New Energy Vehicle Market Development Prospects and Sales
Short-Term Forecast
Junyang Zhou
a
Reading Academy, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China
Keywords: New Energy Vehicle, Regression Analysis Method, Seasonal Time Series Analysis.
Abstract: With the increasing global focus on sustainable energy and reducing greenhouse gas emissions, the new
energy vehicle market is developing rapidly. Technological innovation, government policy support, and
increased consumer awareness of environmental protection and cost-effectiveness have contributed to the
growth of NEV sales. Based on the monthly sales data for 2023 and 2024 provided by the China Association
of Automobile Manufacturers, this paper conducts linear regression analysis and seasonal time series analysis
to predict the sales trend in the coming months. The study found that the new energy vehicle market shows
strong growth potential, although there is a little fluctuation, but it is expected that sales will continue to rise in
the short term, the overall increase over last year, the end of the year is predicted to reach nearly two million.
However, market participants should pay close attention to industry dynamics and changes in the external
environment to adapt to any market volatility that may arise. With technological advances and intensified
global efforts to reduce emissions, the new energy vehicle market is expected to achieve significant growth in
the coming years.
a
https://orcid. org/0009-0009-8709-4452
1 INTRODUCTION
China's new energy vehicle market is rapidly
expanding because the country's establishing a 'dual
carbon' goal and transformative energy structure
(Zeng et al., 2025). Due to the popularity of new
energy cars, urban dwellers will enjoy a better living
environment, which will somewhat enhance green
space and decrease urban noise pollution (Sun, 2024).
After several years of development, new energy has
made significant breakthroughs in power batteries,
drive motors, charging technology, and assisted
driving (Lu, 2023). However, the quantity of new
energy vehicle brands is increasing, and the
competition in the Chinese market is fierce.
Therefore, relevant personnel need to have a deep
understanding of consumer demand and accurately
forecast the market to make effective response
measures. Based on a mathematical model, Wu
(2024) examines the development trend of new
energy electric vehicles in China and concludes that,
as a result of the combined influence of numerous
factors, the sales volume of new energy vehicles will
continue to maintain a positive development trend
over the next ten years, and its market share will
continue to grow. Zhang, Xiang & Yang (2024)
conducted Research on the future development
prospects of new-energy electric vehicles based on
the trend determination method, which shows that the
industry's development is influenced by market
dynamics, such as the increase in sales and expanding
market share of new-energy vehicles. Zhou, Wang &
Zhang (2024) present a study on the development of
new energy vehicles and the forecast of power
demand using the Random Forest model, the results
show that by 2030, it is anticipated that China will see
a sharp increase in the number of new energy cars,
accounting for 25% of all vehicles. For firms, these
researches can quickly modify the research and
development direction of new goods and optimize the
production plan through short-term prediction to
satisfy the market demand and increase the economic
efficiency and market competitiveness of enterprises.
The government can also formulate corresponding
preferential policies and reasonable supporting
facilities according to market forecasts to promote the
development of new energy vehicles and
technological innovation to achieve the purpose of
Zhou, J.
New Energy Vehicle Market Development Prospects and Sales Short-Term Forecast.
DOI: 10.5220/0013814300004708
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy (IAMPA 2025), pages 77-82
ISBN: 978-989-758-774-0
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
77

energy saving, emission reduction, and
environmental protection (Xu & Sun, 2024).
Based on this, this paper uses regression analysis
and seasonal time series analysis to analyze the
monthly sales data of new energy vehicles in 2023
and 2024 collected from China Association of
Automobile Manufacturers in combination with
market development prospects, and makes a
prediction of new energy vehicle sales for the next
year and performs a straightforward analysis. It is
designed to help consumers choose wisely and
producers better prepare for the next year's market.
2 NEW ENERGY VEHICLE
MARKET DEVELOPMENT
PROSPECTS
The new energy vehicle market is ushering in a new
stage of rapid development, and its development
prospects are jointly promoted by three major factors:
technological progress, government policies, and
market competition.
At the technical level, the improvement of battery
energy density and endurance of new energy vehicles
is a key trend. Future models are expected to have a
significantly improved battery life due to the use of
new technologies like solid-state batteries, and the
battery life of some models is expected to exceed 1,
000 kilometers. The progress of assisted driving
technology will also become a highlight of new
energy vehicles, Zhang Yongwei said at the China
Electric Vehicle 100 Forum media communication
conference that the penetration rate of L2 level
assisted driving is expected to reach 65%. Consumers
are also paying attention to enhancing safety
performance and developing fast-charging
technology.
The new energy vehicle industry's progress is
aided by government support, which involves
improving infrastructure, providing car purchase
subsidies, and offering industrial planning guidance.
Government subsidies can promote enterprises'
research and development investment. In the case that
the market regulation mechanism has not played a
significant role, the government can actively regulate
and guide the market through some guiding fiscal
policies to improve enterprises' confidence in
independent research and development investment
(Yan & Chen, 2023). The government has increased
the amount of new energy car charging infrastructure
being built, charging stations have been supported,
and social capital has been encouraged to participate.
Since 2010, there has been a policy in place to
provide financial subsidies to eligible new energy
vehicles. Since 2019, subsidy policies have gradually
shifted to technological innovation-oriented financial
incentive policies, which will further promote
consumer acceptance of new energy vehicles (Tian,
Wang & Zhu, 2024). In the future, the government
will encourage core technology research and
development, shift vehicle subsidies to technology
research and development subsidies, and promote the
transformation of research results from universities
and scientific research institutions to the automobile
production end (Liu, 2023).
At the level of enterprise competition, the
transformation of traditional automobile
manufacturers and the rise of emerging forces jointly
shape the market competition pattern. The
establishment of a strong after-sales service system,
including maintenance and parts supply, is crucial for
improving user experience (Gao, 2024). Traditional
car companies like BYD and Geely have been able to
transform the field of new energy with remarkable
results, while the entry of new forces such as Huawei
and Xiaomi has brought new vitality to the market
with its advantages in intelligence and technological
innovation.
Overall, the new energy vehicle market has broad
prospects for development. Technological
innovation, government policy support, and
competition among enterprises will drive the market
forward. It is anticipated that new energy cars will
significantly increase their market share in the global
auto industry, and the trend of comprehensive
intelligence will be more obvious. A prosperous
future is expected to be brought about by the
continued progress of technology and the continuous
support of policies in the new energy vehicle market.
3 SHORT-TERM FORECAST
OF NEW ENERGY VEHICLE
SALES
3.1 Data Analysis Method
3.1.1 Regression Analysis Method
To analyze the relationship between sales volume and
multiple influencing factors, new energy vehicles are
analyzed using the unitary linear regression model.
The construction of the model involves identifying
variables that affect sales volume, such as price,
policy support, technological progress, etc., and then
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estimating the model parameters through historical
data. The general form of a linear regression model
is:
ε
β
β
++= XY
10
(1)
Where
Y
is the dependent variable (sales
volume),
X
is the independent variable (time),
0
β
is the intercept,
1
β
is the slope, and
ε
is the error
term.
3.1.2 Seasonal Time Series Analysis
SARIMA model is a commonly used method in time
series analysis, which is suitable for time series data
with seasonal fluctuations. The steps to build a
SARIMA model include determining the model's
parameters (p, d, q) and seasonal parameters (P, D,
Q)s, and then using historical sales data for model
fitting and parameter optimization. SARIMA (p, d, q)
(P, D, Q)s, where p represents the number of terms in
the non-seasonal autoregressive part of the model. d
represents the number of differences needed to make
the non-seasonal part stationary. q represents the
number of terms in the non-seasonal moving average
part of the model. P represents the number of terms in
the seasonal autoregressive part of the model. D
represents the number of seasonal differences that
need to be made to make the seasonal part stationary.
Q represents the number of terms in the seasonal
moving average part of the model. s stands for
seasonal repeating cycles.
3.2 Data Preprocessing
The data source is mainly the 2023 and 2024 data
collected by the China Association of Automobile
Manufacturers. As a permanent member of the World
Automobile Organization, the data source has high
reliability. This paper collected monthly New Energy
Vehicle (NEV) sales in 2023 and 2024 as sample size
and variables.
3.2.1 Regression Analysis Method
Through calculation, it can be obtained that in the
regression analysis model of 2023, the intercept (
0
β )
is 34. 0515, the slope (
1
β
)is 4. 6857 and the value
2
R is 0. 9582.
The following are the results of the model test in
2023, the independence test (Figure 1) and normality
test (Figure 2), from which it can be seen that the
applicability and reliability of the regression model
are relatively high.
Figure 1: Independence test. (Photo/Picture credit:
Original).
Figure 2: Normality test. (Photo/Picture credit: Original).
Through calculation, it can be obtained that in the
regression analysis model of 2023, the intercept (
0
β )
is 40. 1379, the slope (
1
β
)is 8. 6762 and the value
2
R is 0. 9176.
The following are the results of a model test in
2024, the independence test (Figure 3) and normality
test (Figure 4), from which it can be seen that the
applicability and reliability of the regression model
are relatively high.
Figure 3: Independence test. (Photo/Picture credit:
Original).
New Energy Vehicle Market Development Prospects and Sales Short-Term Forecast
79

Figure 4: Normality test. (Photo/Picture credit: Original).
Based on the error analysis of the constructed
regression analysis model, the mean square error
(MSE) and Root Mean Square Error (RMSE) in 2023
are 11. 425 and 3. 380, and the MSE and RMSE in
2024 are 80. 508 and 8. 973, respectively.
3.2.2 Seasonal Time Series Analysis
In this paper, the stationarity test of the data was first
carried out, and it was found that the original data
(Figure 5) was unstable, so the first-order difference
was performed. As shown in the result (Figure 6), the
data was stable after the first-order difference was
tested.
Figure 5: Raw data. (Photo/Picture credit: Original).
Figure 6: Data after first difference. (Photo/Picture credit:
Original).
Using the autocorrelation function (Figure 7) and
the partial autocorrelation function (Figure 8) to
establish the appropriate p and q, all parameters
required by the model are determined: p=1, d=1, q=1,
P=1, D=1, Q=1, s=12.
Figure 7: Autocorrelation Function. (Photo/Picture credit:
Original).
Figure 8: Partial Autocorrelation Function. (Photo/Picture
credit: Original).
Based on the error analysis of the constructed
seasonal time series analysis model, the MSE and
RMSE in 2023 are 138. 934 and 11. 787, and the
MSE and RMSE in 2024 are 577. 201 and 24. 025,
respectively.
3.3 Sales Forecast and Analysis
Based on data processing, this paper uses a linear
regression model and SARIMA model respectively to
forecast the number of new energy vehicles sold in
2025, and Figure 9 and Figure 10 are obtained.
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80

Figure 9: Regression analysis method. (Photo/Picture
credit: Original).
Figure 10: Seasonal time series analysis. (Photo/Picture
credit: Original).
According to the forecast of the linear regression
model (Figure 9), the monthly sales volume of new
energy vehicles in 2025 shows a positive growth
trend. In January, sales are expected to be 918, 100
units, and then rise month by month, to the end of
December is expected to reach 1. 653 million units,
reflecting the continued increase in consumer
demand for new energy vehicles and the further
release of market potential, indicating its important
position in the future automotive market, but also for
the development of the industry into a strong
confidence.
According to the prediction of the time series
model (Figure 10), the monthly sales volume of new
energy vehicles in 2025 has a little fluctuation, but the
overall trend is upward. Sales are expected to reach 1,
1005 400 units in January and 1, 951, 900 units in
December. Among them, there may be a large
reduction in February, and there will still be stable
growth in the second half of the year. However, time
series analysis may require more complex models
and more data to improve the accuracy of the
prediction. In subsequent studies, more factors can be
considered to make more complex predictions.
According to the data processing, the MSE and
RMSE of the linear regression model are
significantly lower than that of the seasonal time
series analysis model, and the fitting effect of the
linear regression model is better. Jiang, Mei, Pan &
Wang (2024) collected the sales volume of new
energy vehicles in the past ten years to build the
Autoregressive Integrated Moving Average
(ARIMA) model. The amount of data is large enough,
and the predicted result is more accurate. Zhang &
Zhang (2024) predicted new energy vehicle sales of
different brands and regions through the combination
of ARIMA and LSTM models, and the construction
of the combined model has higher prediction
performance.
4 CONCLUSION
The sales volume of new energy cars is analyzed and
forecasted in this research using regression analysis
and seasonal time series analysis, respectively. The
market for new energy vehicles has a very wide range
of development prospects in the near future.
Although there is a little fluctuation, the sales volume
is expected to continue to rise. This is mainly due to
the strong support of policies, many countries and
regions have introduced subsidies and other policies
to encourage consumers to buy. Consumers are
becoming more aware of environmental protection,
and the number of people who are aware and accept
new energy vehicles is increasing. Moreover,
technological progress has also increased the driving
range of new energy vehicles, accelerated charging
speed, and improved intelligence. Furthermore, new
energy vehicles have low operating and maintenance
costs, which encourages more consumers to choose
them.
In the short term, the competition in the new
energy vehicle market will be more intense, the
market reshuffle will accelerate, and enterprises with
technology, brand, and cost advantages will stand out.
Intelligence and networking will become the focus of
market competition, and the penetration rate of
intelligent driving functions will be further improved.
Plug-in and range extension technology will continue
to advance, fostering the growth of the sales of new
energy vehicles. The infrastructure will also be
continuously improved, and the changing station will
enhance the user experience of consumers. The
hydrogenation station will be gradually increased.
and encourage the expansion of the market for new
energy vehicles.
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