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The new energy vehicle (NEV) is emerging as an important alternative in the automobile industry in its potential to alleviate environmental pollution and contribute to carbon neutrality. The rapid growth of NEVs has been reflected in the scaling up of electric vehicle battery production. The dramatic increase of retired batteries, however, exposes the technological limitations in current recycling operations, which will ultimately impede the sustainable development of the NEV supply chain. Blockchain technology (BT) adoption provides a solution by contributing to the construction of an efficient recycling network. Our research investigates the influence of carbon reduction instruments on the uptake of BT. The key findings are as follows. Under a carbon tax system, (1) carbon emission reduction encourages the battery supplier to adopt BT; (2) BT adoption increases the profits of NEV supply chain stakeholders. Under carbon cap-and-trade regulations, (1) the unit outsourcing fee and the performance of the BT impact the investment decision of the manufacturer; (2) the profit of the third-party enterprise is increased by introducing the BT. Under both policies, improving the efficiency of BT helps to upgrade the traceability level and contribute to carbon neutrality in the NEV supply chain. [ABSTRACT FROM AUTHOR]

The adoption of new energy vehicles (NEVs) is an effective strategy for pollution reduction, especially for high-emitting commercial vehicles. This paper systematically reviews the promotion policies and development status of zero-emission commercial vehicles (ZECVs) in China, with a focus on diverse application scenarios. Comprehensive policies, including subsidies, right-of-way, infrastructure development, and environmental protection incentives, have significantly advanced NEV adoption, as demonstrated by Shenzhen's full electrification of buses and the extensive deployment of zero-emission trucks. Despite the overall slow development of ZECVs, regions in southern China and developed areas exhibit better progress. Medium and large passenger vehicles (MLPVs) have achieved a zero-emission rate of around 40%, contrasting with the significantly lower rates of 1.52% for mini and light trucks (MLTs) and 0.44% for medium and heavy trucks (MHTs). Electrification promotion varies significantly in different application scenarios, with buses leading at over 90% zero-emission rates, followed by the airport (24%) and port (16%) vehicles. The electrification of sanitation, logistics, and key industry transport, through lagging, is enhanced by targeted policies and local industry. Buses are designated as the highest priority (Level 1) for electrification transition while intercity logistics and vehicles in key industries are categorized as the lowest priority (Level 4). In addition, policy recommendations, including tailored strategies for ZECV promotion and emission reductions in traditional commercial vehicles, are put forward to provide guidance and reference for setting future zero-emission promotion goals and policy direction for commercial vehicles in subdivided application scenarios. [ABSTRACT FROM AUTHOR]

The objective of this study is to analyze how knowledge flows within the new energy vehicle industry alliance impact technology commercialization. The findings will provide valuable guidance for practitioners, managers, and policymakers who seek to rethink, reformulate, and reshape organizational processes in alignment with the United Nations' 2030 Agenda. The commercialization of new energy vehicle technology holds strategic importance for enterprises and contributes to sustainable development by promoting green and low-carbon transformation. From the perspective of the innovation ecosystem and stakeholder theory, this study further investigates the under-researched theme of knowledge flow in the new energy vehicle industry. According to the author's knowledge, this is the first study to analyze the practical application of sustainable development goals under the perspective of stakeholder theory, based on panel data from seven provinces and cities in China, using methods such as index system evaluation and regression analysis. Based on the research findings, technological cooperation within knowledge flow and foreign direct investment would promote the commercialization of technology within the industry alliance. Additionally, the maturity of the innovation ecosystem within the industry alliance has a positive moderating effect on the relationship between knowledge flow and technology commercialization. [ABSTRACT FROM AUTHOR]

Developing the new energy vehicle (NEV) industry significantly reduces pollutant emissions in the transportation sector, promotes high-quality carbon peaks, and reduces dependence on oil imports. Industrial policies also support the NEV industry, constantly enhancing its international competitiveness. The Dual Credit Policy, implemented in 2017, has pressured automotive manufacturers to transform their production models, reduce the output of traditional fuel vehicles, and increase the production of NEV. This study analyzes the effects of the Dual Credit Policy on corporate competitiveness before and after implementing it, using listed companies in China's NEV industry as the research subjects. The results indicate that the Dual Credit Policy significantly enhanced corporate competitiveness through substantial innovation. Additionally, the difference-in-differences (DID) model results reveal that the policy's promotional effect is more pronounced in traditional vehicle companies due to higher pressure. Heterogeneity tests show that the policy has a more significant effect on state-owned enterprises and that regional marketization differences lead to a greater promotional impact on enterprises in the central and eastern regions. [ABSTRACT FROM AUTHOR]

The continuous revolution of new energy technologies and the introduction of subsidy policies have promoted green consumers' willingness to purchase new energy vehicles and automotive online service platforms have disclosed vehicle reputation and consumer satisfaction ratings information. However, due to issues such as uncertain data quality, large data volumes, and the emergence of positive reviews, the cost for potential car buyers to acquire useful decision-making knowledge has increased. Therefore, it is necessary to develop a scientific decision-making method that leverages the advantages of large-scale consumer satisfaction ratings to support potential car buyers in efficiently acquiring credible decision-making knowledge. In this context, the Bonferroni mean (BM) is a prominent operator for aggregating associated attribute information, while basic uncertain linguistic information (BULI) represents both information and its credibility in an integrated manner. This study proposes an embedded-criteria association learning BM operator tailored to large-scale consumer satisfaction ratings-driven scenarios and extends it to the BULI environment to address online ratings aggregation problems. Firstly, to overcome the limitations of BM with weighted interaction (WIBM) when dealing with independent criteria, we introduce an adjusted WIBM operator and extend it to the BULI environment as the BULIWIBM operator. We discuss fundamental properties such as idempotence, monotonicity, boundedness, and degeneracy. Secondly, addressing the constraints on interaction coefficients in BM due to subjective settings, we leverage expert knowledge to explore potential temporal characteristics hidden within large-scale consumer satisfaction ratings and develop a method for learning criteria and interaction coefficients. Finally, we propose a conversion method between user credibility-based ratings and BULI. By combining this method with the proposed adjusted BM operator, we construct a multi-criteria group decision-making (MCGDM) approach for product ranking driven by large-scale consumer satisfaction ratings. The effectiveness and scientific rigor of our proposed methods are demonstrated through solving a new energy vehicle selection problem on an online service platform and conducting comparative analysis. The case analysis and comparative analysis results demonstrate that the interaction coefficients, derived from expert knowledge and 42,520 user ratings, respectively, fell within the ranges of [0.2391, 0.7857] and [0.6546, 1.0]. The comprehensive interaction coefficient lay within the range of [0.4674, 0.7965], effectively mitigating any potential biases caused by subjective or objective factors. In comparison to online service platforms, our approach excels in distinguishing between alternative vehicles and significantly impacts their ranking based on credibility considerations. [ABSTRACT FROM AUTHOR]

This paper investigates strategic production selections in scenarios of credit sharing between cooperative fuel vehicle (FV) automakers and new energy vehicle (NEV) automakers under the dual-credit policy. Three coopetition production strategies are formulated: the simultaneous production strategy, the FV priority production strategy, and the NEV priority production strategy. On the basis of these three production strategies, this study examines the optimal strategy for both parties in scenarios of no credit sharing, credit sharing dominated by the FV automaker, and credit sharing dominated by the NEV automaker. The simultaneous production strategy is the most conducive to both parties' coexistence in the vehicle market, and the FV or NEV priority production strategy can be adopted to realize the Pareto optimization of their total profit in certain applicable intervals. Credit sharing will greatly change both parties' applicable intervals and optimal strategy selections, and credit sharing dominated by FV automakers has been proven to effectively improve their social welfare with a low credit price. Interestingly, a high credit price is sometimes more important for the development of NEVs than the NEV cruising range and substitutability under the dual-credit policy. This study also demonstrates the impact of the credit coefficient, credit equilibrium, and NEV substitutability on both parties' production decisions and credit sharing. Our study has important managerial implications and can be utilized as strategic guidance for FV/NEV automakers to pursue coopetition under the dual-credit policy. [ABSTRACT FROM AUTHOR]

Climate change and environmental issues have received increasing attention across the world. China's governmental targets for carbon peak and carbon neutralization show the ambition and efforts necessary in challenging these problems. The transportation industry will be crucial in reducing carbon emissions. Based on the green patent application data in China's new energy vehicle (NEV) industry from 2006 to 2021, this article focuses on risk premium of green technology innovation. In particular, the premium effects of the green technology innovation and the cooperative network are empirically examined. Furthermore, two channels that play a role in generating the premium are investigated, i.e., attracting market attention and reducing financing constraints. The empirical results show that the stock returns are positively correlated to the green technology innovation and the company's central position in the cooperative network, i.e., there exist the premium effects of green technology innovation in China's NEV industry. The positional advantage in the cooperative innovation network can further increase analyst following and reduce financing constraints. The research can provide evidence and policy implications for the government, companies and investors. [ABSTRACT FROM AUTHOR]

The increasing global demand for electric vehicles is evidence of the general recognition among the public regarding issues tied to climate change, carbon emissions, and air pollution. China has experienced a rapid expansion in its New Energy Vehicle (NEV) market over recent years, establishing itself as the largest such market globally. This research endeavors to comprehend how consumers' value systems contribute to their intent to purchase NEVs. The proposed model was assessed using the data collected from a Chinese sample. Employing a multi-faceted approach, this study uncovers the diverse ways in which consumers with varying value orientations evaluate NEVs and formulate intentions to support governmental initiatives. Furthermore, the study examines the interplay between consumers' value assessments, their inclination to endorse governmental efforts, and their intentions to purchase NEVs. The study's findings underscore the role of consumers' value orientation in shaping their perceptions of NEVs. Additionally, it reveals that consumers who align with an environmentally conscious government are more inclined to buy NEVs. • Based on Value-Belief-Norm and Value orientation theory, a new framework of NEVs behavior decision is constructed. • Perceived hedonic and utilitarian values are critical for increasing to buy NEVs. • Value perception and intention to support the government are found to influence the consumer's NEV purchase decision. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: The LM-IEKF algorithm proposed in this paper can effectively estimate the state of charge of a lithium-ion battery, and it is suitable for the estimation of an electric vehicle. The error covariance matrix in the IKEF process is modified by the LM algorithm, and it can still maintain a good convergence speed and estimation accuracy in the face of severe current changes. With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm. [ABSTRACT FROM AUTHOR]

Abstract The new energy vehicle plays a crucial role in green transportation, and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving. This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems. Additionally, it envisions the outlook for autonomous intelligent hybrid electric vehicles, with reinforcement learning as the foundational technology. First of all, to provide a macro view of historical development, the brief history of deep learning, reinforcement learning, and deep reinforcement learning is presented in the form of a timeline. Then, the comprehensive survey and review are conducted by collecting papers from mainstream academic databases. Enumerating most of the contributions based on three main directions—algorithm innovation, powertrain innovation, and environment innovation—provides an objective review of the research status. Finally, to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles, future research plans positioned as “Alpha HEV” are envisioned, integrating Autopilot and energy-saving control.

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. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to consider the joint action of structural, cognitive and relational social capital, and to explore the formation mechanism of the innovation network of new energy vehicles (NEV). The research data come from China's NEV cooperative invention patent applications from 2001 to 2019. This paper uses the exponential random graph model (ERGM) to study the impact of different dimensions of social capital on the NEV industry-university-research (I-U-R) innovation network. The results show that from the perspective of structural capital, the closed network structure has a positive impact on the formation of NEV I-U-R innovation network. From the perspective of cognitive capital, the homogeneity of knowledge base has a positive effect on the formation of the NEV I-U-R innovation network, and the innovation subjects with the same knowledge base breadth and the same knowledge base depth are more inclined to form a cooperative relationship. For relational capital, institutional environment similarity and organizational structure similarity are important factors affecting the formation of NEV I-U-R innovation network to a similar extent. The findings of this study provide scientific references for promoting the sustainable development of I-U-R innovation network in NEV industry. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

New energy vehicles (NEVs) offer a sustainable private transportation alternative. Charging points are the source of power for NEVs; thus, their construction can significantly lower the costs associated with their use, thereby encouraging their adoption. This could potentially impact the subway demand, which is reflected by the relationship between housing prices and subway proximity in this paper, leading to a decrease in the premium for properties near subway stations. Utilizing a comprehensive data set of 599,916 housing transactions in Beijing and a difference-in-differences approach based on the hedonic price model, we found that China's NEV charging point subsidy policy significantly decreases the subway premium of housing prices and mitigates housing price disparities. Furthermore, we explored the spatial heterogeneity of this impact, finding that the policy has less influence on residents living near the city center. Our findings indicate that the policy has resulted in a considerable decrease in the subway premium, ranging from ¥19,217 to ¥55,936 ($2745 to $7991) per transaction, which is equivalent to the annual income for an average individual at the time of the policy. The results address the far-reaching implications and significant role of NEV development in urban transportation. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the aggravation of environmental pollution problems and the reduction of non-renewable energy sources such as oil, new energy vehicles have gradually become the focus of attention, and the application of their power batteries has become more and more widespread. The state of energy (SOE) of the power battery is an important basis for energy scheduling. Therefore, the study used computer technology to develop an analogous model of the power battery and evaluated its properties at various temperatures in order to precisely analyze the performance of the battery under thermal conditions. At the same time, to address the limitations in parameter estimation, the study uses the improved Kalman filter (KF) algorithm to optimize it. The results revealed that the estimation errors of the improved cubature Kalman filter (CKF) algorithm were reduced by 0.52%, 2.91% and 3.10% compared with the traditional CKF algorithm, EKF algorithm and UKF algorithm, respectively. In summary, the research on computer modeling and parameter estimation of the performance of new energy vehicle power batteries under hot working conditions provides important support and reference for the efficient operation and safety of new energy power batteries under hot working conditions.

Freight transport with heavy trucks has emerged as a significant energy-intensive sector and a major contributor to carbon emissions in China's transportation industry. The diesel consumption in this field represents approximately 40 % of China's total annual diesel consumption, while the CO2 emissions account for approximately 50 % of the total annual CO2 emissions in the transportation industry. Under the background of carbon peaking and carbon neutrality goals and energy revolution, it has become an inevitable trend to build a green, secure and efficient road freight mode and promote the low-carbon and clean transformation of heavy freight transport. So far, the electrification options of freight transport with heavy trucks mainly include charging, battery swapping, hydrogen heavy trucks, and electrified highways. This paper mainly introduces the hydrogen heavy truck and electrified highway options. Based on a dedicated coal transport route, the economic efficiency and application scenarios were compared and analyzed between these two options from a perspective of cost estimates. The analysis results show that, for this dedicated coal transport route, when the annual traffic volume is greater than 2.0483 million tons, the electrified highway option is more economical, while on the contrary, the cost of the hydrogen heavy truck option is lower.

Vehicle ‒ energy‒road‒ cloud collaboration seamlessly integrates vehicles, energy, transportation, and the cloud, which is conducive to accelerating the formation of a new intelligent, low-carbon, safe, and efficient ecosystem of travel and life. The collaboration aims to promote the high-quality development of the automobile industry. However, in the new technological situation, further forming a sound ecology that features “intelligent car + interactive energy + digital road + collaborative cloud” and exploiting its value advantages are important tasks for the vehicle‒energy‒road‒cloud collaboration in China. This study analyzes the development trend of China’s automobile industry in the context of new energy transformation, the development of intelligent connected vehicles, artificial intelligence, carbon neutrality, and other new technological situations. It introduces the system composition, current development status, interrelationships, and overall framework of the vehicle‒energy‒road‒cloud industry from five aspects: vehicle, energy, road, cloud, and industry integration. This study also summarizes the opportunities and challenges faced by the integrated development of China’s vehicle‒energy‒road‒cloud industry. Based on this analysis, this study proposes countermeasures and suggestions for the vehicle ‒ energy‒ road ‒ cloud collaboration, covering five aspects: adhering to an application-oriented principle, promoting positive innovation, demonstrating city-level applications, leveraging diversified investments, and promoting the efficient development of circular industries. These suggestions aim to provide references for the high-quality collaboration of China’s vehicle, energy, road, and cloud industries, thereby strengthening the automobile, transportation, and manufacturing sectors of China.

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. [ABSTRACT FROM AUTHOR]

Governments have formulated policies to support companies developing new energy vehicle, and unexpected slow development brings further attention to the policy. Concerns have been raised about how to design a subsidy mode for policies. In this paper, we provide insights into the preference of subsidy mode, government's subsidy strategies and firms' strategies of researching and pricing among product subsidy, innovation subsidy, and product-innovation subsidy. We find that the government and companies prefer the product-innovation subsidy and detest innovation subsidy. Moreover, there is a trade-off in the government's subsidy strategy under the product-innovation subsidy, where the product subsidy payment is higher than that under the product subsidy, and the innovation subsidy payment is lower than that under the innovation subsidy. In terms of price strategies, the manufacturer's price is lowest under the product subsidy, and the supplier's price is lowest under the innovation subsidy. Furthermore, the investment strategies under the three subsidy cases are positively correlated with consumer recognition and firms' R &D capabilities, while the government subsidy strategy under the innovation subsidy show some differences. Government should make the product-innovation subsidy as the first choice, and turning to the innovation subsidy as companies' R &D capabilities and consumer recognition increasing. [ABSTRACT FROM AUTHOR]

Energy flow analysis is an important means to analyze vehicle energy consumption, which is the basis of vehicle energy consumption optimization. The new energy battery underground Load-Haul-Dump (LHD) is a new type of underground mining equipment, and there are few researches on its energy flow and energy consumption efficiency of each component. To investigate the energy loss of its underground operation is the focus of the research to extend its endurance. This paper applied energy flow analysis method to energy consumption analysis of new energy LHDs for the first time, established an energy flow model, designed test groups and carried out working condition tests according to battery temperature and working condition of LHDs, collected test data and studied the test results based on energy flow analysis method. The results show that the energy loss of new energy LHD is concentrated in the traction system and battery system. The ineffective operation caused by the driver's inadaptability to the electric drive operation and the imperfect control strategy of the traction system are the main factors leading to the high energy consumption of the traction system. The accumulated temperature caused by the rapid charging of the battery system will affect the energy distribution of the LHD and reduce its battery life. By optimizing the traction system and battery system, the energy consumption of the new energy LHD is reduced by 13%. The research shows that the energy flow analysis of new energy LHD can intuitively understand their energy flow and energy consumption, which is helpful to find high energy consumption problems and propose optimization directions, so as to reduce energy consumption, improve energy utilization efficiency and extend service life. [ABSTRACT FROM AUTHOR]

In the realm of production and manufacturing, a symbiotic relationship with suppliers underscores the significance of rigorous supplier risk assessment. To address this growing need, our approach comprises four essential stages. Firstly, we have pioneered the development of an interval grey number QFD platform, a pioneering tool designed to discern and prioritize pivotal quality risk factors. Secondly, the Choquet integral is skillfully employed to navigate the intricate web of risk events' interrelations, thereby deriving the essential weightings of these risk factors. Thirdly, a cutting-edge grey clustering evaluation model is meticulously crafted, integrating the weightings of the risk factors. This model, a cornerstone of our methodology, is instrumental in classifying suppliers based on their respective risk profiles, optimizing risk management strategies. Lastly, our method's practicality and effectiveness are unequivocally validated through a real-world numerical example, conclusively showcasing its value in the context of supplier risk management. [ABSTRACT FROM AUTHOR]

New energy vehicles (NEVs) are the main direction for the development of the global automobile industry. Evaluating and analyzing the competitiveness of new energy vehicle enterprises (NEVEs) is of great significance for promoting their development. In order to explore the current situation of NEVEs in Henan Province, this paper firstly constructs a competitiveness evaluation index system for NEVEs, comprising both quantitative and qualitative indexes. Then, a new definition of consistency, the consistency measure level, and corresponding improvement methods for interval multiplicative preference relations (IMPRs) are proposed. On this basis, fuzzy group decision-making models with IMPRs are constructed to deal with the ambiguity and uncertainty of the decision information, where consistency and consensus are both considered. In our case study, decision results are derived using Lingo 11.0 software. The results of this paper show that the degree of specialization has the greatest impact on the competitiveness of NEVEs, and some NEVEs are deficient in this regard. Related suggestions based on expert evaluation results are also provided. In addition, a comparison with other consistency improvement methods of IMPRs reveals that the methods proposed utilize the original information provided to decision-makers to the utmost degree. [ABSTRACT FROM AUTHOR]

New energy sources are transforming the automotive market. This shift has also expanded the possibilities for in-vehicle interaction. Through a literature review, this study categorizes the in-vehicle interaction activities into three types: driving tasks, comfort tasks, and entertainment tasks. This study conducted empirical survey of 377 users to understand their preferences of in-vehicle interaction input methods inside new energy vehicles. The results show that gender, educational level, income, driver's license type and driving experience have significant influence on the perception and preference of the in-vehicle interaction input methods. However, age and experience with new energy vehicle didn't show significant results. The findings of this study can assist manufacturers in developing targeted solutions and meeting the personalized needs of users in future vehicle market segments.

The Chinese new energy vehicle (NEV) industry has developed rapidly, which has become one of the largest NEV markets in the world. The Chinese government has played a pivotal role in supporting and promoting the NEV industry, leading to significant advancements in policies, technology, infrastructure, industrial chain, and market development. This support has been evident through the implementation of numerous favorable policies, including increased support in finance, taxation, and technology innovation, as well as initiatives to promote research, development, and application of NEV products. Furthermore, subsidy policies have been introduced to incentivize consumers to purchase NEVs and enhance their competitiveness in the market. Additionally, regulations mandating a certain proportion of NEV production and sales for automakers have been put in place, contributing to the widespread advancement of the Chinese NEV industry. While the Chinese NEV industry has seen substantial growth, it also faces challenges and opportunities. To further its development, it is essential for vehicle manufacturers to prioritize technological innovation, the government to continue introducing supportive policies, users to increase environmental awareness, and for collaboration between academia and industry to drive research efforts. The development of the Chinese NEV industry is not only in line with the global trend of environmental protection, energy security, and industrial transformation, but also an important link in promoting the progress of the global NEV industry.

Super-capacitors (SCs), as new energy conversion storage elements, have attracted much attention, but there is still a research gap in the design of electrode materials. In this study, the optimization scheme of Metal-Organic Frameworks (MOFs) and cobalt-based MOF composites as electrode materials for SCs in new energy vehicles is explored, and a series of experiments are conducted to evaluate their performance. Scanning Electron Microscope (SEM) images reveal that the cobalt-based MOF composites have a surface morphology of particles with uniform distribution. The electrochemical performance test results show that the specific capacitance of the cobalt-based MOF composites is much higher than the sum of the two individual electrode materials and presents a remarkable increase with the scanning rate. Additionally, in the constant current charge-discharge test, cobalt-based MOF composites exhibit the longest charge-discharge time and good symmetry. Electrolyte particle contact tests for samples at different preparation temperatures display that high-temperature samples have better structural stability and electrolyte ion contact. In Cyclic Voltammetry (CV) and Galvanostatic Charge Discharge (GCD) tests, the 250 °C sample demonstrates the best electrochemical properties and the highest specific capacitance (269 F/g). Moreover, as the current density increases, the specific capacitance of the 600 °C sample decreases at a lower rate, showing stronger stability. However, the use of cobalt-based MOF materials may pose environmental and safety risks, such as the environmental impact of cobalt resource mining, instability under high-temperature conditions, and the possible production of hazardous substances. Therefore, these factors need to be fully considered when designing and using SCs to ensure the environmental friendliness and safety of cobalt-based MOFs. These results provide an important reference for selecting and optimizing electrode materials for SCs in new energy vehicles. Furthermore, this study offers research suggestions for improving new energy materials, filling the research gaps in related fields, and supporting the further development of SC technology.

The new energy vehicle industry has proliferated in the face of global climate change and challenges to sustainable development. Understanding the factors that encourage consumers to purchase new energy vehicles is essential to driving the new energy vehicle industry’s future development. Based on signaling theory, this study aims to investigate the significant influence of various factors, such as perceived ESG and policy incentives, on Chinese consumers’ intention to purchase new energy vehicles and explore the mediating effect of brand image and perceived value. This study adopts a quantitative research methodology by collecting data from 860 potential new energy vehicle consumers in China through a questionnaire survey and analyzing the data using structural equation modeling in AMOS 24.0. The results of the study show that consumers’ perceived ESG significantly affects their intention to purchase new energy vehicles. Brand image and perceived value mediate consumers’ perceived ESG factors and their purchase intention of new energy vehicles. Consumers’ positive attitudes will increase the purchase intention of new energy vehicles. In addition, government incentives also have a positive and significant effect on the intention to purchase new energy vehicles. These results provide sustainable marketing guidance for NEV companies, confirming the importance of good environmental, social, and governance performance, good brand image, and perceived benefits in driving purchases. In addition, this study provides empirical evidence of policy support for the NEV industry, thus reinforcing its importance for policymakers.

This paper investigates strategic production selections in scenarios of credit sharing between cooperative fuel vehicle (FV) automakers and new energy vehicle (NEV) automakers under the dual-credit policy. Three coopetition production strategies are formulated: the simultaneous production strategy, the FV priority production strategy, and the NEV priority production strategy. On the basis of these three production strategies, this study examines the optimal strategy for both parties in scenarios of no credit sharing, credit sharing dominated by the FV automaker, and credit sharing dominated by the NEV automaker. The simultaneous production strategy is the most conducive to both parties’ coexistence in the vehicle market, and the FV or NEV priority production strategy can be adopted to realize the Pareto optimization of their total profit in certain applicable intervals. Credit sharing will greatly change both parties’ applicable intervals and optimal strategy selections, and credit sharing dominated by FV automakers has been proven to effectively improve their social welfare with a low credit price. Interestingly, a high credit price is sometimes more important for the development of NEVs than the NEV cruising range and substitutability under the dual-credit policy. This study also demonstrates the impact of the credit coefficient, credit equilibrium, and NEV substitutability on both parties’ production decisions and credit sharing. Our study has important managerial implications and can be utilized as strategic guidance for FV/NEV automakers to pursue coopetition under the dual-credit policy.

Climate change and environmental issues have received increasing attention across the world. China’s governmental targets for carbon peak and carbon neutralization show the ambition and efforts necessary in challenging these problems. The transportation industry will be crucial in reducing carbon emissions. Based on the green patent application data in China’s new energy vehicle (NEV) industry from 2006 to 2021, this article focuses on risk premium of green technology innovation. In particular, the premium effects of the green technology innovation and the cooperative network are empirically examined. Furthermore, two channels that play a role in generating the premium are investigated, i.e., attracting market attention and reducing financing constraints. The empirical results show that the stock returns are positively correlated to the green technology innovation and the company’s central position in the cooperative network, i.e., there exist the premium effects of green technology innovation in China’s NEV industry. The positional advantage in the cooperative innovation network can further increase analyst following and reduce financing constraints. The research can provide evidence and policy implications for the government, companies and investors.

With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm.

Countries all over the world are paying attention to the growth of the new energy vehicle industry and implementing various subsidy policies to stimulate industry development to enhance the new energy vehicle industry’s innovative capability. This study uses a network DEA model to analyze China’s new energy vehicle industry’s technological innovation capability, decomposing it into two stages: technology development and innovation transformation, and calculating the innovation capability level of China’s new energy vehicle industry from 2012 to 2017. The findings show that due to a disconnect between the efficiency of the technology development stage and the efficiency of the innovation transformation stage, innovation technology cannot serve business operations, resulting in China’s new energy vehicle industry’s overall low level of innovation capability. Based on this, an IT3SLS analysis of the factors influencing innovation capability and phase-by-phase efficiency reveals that while China’s new energy vehicle industry’s subsidy policy has historically failed to significantly improve innovation capability, there is a complementary/substitution effect between labor input, corporate capital, and government subsidies. Based on the findings of this study, important policy recommendations are made to further develop the technological capabilities of the new energy vehicle industry in the context of China’s present new energy policy. [ABSTRACT FROM AUTHOR]

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In the energy crisis and post-epidemic era, the new energy industry is thriving, encompassing new energy vehicles exclusively powered by lithium-ion batteries. Within the battery management system of these new energy vehicles, the state of charge (SOC) estimation plays a pivotal role. The SOC represents the current state of charge of the lithium-ion battery. This paper proposes a joint estimation algorithm based on genetic algorithm (GA) simulating biogenetic properties and support vector regression (SVR) to improve the prediction accuracy of lithium-ion battery SOC. Genetic algorithm support vector regression (GASVR) is proposed to address the limitations of traditional SVR, which lacks guidance on parameter selection. The model attains notable accuracy. GASVR constructs a set of solution spaces, generating initial populations that adhere to a normal distribution using a stochastic approach. A fitness function calculates the fitness value for each individual. Based on their fitness, the roulette wheel method is employed to generate the next-generation population through selection, crossover, and mutation. After several iterations, individuals with the highest fitness values are identified. These top individuals acquire parameter information, culminating in the training of the final SVR model. The model leverages advanced mathematical techniques to address SOC prediction challenges in the Hilbert space, providing theoretical justification for handling intricate nonlinear problems. Rigorous testing of the model at temperatures ranging from −20 ∘ C to 25 ∘ C under three different working conditions demonstrates its superior accuracy and robustness compared to extreme gradient boosting (XGBoost), random forest regression (RFR), linear kernel function SVR, and the original radial basis kernel function SVR. The model proposed in this paper lays the groundwork and offers a scheme for predicting the SOC within the battery management system of new energy vehicles. [ABSTRACT FROM AUTHOR]

In view of the electromagnetic interference problem of the motor drive system inside the electric vehicle, a prediction model of the electromagnetic interference of the vehicle motor drive system is established in this study. Based on the principle of electromagnetic compatibility, the electromagnetic interference mechanism of the in-vehicle motor drive system module is analyzed. The model of the IGBT power semiconductor device that causes interference in the module is constructed, and the influence of the parasitic parameters of the actual device is considered in the modeling process. Through the Simulink+Maxwell+Simplorer collaborative analysis technology, the electromagnetic interference signal acquisition of the motor drive system is realized. The comparison between the simulation and the measured data verifies the effectiveness of the method proposed in this study, and the errors of both are less than 10 dB.Based on the GB/T 18655-2018 electromagnetic compatibility standard, the results of the parts exceeding the standard are optimized and rectified. [ABSTRACT FROM AUTHOR]

With the increasingly serious problem of environmental pollution, new energy vehicles have become a hot spot in today's research. The lithium-ion battery has become the mainstream power battery of new energy vehicles as it has the advantages of long service life, high-rated voltage, low self-discharge rate, etc. The battery management system is the key part that ensures the efficient and safe operation of the vehicle as well as the long life of the power battery. The accurate estimation of the power battery state directly affects the whole vehicle's performance. As a result, this paper established a lithium-ion battery charge state estimation model based on BP, PSO-BP and LSTM neural networks, which tried to combine the PSO algorithm with the LSTM algorithm. The particle swarm algorithm was utilized to obtain the optimal parameters of the model in the process of repetitive iteration so as to establish the PSO-LSTM prediction model. The superiority of the LSTM neural network model in SOC estimation was demonstrated by comparing the estimation accuracies of BP, PSO-BP and LSTM neural networks. The comparative analysis under constant flow conditions in the laboratory showed that the PSO-LSTM neural network predicts SOC more accurately than BP, PSO-BP and LSTM neural networks. The comparative analysis under DST and US06 operating conditions showed that the PSO-LSTM neural network has a greater prediction accuracy for SOC than the LSTM neural network. [ABSTRACT FROM AUTHOR]

New energy vehicles have better clean and environmental protection characteristics than traditional fuel vehicles. The new energy engine cooling technology is critical in the design of new energy vehicles. This paper used oneand three-way joint simulation methods to simulate the refrigeration system of new energy vehicles. Firstly, a k-ε turbulent flow model for the cooling pump flow field is established based on the principle of computational fluid dynamics. Then, the CFD commercial fluid analysis software FLUENT is used to simulate the flow field of the cooling pump under different inlet flow conditions. This paper proposes an optimization scheme for new energy vehicle engines' "boiling" phenomenon under high temperatures and long-time climbing conditions. The simulation results show that changing the radiator's structure and adjusting the thermostat's parameters can solve the problem of a "boiling pot." The optimized new energy vehicle engine can maintain a better operating temperature range. The algorithm model can reference each cryogenic system component hardware selection and control strategy in the new energy vehicle's engine. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To evaluate the fuzzy and uncertain factors brought by human's emotional sensibilities during the design of new energy vehicle models, a scientific design evaluation method was adopted. This study combines the Analytic Hierarchy Process (AHP) and Kansei Engineering to assess the design of new energy vehicle models. It uses both objective criteria and subjective user perceptions. First, it quantified the design imagery vocabulary through Kansei Engineering and identified the target imagery vocabulary. Secondly, the AHP method was used to establish a relationship between design features and imagery vocabulary. Thrid, the study created an evaluation index system for design imagery, and adopted the fuzzy comprehensive evaluation matrix to rank the representative vehicles and brands in the Chinese new energy vehicle market. Based on the weight analysis results, the vehicle functionality (whether it is safe and reliable), the elegance of design, and the intricacy of vehicle structure were identified as the top three aspects that affect the impression of new energy vehicles. Therefore, we need to focus on these aspects during the design of new energy vehicles. Additionally, by integrating objective evaluation and emotional analysis results, we assessed the design for each new energy vehicle brand. The results of this study are important for developing the new energy vehicle market.

The study constructs a TRIZ-based consumer purchase decision model through the consumer purchase decision, TRIZ theory, and EKB model. Functional models and object-field models are constructed through intergroup analysis and structural analysis. The application effect of the TRIZ consumer purchase decision model is explored. A brand new energy vehicle enterprises began to adopt the TRIZ-based consumer purchase decision model in January 2020, and their annual sales volume of new energy vehicles in 2020 is 2,507,625,000 units, which is an increase of more than 2,000,000 units compared with that of the annual sales volume without adopting the TRIZ purchase decision model. A brand-new energy vehicle’s market share increased from 1.64% in Q1 2019 to 13.47% in Q4 2020. The TRIZ-based consumer purchase decision model has a significant impact on the increase in sales and market share of new energy vehicles manufactured by brand A. In 2020, new energy vehicles from brands A and B will sell 2,507,625 and 691,370 units, respectively. The TRIZ-based consumer purchase decision model has a significant advantage over the traditional purchase decision model, as can be seen.