Market Penetration Of Electric Vehicles Research Articles

Understanding the Economics of Aged Traction Batteries: Market Value and Dynamics

The growing demand and market penetration of electric vehicles (EVs) have led to an expansion in the size of the market for used EVs, accompanied by a continuous increase in the return rate of aging battery systems. Consequently, a second-hand market for aged battery systems, known as second-life batteries, is slowly emerging. Understanding this market is crucial for enabling a functioning circular economy for batteries. This paper analyzes the market mechanisms influencing price formation for used goods, drawing parallels to the largest second-hand market, the used car market, and applies them to the second-life battery market. By examining these mechanisms, insights are provided into the dynamics of the second-life battery market, facilitating the development of strategies to optimize resource utilization and sustainability in the EV industry. Finally, the second-life battery price index is introduced, increasing the transparency of prices for lithium-ion batteries and the circular economy.

Charging Ahead- Addressing Key Barriers to Electric Vehicle Market Penetration in India

The transition to electric vehicles (EVs) represents a pivotal step towards sustainable transportation, particularly in the context of India's burgeoning population and rapid urbanization. Despite significant potential, the widespread adoption of EVs in India is hampered by various barriers, including infrastructure limitations, policy gaps, high upfront costs, and consumer perceptions. This research paper aims to analyze these key barriers and propose strategies to accelerate the market penetration of EVs in India. Through a comprehensive literature review, policy analysis, and examination of case studies, this paper identifies the challenges hindering EV adoption and offers recommendations for addressing them. Proposed strategies include infrastructure development, policy interventions, consumer awareness initiatives, and technological innovations. By implementing these recommendations, India can overcome barriers to EV adoption and realize the environmental and economic benefits associated with clean mobility.

Locating and sizing charging station in multi-period to promote electric vehicles adoption in urban areas

The layout of public fast charging stations (CSs) often fails to meet the charging demand of electric vehicle (EV) users, resulting in charging anxiety. To address this issue and promote the widespread use of EVs in urban areas, this paper presents a multi-period locating and sizing optimization model under dynamic demand. The model incorporates GPS trajectory extraction data to identify potential charging demand points and considers the mobile load characteristics of EVs for fusion modeling. A multi-period change formula is proposed to reflect the interaction between public fast CSs and EV market penetration, incorporating charging opportunity and waiting time satisfaction as measures of charging service quality. To solve the model, a heuristic algorithm combining genetic algorithm (GA) and k-medoids clustering algorithm is designed. We apply the model to an actual case of Shenzhen and find that increasing investment in charging infrastructure can boost EV market penetration in the later planning period. Moreover, the gap between charging opportunity and waiting time satisfaction tends to narrow as the planning progresses. Our findings highlight the importance of optimizing the location and size of public fast CSs to improve the charging service quality and promote the adoption of EVs.

Unsymmetrical Dual-Input Isolated High Gain Bidirectional DC–DC Converter With Inherent Current Sharing for Light EV Applications

In hybrid Electric Vehicles (EVs), multi-input DC-DC converters with different sources of energy storage are preferred to enhance the EV market penetration. The proposed new topology is a modified SEPIC converter with integrated boost cell, capable to handle the battery and ultracapacitor with different terminal voltage. It is also having single source transition capability. This multi-input DC-DC converter provides the high voltage gain, input-output isolation and bidirectional power flow feature. The essential of regeneration in the EV is demonstrated to achieve the better energy utilization. The current sharing between the energy storage systems (battery and ultracapacitor (UC)) is inherently provided by the topological structure of the proposed converter. Hence, it eliminates inner current control loop requirement. Accordingly, cost and control complexity is minimized with improved storage utility. Therefore, the proposed converter’s functional capabilities are suitable for battery/UC based Light EV applications. The operating performance is evaluated on a 250 W hardware prototype to validate the functional capability of the proposed converter.

Risk Assessment Framework for Electric Vehicle Charging Station Development in the United States as an Ancillary Service

Promoting the accelerated adoption of electric vehicles (EVs) in the United States (US) is one of the main strategies for reducing risk related to climate change. However, the lack of public charging stations (EVCSs) in the US has been identified as a grave obstacle to EV market penetration. The US Federal Government is providing extensive financial incentives to promote EVCSs. This allows diverse businesses to offer EV charging as an ancillary service, without the risks associated with traditional fuel facilities. Locations offering these novel services will reduce their financial operational risks, increase customer traffic and receive additional revenue. However, selecting unsuitable equipment for particular business segments and locations creates a severe risk of underuse and disrepair, leading to the potential failure of these new projects. Furthermore, these unsuccessful EVCSs exacerbate consumer reluctance to EV adoption and foster social opposition to this new technology. This study provides stakeholders with a framework for the optimal placement of EVCSs to maximize their successful deployment and incentivize continuous growth in the EV market. It identifies risk factors related to the placement and operation of EVCSs, aiding in optimal equipment selection for each location. Results from this study highlight EVCS location trends based on location and type of business, with the potential for some retrofitting projects. This framework provides relevant geospatial results for business owners, policy makers, consumers and other stakeholders in the adequacy of new charging infrastructure.

Green premium modeling based on total cost ownership analysis: From the Chinese Electric Vehicle sales forecasting perspective

In the field of energy economics, the concept of “green premium” refers to the cost difference between zero-emission or emission-reducing technologies and emission-emitting technologies. This cost difference plays a crucial role in the adoption and commercialization of renewable energy technologies, which is essential for addressing climate change. However, the green premium, as an abstract concept, is often used qualitatively, and research on quantifying the green premium is very limited. This has significant adverse effects on both the formulation of business strategies for companies and the planning of government policies. The Total Cost of Ownership (TCO) method is a widely used cost analysis approach in businesses and organizations. It can quantitatively assess and understand the overall costs associated with specific products, assets, or services, thereby assisting decision-makers in making informed choices and managing resources. Therefore, this study introduces, for the first time, a TCO-based green premium modeling model to address the challenge of quantifying the green premium. Considering that China’s Electric Vehicles (EVs) industry has successfully overcome the green premium barrier and entered the commercialization phase, it is an appropriate domain for studying the green premium. To further demonstrate the effectiveness and scalability of the modeling, this paper enhances the classic Bass model for sales forecasting of electric vehicles. It innovatively incorporates the influence of the green premium in the generalized Bass model. Additionally, the study uses primary data from industry research to construct a multi-factor green premium model for EVs based on TCO analysis. It also develops a generalized Bass diffusion model that considers the green premium and selects time-series data on EV diffusion in the Chinese market from 2010 to 2021. A genetic algorithm is employed to fit the model parameters, and different scenarios are considered to forecast EV market penetration over the next decade. Experimental results validate that incorporating the green premium in the generalized Bass model leads to better forecasting performance compared to methods that do not consider the premium which proved these related green premium modeling approaches exhibit good accuracy and scalability.

Predicting the Market Penetration Rate of China’s Electric Vehicles Based on a Grey Buffer Operator Approach

On the decision of whether to continue to implement the industrial support policy, two scenarios are set to predict the market penetration rate of China’s electric vehicles (EVs) (In this paper, the term Electric Vehicles (EVs) refers to both full-battery EVs and plug-in hybrids). In order to weaken the disturbance caused by international oil prices and industrial policies, the grey buffer operator was firstly applied, to preprocess the original data series. The sales data for EVs and fuel vehicles were buffered for second order and first order, respectively. Based on the obtained buffer data sequence, the GM (1, 1) model was used to predict the sales of EVs and fuel vehicles between 2022 and 2025 in China. The results demonstrate a significantly improved fit compared to directly modeling the raw data. This method is suitable for studying the market penetration rate prediction of China’s EVs. If the industry support policies continue (Scenario I), an EV market penetration rate of 22.45% can be achieved in 2024, and the expected target can be achieved one year ahead of schedule. Even if the corresponding industrial support policies are no longer implemented (Scenario II), the EV market penetration rate will reach 20.58% in 2025, and the set target of 20% will be achieved on schedule.

Driving Cycle-Based Energy Management Strategy Development for Range-Extended Electric Vehicles

<div>Environmental concerns and technological progress push the development and market penetration of electric vehicles (EVs) and hybrid electric vehicles (HEVs). On the other hand, transportation systems are becoming more efficient by improved communication systems within vehicles and between vehicles and infrastructure. In this study, a driving cycle-based energy management strategy is developed for range-extended electric vehicles (REEVs) to increase system efficiency and equivalent vehicle range. A validated vehicle model is developed by critical subsystem testing and a comparative study is conducted to assess the developed strategy. The results showed that the optimized strategy can save CO<sub>2</sub> emission by 6.21%, 1.77%, and 0.58% for heavy, moderate, and light traffic, respectively. Furthermore, the efficient use of a range extender (REx), guided by traffic data, extends the vehicle range, especially in heavy traffic conditions.</div>

Anticipatory planning for equitable and productive curbside electric vehicle charging stations

Two challenges to planning public electric vehicle (EV) charging networks remain in U.S. cities, including uneven productivity of charging stations and inequitable charging access across user groups. The unpredictable EV market penetration over the long term further complicates the relevant infrastructure planning. However, the extant planning approaches are limited in addressing both challenges simultaneously when considering future uncertainties. Therefore, we propose a data-driven anticipatory framework to plan for EV charging station allocation near urban amenities based on charging-while-parking behavioral patterns. We focus on two user groups, i.e., multi-family and single-family residents. We compare the productivity-equity outcomes of allocation scenarios under three planning strategies and four possible ratios between both user groups. The framework addresses the incremental charging demands at different market levels for each scenario. An in-depth case study of Alachua County, FL, shows that over-emphasizing multi-family charging demands when placing EV charging stations may undermine their overall productivity. We then suggest three pathways to balance equitable access and optimized productivity for the community based on the comparison of planning scenarios. The proposed framework is generalizable to other EV-initiating communities. This study sheds light on future-oriented adaptive planning for transportation infrastructure during the energy transition.

Clustering-based optimal operation of charging stations under high penetration of electric vehicles

With the rapid growth of technologies related to electric vehicles (EVs), the market penetration of EVs has increased significantly in recent years and has resulted in an increase in the charging load demand for charging stations. In addition, the uncertainty in EVs’ schedules introduces various difficulties for optimal operation of charging stations. Operation of a charging station with consideration of individual EV behaviors may result in computational complexity and lead to infeasibility. Therefore, this study proposes an operational strategy to reduce the computational burden while maintaining EV schedules for charging stations under high penetration of EVs. The proposed method consists of two major stages, i.e., clustering of EVs and optimizing the operation of each cluster. In the first stage, EVs with similar attributes and behaviors are grouped into different clusters, where EVs in the same cluster share similar schedules and operational constraints. This significantly reduces the computational burden as well as the complexity of optimization modeling. In the second stage, instead of considering the behavior of hundreds of EVs, the optimal operation of each EV cluster is considered. In addition, the proposed method allows cooperative operation among EV clusters via vehicle-to-vehicle (V2V) services. The proposed cooperative operation strategy not only reduces the imported power from the utility grid during peak price intervals, but also significantly reduces the amount of energy storage required in the charging stations. This results in the minimization of the investment and operation costs of the entire system. Finally, the effectiveness of the proposed method is verified for an EV charging station with over 180 EVs.

Strategies of Practical Implementation of Chemical Pre-Lithiation Using Lithium Arene Complex Solutions: A Systematic Study on Silicon-Based Anodes

Currently, Lithium ion batteries (LIBs) are state-of-the-art (SOTA) energy storage systems. There is an urgent need for further improvements in terms of gravimetric and volumetric energy of LIBs, for a successful market penetration of electric vehicles. Therefore, development of advanced negative electrode materials is of high interest. Here, silicon (Si) is a promising active material to replace SOTA graphite due to its ~10-fold higher specific capacity and being also high abundant.However, Si undergoes severe volume changes up to 280% during (de-)lithiation, resulting in fast capacity fading and short cycle life due to the continuous re-formation of the solid electrolyte interphase (SEI), leading to active lithium losses (ALL). In order to counteract ALL, research is focusing on suitable pre-lithiation processes. Among different pre-lithiation methods, chemical pre-lithiation by application of lithium arene complex (LAC) solutions is promising as it is a fast, easy and cost-effective method.In this work, the stability of three different solvents to produce a 4,4’-dimethylbiphenyl (4,4’-DMBP) LAC is investigated via solid phase microextraction gas chromatography-mass spectrometry method (SPME-GC-MS). The optimized LAC solution is used to evaluate the stability of different binder systems with respect to electrode manufacturing. Based on these fundamental insights, different parameters such as the reaction temperature (T) and pre-lithiation time (PL-t) are systematically investigated regarding suitable degrees of pre-lithiation (DOPL) of the negative electrode. DOPLs up to a plateau of 55% can be obtained by using 0.5 M 4,4’-DMBP in tetrahydrofuran (THF) as LAC solution. Higher temperature during the reaction reduces the PL-t until achieving a pre-lithiation plateau. The impact of the parameters T and PL-t towards the electrochemical performance of Si-based LIB full cells is thoroughly investigated. It is shown that this approach improves the cycle life of a silicon nanowire graphite composite negative electrode up to 600% compared to the pristine electrodes

An agent-based model for regional market penetration of electric vehicles in Brazil

Electrified vehicle (EV) are increasingly being adopted worldwide, making them promising elements in the decarbonisation process of the automotive sector. However, forecasting EV demand presents a significant challenge for policymakers worldwide. Our study proposes an agent-based simulation model that aggregates the behaviour of individual agents and generates cumulative adoption numbers through the Bass diffusion model. This approach enables using different parameters and extends previous research on actions to improve EV adoption rates in Brazil. Our investigation of the market introduction of EV using a multi-year dataset of EV buyers and marketed vehicle models reveals that acquisition cost is the primary driver of the adoption process. Still, barriers to electric mobility are also associated with vehicle-specific factors such as range, battery capacity, maintenance cost, recharging time, acquisition cost and number of EV sold. These findings emphasise the need to develop more effective policies and strategies for promoting EV adoption in Brazil, considering the factors that affect consumer behaviour and the adoption process. Doing so can accelerate the transition towards a more sustainable transportation system and reduce carbon emissions in the automotive sector.

The impact of electric vehicle charging infrastructure on the energy demand of a city

With the increased market penetration of electric vehicles (EVs), energy consumption is growing at a quicker rate. Electric vehicles are slowly but surely gaining a foothold in India’s rapidly expanding market. With more EVs on the road, there will be more demand for charging and more energy use in some places, which can put a lot of stress on the power grid. In the absence of sufficient planning, there is a risk of power system interruption and failure. Therefore, the need for energy in cities will shift in light of the advent of EVs in the next decade or two. Accordingly, Surat has been chosen as the research region for the planning of charging infrastructure in the current study. After reviewing the literature, primary data was acquired to estimate citizen travel demands. Vehicle registration statistics were compiled from the Regional Transport Office (RTO) for the purpose of EV forecasting. After collecting data, the EV population and demand for public charging stations by 2025 and 2030 were calculated. This study aims to meet the public charging demand in the city of Surat by analyzing the electric vehicle growth rate and vehicle forecast. This anticipated data was then utilized to compute the energy demand consumption for Surat city by calculating vehicle kilometers traveled. The demand for public charging comes out to be 42.4 MWh/day in 2025 and 358.05 MWh/day in 2030, respectively. The study’s objective is to determine energy demand per region in order to better understand grid planning due to the imbalance in electricity demand.

Assessment of Selected Determinants Affecting the Acceptance of the Development of Electromobility by the Private and Business Sectors—A Case Study in Portugal

The energy transition requires widespread electrification of the transport sector. To promote the penetration of electric vehicles (EVs), it is essential to understand consumers’ perceptions and behavior, particularly regarding the main determinants of EV purchase and the acceptance of electric mobility (EM). With this aim, we focused on an industrialized city in Portugal, addressing the differences between the effective ownership of an EV and the acceptability of EM and between the domestic sector (DS) and the business sector (BS) through questionnaires. Our results indicate that sociodemographic variables are the main determinants of the purchase of EVs and the acceptance of EM in the DS. Men and higher income individuals are more likely to own an EV. On the other hand, younger generations are more likely to have high EM acceptance. Individuals who already own an EV are the ones that have the desire and economic means to do so, regardless of any incentives. Still, widespread market penetration of EVs requires incentives for individuals who desire to own one of these vehicles but do not have the economic power to do so. Additionally, the DS and the BS behave differently; hence, specially designed policies are needed.

Price Prediction and Feature Importance Analysis of German Electric Vehicles Based on Boosted Decision Tree Model

As people realize the importance of non-renewable energy sources, more and more industries are looking for alternative sources of energy that can be used. The automotive industry is a prime example. Electric vehicles are having an increasingly dominant role in the automotive market. This paper investigates the price of electric vehicles (EVs) and the importance of consumers' characteristics for electric vehicles in the European region. In addition, feature importance is discussed with disordered data in selecting the best regression model. Finally, the Boosted decision tree model predicts future electric vehicle prices in the German region. The results show that consumers place more importance on the size of the car, the battery's durability, and acceleration. Although the penetration of electric vehicles in Germany is still tiny, government incentives for electric vehicle companies and consumers will increase the market penetration of electric vehicles. So we believe that in the future, people will be able to buy good-performing electric cars at more affordable prices.

An agent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale

As the market penetration of electric vehicles (EVs) increases, the surge of charging demand could potentially overload the power grid and disrupt infrastructure planning. Hence, an efficient deployment strategy of electrical vehicle supply equipment (EVSE) is much needed. This study attempts to address the EVSE problem from a microscopic perspective by formulating the problem in two steps: public charging demand simulation and charging station location optimization. Specifically, we apply agent-based modeling approach to produce high-resolution daily driving profiles within an urban-scale context using MATSim. Subsequently, we perform EV assignment based on socioeconomic attributes to determine EV adopters. Energy consumption model and public charging rule are specified for generating synthetic public charging demand and such demand is validated against real-world public charging records to guarantee the robustness of simulation results. In the second step, we apply a location approach – capacitated maximal coverage location problem (CMCLP) model – to reallocate existing charging stations with the objective of maximizing the coverage of total charging demands generated from the previous step under the budget and load capacity constraints. The entire framework is capable of modeling the spatiotemporal distribution of public charging demand in a bottom-up fashion, and provide practical support for future public EVSE installation.

Energy transition scenarios in the transportation sector in Brazil: Contributions from the electrical mobility

During COP 21, the Paris Declaration on Electro-Mobility and Climate Change & Call to Action defined the global goal of reaching 100 million electric vehicles (EV) and 400 million two- or three-wheel vehicles by 2030. As a result, several countries have set national targets for implementing EV as an integral part of low-carbon energy transition. In Brazil, the technological route of biofuels has stood out from the National Alcohol Program (PROALCOOL) in the 1970s to the current National Biofuels Policy (RenovaBio). However, in recent years, the EV market has been gaining a prominent role in business, governmental and academic debates. Thus, this study seeks to analyse the main factors that drive electric mobility in Brazil, from the definition of technical, economic and fiscal parameters, in addition to evaluating hypotheses on the evolution of public policies and prices of EV in the country. Some scenarios were developed to identify the behaviour of the market penetration of EV in the country, based on Bass' technological diffusion model and dynamic systems. Results indicate that the EV purchase price is consolidated as the most relevant variable for the diffusion of electric mobility in Brazil. From the gains derived from economies of scale, a reduction in the price of vehicles is expected given the decrease in the production costs, as well as in price of batteries. We also concluded that the establishment of a regulatory framework and the development of public policies are fundamental to the promotion of electric mobility in the country.

Retired Lithium-Ion Battery Pack Disassembly Line Balancing Based on Precedence Graph Using a Hybrid Genetic-Firework Algorithm for Remanufacturing

Abstract Electric vehicle production is subjected to high manufacturing cost and environmental impact. Disassembling and remanufacturing the lithium-ion power packs can highly promote electric vehicle market penetration by procuring and regrouping reusable modules as stationary energy storage devices and cut life-cycle cost and environmental impact. Disassembly efficiency is crucial for battery remanufacturing companies in reverse supply chains. However, disassembly planning suffers from high computational complexity and inferior solutions. This paper developed a multi-objective mathematical model and presented a novel hybrid genetic-firework algorithm based on the precedence graph for obtaining solutions to disassemble the electric vehicle power pack into module levels in an efficient manner. The objectives for the model include not only smoothness of working stations, cycle time, and economic returns, but also consider operation safety and energy consumption. The proposed hybrid algorithm explored the performance of the novel solution searching mechanism of combining the firework and genetic algorithms. The proposed approach is compared with the commonly used multi-objective evolutionary algorithms in the literature, showing its feasibility and effectiveness.

Integrated cooling/HVAC system design and control strategy for reconfigurable light electric vehicle

In the recent panorama, the use of conventional passenger vehicles has been discouraged from European level up to local municipalities. “Reconfigurable light electric vehicle” REFLECTIVE project targets at developing a light, fully electric vehicle capable of performing safely, efficiently, and multifacetedly in urban environments. By this way, it would be possible to fill the gap with the final user perception and electric vehicles market penetration. Moreover, in order to improve occupants’ comfort, HVAC system has been considered as well, a feature which is not usually implemented in such a kind of vehicles. This paper will show results obtained for an integrated solution for cooling/HVAC circuits in terms of energy efficiency and occupants’ comfort, with particular focus on proper battery operation. In fact, the battery thermal management is integrated into the HVAC circuit. The integrated circuit will take care of components’ cooling so to guarantee proper operating conditions, HVAC for passenger comfort and battery thermal management so to assure proper operating conditions at the battery itself throughout different scenarios. Finally, the effectiveness of the control logic specifically developed for such an integrated circuit (HVAC/Cooling/Battery) will be shown in a urban driving cycle during winter season, so to maximize the heating loads both at powertrain and battery, with always assuring comfort conditions into the cabin.

Electrical architecture for ultrafast charging station

EVs will play an important role in transport, climate change and electrification. However, there is a consensus that one of the main obstacles to full market penetration of EVs is the so-called range anxiety (fear of not having enough charge in the vehicle). Electric vehicle charging infrastructures have two opposing approaches to their use. For urban routes, where distances travelled, and EV usage times are low, on-board alternative current (AC) chargers (Level 1 and 2) can meet the charging requirements in most situations. The solution is a gradual increase of the existing electrical infrastructure and charging points, together with the integration of new energy sources to cope with the new loads. On the other hand, on highway routes, EVs require large amount of energy in a short time in order to provide a continuous trip. Nowadays, electric charging stations provide fast charging points for vehicles by connecting external chargers that supply direct current (DC) directly to the battery bank (Level 3) and these can take up to 30 minutes. However, a time of more than 10 minutes is considered to be a very long time in contrast to the time required to fill the tank of internal combustion vehicles. In this context, ultrafast charging is defined with charging time to 10 minutes or less. This report proposes the electrical architecture of an ultrafast charging station for electric vehicles (EVs), which is based on a hybrid energy distribution system made up of an AC bus and two DC buses, each one of different voltage. The proposal is the result of a statistical analysis based on Monte Carlo simulations, which uses real traffic data and stochastic models to estimate the number of vehicles, the energy required and the time to charge each EV that would access the station. Once the architecture is established, the operating modes of the station are defined, where a storage system is contemplated to provide service reliability, and which is sized following load levelling and load shifting strategies with static and proposed dynamic charge counting methods. As a result of the report, an event simulation incorporating the operating modes and a 500 kWh storage system to serve 100 EVs has been performed, in which case 99% service was achieved even under shutoff of the grid.