Search

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.

Under the influence of the new energy strategy, the new energy vehicle has developed vigorously. As an important supporting infrastructure, the rationality of its location construction has become the key factor for the popularization of the cars. In this paper, the influence of economy, environmental protection and convenience is comprehensively considered in the model. The comprehensive optimization objective is to minimize the annual cost of construction and operation of the stations, minimize the additional carbon emissions caused by charging, and maximize the service capacity. Considering the layout and capacity constraints of charging facilities in charging stations, the AHP is used to weight each objective function. Thus, the multi-objective optimization model can be simplified, and the chicken swarm algorithm is used to settlement this question.

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.

Several incidents of electric vehicle combustion across various regions in China have brought the safety concerns associated with new energy vehicles into sharp focus within public discourse. Addressing these concerns, this paper explores maintenance decision-making for new energy vehicles through the application of decision trees and machine learning techniques. Initially, the study analyzes how decision trees and machine learning are employed in crafting maintenance decisions for these vehicles. It involves collecting data through internet searches, followed by statistical analyses and preprocessing to set the groundwork for further inquiry. Furthermore, the research advances by developing and refining decision tree models, which facilitate the integration of fault diagnosis and maintenance decision-making processes for new energy vehicles. This effort culminates in the establishment of a robust decision tree model specifically designed for the maintenance of new energy vehicles, which is subsequently evaluated through a detailed case study. The results are presented: the approximation degree of new energy vehicle fault diagnosis based on the decision tree model is 90.58%, 90.67%, 88.09%, 91.28%, and 90.19% at the significant level of α taking the value of 0.01, 0.05, 0.100, respectively. This study provides theoretical guidance on the application of decision trees for the diagnosis and repair of faults in new energy vehicles so that the development of new energy vehicles in China can be ensured.

The graded utilization of waste batteries has gained research significance due to recent reports of new energy vehicle lithium-ion batteries exploding whilst awaiting recycling or in end-of-life storage. In this study, we innovatively selected battery performance parameters such as the internal resistance, charge and discharge rate, and current maximum available capacity to evaluate the safety of retired power batteries from the perspective of inducing thermal runaway. A fractional calculus theory was then introduced, and the fractional second-order resistance as well as a capacitance model and an adaptive genetic algorithm were established for the identification of the parameters. An improved dual-scale filtering algorithm was generated, which combined the extended Kalman filter algorithm and the unscented Kalman filter algorithm to improve the accuracy of the parameter estimation. The final test outcomes indicated that the equivalent circuit model optimized by incorporating multiple filtering algorithms had error rates of 1.87 %, 1.65 %, and 1.27 % for the state of charge of the battery in three different operating condition testbeds, with average errors of 0.62 %, 0.69 %, and 0.59 %, respectively. When an initial experimental platform was constructed for the detection of the parameters, the voltage error quickly stabilized to within 0.03 V. It also displayed many advantages of data detection and calculation, such as faster convergence, faster tracking, and the highest result accuracy when compared with the battery model using other algorithms. This experiment highlighted that a fractional second-order resistive–capacitive circuit equivalent battery state detection model incorporating various filtering algorithms has practicality and feasibility.

New energy vehicles (NEVs) can effectively reduce emissions and alleviate global climate problems, and are crucial to global sustainable development. China is the world's largest seller of NEVs, but charging stations have always been a major constraint to the development of NEVs. To clearly characterize the structural characteristics of an innovation association network of NEV charging stations, this study analyzes the structural characteristics of China's innovation association network of NEV charging stations with the patent data since 2007 in the Incopat Global Patent Database from the three perspectives of province, city and patent applicant using method of social network analysis (SNA). The findings show the following: (1) The innovation association network of NEV charging stations has been expanded constantly, with the association strength evidently increasing. (2) The innovation association network of NEV charging stations exhibits a typical core-periphery structure, with the quantity of nodes with high association strength varying little, and the quantity of nodes with low- and medium-association strengths exhibiting an evident increasing trend. (3) The effect of regional central cities is enhanced, with an evident group structure. There has been no information barrier caused by excessive small group formation in the province association network, whereas there are small group development trends in city and patent applicant cooperation networks. In addition, policy recommendations for further expanding technological innovation in charging stations to promote the development of the NEV industry are discussed based on the study results.

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]

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.)

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]

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.)

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]

Promoting electric vehicles is an important way to achieve a low-carbon transport sector for the carbon peak and neutrality target. A better understanding of future scenarios of EV ownership is fundamental for more policy-relevant results. This study develops a system dynamic model for simulating EV ownership in China by considering the interactions among factors influencing the application of electric vehicles and synthetic scenarios designed. The result shows that under the baseline scenario, China's EV ownership would increase by 36 times, from 4.3 to 157.2 million during 2020–2035, and the new EV sales will account for 60% of total car sales, meaning that it would reach the target of promoting electric vehicles set by the Chinese government. However, according to the scenario analysis, the efficient strategies to accelerate the promotion of EVs in China are speeding up the construction of charging facilities, improving battery lifespan, and reducing daily usage fees, which are estimated to increase the EV ownership in 2035 by 59.6%, 27.3%, and 26.8%, respectively. In comparison, the effect is not so obvious for measures like duty-free and subsidy policy. The developed model can offer policymakers a reliable tool and reference to assess the outcomes of policies to promote EV application ahead of their implementation. [ABSTRACT FROM AUTHOR]

Motivated by the electric vehicle battery life performance, we studied the optimal investment decision-making behaviour of duopoly automakers. Based on the framework of game theory, this paper explores the influence of various parameters in the static game and dynamic game on the results, in combination with consumers' preference for the battery life of electric vehicles. In the static game, a smaller investment coefficient is more beneficial to a firm that adopts an investment strategy rather than a firm that does not. When the investment coefficient increases, the difference between the two manufacturers will become smaller. The change of parameters in the dynamic game system may lead to complex dynamic phenomena, and the system will experience period-doubling bifurcation and N-S bifurcation from a stable state into a chaotic state. It will also significantly impact the basins of attraction, which affect the decision-makers' initial choice. Consequently, we can use the control method to return the unstable system to stability. Based on these findings, some management insights and suggestions are presented. [ABSTRACT FROM AUTHOR]

In the field of Intelligent Traffic Systems (ITS), vehicle recognition is a hot research topic. Although different types of vehicles can already be recognized, further identification and statistics of new energy and fuel vehicles in unknown and complex environments remain a challenging task. In this paper, we propose a New Energy Vehicle Recognition and Traffic Flow Statistics (NEVTS) approach. Specifically, we first utilized the You Only Look Once v5 (YOLOv5) algorithm to detect vehicles in the target area, in which we applied Task-Specific Context Decoupling (TSCODE) to decouple the prediction and classification tasks of YOLOv5. This approach significantly enhanced the performance of vehicle detection. Then, track them upon detection. Finally, we use the YOLOv5 algorithm to locate and classify the color of license plates. Green license plates indicate new energy vehicles, while non-green license plates indicate fuel vehicles, which can accurately and efficiently calculate the number of new energy vehicles. The effectiveness of the proposed NEVTS in recognizing new energy vehicles and traffic flow statistics is demonstrated by experimental results. Not only can NEVTS be applied to the recognition of new energy vehicles and traffic flow statistics, but it can also be further employed for traffic timing pattern extraction and traffic situation monitoring and management. [ABSTRACT FROM AUTHOR]

There are many controversies about the actual effect of China's new energy vehicle (NEV) industry policy in promoting the innovation of NEV enterprises. This study classifies China's NEV industry policies into two categories, subsidised policy and nonsubsidized policy, and divides the innovation behaviour of NEV enterprises into high- and low-quality innovation. We selected the data from 2010 to 2019 to analyze the impacts of the abovementioned policies on the two innovations, the differences during the various development stages of the industry, and the moderating effect of the political connections of NEV enterprises. The results demonstrated that the promoting effect of subsidised policy on innovation increased at first and then decreased, and the policy is more likely to cause enterprises to prefer low-quality innovation. The promoting effect of nonsubsidized policy on innovation gradually increases, and the policy has a greater impact on high-quality innovation. Further analysis revealed that the political connections positively moderate the influence of subsidised policy on low-quality innovation, but negatively moderate the influence of nonsubsidized policy on high-quality innovation. [ABSTRACT FROM AUTHOR]

As a manifestation of technological innovation achievements, patents reflect the frontier of technological development in the field. The aim of this research is to investigate the spatial evolution of patent collaboration networks and cooperation activities in the Chinese new energy vehicle (NEV) industry. We hypothesize that the higher-order collaboration networks will exhibit the formation of triangle groups centered around core nodes and the emergence of key edges indicating their importance. Key organizations and partners will play a significant role in shaping the future direction of cooperative innovation. The research utilizes data on collaboration patents in the NEV industry in China and constructs higher-order interaction collaboration innovation networks. The spatial evolution of networks and patent cooperation activities are examined using simplex methods. The results indicate that the generalized degree distribution of nodes and edges follows a power-law distribution. Two-dimensional simplex networks gradually form triangle clusters centered on core nodes and key edges. Large companies and research institutes prefer high-depth collaboration, while universities prefer high-breadth collaboration. Furthermore, the development of the new energy vehicle industry has gradually shifted from the eastern region to the central region. In the two-dimensional simplex network, Beijing and Jiangsu play a crucial role as key bridges in fostering stable and deep collaborations. The findings of this study shed light on the spatial evolution of patent collaboration networks and cooperation activities in the Chinese NEV industry. The identification of key organizations and partners, as well as the central positions of certain regions, provides valuable insights for innovation organizations in navigating innovation development and selecting collaboration partners. [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.)

Lithium-ion batteries (LIBs) with relatively high energy density and power density are considered an important energy source for new energy vehicles (NEVs). However, LIBs are highly sensitive to temperature, which makes their thermal management challenging. Developing a high-performance battery thermal management system (BTMS) is crucial for the battery to retain high efficiency and security. Generally, the BTMS is divided into three categories based on the physical properties of the cooling medium, including phase change materials (PCMs), liquid, and air. This paper discusses the effect of temperature on the performance of individual batteries and battery systems, at first. Then, a systematic survey of the state-of-the-art BTMS is presented in terms of liquid-based, PCM-based, and air-based BTMS. To further utilize the heat source of the vehicle, the BTMS integrated with the vehicle thermal management system (VTMS) is discussed. Finally, the challenges and future prospects for BTMS with the ability to cut off the thermal runaway are discussed. The primary aim of this review is to offer some guidelines for the design of safe and effective BTMS for the battery pack of NEVs. [ABSTRACT FROM AUTHOR]

With China's vigorous implementation of the two-carbon policy. the automotive industry is transitioning from traditional to alternative energy sources. Accurate forecasting of new energy vehicle sales can provide statistical support for policy planning. This paper proposed a grey Lotka-Volterra model based on an HP filter to forecast new energ vehicle sales trends. The proposed model overcomes the limitations of the original model in forecasting cyclical data and improves its applicability in small sample data systems. And the competitive forecasting model allows us lo explore the market competition or cooperation between various vehicle types. Compared to the two existing models, the new model perfbrms better in forecasting vehicle sales. The study further applies the LCA method to assess the scale of energy consuniption and GHG emissions in the automotive sector. The prediction and evaluation results show that new energy vehicle sales will surpass traditional fuel vehicles by 2035 and dominate the vehicle market before 2050. Unfurtunately, vehicle electrification has not significantly reduced the transportation industry's environmental concerns. To fully utilize the potential for enet·gy-savings and emission-reduction of new energy vehicles, it is urgent to pursue technological advancements in battery production and power-generating structure. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material 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 Automobile Technology & Material is the property of Automobile Technology & Material 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.)

Most experts try their best to enhance investment performance of multifactor strategy by appending new useful factors or by optimizing existing factors. However, few people take the supply chain into consideration during multifactor strategy designing. Indeed, supply and demand relationships in the industry can reflect the running situation of the relevant companies, so the relationships would be efficient in stock choosing. In this work, we propose a midterm strategy of multifactor, named MUSU, by introducing the industrial supply chain into the multifactor model. We verify the model by taking the industry of new Energy Vehicle as Example. The results show that MUSU performs much better than the traditional models, no matter with market value choosing mechanism or not. It reveals that the introduction of supply chain in multifactor model considerably improves multifactor strategy's profitability and stability. [ABSTRACT FROM AUTHOR]

High-speed and complex road conditions make it easy for vehicles to reach limit conditions, increasing the risk of instability. Consequently, there is an urgent need to solve the problem of vehicle stability and safety. In this paper, adaptive stability control is studied in BEVs driven by in-wheel motors. Based on the sliding model algorithm, a joint weighting control of the yaw rate and sideslip angle is carried out, and a weight coefficient is designed using a fuzzy algorithm to realize adaptive direct yaw moment control. Next, optimal torque distribution is designed with the minimum sum of four tire load rates as the optimization objective. Then, combined with the road adhesion coefficient and the maximum motor torque constraint, the torque distribution problem is transformed into a functionally optimal solution problem with constraints. The simulation results show that the direct yaw moment controller based on the adaptive sliding mode algorithm has a good control effect on the yaw rate and sideslip angle, and it can effectively improve vehicle adaptive stability control. In the optimal torque distributor based on road surface recognition, the estimated error of road adhesion is within 10%, and has a greater margin to deal with vehicle instability, which can effectively improve vehicle adaptive stability control. [ABSTRACT FROM AUTHOR]

This study constructs an evaluation index system based on demand competitiveness, basic competitiveness, industrial agglomeration, industrial competition, industrial innovation, supporting industries, and government policy competitiveness. The study selected 13 provinces with good development of the new energy vehicle (NEV) industry as the sample. Based on the competitiveness evaluation index system, an empirical analysis was conducted to evaluate the development level of the NEV industry in Jiangsu with grey relational analysis and three-way decisions. The results reveal: 1) Under the absolute level of temporal and spatial characteristic attributes, the development of Jiangsu's NEV industry is in a leading position in the country, and the competitiveness level is closer to that of Shanghai and Beijing; 2) Under the incremental level, Jiangsu's incremental level ranks in the upper and middle reaches of 13 provinces, second only to Shanghai; 3) Under the volatility level, Jiangsu's industrial development fluctuates greatly, and the level of volatility lies in the middle reaches of the country. There is a big gap with Shanghai; 4) From the perspective of overall temporal and spatial characteristics, Jiangsu's overall industrial development level is in the first echelon in China, second only to Shanghai and Beijing, indicating that Jiangsu's NEV industry has a relatively good overall development level.

In this paper, by formulating a unified test scheme and integrating industry forces, the data collection of vibration condition for driving motor system of 21 different types of new energy passenger vehicles was completed based on the strengthened road surface in the test site. The above data are processed by selecting the maximum vibration energy value of each frequency segment and selecting envelope of vibration curve in each direction. Finally, a random vibration curve is formed for the tested components installed in the three positions of the new energy vehicle powertrain, body and drive motor. The final vibration curve were compared with the ISO 16750.3 CD draft. It is found that the general trend of vibration conditions constructed in this paper is consistent with the ISO draft. However, the vibration energy value is higher than ISO draft and higher than the measured vibration energy value of each enterprise. It is proved that the vibration condition formed by taking envelope of vibration curve is different from that of the actual driving motor. In this paper, further rectification measures are proposed to ensure the rationality of the subsequent construction of working conditions.

Analyzing the development trend of the scale of new energy vehicles is of great significance to the government regulation,the development direction of vehicle enterprises and the decision-making of the energy department. A new energy vehicle scale prediction method with nonlinear residuals is proposed in this paper. Firstly,the interval prediction method is used to study the uncertainty of economic policy. Secondly,considering the residual of scale prediction,the combined prediction model and support vector regression (SVR) model are used to predict the linear component and nonlinear residual respectively. Finally,the range of new energy vehicles scale in the future is obtained by taking the scale of national new energy vehicles as an example. By comparing different prediction methods,the effectiveness of the proposed method is verified,and the impact of different policy factors on the scale evolution of new energy vehicles is analyzed. The proposed method provides a corresponding reference for later charging facilities and other related planning.

From 2020 to 2022, the price of gasoline in China rose sharply, which may cause consumers to adopt new energy vehicles. However, the government subsidies in the yearly retreat for 2023 were completely abolished, which could hinder consumers' switching behavior. The combination of these factors may affect consumer decision-making, making accurate analysis of consumer willingness to switch to new energy vehicles crucial for the development of this industry. The current study aims to investigate consumers' vehicle switching intentions affected by multiple factors such as fuel prices, and we attempt to analyze these by combining personal and environmental factors from social cognitive theory. Data were collected through an online platform survey using developed reliable scales. The 464 responses were then synthesized using structural equation modeling and Bayesian networks, and the results showed that approximately 51% of consumers had high-level switching intentions to new energy vehicles; attitude, self-efficacy, environmental consciousness, and infrastructure barriers had the strongest effect on consumers' vehicle switching intentions. This study assists in identifying the psychological demands of consumers switching to new energy vehicles and provides ideas for vehicle manufacturers and governments in terms of marketing strategies and policy formulation at crucial stages when new energy vehicles are in accelerated development. [ABSTRACT FROM AUTHOR]

Eco‐innovation has turned into the hotspot of numerous enterprises and governments to gain sustainable development. This study examines how enterprises acquire green knowledge in interorganizational cooperative eco‐innovations, exploring the influence of different collaborative modes and the direct and moderating roles of specific investments. In accordance with interorganizational interaction level, the widely used collaborative modes can be attributed to low‐ (e.g., commissioned research and development [R&D]), medium‐, and high‐interaction modes (e.g., joint R&D and co‐construction of R&D entities). Results from a survey of 172 Chinese firms operating in the new energy vehicle industrial chain support that both specific investments and collaborative eco‐innovation have positive effects on green knowledge acquisition. Furthermore, the positive effect of collaborative eco‐innovation is stronger for high‐interaction collaboration modes than that for low‐interaction collaboration modes. Specific investments also play a moderate role in the link of collaborative eco‐innovation and green knowledge acquisition; similarly, the positive moderating effect is stronger for high‐interaction collaboration modes than that for low‐interaction collaboration modes. These findings suggest that when managers formulate the environmental policy, they should encourage collaboration, interorganizational interactions, and specific investments to acquire green knowledge. This paper mainly contributes to open eco‐innovation literature by revealing the mechanisms and the boundary conditions under which collaborative mode could effectively promote green knowledge acquisition. [ABSTRACT FROM AUTHOR]

New energy vehicles have gradually become the preferred means of transportation for people to travel greenly. Lithium batteries, as batteries for new energy vehicles, its quality directly affects the safety of vehicles and mileage is also the core data that people consider when choosing vehicles one. Therefore, the research uses big data to predict and test the battery life and failure of new energy vehicles. When predicting the battery life, the improved P-GN model has a good prediction effect and the model reaches the convergence state only three iterations and converged to 0.83. The optimal fitness converges at the beginning of iteration, reaching the optimal value of 1.946 after only four iterations. The error between the predicted value and the actual value of the remaining cruising range is within an acceptable range. When the weather condition is good, the prediction effect of the remaining cruising range is excellent and the fluctuation of the prediction difference is small. When the weather conditions are severe, the model can still predict the cruising range of the battery pack normally. When performing fault detection on the battery pack, the fault detection system can accurately and quickly detect the type of fault and effectively analyse the inconsistency of the battery and be accurate to the single faulty battery. When analysing the single faulty battery, it is proposed that the fault detection system can accurately diagnose the fault in the test battery, which not only takes a short time but also has good feasibility. [ABSTRACT FROM AUTHOR]

The recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing; (b) the dynamic reverse logistics network is superior to its static counterpart; and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost. [ABSTRACT FROM AUTHOR]

Automotive forums offer abundant and genuine data from car enthusiasts, enabling larger-scale customer satisfaction research. Current studies utilizing forum data for customer satisfaction measurement tend to be coarse-grained due to the intricacy of automotive products. As such, we introduce an improved aspect attention-based BiLSTM with aspect embedding (AATAE_BiLSTM) model, precisely identifying users' aspect-level sentiment tendencies. Employing a hierarchical indicator system, we capture users' sentiment tendencies at three levels, from fine-grained to coarse-grained. Moreover, we present a novel comprehensive quantitative model of satisfaction and attention based on sentiment tendencies, facilitating the measurement of users' satisfaction and attention across different indicator levels. We analyze BYD's NEV word-of-mouth comments data from AutoHome to gauge user satisfaction with BYD NEVs. Our study offers valuable insights for NEV companies to enhance their products and increase competitiveness. [ABSTRACT FROM AUTHOR]

To reduce greenhouse gas emissions from passenger vehicles, new energy vehicles are actively promoted by China's government. Various power system types are being developed and their sales keep increasing. However, there is uncertainty about the greenhouse gas emission of different vehicle types. This paper studies the life cycle carbon emissions of passenger vehicles in China. A calculation model is established with consideration of all types of power systems, model classes, and electric driving ranges. In order to calculate and compare the effect of carbon emission reduction on all types of vehicles, a sensitivity analysis is conducted in two ways to study three of the main influencing factors. The results show the carbon emission-reducing effect of different factors on different stages in the life cycle. It is known that different influencing factors have different effects on these stages. Since there is a variation in different vehicle types, the carbon reduction effect caused by these factors is different for these vehicle types. This paper describes a sensitivity analysis of three main influencing factors and puts forward relevant policy recommendations to reduce the carbon emissions of passenger cars during their life cycle based on these results. It is necessary to take the vehicle life cycle as a whole for carbon emission management. The conclusions of this paper can be used for vehicle manufacturers to decide the focus of technology research, and also have important reference significance for enterprises when making life cycle carbon reduction strategies for their products. It is also of certain value for China to formulate a medium- and long-term carbon emission reduction strategy for the passenger car industry. [ABSTRACT FROM AUTHOR]

Under the dual pressure of energy transformation and environmental protection, how to use the innovative network and enhance technical innovation (TI) are significant problems for new energy vehicle (NEV) enterprises in China. Based on the patent data of China's NEV industry from 2001 to 2022, combined with the logical framework of "Patent Analysis—Network Evolution—Empirical research", this study deeply discusses the influence of enterprises' innovative network on TI. It is shown that there is an inverse U-relationship between the cooperation breadth of the enterprises, and that the appropriate cooperation breadth favors TI. At the same time, structural holes, knowledge diversity and technical value have a significant positive impact on TI. In addition, the study performs a series of robustness tests and heterogeneity checks. The conclusions are conducive to further improving the study of the relationship between innovative networks and TI, which is important both theoretically and practically for promoting the energy transformation of automotive enterprises. [ABSTRACT FROM AUTHOR]

Under the background of green development, new energy vehicles, as an important strategic emerging industry, play a crucial role in energy conservation and emission reduction. In the post-epidemic era, steadily promoting the promotion of new energy vehicles will be a hot topic. Based on multi-source heterogeneous data, combined with the latent Dirichlet allocation topic model, social network analysis, and econometric methods, this paper explores whether individual purchase decisions and company-level cooperative research and development will promote the promotion of new energy vehicles. The results show that whether it is battery electric vehicles, hybrid electric vehicles or plug-in hybrid electric vehicles, users are more concerned about space dimension, power performance, and design style. Patent collaboration network analysis indicates that new energy vehicle enterprises are establishing close partnerships, which will urge the promotion of new energy vehicles. An interesting test result found that for short-term innovation, new energy vehicles enterprises should invest more patent research and development in battery electric vehicles and hybrid electric vehicles models to better accelerate the promotion of new energy vehicles. [ABSTRACT FROM AUTHOR]

Policies play a pivotal role in fostering the growth of new energy vehicles. This paper delves into the benefits and risks associated with the development of new energy vehicles under varying policy landscapes. Firstly, the intricate relationship between the competition and growth of new energy vehicles and internal combustion engine vehicles is captured through the application of the extended Lotka-Volterra model. Secondly, a simulation flow diagram is constructed, taking into account the actual mediums of policy impact and drawing insights from diverse research methodologies. This flow diagram enables a comprehensive evaluation of environmental benefits, health risks, and traffic pressure. Finally, a critical evaluation of the actual impact of policies on new energy vehicles is conducted, along with an exploration of the root causes of potential policy paradoxes. The findings reveal that a standalone infrastructure support policy, research and development support policy, or carbon tax policy can, in certain periods, lead to an increase in carbon emissions and traffic pressure. Although the driving restriction policy does not exert negative impacts on the environment, health, and traffic at the national level, it can result in a surge in the total vehicle population. Therefore, it is imperative to adopt a combination of policies to mitigate the potential risks associated with any single policy.

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.

The dual credit policy is currently the main starting point for China to achieve the green and sustainable development of the auto market. However, the policy's impact on future market development remains unclear. It is necessary to consider the market capacity constraints and the market competition environment. By researching the impact of the dual credit policy on the micro-decision-making of automakers and the long-term evolution of the macro-auto market, the effect of the dual credit policy on the Chinese auto industry is obtained. This paper considers the market capacity constraint, combines the competitive relationship and game payment matrix between NEV makers and CFV makers, constructs a game model of the competition density between NEVs and CFVs, simulates the development and evolution of China's auto market size, and analyzes the effect of the quantitative parameters of the dual credit policy on the auto market. The results show that: (1) the increase in NEV makers' sale of credits will stimulate their production incentives, and the increase in conventional fuel vehicle (CFV) makers' cost of purchasing credits will reduce their production incentives; (2) tightened fuel consumption standards for CFVs has an enhanced stimulating effect on the increase in the market share of NEVs, which can effectively reduce the market share of CFVs; (3) the price of credits facilitates the growth of the NEV market share, but credit prices that are too high do not stimulate the growth of the NEV market share to a significant degree; (4) the increase in the proportion of credits required for NEVs and the increase in the price of credits together help to reduce the market share of CFVs and increase the market share of NEVs. [ABSTRACT FROM AUTHOR]

China has developed a preliminary policy system for the development of new energy vehicles regarding the law, electricity price, grid-connected standards, project management, and financial support, however, defects remain in the policy and market environment, market mechanism, control technology, infrastructure, etc. We analyze new energy vehicles based on the analysis of basic data such as the number of electric vehicles and charging facilities, focusing on industrial development strategies, related subsidies, and tax policies. First, this paper summarizes the development status of China's new energy vehicles in different scenarios. In 2021, China's new energy vehicle production was 3545 thousand, and sales amounted to 3521 thousand. According to preliminary estimates, the number of new energy vehicles will exceed 15 million in 2030. The research route for the development of new energy vehicle bottlenecks is proposed. Secondly, we analyze foreign and Chinese policies on different stages and construct the implementation path for the healthy and stable development of China's new energy vehicles. By comparing the basic indicators, related policies, and related innovation activities of new energy vehicles in China, we conclude that the development of the national electric vehicle industry needs to be increased in terms of government policies, business model innovation, and public awareness. [ABSTRACT FROM AUTHOR]

Considering the influence of new energy vehicle enterprises innovation input is affected by a variety of non-linear and uncertain factors, an automatic coding machine mixed with RBF neural network model is presented in this paper, and the Gaussian distribution of training data optimization method and the Gaussian transfer function training module are put forward to make innovation input higher prediction precision and stronger universality. By comparing the prediction data of the proposed model with that of the traditional neural network model, the accuracy of the improved model is verified. Therefore, the proposed model can provide theoretical basis and decision support for technological innovation decision-making of new energy vehicle enterprises. [ABSTRACT FROM AUTHOR]