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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.

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.

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.

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.

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.

The new energy vehicle is a robust measure to solve the problem of global warming. However, the new energy vehicle generally has the disadvantages of short mileage and difficulty in finding public chargers. The combination of wind energy harvest and new energy vehicle can be conducive to the promotion of the new energy vehicle. This paper proposes a novel adjustable Savonius vertical axis wind turbine (SVAWT). It contains three parts: an energy absorption module, an energy recovery module, and an energy conversion module. The energy absorption module includes four blades with staggered distribution in two layers. The overlap ratio of the blades can be adjusted by the wind speed, which can ensure the SVAWT has a higher energy transfer efficiency. The energy recovery module adjusts the overlap ratio of the blades without interruption by utilizing the self-rotation and the orbital revolution of the gears. The energy conversion module converts mechanical energy into electric energy and supplies power for the vehicle after adjustment by the voltage regulator module. Based on actual operating data, it can be found that the variation trend of power of the blades absorbing is consistent with wind speed and increases with the wind speed. Under four actual operating conditions, the root mean square value of the blades absorbing power are 7.0 W, 7.1 W, 3.9 W, and 5.1 W, respectively. These results reveal that the proposed novel adjustable SVAWT has high recovery power potential and can provide a valuable solution to the practical applications of wind energy harvesting.

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.

The motor is an important component that affects the output performance of new energy vehicles (using new energy sources such as electric energy and hydrogen fuel energy to drive the motor and provide kinetic energy). Motors with high power and low noise can effectively improve the dynamic performance, passability and smoothness of new energy vehicles and bring a comfortable experience to driver and passengers. The magnetic field analytical model of the inner-mounted permanent magnet synchronous motor (IPMSM) is studied to improve its output quality. The motor is divided into four subdomains: the stator slot subdomain, the stator slot notch subdomain, the air-gap subdomain, and the permanent magnet (PM) subdomain. The general solution of the vector magnetic potential of each subdomain is solved, and the expression of magnetic flux density of each subdomain is derived. Meanwhile, the analytical model of the non-uniform air gap is established according to the uniform air-gap model. The model’s accuracy is verified by finite element analysis and prototype tests. The results show that the calculation results of the analytical model are effective. The model can be applied to predict the no-load back electromotive force (EMF) and cogging torque of the motor under different main air gaps. It also provides an effective and fast analysis method for the design and optimization of IPMSM for new energy vehicles.

The automotive industry is undergoing a transformational period where more and more new energy vehicles (NEVs) are being produced and delivered to the market. Accordingly, some new challenges arise during the manufacturing process for car companies. Since the mixed-model assembly line has been widely used, how to integrate the NEVs into the existing assembly system that was designed for the production of gasoline cars is a key issue. A practical approach assigning a specific workforce to handle NEV assembly work is applied at the BMW assembly shop. This work studies this new production pattern and focuses on the design of the assembly system under this pattern. This work aims to develop a method for minimizing the production cost of NEV assembly. Thus, an exact algorithm for hierarchically solving the assembly line balancing problem and vehicle model sequencing problem is proposed. Mixed integer programming mathematical models that describe these two problems are formulated for the first time. Three new benchmark problems and one industry case that include the NEV models are created to evaluate the effectiveness of the proposed method. Results of numerical tests demonstrate that the developed algorithm can quickly generate reconfiguration solutions of the assembly line for various model mix scenarios and production rates. High flexibility of the manufacturing system can be obtained using the proposed approach.

With the promotion and financial subsidies of the new energy vehicle (NEV), the NEV industry of China has developed rapidly in recent years. However, compared with traditional fuel vehicles, the technological maturity of the NEV is still insufficient, and there are still many problems that need to be solved in the R&D and operation stages. Among them, energy consumption and driving range are particularly concerning, and are closely related to the driving style of the driver. Therefore, the accurate identification of the driving style can provide support for the research of energy consumption. Based on the NEV high-frequency big data collected by the vehicle-mounted terminal, we extract the feature parameter set that can reflect the precise spatiotemporal changes in driving behavior, use the principal component analysis method (PCA) to optimize the feature parameter set, realize the automatic driving style classification using a K-means algorithm, and build a driving style recognition model through a neural network algorithm. The result of this paper shows that the model can automatically classify driving styles based on the actual driving data of NEV users, and that the recognition accuracy can reach 96.8%. The research on driving style recognition in this paper has a certain reference value for the development and upgrade of NEV products and the improvement of safety.

In recent years, battery-swapping heavy-duty trucks have seen rapid growth in China. Summarizing from the practical experiences gained in this development, and starting from market research and analysis of the most typical city of application case, Beijing, we aim to achieve the following: (ⅰ) Establish an operating cost model for battery-swapping heavy-duty trucks throughout a full operation cycle from the perspective of a heavy-duty truck freight transport capacity operator, based on four key cost dimensions, including transportation equipment, operation and maintenance, environmental protection compensation, and battery recycling compensation. (ⅱ) Calculate and compare the operating cost economy of battery-swapping heavy-duty trucks and other types of heavy-duty truck under different energy supplement modes, including charging, hydrogenation, and diesel. (ⅲ) Propose suggestions for faster and more successful heavy-duty truck electrification. The results indicate that battery-swapping heavy-duty trucks have good cost economy in a full operation cycle under specific scenario, and their economy will be improved with the popularization of battery-swapping stations.

The corporate average fuel consumption (CAFC) and new energy vehicle (NEV) credit policy (2021–2023) was officially released in June 2020. As a mandatory regulation for automobile manufacturers to produce new energy vehicles, its impact on the output of new energy vehicles needs to be systematically evaluated. In this study, we build an enterprise policy compliance model to simulate the dual-credit policy requirements for the production of new energy vehicles from 2021 to 2023 under different scenarios. The results show that the production of new energy vehicles from 2021 to 2023 is required to reach 1.78 to 3.97 million under different scenarios. Three factors, i.e., switching from New Europe Driving Cycle (NEDC) to World Light Vehicle Test Procedure (WLTP) fuel consumption improvement of conventional vehicles, and credit per new energy vehicle, have a more significant impact on the new energy vehicle production than others. Under the minimum guarantee scenario, a 10% change in the above three factors will lead to a 2.5%, 1.5%, and 0.5% reduction in the production requirement for new energy vehicles.

To better understand the multilevel mechanism of innovation and reveal the distribution and geographical formation of China’s NEV industry innovation, we collect patent data in China’s NEV industry from SIPO and use ArcGIS and HLM to map and analyze its geoinformation from 2009 to 2014. The results show that innovation activities are agglomerated at different scales, but the distribution of individual innovation and collaborative innovation are not similar, especially Pearl River Delta, which shows a great shortage of collaborative innovation. The multilevel effects are complicated. Institutional environment, resources, the regional institutional environment, and infrastructure have positive effects at the local level. Institutional environment is the only positive factor at the regional level. In general, regional factors enhance the positive effects of local factors. However, multilevel mechanism of collaborative innovation is different from the individual innovation, as it is less depended on policies stimulation and local knowledge institutions play a more important role in it. Furthermore, we put forward multilevel policies suggestions that central government should pay more attention on NEV products regulations, institutional environment should be improved at regional and local government should develop knowledge facilities in post-subsidy era. Our study highlights that locals that have advantages would benefit more from multilevel systems.

With the rapid development of new energy vehicles (NEVs) industry in China, the reusing of retired power batteries is becoming increasingly urgent. In this paper, the critical issues for power batteries reusing in China are systematically studied. First, the strategic value of power batteries reusing, and the main modes of battery reusing are analyzed. Second, the economic benefit models of power batteries echelon utilization and recycling are constructed. Finally, the economic benefits of lithium iron phosphate (LIP) battery and ternary lithium (TL) battery under different reusing modes are analyzed based on the economic benefit models. The results show that when the industrial chain is fully coordinated, LIP battery echelon utilization is profitable based on a reasonable scenario scheme. However, the multi-level echelon utilization is only practical under an ideal scenario, and more attention should be paid to the first level echelon utilization. Besides, the performance matching of different types of batteries has a great impact on the echelon utilization income. Thus, considering the huge potentials of China’s energy storage market, the design of automobile power batteries in the future should give due consideration to the performance requirements of energy storage batteries. Moreover, the TL battery could only be recycled directly, while the LIP has the feasibility of echelon utilization at present. At the same time, it will strengthen the cost advantage of the LIP battery, which deserves special attention.