Research Progress and Application Prospects of Solid-State Hydrogen Storage Technology.

Solid-state hydrogen storage technology has emerged as a disruptive solution to the "last mile" challenge in large-scale hydrogen energy applications, garnering significant global research attention. This paper systematically reviews the Chinese research progress in solid-state hydrogen storage material systems, thermodynamic mechanisms, and system integration. It also quantitatively assesses the market potential of solid-state hydrogen storage across four major application scenarios: on-board hydrogen storage, hydrogen refueling stations, backup power supplies, and power grid peak shaving. Furthermore, it analyzes the bottlenecks and challenges in industrialization related to key materials, testing standards, and innovation platforms. While acknowledging that the cost and performance of solid-state hydrogen storage are not yet fully competitive, the paper highlights its unique advantages of high safety, energy density, and potentially lower costs, showing promise in new energy vehicles and distributed energy fields. Breakthroughs in new hydrogen storage materials like magnesium-based and vanadium-based materials, coupled with improved standards, specifications, and innovation mechanisms, are expected to propel solid-state hydrogen storage into a mainstream technology within 10-15 years, with a market scale exceeding USD 14.3 billion. To accelerate the leapfrog development of China's solid-state hydrogen storage industry, increased investment in basic research, focused efforts on key core technologies, and streamlining the industry chain from materials to systems are recommended. This includes addressing challenges in passenger vehicles, commercial vehicles, and hydrogen refueling stations, and building a collaborative innovation ecosystem involving government, industry, academia, research, finance, and intermediary entities to support the achievement of carbon peak and neutrality goals and foster a clean, low-carbon, safe, and efficient modern energy system.

Deciphering urban traffic impacts on air quality by deep learning and emission inventory.

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 µg/m3 to 12.283 µg/m3 compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction. We also infer that the promotion of vehicular electrification would achieve further alleviation of PM2.5 about 8.45% by 2030 gradually. These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control, and eventually benefit people's lives and high-quality sustainable developments of cities.

Examination of the adoption intention of new energy vehicles from the perspective of functional attributes and media richness.

Drawing on the theory of media richness, this paper aims to explore the impact of media richness on consumers' adoption intention through their perception of new energy vehicle (NEV) function attributes, and assess the moderation roles of brand familiarity and locus of control. A structural equation model is applied to analyze the data collected from 427 respondents. Empirical results demonstrate that consumers' perception of an electric attribute (i.e., charging efficiency) and two intelligent attributes (i.e., car networking and self-driving) are determinants of their adoption intention of NEVs. The other electric attribute (range) is trivial in consumers' perception. We also find that low, medium, and high-richness media significantly affect consumers' perception of NEVs' functional attributes. Compared to the high-richness, medium-richness correlates significantly with two types of NEV functional attributes. Regarding moderating effects, consumer familiarity with NEV's brand negatively impacts the relationship between media richness and adoption intention. Furthermore, low and medium-richness media effectively stimulate individuals with external control to adopt NEV, while high-richness media adversely influence individuals with internal control.

Regenerative braking system effectively reduces the formation of brake wear particles.

Brake wear particles (BWPs) are considered one of the most significant non-exhaust particle emission sources from motor vehicles. Previous studies have primarily focused on BWPs from conventional fuel vehicles (CFVs), with limited research available on BWPs from new energy vehicles (NEVs). We developed an independent BWP emission testing system applicable to NEVs and conducted BWP emission tests on representative NEVs and CFVs under various testing cycles via a chassis dynamometer. The BWP emission characteristics of the NEVs equipped with regenerative braking system significantly differed from those of gasoline vehicles. For transient emission characteristics, gasoline vehicles exhibited higher peak concentrations during brake events than brake drag events, while those with regenerative braking exhibited the opposite feature. Under continuous braking, the concentration of ultrafine particles emitted by NEVs was reduced by more than 3 orders of magnitude compared to gasoline vehicles. In terms of single-particle morphology, BWPs could be mainly divided into three categories: carbonaceous particles, iron-rich particles, and mixed metal particles. We obtained realistic emission characteristics of BWPs from NEVs, which could provide data support and a scientific basis for the formulation of relevant emission standards and control measures in the future.

Energy-saving and CO2 reduction strategies for new energy vehicles based on the integration approach of voluntary advocacy and system dynamics.

The low-carbon development of new energy vehicles (NEVs) is critical to achieving the goals of carbon peaking and carbon neutrality. As such, combining gray model theory with system dynamics (SD-GM) and based on the bidirectional-cycle prediction theory, we propose a NEV annual average mileage algorithm considering the impact of the epidemic in China, taking private cars as an example. Then, combining a voluntary advocacy strategy (VA) with the SD-GM theory (VA-SD-GM integration), we establish an energy-saving and carbon-reduction management model. To evaluate the proposed algorithm, we performed a dynamic simulation. The results indicate that the enhanced green scenario enabled significant energy-saving and CO2 reduction performance but would cause side effects in the long term. Compared with the enhanced green scenario, the linkage mode reduced the impact of parking space tension, the number of NEV trips, and the intensification of traffic congestion by approximately 33%, 50%, and 34%, respectively. It effectively suppressed the continuous increase in side effects and had a synergistic effect of carbon reduction, energy conservation, congestion alleviation, and side-effect reduction. The study provides a theoretical basis for optimizing the energy-saving and CO2 reduction path of NEVs.

Acceptance model of new energy vehicles based on PLS-SEM model.

The current energy crisis is worsening worldwide, and in China, urban expansion and per capita vehicle ownership have created a growing energy imbalance and increased pressure to reduce carbon emissions.The popularization of new energy vehicles (NEVs) can provide a step forward to solving energy shortage problems, environmental pollution, and global warming. In 2022, the average penetration rate, which is ratio of new energy vehicle sales to vehicle sales, is just 19.1 %. This paper analysed the reasons for the differences in the penetration rates of new energy vehicles in China's 269 prefecture-level cities, using a Geo Detector approach, and the results showed that the level of economic development, the average annual temperature difference, the density of charging piles, the charging price and the number of population all had significant effects(q＞0.12) on the penetration rate. Based on the above studies, a questionnaire was used to investigate the public's acceptance of new energy vehicles in Xinjiang Uygur Autonomous Region, and a PLS-SEM regression analysis was conducted. The results showed that men, young people and people with a certain level of basic education were 5 % more likely to accept new energy vehicles.Unlike previous studies, perceived cost had no significant correlation with the acceptance of new energy vehicles. Perceived risk had a significant negative correlation with the acceptance of new energy vehicles,the path coefficient is -0.1.The acceptance of new energy vehicles was significantly and positively correlated with vehicle quality and service, the public's understanding of new energy vehicles, and subjective norms, their average path coefficients are above 0.1. We argues that the government should maintain a certain level of promotion of new energy vehicles and accelerate the construction of charging piles, based on the aforementioned results.

Electronic parking algorithm of new energy vehicle on slope based on FMPC.

Since the unique structure and transmission mode of new energy vehicles, the problem of parking prediction and control has always been the focus of its research. Therefore, to handle the predictive control problem of new energy vehicles in slope environments, the FMPC algorithm is applied to replace the core PID controller of new energy vehicles, and it is effectively validated using automotive simulation software on different slopes and vehicle masses. The experimental results indicate that new energy vehicles with FMPC algorithm can complete parking in about 6 s under different slopes and vehicle masses, while the PID algorithm may experience shaking and displacement. The FMPC algorithm proposed in the study achieves better results in electronic parking control of new energy vehicles on slopes, effectively improving the parking stability of new energy vehicles, which has practicality in practical slope parking of new energy vehicles.

A Sustainable Road Transport Decarbonisation: The Scenario Analysis of New Energy Vehicle in China.

Due to the prosperous development of the economy, the emissions of carbon dioxide (CO2) and other greenhouse gases (GHGs) have intensified and attracted attention worldwide. China has set the "dual-carbon" aim to pursue sustainable development in the transport sector. Thus, this study created a generalised Bass model to forecast new energy vehicle (NEV) ownership by introducing a new factor, charging piles, to reflect the infrastructure effects. Using the improved model with the hypothesis of annual mileage, an empirical analysis was conducted with the subject of NEVs in China by using the NEV-related panel data from 2010 to 2020, and the forecast result is outstanding with a goodness-of-fit of 99.7%. With the forecasts, carbon emission reduction was calculated with a bottom-up method. To further discuss the pathway to achieve carbon neutrality in the transport sector of China, a scenario analysis was conducted with ideal, enhanced, and radical constraints. The results show that if all factors remain "as is" until 2050, China will be far from carbon neutrality. Thus, this paper proposes relevant policy implications to assist the government to obtain effective methods to assess carbon reduction benefits and find viable pathways to a sustainable road transport system.

Can environmentally friendly technology help China to achieve a carbon neutrality target by 2060? An asymmetrical based study in China.

Climate change-related environmental challenges are prompting an increasing number of countries to set carbon-neutral targets. Since 2007, China has pursued numerous initiatives to attain carbon neutrality by 2060, including increasing the percentage of non-fossil energy, developing zero-emission and low-emission technologies, and taking actions that reduce CO2 emissions or boost carbon sinks. As a result, utilizing quarterly data from 2008/Q1 to 2021/Q4, and applying the nonlinear autoregressive distributed lag (NARDL) approach, this study evaluates the effectiveness of the measures taken by China to improve the ecological situation. The results of the study show that the measures enacted to reduce CO2 emissions did not accomplish their ultimate purpose. Specifically: (i) high-speed railways and new energy vehicles do not improve the environment in the long run; (ii) investments and patents in the energy sector, as well as low-carbon sources, will degrade the environment; (iii) only investments in the treatment of environmental pollution will improve the ecological situation. Various policy implications are suggested based on the empirical results in order to attain environmental sustainability.

Exploring the promotion of green technology innovation in the new energy vehicle industry: An evolutionary game analysis.

This paper aims to explore how to promote green technology innovation (GTI) among new energy vehicle (NEV) manufacturers and the strategic changes among the government, manufacturers, and consumers. From the perspective of evolutionary game theory, a tripartite evolutionary game model is established to analyze the influence of key factors on the tripartite strategies in the context of the government's willingness to subsidize gradually decreases. The main findings are as follows: (1) government subsidies provided to manufacturers better promote their willingness to participate in GTI. The relationship between government subsidies and GTI is not linear, and the government cannot blindly increase the level of subsidies. (2) The willingness of NEV manufacturers to engage in GTI is influenced by price and consumer purchase preferences. The higher the price of new energy vehicle using green technology (NEVG) is not better, and lowering the price of NEVGs can promote manufacturers' participation in GTI and consumers' purchase of NEVGs. (3) Increasing the mileage of NEVGs and consumers' green consumption preferences will effectively increase consumers' willingness to purchase. Accordingly, this study suggests that to enhance manufacturers' participation in GTI, the government should increase subsidies and encourage green consumption among consumers. In addition, manufacturers should focus on improving the mileage of NEVGs and reducing their prices to make them more accessible to consumers.

Can the Exemption of the New Energy Vehicle Purchase Tax Policy Induce Technological Innovation of Automobile Companies?

Implementing industrial policies to develop the new energy vehicle industry and improve technological innovation related to new energy vehicles is essential for countries dealing with environmental pollution and climate change. This paper first examines the impact of a demand-side industry policy related to new energy vehicles on the technological innovation level of supply-side vehicle enterprises. Regarding the new energy vehicle purchase tax exemption policy implemented by the Chinese government in 2014 as a quasi-experiment, this paper constructs a multi-period difference-in-differences (DID) model to identify the impact of the demand-side preferential tax policy on the technological innovation level of vehicle enterprises. In our DID design, 178 enterprises are in the treatment group and 166 enterprises in the control group, with 3,440 balanced panel data of observations. The results show that this policy can significantly promote the technological innovation of new energy vehicle enterprises. This promotion effect is greater in magnitude in large-scale enterprises, high-tech enterprises, and enterprises producing passenger vehicles. Theoretically, we believe that the demand-side tax exemption policy mainly indirectly promotes the technological innovation level of automobile enterprises through the signal channel and profit channel.

A hybrid DEMATEL-COPRAS method using interval-valued probabilistic linguistic term set for sustainable hydrogen fuel cell supplier of new energy vehicles.

With the continuous development of the global economy, global environmental pollution, climate degradation and global warming are becoming increasingly serious. In order to deal with the increasingly serious environmental problems, the government is vigorously supporting and promoting the development of new energy vehicles (NEVs). As the core unit of NEVs, one of the main challenges faced by hydrogen fuel cell (HFC) supplier is to select the best supplier for their business among all possible suppliers. Selecting the optimal supplier is a key decision in green supplier management. Therefore, it is extremely important and meaningful to select an optimal HFC supplier to provide power for NEVs. This paper proposes a new decision-making framework based on Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and Complex proportional assessment (COPRAS) method under interval-valued probabilistic linguistic environment to select the appropriate HFC supplier of NEVs. Firstly, this paper establishes the evaluation criteria system of HFC supplier assessment which is the synthesis of economical, environmental, social, technical, organisation and service aspects. Then, in order to express the uncertainty of expert decision-making, this paper uses interval-valued probabilistic linguistic term set (IVPLTS) to describe the evaluation information. Next, the interval-valued probabilistic linguistic term set decision-making trial and evaluation laboratory (IVPLTS-DEMATEL) method is applied to calculate the criteria weights. Moreover, this paper constructs the interval-valued probabilistic linguistic term set Complex Proportional Assessment (IVPLTS-COPRAS) model for the selection of HFC supplier of NEVs. Finally, a case in China with sensitivity analysis and comparison analysis are executed to illustrate the feasibility and validity of the proposed approach. This paper provides valuable references for investors and companies to select the most appropriate HFC supplier of NEVs under uncertain environment.

Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting.

Low-carbon and environmentally friendly living boosted the market demand for new energy vehicles and promoted the development of the new energy vehicle industry. Accurate demand forecasting can provide an important decision-making basis for new energy vehicle enterprises, which is beneficial to the development of new energy vehicles. From the perspective of an intelligent supply chain, this study explored the demand forecasting of new energy vehicles, and proposed an innovative SARIMA-LSTM-BP combination model for prediction modeling. The data showed that the RMSE, MSE, and MAE values of the SARIMA-LSTM-BP combination model were 2.757, 7.603, and, 1.912, respectively, all of which are lower values than those of the single models. This study therefore, indicated that, compared with traditional econometric forecasting models and deep learning forecasting models, such as the random forest, support vector regression (SVR), long short-term memory (LSTM), and back propagation neural network (BP) models, the SARIMA-LSTM-BP combination model performed outstandingly with higher accuracy and better forecasting performance.

Decentralized multi-agent collaborative innovation platform for new energy vehicle core technology breakthrough with digital empowerment: From the perspective of prospect theory.

Under the background of digital economy and energy crisis, based on the theory of digital empowerment and prospect theory, aiming at the problems of inefficient collaborative innovation mode, long principal-agent chain, imperfect collaborative innovation mechanism and insufficient digital collaborative innovation degree of the core technology of new energy vehicles, this study constructs a decentralized multi-agent collaborative tripartite evolutionary game model with the participation of government building platform, new energy enterprises and academic research institutions, analyzes the evolution law and key factors, and finally compares the examples of the United States, China and European countries. Results show that: (1) Government subsidies should meet the sum of the strategic income difference and the credibility income is greater than the subsidies to enterprises and research institutions; (2) The subsidy structure and innovation performance show an inverted U-shaped structure. The management mechanism of platform should be optimized; (3) Government should improve the threshold for innovators to enter the decentralized new energy vehicle core technology breakthrough platform, and the contract maintenance strength is conducive; (4) The innovation subject has a dynamic income cost distribution coefficient, and reasonable control of the income and cost sharing coefficient is important; (5) Digital empowerment can make the above four mechanisms achieve dynamic optimization iteration and achieve better equilibrium solutions. Finally, practical countermeasures for the government are put forward, which improves the theoretical research and practical exploration.

Who decided the new energy vehicles policy in China? From the perspective of policy objects and policy makers.

The new energy automobile industry is a comprehensive system that contains Exploration and Manufacture, Consumption and Promotion, Infrastructure Construction and Supporting Industries, which coordinate and supplement with each other. Accordingly, from the perspective of policy object, NEVs policies since 1991 to 2022 could be divided into four fields in China. With policy bibliometric analysis and social network analysis, in each field of policies, its policy networks can be drawn, with statistic of policies released separately, in order to comprehensively analyse the features of NEVs policy making. It is found that: (1) The structure of policy system is balanced among four fields of NEVs policies in China, though with a bias towards Consumption & Promotion, Exploration & Manufacture. (2) Policy makers in all four fields of NEVs policies preferred slightly to formulate policies jointly, rather than acting alone. While policies made by sole actors are part of policy system. (3) GOOSC, MIIT and MOT, as sole actor, played more significant roles in industry-wide, supply-side and demand-side of NEVs industry respectively. (4) Policy networks of all four fields started with the "iron four" (MIIT, NDRC, MOF, MOST), ultimately forming two different ways of development, specialization and sociability. (5) In addition to the government departments, social organizations and enterprises also influenced the policy network, at the edge of network. This paper is of positive significance for understanding the current status and characteristics of policy making in different fields of the NEV industry, beneficial to distinguish potential effective ways to impact on NEVs policy system in China.

[Life Cycle Prediction Assessment of Energy Saving and New Energy Vehicles for 2035].

The development of energy saving and new energy vehicles is an important technology path to reduce carbon emissions for the transportation industry. To quantitatively predict the life cycle carbon emissions of energy saving and new energy vehicles, this study used the life cycle assessment method and selected the fuel economy level, lightweight level, carbon emission factor of electricity structure, and carbon emission factor of hydrogen production as key performance parameters to establish inventories of internal combustion engine vehicles (ICEV), mild hybrid electrical vehicles (MHEV), heavy hybrid electrical vehicles (HEV), battery electrical vehicles (BEV), and fuel cell vehicles (FCV) based on automotive-related policy and technical routes. The sensitivity of carbon emission factors of electricity structure and different hydrogen production methods were analyzed and discussed. The results showed that the current life cycle carbon emissions (CO2 equivalent) of ICEV, MHEV, HEV, BEV, and FCV were 207.8, 195.2, 149.9, 113.3, and 204.7 g·km-1, respectively. By 2035, BEV and FCV were predicted to have a significant reduction of 69.1% and 49.3%, respectively, compared with ICEV. The carbon emission factor of electricity structure had the most significant influence on BEV life cycle carbon emissions. In terms of different hydrogen production methods of FCV, hydrogen demand should be mainly supplied by industrial hydrogen by-product purification in the short-term future, whereas hydrogen energy production by water electrolysis and hydrogen production from fossil energy combined with carbon capture, utilization, and storage technology should be used to meet the hydrogen demand of FCV in the long-term future, so as to achieve a significant improvement in the life cycle carbon reduction benefits of FCV.

Health Benefits Quantification for New-Energy Vehicles Promotion: A Case Study of Beijing.

Considering that the promotion of new-energy vehicles (NEVs) is a potential measure to address urban air pollution, the Chinese government has launched subsidy schemes to improve its market penetration. The purpose of this study is to quantify the economic benefits of NEV promotion from 2016 to 2019 and compare them with the cost of government subsidies in Beijing, so the effectiveness of the NEV policies can be evaluated and valuable recommendations can be provided for decision-making. The exposure-response model and the cost of illness approach was applied to evaluate the health and economic benefits of NEV promotion. Our results are as follows: (1) promoting NEVs can reduce the PM2.5 concentration significantly, the average annual concentration reduction was between 3.23 µg/m3 and 4.61 µg/m3; (2) at least 37,545 illnesses and deaths in Beijing could be reduced through NEV promotion annually, internal disease (15-64) was the most affected illness; (3) the economic benefits of NEV promotion were far more than the cost and the net benefits stock reached 33.71 billion CNY in 2019, accounting for 0.95% of Beijing's GDP in the same period. This study provides references in the perspective of environmental economics for the policy formulation of promoting NEVs.

The health effects of traffic-related air pollution: A review focused the health effects of going green.

Traffic-related air pollution (TRAP) is global concern due to both the ecological damage of TRAP and the adverse health effects in Humans. Several strategies to reduce TRAP have been implemented, including the use of sustainable fuels, after-treatment technologies, and new energy vehicles. Such approaches can reduce the exhaust of particulate matter, adsorbed chemicals and a range of gases, but from a health perspective these approaches are not always successful. This review aims to discuss the approaches taken, and to then describe the likely health effects of these changes.

Exploring purchase intentions of new energy vehicles: Do "mianzi" and green peer influence matter?

New energy vehicle is an innovative means of transportation, and its development has been widely concerned all over the world. However, few studies investigate the purchase intention of new energy vehicles (NEVs) from the perspective of combining altruism and cultural factors. Based on the extended norm activation model (NAM), this study explores the influencing factors of NEVs' purchasing intention and the moderating effects of "mianzi" and green peer influence. According to 302 valid questionnaires, the results indicated that the extended NAM model is useful in predicting consumer purchasing behavior with an improved explanatory power in purchase intentions of NEVs from 15 to 26%. The awareness of consequences, the ascription of responsibility, and green self-identity have a positive impact on the personal norm. Personal norm and green self-identity are positively associated with purchase intention. "Mianzi" and green peer influence positively moderate the relationship between green self-identity and intention to purchase. The findings give new insights into the impact of cultural factors on purchasing NEVs and profound suggestions for policymakers and enterprises to promote the development of NEVs.

How can new energy vehicles become qualified relays from the perspective of carbon neutralization? Literature review and research prospect based on the CiteSpace knowledge map.

Analyzing the feasibility of fuel vehicle transition will be conducive to the realization of the carbon neutralization goal. However, at present, there are few studies specifically aimed at the transition of fuel vehicles. Therefore, this study first analyzed the necessity for the transition of fuel vehicles and then used CiteSpace to analyze 2081 articles in the core Web of Science database in the past decade with "fuel vehicle emission reduction" as the search keyword. After clarifying the research context and development frontier of fuel vehicle emission reduction, we found that most of the literature with the research theme on this topic ends with the research of electric vehicles. Therefore, we took new energy vehicles represented by electric vehicles as the starting point to explore the realization path of carbon neutralization by analyzing the development dilemma and residents' feedback on electric vehicles. Finally, the research review and research prospects were carried out. The study found that although the development of new energy vehicles has made obvious progress at this stage, there are still some problems in comprehensively promoting electric vehicles, such as battery power, charging facilities, and the weak willingness of consumers to accept electric vehicles. Therefore, improving the usage efficiency of new energy vehicles can more effectively force fuel vehicles and new energy vehicles to complete the relay from the perspective of market attraction. This study will provide a more scientific solution and implementation path for the transition of fuel vehicles in various countries.