New Energy Vehicles Research Articles

Exploring the technology changes of new energy vehicles in China: Evolution and trends

In the sustainable development context, the automotive industry is shifting towards new energy vehicles (NEVs) to reduce carbon emissions. China leads in NEVs production and technology but faces challenges in innovation capacity due to increasing market competition and technological demands. Consequently, to explore the situation of technology innovation of China’s NEVs, this paper delves into the technology changes in this field with the invention granted patents from 2000 to 2021, employing the basic patent-metrics method and Latent Dirichlet Allocation (LDA) topic model for evolution analysis and vector autoregression (VAR) model for trend predictions. The research findings indicate that: (1) the patents pertaining to NEVs have experienced an average annual growth rate of 23%, with notable regional disparities in the spatial distribution of said patents. (2) Among the 8 key technology topics identified, battery equipment technology, brake control system technology, and supplying system technology emerge as the top three technology topics. (3) As NEVs progressively become more intelligent, there is an anticipated surge in interest regarding vehicle braking control technology over the ensuing five years.

Intelligent evaluation system for new energy vehicles based on sentiment analysis: An MG-PL-3WD method

As a critical strategic tool within the framework of the digital economy (DE), artificial intelligence (AI) plays a crucial role in realizing an AI-assisted economy. This novel paradigm has gained widespread acceptance from governments globally, positioning itself as a cornerstone in the practical implementation of the DE. The increasing reliance on digital means for economic transformation, the treatment of data as a critical resource, and the integration of information technologies highlight the synergy between AI and the DE. Against this backdrop, online reviews and SA play a pivotal role, offering rich data streams continually emerging on various platforms. Therefore, this article utilizes a probabilistic linguistic term set (PLTS) to establish a multi-granularity probabilistic linguistic (MG-PL) information system. This approach, through sentiment analysis (SA), enables quantitative analysis of user emotional expressions. The integration of these technological aspects holds the promise of enhancing decision-making processes, facilitating the intellectualization of economic activities, and addressing existing challenges in the application of AI within the DE domain. Initially, SA is conducted on text-based online comment data, forming a probabilistic linguistic incomplete information system. The maximum similarity method is employed to establish a probabilistic linguistic complete information system. Subsequently, an adjustable MG-PL decision-theoretic rough set (DTRS) is developed, followed by the establishment of a three-way group decision model using three-way decisions (3WD) and a directed graph. The model’s applicability, stability, and feasibility are demonstrated via a case study centered on the acquisition of new energy vehicles (NEVs).

Investigating emotional design of the intelligent cockpit based on visual sequence data and improved LSTM

To enhance affective experience and customer satisfaction in the intelligent cockpit of new energy vehicle (NEV-IC), this article proposes a novel method that combines the visual sequence data of eye movements with the sentiment prediction using improved Long Short-Term Memory (LSTM). Specifically, we used eye-tracking technology to capture users' visual sequence of design morphology for NEV-IC. We then adopted entropy-TOPSIS to compute the ranking of morphological components based on experts’ opinions, establishing the coupling between users' visual perception and experts' opinion to obtain the key morphological dataset of NEV-IC based on user visual sequence. To tackle the shortcomings of LSTM, meanwhile, we employed the sparrow search algorithm (SSA) to optimize the hyperparameters of the LSTM model. Moreover, an attention mechanism has been introduced to address LSTM's difficulty in preserving key information when processing the sequential data, enabling a stronger focus on critical sequential features within the user's visual path. To assess the efficacy of the proposed SSA-LSTM-Attention model, a dataset incorporating user emotional imagery was constructed, within the research framework of Kansei engineering (KE). This dataset, in conjunction with the morphological dataset of visual sequential features, was applied to our model. The study results indicated that compared to traditional machine learning models like BP neural network (BPNN), support vector regression (SVR), and LSTM, our model performed better in capturing the nonlinear relationship between user sentiment and design features. Additionally, it exhibited higher predictive accuracy, better generalization ability and stronger robustness.

For-profit or for-CSR: decisions of power batteries recovery in the supply chain

Abstract Accepted by: M. Zied Babai The importance of recovering retired batteries from new energy vehicles (NEVs) has garnered widespread attention in both scholarly literature and practical applications. This paper discusses the decisions of battery recovery in a supply chain consisting of a supplier and a manufacturer. Utilizing the Stackelberg game, we construct relevant models and compare the equilibrium solutions in two decision-making modes, for-profit and for-CSR (corporate social responsibility). We shows that the firms’ pursuit of CSR contributes to increasing collection prices. However, if environmental damage is severe, the firms’ pursuit of CSR leads to high retail prices, which is detrimental to the sale of NEVs. Furthermore, although not all members’ collection amounts increase when considering CSR, the overall collection effect will be better. Meanwhile, we find that both the supplier and the manufacturer have incentives to practice CSR because their pursuit of CSR leads to high profits. Finally, this paper has verified all the above conclusions using theoretical data.

Design and selection of government regulations for vehicle supply chains: A Chinese perspective

New energy vehicles (NEV) have become a major trajectory changer for the global auto industry and an important catalyst for global sustainable economic growth. The implementation of carbon cap and dual credit regulations has altered the competitive environment and strategy of conventional vehicles (CV) and NEV, and has attracted extensive scholarly attention. However, the collaborative governance of the two regulations is relatively limited. This paper constructs two stylized production models based on Cournot game to examine the impacts of different combinations of two regulations, including no regulatory intervention (NC, ND), consideration of dual credit regulation without carbon cap regulation (NC, D), consideration of carbon cap regulation without dual credit regulation (C, ND), and consideration of both regulations (C, D), on vehicle production decisions, and determine the optimal regulatory scheme and intervention intensity under varying conditions. The Lagrange functions and Karush-Kuhn-Tucker (KKT) optimization conditions are employed to solve for the optimal CV and NEV production decisions. The results show that carbon cap regulation not only reduces carbon emissions but also expands the NEV market size. Different regulatory combinations are applicable in different contexts, while collaborative efforts between carbon cap and dual credit regulation can enhance social, economic, and environmental potential within the auto industry under specific conditions. These findings provide important theoretical guidance for expanding NEV market and improving government intervention mechanism.

Brand Awareness Influence on the Purchase Intention of New Energy Vehicles under the Moderation of Green Consumption Value, Mediated by Perceived Value

To explore the relationship between brand awareness and purchase intention of new energy vehicles (NEV) and their mechanism, 560 users with recent purchase intention of NEV were surveyed by using the brand awareness scale, perceived value scale, purchase intention scale, and green consumption value scale. The results show that: (1) under the condition of controlling for gender and age, brand awareness can significantly and positively predict the purchase intention of NEV users, (2) brand awareness predicts the purchase intention of NEV users through the mediation of perceived value, and (3) the relationship between brand awareness and the purchase intention is moderated by the green consumption value. Specifically, individuals with a higher level of green consumption values exhibit a stronger correlation between brand awareness and purchase intention compared to those with lower levels of consumption values. The findings of this study shed light on the mechanisms underlying the purchase intention of NEV brand users and offer valuable enlightenments for the marketing strategies of NEV brands.

Variation of New Energy Vehicle Product Scores Based on Fuzzy Rough Set Theory and Cellular Automaton

This study investigates the fluctuation in product scores of new energy vehicles (NEVs) using a combination of fuzzy rough set theory and cellular automaton. By integrating these two methodologies, we aim to provide a comprehensive understanding of how NEV product scores evolve over time. Firstly, the fuzzy rough set theory is employed to handle the uncertainty and imprecision inherent in NEV product evaluation, optimizing the selection of influential factors. Subsequently, a cellular automaton model is utilized to simulate the dynamic changes in NEV product scores, incorporating factors identified through fuzzy rough set theory. Through this combined approach, we can continuously monitor and analyze the variations in NEV product scores, enabling stakeholders to make informed decisions for improving product competitiveness and market performance. This study contributes to the advancement of methodologies for evaluating NEV products and offers insights into the dynamic nature of their competitive landscape.

Does the new energy vehicles subsidy policy decrease the carbon emissions of the urban transport industry? Evidence from Chinese cities in Yangtze River Delta

New energy vehicles (NEV) have become an important driving force for carbon reduction in the transportation industry. This paper adopts the extended environmental pressure assessment (STIRPAT) model and the time-varying difference in differences (DID) model to evaluate the effectiveness of the NEV subsidy policy to achieve sustainable governance. The results show that the NEV subsidy policy can significantly decrease the carbon emission of the transport industry. The mechanism analysis results indicate that the NEV subsidy policy can decrease carbon emissions by promoting a green transformation of the consumer car purchasing structure. Further analysis found that a “fraudulent subsidy” can significantly weaken the policy effect of subsidies. The results of heterogeneity analysis indicate that the subsidy policies introduced by different levels of government have different effects. The subsidy policy of the central government has a more significant effect, and repeated subsidies from central and local governments will weaken the policy's effectiveness. These findings provide valuable insights for policymakers in developing countries to optimize subsidies for new energy vehicles in reducing carbon emissions in the transportation industry.

The Development of China's New Energy Battery and Automotive Industry

This paper provides an in-depth analysis of the development of China's new energy battery and automotive industry, focusing on the transition from traditional vehicles to new energy vehicles (NEVs) due to global energy restructuring and environmental concerns. The Chinese government's significant policy support for NEVs and related power battery sectors is highlighted, emphasizing their role in reducing carbon emissions and promoting environmental conservation. The paper traces the evolution of China's new energy battery and automobile industry, characterized by rapid technological progress and strategic national support. It examines various battery technologies, including lead-acid, nickel-based, and lithium-ion batteries, each with unique benefits and challenges. Lithium-ion batteries, despite their high cost and safety concerns, are preferred for their superior energy density and cycle life. The current development status of the industry is discussed, addressing rapid growth, technological innovations, and challenges such as battery inconsistency, safety, and recycling. The paper emphasizes the significance of technological advancements in power batteries, drive motors, and NEV system configurations. Looking to the future, the paper anticipates continued growth in China's NEV and power battery sector, driven by technological innovations, policy support, and alignment with China's dual-carbon strategy. Challenges such as cost control, safety, and battery recycling are recognized as critical areas for future development. In conclusion, the paper asserts that while China's new energy battery and automobile industry is poised for significant growth, it faces multifaceted challenges that require collaborative efforts from the government, industry, and research bodies to ensure sustainable and safe advancement.

Identifying Heterogeneous Willingness to Pay for New Energy Vehicles Attributes: A Discrete Choice Experiment in China

New energy vehicles (NEVs) have emerged as a promising solution to reduce carbon emissions and address environmental concerns in the transportation sector. In order to effectively accelerate market acceptance, it is crucial to prioritize the heterogeneity of consumer preferences for NEV attributes. This study employs the multinomial logit model (MNL) and latent class model (LCM) to investigate both observed and unobserved preference heterogeneity based on stated preferences obtained from a discrete choice experiment conducted across seven cities in China. Results from the MNL model indicate that all attributes significantly influence alternative utility. In particular, there are differences in the willingness to pay (WTP) for attributes of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). Analysis of MNL subgroups reveals observed heterogeneity in WTP for identical attributes among consumers from regions with different latitudes and markets with different NEV penetration rates. Furthermore, the LCM model uncovers unobserved preference heterogeneity by classifying respondents into four distinct classes and identifies specific socioeconomic variables associated with each class. The recognition of heterogeneous WTP for NEV attributes across vehicle types, regions, markets, and consumer classes provides important implications for formulating targeted policies that promote the sustainable development of the NEV industry.

Impact of Consumer Subsidy Considering Subsidy Threshold on New Energy Vehicle Firms and Environmental Benefits

This paper investigates a competitive market consisting of two new energy vehicle (NEV) firms with a technology gap and consumers with green preferences. By employing a Hotelling model, we obtain the equilibrium outcomes of two competing firms without and with segmented consumer subsidy (SCS) following Nash game theory. We further explore the incentive effect, effective scope of SCS policy and the impacts of subsidy threshold and technology improvement on it, which fills the gap in the literature and provides managerial insights. We find that SCS can only play a role when the threshold is intermediate, and the government can expand the effective scope of subsidies by cultivating consumers’ green preferences and strengthening the intensity of subsidies. Moreover, the government can change the competitiveness and green level gap of two firms through the threshold. When threshold is small (large), increasing it can narrow (expand) the green level gap between two firms but widen (narrow) the market gap. We also find that implementing an SCS policy has a positive impact on environmental benefits and technological improvement is more likely to cause environmental damage. However, the government can effectively decrease the degree and probability of damage by raising the subsidy threshold and reducing the subsidy amount.

Synergistic effect of government policy and market mechanism on the innovation of new energy vehicle enterprises

To accelerate the realization of the "carbon neutrality and carbon peaking" (dual carbon) goal, the "Opinions on Financial Support for Carbon Neutrality and Carbon Peaking" (the "Opinions") advocates the comprehensive adoption of tax incentives, government procurement and market-diversified investment measures to support the green low-carbon innovation transformation of key industries. Taking China's strategic new energy vehicle (NEV) industry from 2009 to 2021 as a sample, this paper empirically investigates the innovation effect of the measures in the "Opinions" on listed NEV enterprises. The results show that: (1) Both "supply-side" tax incentives and "demand-side" government procurement encourage NEV enterprises to innovate independently, jointly and efficiently, and the "supply-side" tax incentives have a stronger innovation effect. At the same time, compared with joint innovation, government policies are more conducive to independent innovation of NEV enterprise. (2) Market venture capital also encourages NEV enterprises to innovate. Government policy signals to the market can attract venture capital, and the "demand side" government procurement signal effect is stronger. (3) Tax incentives and venture capital, government procurement and venture capital have played an innovative synergistic effect on NEV enterprises. (4) There is an innovation failure in the combination of tax incentives and government procurement, but the innovation effect appears after the introduction of market venture capital. This paper not only supports the effectiveness of the innovative measures in the "Opinions", but also recognizes the continuation of the vehicle purchase tax exemption policy for NEVs.

Exploring the Cross-Correlations between Tesla Stock Price, New Energy Vehicles and Oil Prices: A Multifractal and Causality Analysis

The interaction between new energy vehicle (NEV) stock prices and the crude oil market is crucial for resource allocation and risk management. This study employs Multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the multifractal characteristics of the cross-correlation between Tesla stock price (TSLA) and crude oil price (Brent), as well as between TSLA and other NEV stocks (excluding TSLA). The experimental results reveal long-term persistence and multiple fractal characteristics in the cross-correlations. Additionally, multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) demonstrates the asymmetry of the cross-correlation during upward or downward trends between TSLA and Brent, as well as between TSLA and other NEV stocks (excluding TSLA). Furthermore, utilizing the transfer entropy (TE) method, we assess the strength and direction of information flows between TSLA and Brent, and between TSLA and other NEV stocks (excluding TSLA). Interestingly, we observe bidirectional information transmission between TSLA and other NEV stocks, while only unidirectional information transmission from NIO to TSLA is evident. These findings provide valuable insights for resource allocation, supply chain management and sustainable development strategies for decision-makers in the NEV market.

Exploring the influence of social capital on innovation networks in emerging industries: the mediating role of knowledge sharing

ABSTRACT In the face of rapidly evolving global markets, understanding the drivers of innovation network in emerging industries is crucial. Drawing on cooperative patent data from China’s new energy vehicle (NEV) industry from 2001 to 2019, this study uses social network analysis (SNA) and the quadratic assignment procedure (QAP) to empirically assess the effects of social capital on the innovation networks of emerging industries. The results revealed that during the growth and prosperity periods, the bonding structure has a positive impact on the formation of cooperative relationships, and the bridging structure has no effect. Cognitive and relational capital have continuous and significant positive impacts on the formation of cooperative relationships. In addition, knowledge sharing plays a significant mediating role in the process through which social capital affects innovation networks. This study not only advances our understanding of the dynamics within emerging industries but also provides valuable insights for policymakers and industry leaders seeking to foster innovation-driven growth. The implications of these findings are particularly pertinent in guiding strategic decision-making and policy formulation in sectors akin to the NEV industry.

New energy vehicles' technology innovation coordination strategy based on alliance negotiation under dual credit policy.

The development of new energy vehicles (NEVs) is one of the effective ways to alleviate carbon emissions, environmental pollution, and energy scarcity in the transportation sector. The Chinese government has innovatively proposed the "dual credit policy," but it is still a hot topic whether it can promote the NEVs' technological innovation. In this study, we construct game models and obtain the technological innovation strategies for NEVs under the dual credit policy, considering that the NEV supply chain contains one manufacturer and N suppliers. Further, we construct bargaining game models and study how to encourage manufacturers and suppliers to enhance technological innovation, realize supply chain coordination, and give the alliance strategy to maximize suppliers' profit. We found that the dual credit policy can effectively stimulate technological innovation, and the higher the credit price or technological innovation credit factor, the higher the technical level of NEVs. The findings could guide the government to adjust and revise the policy. Second, we found that the bargaining games could coordinate the NEV supply chain so that decentralized enterprises can achieve optimal technological innovation under centralized decision-making. Third, we found that suppliers can improve their profits by choosing a suitable alliance strategy under the manufacturer's different negotiating power.

A new energy vehicle battery supplier selection using SWARA-MEREC-MARCOS approach under probabilistic triangular intuitionistic hesitant fuzzy environment

In this manuscript, we construct a Multi-Criteria Decision-Making (MCDM) model to study the new energy vehicle (NEV) battery supplier selection problem. Firstly, we select criteria to build an evaluation index system. Secondly, SAWARA and MEREC methods are used to calculate subjective and objective weights in the ranking process, respectively, and PTIHFS (Probabilistic Triangular Intuitionistic Hesitant Fuzzy Set) is employed to describe the decision maker’s accurate preferences in performing the calculation of subjective weights. Then, the game theory is used to find the satisfactory weights. We use TFNs to describe the original information in the MARCOS method to obtain the optimal alternative. Finally, a correlation calculation using Spearman coefficients is carried out to compare with existing methods and prove the model’s validity.

The impact of consumer preferences on the evolution of competition in China's automobile market under the Dual Credit Policy-A density game based perspective.

The evolution of the automobile market is a macro-expression of the behavior of automakers' production decisions. This study examines the competitive environment between new energy vehicles (NEVs) and conventional fuel vehicles (CFVs) and develops a game-theoretical model incorporating consumer utility, automaker profit, and the competitive density of NEVs and CFVs. It aims to assess how consumers' preferences for vehicle range and smart features influence automakers' strategic decisions and the broader market evolution under the Dual Credit Policy. The findings indicate: (1) A low NEV credit price facilitates NEV market size growth, but this growth rate diminishes beyond a certain price threshold; (2) The lower the consumer's range preference, the higher NEV credit price can accelerate the development of new energy vehicles to their saturation value. However, when consumers in the market prioritize smart features, increasing the NEV credit price does not significantly influence the growth of NEV market size. (3) Higher consumer preferences for both range and smart features, combined with increased NEV credit prices, can synergistically accelerate the speed of the NEV market to reach the saturation value and also raise the saturation value of the scale of NEVs. And higher consumer range preference combined with increased NEV credit prices has a more significant effect on the promotion of NEV market size than the combined effect of higher consumer smart preference and increased NEV credit prices. The actual data of China's automobile market is used in the simulation of this model. The model and its simulation results effectively explain and reveal the evolutionary impacts of consumers' range and smart feature preference on the promotion of China's NEVs under the Dual Credit Policy to provide effective technological and theoretical support for the promotion of the sustainable development of China's NEV industry.

Fast or stable: Research on dynamic behaviors of duopoly automakers under the background of energy transformation in the automobile industry

New energy vehicles (NEVs) are crucial way to solving environmental and energy dilemmas. However, the transition from traditional energy vehicles (TEVs) to NEVs is extremely intricate. If the process for TEVs switching to NEVs falls into out order, the automobile industry will be shaken. This paper takes electric vehicles (EVs), representing NEVs, and internal combustion engine vehicles (ICEVs), representing TEVs, as research objects, and constructs a dynamic Gounod duopoly model for the production decisions of two oligopolistic automakers under the dual credit policy. Combining the nonlinear dynamic theory and Cournot game, the influence of policy, market, technology, and cost on the stability of duopoly automakers’ dynamic production game equilibrium and the nonlinear evolution characteristics of Cournot-Nash equilibrium are analyzed systematically. Results show that: (1) In the transition from ICEVs to EVs, if the speed of production adjustment is too fast, the automobile system would trap into chaos and shakeouts, and the social welfare would show volatility. (2) The revised dual credit policy increases the credit ratio and decreases the credit coefficient which enhances the stability of automobile system. This revision may accelerate the transformation from ICEVs to EVs in the automobile industry. Especially, the credit coefficient plays a more significant role. (3) The consumer acceptance for vehicle prices and the range of EV are positively correlated with the production of EVs and the social welfare, and negatively correlated with the production of ICEVs. Nevertheless, the cost of the two types of vehicle had the opposite effect compared price acceptance and range of EVs.

Tire noise prediction based on transfer learning and multi-modal fusion

Vehicle traffic noise has become an important factor in urban noise pollution. With the increase in the number of new-energy vehicles, the current situation of tire/road noise as one of the main noise sources of automobiles is becoming increasingly prominent. Tire noise prediction is the basis for optimizing the design of low-noise tires, providing a reference basis for tire design with low-noise performance. This paper used deep learning methods to predict the noise performance of TBR radial tires. We obtained descriptive statistical features of tire structure and tread pattern images as input to the model, and used measured tire/road noise as output, then we constructed a multi-modal tire noise dataset. Comparing the prediction performance of three pre-trained transfer models such as Resnet18, VGG16, and Inception V3 on the tire pattern images, the Resnet18 had the best prediction effect. Resnet18 was selected as a feature extractor to extract image features, which fuse with the tire structural features at the feature level. The experiment constructed the TLMF-TRNP model to predict tire noise. The experimental results of the TLMF-TRNP model indicated that RMSE, MAE, and R2 were 0.1337, 0.0948, and 0.9864 respectively, achieving ideal prediction accuracy on a small-scale tire noise dataset and controlling the absolute error of the test tires within ±0.4 dB effectively. An accurate tire noise prediction model will provide a theoretical basis for tire design with low noise tires.

Competition and Coordination: Regional Dynamics in the Rise of China’s New Energy Vehicle Industry

Studies on China’s success in the electric vehicle industry overlook the role of regional clustering and regional electric vehicle policies in supporting sustainable production and consumption. This paper adopts a mixed-method policy inventory and analysis to examine the rise of the new energy vehicle (NEV) industry in China’s Greater Bay Area. It also systematically analyses the policies implemented by the central government, province, and cities in building a sustainable regional industry. A core dilemma is that cities are competing with one another for NEV development, yet regional industrial development requires coordination and cooperation. Examining the four policy areas of production, consumption, infrastructure support, and pollution suppression reveals that the competition among cities can be a driving force for the adoption of sustainable technology when supported by regional policy coordination.