Development Of Electric Vehicle Industry Research Articles

New Energy Electric Vehicle Industry Development Trends Forecast

This study focuses on developing the MLPRegressor model for predicting the future trends in China's new energy electric vehicle industry over the next decade. We employed a dataset comprising 16,500 records, encompassing 12 features, such as the number of public electric vehicles, the length of electric vehicle operational routes, and the number of electric vehicles owned by residents, for model training. We compared our model with traditional models like ARIMA, XGBRegressor, and Random Forest Regressor, using MAPE and MSLE as evaluation metrics. The final results revealed that our model exhibited superior performance in terms of loss, outperforming the other models significantly. This research not only provides businesses with more accurate strategies for developing the new energy electric vehicle industry but also serves as a crucial reference for the government in formulating environmentally friendly policies.

Electric vehicle industry and sustainable economic growth: A measurement, coupling and causal analysis

This study examines the electric vehicle (EV) industry's impact on sustainable economic growth (SEG). Using the entropy weight method, it evaluates the EV industry's development and SEG levels. A coupling coordination degree model measures their interaction. Results show a positive trend in both areas, with charging infrastructure expansion and foreign investments being key drivers. The study finds a progressive shift from imbalance to high-quality coordination between the EV industry and SEG, highlighting their increasingly synergistic relationship. This growth in the EV industry fosters technological innovation and drives green advancements throughout the industry chain, thereby enhancing SEG.

What factors affect the development of public charging infrastructures? a study from the perspective of potential users

The rapid development of the electric vehicle market has greatly stimulated the demand for public charging infrastructure (PCI) and made it a pertinent topic to improve its charging service quality in the industry. Data from perceived preference, PCI and electric vehicles cannot reflect the factors influencing potential users’ anxiety. This study designed a PCI development evaluation framework to investigate the impact of economic, technological, market, policy, and social environmental factors on PCI development from the perspective of potential users. We collected a dataset including 386 potential users in Tianjin, China, and employed structural equation model to survey the implementation of PCI. The following conclusions were drawn from this study: 1) Reducing operating costs will not significantly increase PCI charging user demand and improve use efficiency for potential users; 2) Technical factors cannot directly promote the development of PCI but will reverse lag the development of the market; 3) The growing market demand is the main impetus to the development of electric vehicle industry, and the incentive policy and social environment can indirectly incentivize PCI development. The policy implications suggest that the PCI industry can experience sustainable development by continuously innovating market-oriented business models, and improving policy systems and industry mechanisms. This study provides analytical foundation and decision support for policymakers and pertinent industry participants, promotes the development of electric vehicle-related industries, and helps achieve the strategic goal of carbon neutrality.

High-precision Prediction Method of Electric Vehicle Trading Power based on Neural Network

Introduction: Electric vehicles have become a development trend due to their good environmental protection and energy saving, etc. Prediction of electric vehicle charging volume can help relevant departments optimize power supply, service, and construction. Methods: In this paper, the Support Vector Machine (SVM) model and the combined Long Short Term Memory (LSTM) and Support Vector Regression (SVR) prediction model are constructed for the prediction of charging capacity, and simulated by actual trading power data in Hubei Province. Results: The results show that the prediction effect of the two methods is good, and the LSTMSVR algorithm is judged to have better performance and less error in predicting the fluctuation of transaction power. Conclusion: LSTM-SVR can be used as a charging prediction method to provide a reference basis for the power control strategy of electric vehicles charging management platform, which is conducive to the healthy development of electric vehicles industry.

Comparative life cycle assessment of sodium-ion and lithium iron phosphate batteries in the context of carbon neutrality

The promotion of electric vehicles (EVs) is of great significance to reduce the use of fossil fuels, decrease vehicle emissions and promote the transformation of the automotive industry to a green and low-carbon direction within the context of the global “carbon neutrality” goal. However, with the rapid development of EV industry, the environmental problems of power batteries represented by lithium batteries are increasingly prominent, and there is an urgent need to develop new high-efficiency and environmentally friendly power batteries to promote the further development of the automotive industry. New sodium-ion battery (NIB) energy storage performance has been close to lithium iron phosphate (LFP) batteries, and is the desirable LFP alternative. In this study, the environmental impact of NIB and LFP batteries in the whole life cycle is studied based on life cycle assessment (LCA), aiming to provide an environmental reference for the sustainable development of electric vehicle industry and the improvement of new power batteries. During the study, four research scenarios were set up combining battery gradient utilization technology and various battery end-of-life recycling technologies, focusing on the carbon footprint of NIB and LFP and the corresponding environmental impacts. The results show that LFP batteries have better environmental performance in the production stage, but NIB seems to be better in the long-term development perspective. In the battery recycling stage, hydrometallurgical recycling has better environmental performance, while battery gradient utilization further exploits the residual value of the battery and is more suitable for real-life disposal of retired batteries. In the future, NIB is expected to further develop and replace LFP for sustainable battery development.

Dynamic pricing for fast charging stations with deep reinforcement learning

With the rapid development of electric vehicles (EVs) and charging infrastructures, the unbalanced utilization rate of fast charging stations (FCSTs) and the long waiting time for charging have aroused considerable attention. The incurred low operation profit of FCSTs and low satisfaction of EVs impose difficulties on the further development of EV industry. Existing literature ignored the influence of real-time charging price changes on traffic flow variation and EV charging determination during the dynamic price regulating process. This paper focuses on solving these crucial issues in the dynamic pricing for FCSTs with deep reinforcement learning (DRL). Firstly, considering the spatial–temporal interactions of different roads, a traffic flow prediction model is proposed based on the LSTM combined with the GNN-FiLM. Then, the Origin-Destination (OD) estimation is used to estimate the charging requirements of EVs based on the predicted traffic flow, and a charging demand prediction method for FCSTs is developed by converting the EV satisfaction into economic costs with different dimensions. Then, the vehicle–road learning environment is built with the Markov decision process (MDP), and a dynamic pricing strategy based on the Deep Deterministic Policy Gradient (DDPG) learning is proposed to achieve the optimal charging prices of FCSTs with maximum operation profit. Moreover, during the learning process, the real-time charging price is renewed based on the predicted charging demand, and the future charging demand is further predicted under the renewed charging price until the optimal price is achieved. Finally, simulation results validate that the proposed dynamic pricing strategy effectively improves the profit of FCSTs, alleviates the road congestion, and improves the users’ satisfaction.

An Exploring Human Resource Development in Small and Medium Enterprises in Response to Electric Vehicle Industry Development

Transitioning the automotive industry from internal combustion engines (ICE) to electric vehicles (EV) has arisen as a critical challenge for global countries in achieving human resource development, owing to the need of new labor skills and replacement of automation systems. By applying a case study of Thailand’s automotive industry in response to this challenge, we aimed to plot out the trajectory of changes involved in the industry’s workforce during its transition with a central focus on Small and Medium-sized Enterprises (SMEs), in order to make sound suggestions to the government on how to build an effective policy for industry growth. An exploratory research design was adopted for the investigation. Both primary and secondary sources were collected. Interviews with key stakeholders, including official agencies, organizations in the business sector, and academic institutions, were conducted in a semi-structured format to collect as primary data. Meanwhile, secondary data were gathered from reports and other scholarly contributions that are relevant. All of the data that were collected were subjected to qualitative methods of analysis, including content and theme analysis. We found that the advances in technology and associated skills have posed challenges to the SMEs for the workforce relocations in terms of occupational shifts and skill development, with engineering demand potentially growing 10% while low labor skills declined by nearly 70%. We emphasized that without effective policies for establishing EV roadmap and coordination practices between public and private stakeholders, this transition would have a detrimental effect on the workforce development of SMEs, which would ultimately have a harmful impact on the automotive industry and the economy.

The impacts of critical metal shortage on China's electric vehicle industry development and countermeasure policies

Electric vehicle (EV) has been taken as the key technology in the road transportation sector to achieve the carbon neutrality target. However, considering China's critical metal resource endowment as well as its supply status, the potential metal supply shortage and its impact on China's EV industry development goal need to be analyzed. Therefore, in the present study, several supply and recycling scenarios are proposed and a dynamic stock-flow model is adopted to calculate the impacts of potential critical metal shortages including nickel, cobalt, and lithium and their countermeasures. The obtained results show that: i) The current resource endowment in China cannot meet the metal demand for EV industry development target. From 2022 to 2050, the metal shortage will cut the accumulative EV production potential by 459.7–384.6 million units, leading to extra greenhouse gas emissions; ii) In terms of specific metal types, the shortage of lithium will occur in 2026 and 2034 under different metal supply scenarios, which will hinder the EV production significantly; iii) In total, there are 101.9–190.8 Kt, 20.3–38.1 Kt, and 40.2–78.9 Kt nickel, cobalt, and lithium resource can be obtained from battery recycling system under different scenarios. However, the metal shortage issue still exists even under the highest recycling ratio scenario. The findings of this study are significant for policymakers to further understand the nexus between emission abatement and mineral conservation in the process of EV industry development and propose relevant policies.

MILAD: Robust Anomaly Detection for Electric Vehicles with Label Noise

One of the bottlenecks restricting the development of electric vehicle industry is the safety problem. Although numerous of anomaly detection algorithms for electric vehicles have been proposed, most of them may perform poorly due to the complexity and unpredictability of real scenes. We consider that there may be a certain degree of potential safety hazard in the battery system of electric vehicles before, during and after the process of faults in the real scenes, that is, label noise. In order to solve this problem, we propose a Multi-Instance Learning based Anomaly Detection (MILAD) framework, to perform anomaly detection for electric vehicles with label noise problem. Extensive cross validation experiments fully verify that the framework can effectively detect the existence of abnormal conditions in the presence of label noise in multivariate time series data.

Study on the Battery Safety in Frontal Collision of Electric Vehicle

With the rapid development of electric vehicle industry, more and more attention has been paid to the safety of the automotive battery. The safety of battery in electric vehicle’s frontal collision is the focus of this paper. In the process of simulated collision, the research object is simplified into a battery box. The mechanical properties of the battery monomers were investigated to summarize the mechanical properties of the internal failure of the monomers. Constructing the finite element model of the research object, we focus on the analysis of collision simulation results and propose the improvement measures. According to the test requirements in C-NCAP, the crash simulation of battery box is carried out by using the finite element software called LS-DYNA, which is used in automobile collision to analyze the deformation, stress of the battery box and the most dangerous battery monomer during the frontal collision. The simulation results show that the deformation of the case is obvious, and the protection of the battery is lost in the 50km/h frontal collision condition. By adding EVA foam, the maximum deformation of the battery monomer is reduced by 8.2%. By improving the material of battery case, the maximum deformation is reduced by 12.65%.

Electric vehicle industry sustainable development with a stakeholder engagement system

Electric vehicles emerge as the possible strategy for decarbonization and green transportation due to social demand. Researchers have made multiple efforts and initiatives as the demand surge for sustainable development in the electric vehicle industry. This study analyzes the relevant research of the industry, thereby explores electric vehicle industry development trends with a scientometrics-based data evaluation system, where three key topics are detected: “Vehicle Exhaust Emissions”, “Climate Change”, and “Integration”. The results are visualized in the view of clusters, timeline, and time zone to explore the dynamic direction and future trends of the industry. Further trend detection and forward-looking analysis suggest the significance of stakeholders and their interconnection. In response to the significant challenges in sustainable development, this study proposes the stakeholder engagement system in a comprehensive perspective. The system firstly reveals the driving forces behind the industry development, that is how different motivation and strategies orientate the industry towards sustainability. Then, it furtherly analyzes the commitments and efforts needed from multiple stakeholders, through policy implications, leading factors on demand side, and technology innovation requirements on supply side. The stakeholder engagement system may contribute to stakeholder synergy and resource optimization hence for electric vehicle industry sustainable development and novel ideas to other relevant research fields.

Research on Electric Vehicles Collaborative Charging of DC and Equalizing Charger for Improving Battery Life

With the rapid development of electric vehicle industry, repeated charging and discharging in the process of vehicle use will lead to the deterioration of voltage consistency of single battery. How to improve battery life through battery balance has become the focus of research. The structure of charging device which cooperates with DC charging pile is described. The equalizing charging device and charging pile can be cooperated to charge the electric vehicle battery, so as to achieve the balance of battery capacity when each single battery is fully charged by means of the communications among charging pile, main control module and battery BMS. Based on the charging structure, the charging system control strategy is proposed. The shortest charging time, the smallest charging power consumption and the combination of satisfaction are considered to construct the optimal algorithm.

Location and capacity determination of charging station based on electric vehicle charging behavior analysis

The infrastructure construction of charging facilities for electric vehicles (EVs) is one of the key factors influencing the development of electric vehicle industry. A reasonable location and capacity determination scheme is very crucial to this issue. In this study, a probability calculation model that fully analyses the charging behavior of the owners is proposed to predict the charging load of the planned land. Based on the results of electric vehicle load forecasting, a location model with the lowest users travel cost is established. The location of charging station is optimized by genetic algorithm to obtain the location and capacity library of charging station. Especially, the two‐way cost for both the owners and the operators is considered for the location and capacity decision, and the optimal scheme is selected from the location and capacity library as the final planning result. An example analysis shows that the proposed method is effective and feasible for the location and capacity determination of electric vehicles charging stations in cities. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Flexible Power Distribution Technology Based on Modular DC Charging Pile

With the rapid development of electric vehicle industry in China, the contradiction between the increasing users' demand for real-time charging and the unbalanced planning and design of charging piles is increasingly apparent. DC charging pile on the market is developed rapidly, but its structure is with multi-gun to one-pile. It is difficult to meet the demand of different kinds of electric vehicle battery charging, different battery capacity and the user expected charging time. To solve the problems, a flexible power distribution method of DC charging pile is proposed in this paper. When multiple EV in different types and states are charged at the same time, and the actual charging demands of each user and the charging battery are considered, the power module can be dynamically switched to realize the flexible power distribution, the idea of flexible power distribution is described. Then the algorithm steps of power distribution were presented. Finally, the Matlab/Simulink was used for example analysis to simulate the power distribution process when different initial SOC batteries were charged simultaneously.

The Impacts of Critical Metal Shortage on China's Electric Vehicle Industry Development and Countermeasure Policies

Electric vehicle has been taken as the key technology in road transportation sector to achieve carbon neutrality. However, considering China’s critical metal resource endowment, the impacts of potential metal supply shortage on the EV industry development goal need to be considered. Therefore, in the present study, a dynamic stock-flow model is proposed to calculate the impacts of potential critical metal shortages (including Nickel, Cobalt, and Lithium) and the countermeasures’ roles. The obtained results show that: i) The current resource endowment in China cannot meet the metal demand for electric vehicle industry development target. From 2020 to 2050, metal shortage may cut the accumulative electric vehicle production potential by 63.3 million units, leading to 23.8 Bt extra greenhouse gas emissions; ii) In terms of specific metal types, Lithium and Nickel shortage will happen in 2032 and 2037; iii) There are 87-200.1 Kt, 17.4 - 40 Kt, and 32.8 - 72.8 Kt Nickel, Cobalt, and Lithium resource respectively can be obtained from battery recycling system under different scenarios. The findings of this study are significant for policy makers to further understand the nexus between emission abatement and mineral conservation in the electric vehicle industry development and propose relevant polices.

A solvothermal route to prepare enhanced LiNi0.88Co0.09Al0.03O2 cathode material taking isopropyl alcohol as solvent

Ni-rich cathode material is taking more and more important position in energy storage and conversion because of their high capacity. But the gap between the demanded and actual capacity still hinder the development of electric vehicle industry. Herein, the Ni-rich (Ni content > 85%) cathode material has received extensive concern. In our research, LiNi0.88Co0.09Al0.03O2 was prepared with solvothermal method by two steps. Meanwhile, the different solvents in solvothermal route are investigated. It is found that LiNi0.88Co0.09Al0.03O2 prepared by isopropyl alcohol shows an excellent capacity retention which is up to 82.3% after 100 cycles at 55 °C, much higher than that prepared by ethyl alcohol (64.0%). The enhanced performance could be ascribed to the specific morphology formed in solvothermal period by the proper solvent.

Co-Evolution of Technology and Institutions in Emerging Industries: Case from Electric Vehicles in China

Technology and institutions are important driving forces for industrial development, but the relationship between them has not yet reached a consensus due to different economic theories. On the basis of the evolutionary theory, this paper aims to study the roles co-evolution of technology and institutions played in the development of emerging industry. Taking electric vehicles in China as a case study and the five-year plans for the nodes of industrial development, this paper analyzes the co-evolutionary process of technology and institutions at different stages of industrial development, and concludes that it was institutions that promoted technology innovation during the industrial incubation and infancy periods, while during the growth period, it was technology that drove institutions’ innovation. In order to promote the development of electric vehicle industry, it is necessary to further strengthen institutional innovation for technological and industrial development.

Research on Interruptible Load Management of Electric Vehicle Charging Considering Price Risk

The rapid development of electric vehicle industry promotes the construction of charging infrastructure and charging station. In the operation of charging station, how to ensure the stable profit of charging equipment under the condition of excessive charging load has become a crucial issue. Based on this, this paper focuses on how charging stations sign interruptible load contracts with users during peak load period and consider their risk coefficients to formulate optimal competition strategies so as to maximize expected profits. Finally, the problems and conclusions are summarized.

Research on Green Transition Development of China’s Electric Vehicle Industry in the New Era Guided by Low Carbon Economic Theory

The shortage of petroleum resources and the aggravation of environmental pollution at present. There is no doubt that China’s automotive industry has put forward higher requirements for energy conservation and environmental protection. How should China base itself on its own situation? To formulate a reasonable technological development route and industrialization strategy to stimulate the development of electric vehicle industry. It is an urgent problem to be solved at present.

A DEMATEL-TODIM based decision framework for PV power generation project in expressway service area under an intuitionistic fuzzy environment

Abstract The expressway service area photovoltaic (ESAPV) projects have been greatly promoted due to the increasing passenger volume and the current development of electric vehicle industry. Site selection based on sustainability perspective is critical to the future construction of ESAPV. This paper puts forward a decision-making framework to ensure the validity of the ESAPV site selection. First, the index system meeting the characteristics of the ESAPV project is established. Second, in order to take into account the influence of subjective preference and objective justice on the results, this study chooses the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to determine the weights of first-level indicators that lack objective data support and employs an integrated weighting method to determine the weights of second-level indicators. Third, the TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method which considers decision makers’ risk aversion psychology is extended to triangular intuitionistic fuzzy environment combined with group decision-making theory. Finally, a case study in Hebei of China is carried out to verify the practicability of the systematic framework. The results indicate that the optimal project is located in Shijiazhuang.