyzed
in depth, breadth, and threshold, and standard and
strict data sets are constructed. Studies show that
cluster analysis technology can improve the accuracy
and stability of new energy vehicle data information,
which can improve the data information of new
energy vehicles Evolutionary data mining analytics.
However, with the development of new energy
vehicle technology and the continuous improvement
of user demand, further research should be done on
the development of new energy vehicles.
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