Market Share Of Electric Vehicles Research Articles

Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs

Introducing electric vehicles is critical to maintaining air quality and reducing carbon emissions. The Indonesian government has issued several regulations to stimulate the diffusion of electric vehicles. This research attempts to forecast the electric vehicle market share, especially electric motorcycles, by involving the policy mix and sustainable life cycle costs. We propose a system dynamics approach that takes into account a policy mix including 0% down payment without credit interest subsidies, tax abolition, expansion of charging station network, and sustainable life cycle costs, i.e., total cost of ownership, social, and environment. The system dynamics model has four modules: the electric motorcycle cost, the conventional motorcycle cost, the economy module, and the consumer market. The simulation results show that the electric motorcycle market share will increase positively in 2021–2030, reaching 5.7% in 2030. Based on the scenario simulation results, providing more charging stations and vehicle tax abolition can significantly boost the market share of electric motorcycles in Indonesia. The study provides valuable insights for policymakers in formulating more appropriate policy instruments to promote electric vehicle diffusion in Indonesia.

Identifying electric vehicle charging styles among consumers: a latent class cluster analysis

The market share of electric vehicles (EVs) is growing rapidly, making it crucial to understand the charging behaviour of current and prospective users. Such understanding is essential for designing policies that positively influence consumers’ charging behaviour and facilitate EV adoption. In this study, we examined the heterogeneity in charging preferences of 994 respondents across Australia using a latent class cluster model that considers indicators of charging behaviour as outcomes of interest. We used sociodemographic characteristics, travel needs, and EV adoption status as covariates to predict class membership. Our findings indicate five segments of consumers with distinct charging preferences: routine-focused frugals, cost-oriented deliberators, range seekers, flexibility seekers, and indifferent late adopters. These segments differ in the importance they attach to charging attributes, their coping strategies with limited battery resources, and their risk attitude. Our results suggest that a uniform approach to EV-related policies is not appropriate, as each consumer segment has unique charging preferences and requirements. Furthermore, the study emphasizes the significance of accounting for charging behaviour heterogeneity in demand modelling, as assumptions in current models may not accurately represent the decision-making of most segments.

Moving Towards Sustainable Purchase Behaviour: Determinants of Consumers’ Acceptance of Electric Vehicles in Malaysia

Electric vehicles (EVs) are becoming more common in the transportation industry in recent times. However, the market share of EVs is still unrivalled by that of conventional internal combustion engine vehicles (ICEVs), particularly in developing countries such as Malaysia. This study seeks to investigate the factors of consumer acceptance of EVs in Malaysia. This study specifically investigates six selected factors that may have a significant role in determining EV acceptance. The research was conducted using a quantitative approach, with data collected via an online questionnaire survey among consumers using snowball sampling. Based on 243 valid responses, the findings revealed that environmental concern, energy efficiency of EVs, cost consideration of EV purchases, government policy towards EVs, availability of charging infrastructure, and social influence are positively and significantly associated with consumer acceptance. This study highlights the importance of improving the user experience, resolving practical issues, and emphasising the benefits of EVs to expedite the transition to sustainable and eco-friendly EVs in the context of developing countries.

Real‐World Vehicular Source Indicators for Exhaust and Non‐Exhaust Contribution to PM2.5 During Peak and Off‐Peak Hours Using Tunnel Measurement

AbstractThe benefit of real‐world applicable indicators in resolving robust traffic‐related source contributions was investigated using tunnel measurement. PM2.5/CO was introduced as a metric to present evidence in non‐exhaust identification indirectly in terms of PM2.5 accumulation and dilution scenarios, and the rates (hourly variation of the difference between PM2.5 and PM2.5/CO) were 0.59−1.88 and −0.79 to −0.65, corresponding to peak and off‐peak hours, respectively. Real‐world PAHs indicators were examined, among which the exhaust indicators showed stable applicability from BghiP, and the non‐exhaust indication noted by BkF and DahA in peak hours were largely weakened in off‐peak hours, showing noticeable profile mixing between exhaust, brake and tyre wear. Source contributions were resolved by principal component analysis (PCA) with support of linear discriminant analysis (LDA) on inter‐group centroid diagnosis. The rate of vehicular non‐exhaust (brake and tyre wear) contribution lifted 10.3 times in peak hours compared with off‐peak hours, and its emission factor was noticeably enhanced 16 times, from 0.03 mg/(km·veh) to 0.48 mg/(km·veh). Guided by global vehicle electrification, the contribution of non‐exhaust in vehicular emissions were estimated to increase with larger ratio of emission factor between non‐exhaust and exhaust, and the growing market share of electric vehicles, under each mode of regenerative braking. The monetary impact of non‐exhaust caused by electric vehicles was calculated reaching the exhaust when electric vehicles increase to 50% in busy commuting megacities. The results provide applicable indicators for accurate source apportionment and support data for the refined control of non‐exhaust emission under rapid vehicle electrification.

Optimization Study of Fire Prevention Structure of Electric Vehicle Based on Bottom Crash Protection

As the market share of electric vehicles continues to expand, fire accidents due to impacts from the power battery located at the bottom of the electric vehicles are receiving increasing attention. Lithium-ion batteries, as the mainstream choice of power battery for electric vehicles solving the problem that they are prone to thermal runaway due to damage when impacted, are the key to preventing and controlling fire accidents in electric vehicles. To address the protective problem of the bottom power battery of electric vehicles when it is impacted by road debris, two new types of sandwich structures with an enhanced regular hexagonal structure and semicircular arch structure as the core layer, respectively, are innovatively proposed in this article. They are used to protect the bottom power battery of electric vehicles and are compared with the traditional homogeneous protective structure in terms of protective performance. A local finite element simulation (FEM) of an electric vehicle containing the necessary components was established for simulation. Stress distribution, deformation, and energy absorption data for each component of an electric vehicle assembled with a protective structure when subjected to a bottom impact were obtained safely and cost-effectively. Three evaluation coefficients, namely, the cell shape variable (Bcmax), the protective effect parameter (ƒPE), and the total energy absorption of the structure (Ea), are proposed to compare and analyze the simulation results of different protective structures under equal mass conditions. The maximum values of the battery deformation of arched sandwich construction and reinforced honeycomb sandwich construction were 0.35 mm and 0.40 mm, respectively, which are much smaller than that of the maximum deformation of the battery under the protection of a homogeneous protective structure, which is 0.62 mm. Their protective effect parameters are 43.55 and 35.48, respectively, which proves that the optimization degree of the protective structure of the bottom of the electric vehicle after the application of the new structure is 35% or more. The total energy absorptions of the two structures are 91.77 J and 87.19 J, respectively, accounting for more than 70% of the kinetic energy in the system, which proves that the deformation of the sandwich structure can effectively absorb the kinetic energy of the collision between the road obstacle and the bottom of the car. The final results show that the arched sandwich structure showed the best impact resistance in the simulation, which can be used for the power battery’s protective structure on the electric vehicle’s bottom. This study fills a gap in local finite element modeling in electric vehicle crash simulations and provides ideas for fire prevention designs of electric vehicle structures.

How does local context matter? Assessing the heterogeneous impact of electric vehicle incentive policies in China

This study reassesses the effectiveness of electric vehicle (EV) incentive policies in China and explores potential disparities in policy impact across different local contexts. Initially, employing panel data analysis, it evaluates policy effectiveness using data from 80 Chinese pilot cities spanning 2010 to 2022. Subsequently, cities are stratified based on varying income levels, traffic conditions, population density, and administrative hierarchy to facilitate an in-depth examination of the heterogeneous impacts of EV policies. Additionally, the Difference-in-Differences (DID) estimation method is employed to scrutinize the dynamic effects of these policies over time, enhancing the credibility of the panel model results. The findings reveal that purchase subsidies, parking benefits, driving privileges, and charging infrastructure have significant positive impacts on EV market share. Specifically, financial incentives are more effective in low-income, low-traffic, low-density cities, as well as in provincial capital cities while driving privileges are more suitable for high-income, high-traffic, and high-density areas. This study also finds that usage-phase policies increasingly drive EV adoption over time, offsetting the negative impact of subsidy reductions. The results of this study hold substantial implications for policymakers, providing insights to design context-based EV policies tailored to specific local conditions.

Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework

As the market share of electric vehicles (EVs) continues to grow, driving range capability has emerged as a primary concern for drivers, car manufacturers, and policymakers. Accurate real-time energy consumption prediction is essential in mitigating range anxiety and fostering the adoption of EVs. Consequently, an EV energy consumption prediction framework that comprehensively considers vehicle and environmental factors, with special attention to individual driving styles and driving conditions, constructed based on long short-term memory (LSTM) and Transformer models is proposed. This framework addresses the issue of long-term dependencies and captures characteristics of time-series data with increased efficiency. Upon rigorous evaluation, the model achieved the mean absolute percentage error (MAPE) of 4.63 % for state of charge (SOC) of EV's battery in experimental dataset, outperforming LSTM and multivariate regression (MLR). The ablation experiment shows that the MAPE of the model is reduced by 18.47 % and 15.27 % respectively after considering the individual driving style of drivers and the driving conditions of vehicles for both two types of vehicles. Based on this framework, a long-distance EV energy consumption prediction strategy based on short-distance is proposed, with a MAPE of 6.7 % when SOC value is reduced from 90 to 40 in the selected dataset.

System Dynamics Simulation of China's Electric Vehicle Market Development Trends

The global new energy vehicle industry is currently in a phase of rapid development, with the growth of China's new energy electric vehicle sector being dynamically regulated by various factors within the overall market. Consequently, this study considers the competitive relationship with traditional fuel vehicles and the policy and economic factors present in international trade. Utilizing system dynamics models and dynamic simulations based on graph theory, the research analyzes the future development trends of the overall automobile market and China's new energy electric vehicles. The results indicate that electric vehicles globally will have a disruptive impact on the traditional fuel vehicle market. In situations where China's new energy electric vehicles significantly challenge the traditional fuel vehicle market, the market share of electric vehicles is expected to grow at a high rate over the next 30 years, rapidly increasing from 0.4 to 0.87. Different governmental guidance measures can create up to a 17% difference in market share. In foreign markets, under the global context of 'carbon peaking' and other low-carbon initiatives, or in scenarios where Chinese electric vehicles have a competitive advantage, the impact on the development of China's new energy vehicle industry is relatively minor.

An Agent-Based Modeling Approach for the Diffusion Analysis of Electric Vehicles With Two-Stage Purchase Choice Modeling

Abstract Diffusion research of innovative technologies is crucial for new product positioning and strategic planning in product design. As a versatile system simulation method, agent-based modeling (ABM) has been used in many previous studies on the diffusion analysis of electric vehicles (EVs). In these simulations, modeling consumers' purchase decisions is a significant step. Previous studies often adopt simple rule-based decision criteria in this step, while an accurate purchase decision model can contribute to a more reasonable diffusion analysis of EVs. To fill this gap, this brief presents an agent-based modeling approach for the diffusion analysis of electric vehicles with two-stage choice modeling. The core idea is to separate consumers' decision-making process for purchasing cars into two stages. Consumers first form a small choice set from the whole auto market. Then, they make the final choice from the choice set built in the first stage. In addition, the word-of-mouth (WOM) effect and consumers' social networks are also considered in the ABM, which can further improve the accuracy of the diffusion analysis. A case study using data collected from Shanghai, China, is presented to demonstrate the proposed approach. Our approach outperforms other ablation models as well as traditional statistical models in the prediction accuracy of EV's market share. The influence of factors such as government policy and technological improvement on the diffusion of EVs is also discussed. These insights can assist automakers in improving their product design and enhancing their market competitiveness.

Driving Toward a Green Future: A System Dynamics Modeling of Electric Vehicle Market Share in Taiwan

Taiwan has gradually increased its electric vehicle (EV) market share over the past few years. The increasing availability of affordable EV models has also contributed to the rising market share of EVs in Taiwan. This study provides a system dynamics model for evaluating the impact of various factors on the electric vehicle market share in Taiwan. The model incorporates key variables such as consumer preferences, financial acceptability, charging infrastructure and accessibility, EV performance, and customer acceptance to simulate the dynamic behavior of the EV market. Moreover, the model utilizes historical data and the government's target plan to calibrate and validate a model that accurately captures the current state of Taiwan's electric vehicle (EV) market share. Results indicate that charging stations and financial issues impact EV adoption significantly. Overall, this study demonstrates the usefulness of system dynamics modeling in evaluating policy interventions and identifying critical drivers for promoting the transition to a greener transportation system.

Automated Disassembly of Battery Systems to Battery Modules

The increasing market share of electric vehicles leads to a growing demand for raw materials such as lithium and cobalt, where the supply situation is fraught with risk. Product-specific challenges regarding high voltage, product state/variance, and labor shortage require flexible automated non-/ semi-/ destructive disassembly. However, there is a need to implement industrial, scalable automation approaches to deal with this problem. This paper addresses the development of a flexible robotic cell for the fully automated disassembly of battery modules from battery systems. The paper presents all required tools and processes for battery diagnoses, machine learning-based object recognition, loosening and removing fasteners, opening sealings, gripping components, separating cables and plugs, and removing the battery modules. Based on this, a sorted disposal/recycling of the material can be realized. Finally, the holistic robot cell, including a safety concept, is described using the example of an actual disassembly process.

Optimal Operation of an Innovative Electric Vehicle Charging Hub directly fed by Renewables

The reduction of Green House Gases (GHGs) emissions has been the top priority of the European Union (EU) in recent times. The increase in use of Electric Vehicles (EVs) to combat climate change is viable only with the use of Renewable Energy Sources (RESs) to power them. Since the Paris Agreement in 2015, the integration of RESs into the charging infrastructure has increased in the EU. The increasing market share of EVs in various categories has called for immediate changes in the EV charging infrastructure and in the operation of charging hubs. This paper aims to present an Energy Management System (EMS) to efficiently manage an EV charging hub fed by RESs, reducing the daily costs of operation and GHG emissions. The mathematical model of the EMS is developed in Matlab as a Mixed Integer Linear Programming (MILP) model. The MILP-based EMS is applied to the real-world case of an innovative EV charging hub located in the Ligurian region of Italy and the results of the optimization are reported.

Investigating the Impact of New Energy Policy on the Market for New Energy Vehicles

As global concerns about environmental sustainability continue to rise, electric vehicles (EVs) are gradually becoming a significant component of the global automotive market as a clean and low-carbon transportation option. This study, through a review of relevant literature and an analysis of market data, examines the influence of new energy policies on the international market for electric vehicles. The research findings indicate that the market share of electric vehicles has steadily increased over the past few years. Government environmental policies and incentive measures have played a crucial role in promoting electric vehicles. Furthermore, ongoing technological advancements and cost reductions have also contributed to the widespread adoption of electric vehicles. However, electric vehicles face certain challenges, such as inadequate charging infrastructure and limitations in battery technology. In the future, with further technological developments and policy support, electric vehicles are poised to play a more significant role in the international market and contribute to global sustainability efforts.

Investigating the factors influencing the electric vehicle market share: A comparative study of the European Union and United States

Electric vehicles (EVs) are gaining worldwide popularity as a means to reduce greenhouse gas emissions and decrease dependence on fossil fuels. This comparative study examines the factors influencing the EV market share in the European Union (EU) and the United States (US), aiming to identify regional differences and similarities using statistical and spatial models. The study findings indicate that in the EU, ownership of charging outlets, personal income, education levels, and ages over 55 are positively correlated with the EV market share. However, population density does not support the adoption of EVs. In contrast, in the US, ownership of charging outlets is positively associated with the EV market share in the Central US, while higher personal income is strongly negatively correlated. These findings emphasize the need for tailored location-specific policies that target specific sociodemographic groups and prioritize the development of robust charging infrastructure.

Can financial subsidy increase electric vehicle (EV) penetration---evidence from a quasi-natural experiment

Electric vehicles (EVs) are considered a promising solution for reducing emissions in urban transportation and addressing energy crises. Several countries, including China, have implemented direct financial subsidies to encourage the adoption of EVs. However, there is a lack of comprehensive evaluation regarding the effectiveness of these subsidies and their interaction with non-financial incentive policies. Additionally, the design of subsidy policies can be endogenous, potentially leading to biased estimations of their effects on EV penetration. To address these issues, this study utilizes a “quasi-natural experiment" approach, taking advantage of the gradual reduction of nationwide EV subsidies by the Chinese government due to financial constraints, rather than market conditions, thus mitigating the endogeneity problem. The study collects panel data from 224 prefecture-level cities in China and employs a modified panel data fixed effects model to quantify the impact of financial subsidies on EV sales and market shares. The analysis focuses on subsidy variations during six periods. The main empirical findings are as follows: (1) 1 % increase in purchase subsidies leads to a 1.36 % increase in EV sales and a 2.31 % increase in EV market share. (2) Non-financial policies, such as parking benefits, promotional goals, and restrictions on the number of vehicles, also contribute to boosting EV sales. (3) The effects of purchase subsidies vary across cities with different socio-economic conditions. (4) Some non-financial policies are found to enhance the positive effects of subsidy policies.

Automated Battery Disassembly—Examination of the Product- and Process-Related Challenges for Automotive Traction Batteries

As the market share of electric vehicles continues to rise, the number of battery systems that are retired after their service life in the vehicle will also increase. This large growth in battery returns will also have a noticeable impact on processes such as battery disassembly. The purpose of this paper is, therefore, to examine the challenges of the battery disassembly process in relation to the required increase in the degree of automation. For this purpose, a survey of various experts along the battery value chain was conducted, and product-side hurdles, such as the wide range of variants, and process-side challenges, such as the opening of the housing cover or the removal of cables and connectors, were identified. Together with an assessment of the potential degree of automation in the context of downstream processes (reuse, repair, remanufacturing, and recycling), this results in a variety of streams for future research in the field of automated battery disassembly. The core aspect in this context is data availability consisting of product and component data as well as process-relevant parameters.

Why are consumers switching to electric vehicles? Analyzing consumers preferences for electric vehicles

Turkey is highly dependent on foreign oil supply and the number of automobiles and fuel consumption is increasing day by day. In terms of greenhouse gas emissions, road transportation is second only to electric power generation. This study studies people's readiness to transition to electric vehicles in order to enhance their environmental and social quality of life, as well as the most effective strategy to increase the market share of electric vehicles (EVs) by 2030. We polled 466 automobile owners from 76 places throughout the country to determine their preferences. The intention to transition to EVs is the dependent variable in our study, with three ordinal responses ranging from “Certainly will switch” to “Definitely will not switch”. The survey used an ordinal response logit (ORL) model with four policy scenarios. These scenarios are: abolishing the special consumption tax (SCT), providing government-backed loans, introducing an age limit for conventional vehicles, and increasing the number of EV stations. In the absence of any policy action by the government or automakers, we estimate that the market share of EVs in Turkey would be 0.1% and the potential customer base would be 1% by 2030. The abolition of special consumption tax (SCT) on EVs would boost the intention to absolutely switch and the likelihood of switching to EVs by 19.5% and 53.2%, respectively. This result shows that the tax on vehicles is the main factor influencing consumers to switch to EVs. The results of this paper suggests that other scenarios implemented by the government will also increase the market share of electric cars in Turkey.

Adoption of Electric Vehicles (EV) among millennial through the Diffusion of Innovation (DOI) framework: The role of Pro-Environmental Attitude

The government is currently encouraging the use of electric vehicles to support environmentally conscious sustainable development. However, Malaysia's current EV market share is still dismal. The adoption rate is far lower than what has been accomplished in neighboring countries, with just less than 3% of the market in 2021. This study aims to learn more about the adoption of electric vehicles among millennials, who are thought to be more environmentally conscious. Based on Diffusion of Innovation (DOI), this study proposes relative advantages, complexity, compatibility, observability, and trialability have a substantial influence on the desire to adopt electric vehicles while being moderated by pro-environmental attitudes. In practice, this study aims to provide comprehensive information on factors that affect intention for EV adoption from the millennials perspectives to policy makers, researchers, and industry players.

Solar parking lot management: An IoT platform for smart charging EV fleets, using real-time data and production forecasts

The fast transition to the electrification of the energy system, combined with an exponential growth of the market share of electric vehicles, is leading to a tight interrelation between electric energy production and transportation, two prominent sectors in fossil fuels consumption and greenhouse gas emissions. Accelerating this process, the management of electric fluxes, aiming at optimizing production and demand coupling, plays a crucial role in reaching the net-zero emission target. The proposed software platform is designed to optimally manage the energy fluxes for a solar powered parking lot, serving a fleet of electric vehicles; the real-time knowledge of energy production and demand, in conjunction with forecasted power generation, allows the maximization of renewable energy self-consumption, thus reducing the exchange with the external grid. The software platform can work either in design mode, allowing the dimensioning of the various parking lot components, or in real-time mode managing instantaneously the energy balance. As a case study, it is tested on the 2019 parking lot mobility data of a research center, assuming a complete transformation of the then existing fleet of employees' cars to electric vehicles. A comparison of the resulting energy flows with those projected by an established commercial tool is performed, as well as a preliminary economic evaluation. Both consistency of the simulation results and favorable economics validate the presented smart charging algorithm and Internet of Things platform for the real-time energy management of a solar parking lot.