Diffusion Of Electric Vehicles Research Articles

Mapping the technology diffusion of battery electric vehicle based on patent analysis: A perspective of global innovation systems

Battery electric vehicles (BEVs) have been in the fourth wave of the low emission vehicle development since 2006. Moreover, the BEV technology diffusion is beyond national boundaries due to the changes in the innovation mode and product characteristics. The paper adopts priority patent applications and international patent families to map the BEV transnational technology diffusion from the perspective of global technology innovation (GTI). Our study finds the sources and recipients of BEV technology are concentrated in a few countries or regions, while the transnational technology diffusion is uneven, especially in the core group. The key drivers of technology diffusion are the innovation capacity of countries and the scale of countries’ market demand for BEV technology. Our findings enrich the literature on transnational technology diffusion and help policy-makers or company managers better understanding of why some countries may achieve success while others made little progress in the similar context of promoting BEV development.

Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design

The future Autonomous Vehicles (AVs) are likely to be electric. We started with a review of the adoption of AVs, Electric Vehicles (EVs) and Autonomous Electric Vehicles (AEVs), as well as the six associated urban sub-systems, namely transportation, land use, environment, energy, economy, and population systems, in order to find evidence about the linkages and interactions between the diffusion of AEVs and the six sub-systems. Based on the review, we argued that an integrated urban model, which takes the linkages and interactions into account, was needed to fully understand the adoption and impacts of AEVs. Furthermore, we conducted a conceptual design of an integrated model for AEVs (without explicit modelling), and demonstrated how to update an existing agent-based Land Use and Transport Interaction (LUTI) model by incorporating AEV components. The resulting integrated model of AEVs would help different AEV-related stakeholders (e.g., local authorities) in their decision-making.

Electric vehicles diffusion: changing pavement acoustic design?

Abstract Electric vehicles (EVs) are progressively entering into the current noisy urban ecosystem. Even though EVs are apparently quieter than traditional Internal Combustion Engine Vehicles (ICEVs), they have an impact on noise maps and road pavement designers should take this into consideration when designing future low-noise road pavements. Consequently, the main objective of this study is to define what are the most important aspects that road pavement designers should take into account. For this reason, in this paper, the noise emitted by EVs was analysed, considering parameters (e.g., speed and frequency) and comparisons, in order to identify crucial characteristics. Results show that EV noise could call for the improvement of pavement acoustic design due to the Acoustic Vehicle Alerting System (AVAS), high-frequency peaks, and noise vibration harshness.

A techno-economic evaluation of the impact of electric vehicles diffusion on Italian customer billing tariffs

The sales of electric vehicles (EV) are recently experiencing an exponential growth. There is scientific evidence that EVs, in their lifecycle, generate a lower amount of greenhouse gases emissions and local pollution with respect to ICE ones. However, a fast EVs diffusion will increase the load on the power system in a still unclear manner. It is recognized that EV charging is a power intensive operation and that dumb charging strategies will increase security problems for grid management. This will likely happen at the end of business, when a huge quantity of EVs gets connected at the domestic socket at the same time, thus contributing to the ramp up needs from thermal generators to cope with the increasing slope in the “duck curve” at sunset. The presented model estimates the burden that different EV penetration scenarios will have on Italian power system in a short term (2025). This is evaluated in terms of energy flows and load profiles for the charging operations. A behavioural analysis of EV charging operations and a thorough investigation of Italian electricity tariff allows to calculate the economic impact of a base and a strategically regulated scenario. In the regulated scenario, featuring a regulatory toolbox to steer EVs diffusion efficiently, the impact on the power system is mitigated even in case of a larger EV penetration.

Public Charging Infrastructure and Electric Vehicles in Norway

We study whether public charging infrastructure drives battery electric vehicle adoption. Our analysis is based on granular, annual information on the location of public charging infrastructure and the battery electric vehicle ownership rate across 356 Norwegian LAU-2 municipalities between 2009 and 2019. We focus on areas in which the first public charging infrastructure was installed in this time period. In these mostly rural areas, the establishment of a first public charging station initiated adoption. We find, on average, an increase of the local electric vehicle ownership rate by 1.5 percentage points or 200% over 5 years. Our results are robust to anticipatory effects. They also remain unaffected from different treatment thresholds: the median number of public chargers in a municipality between 2009 and 2019 or the median density of public charging points per 1,000 inhabitants in the same time frame. While we cannot fully rule out reverse effects, we identify public charging infrastructure to serve as a stimulus to the diffusion of battery electric vehicles.

The Role of Government in the Market for Electric Vehicles: Evidence from China

Governments in many countries have offered various incentives to promote the diffusion of electric vehicles (EVs).This study examines the effectiveness of various policy measures that underlie the rapid development of the EV market in China, by far the world's largest such market. The analysis is based on detailed data on EV sales, local and central government incentive programs, and charging stations in 150 cities from 2015 to 2018. The empirical framework addresses the potential endogeneity of key variables, such as local policies and charging infrastructure, by using a city-border-regression design and instrumental variable approach. We find that consumer subsidies for vehicle purchases accounted for more than half of EV sales in China. Nevertheless, investments in charging infrastructure were much more cost-effective than consumer subsidies. An inexpensive policy that merely provided EVs with a distinctive, green license plate was strikingly effective. These findings demonstrate the varying efficacy of different policy instruments and highlight the critical role of the government in promoting clean technologies.

How has external knowledge contributed to lithium-ion batteries for the energy transition?

SummaryInnovation in clean-energy technologies is central toward a net-zero energy system. One key determinant of technological innovation is the integration of external knowledge, i.e., knowledge spillovers. However, extant work does not explain how individual spillovers come about: the mechanisms and enablers of these spillovers. We ask how knowledge from other technologies, sectors, or scientific disciplines is integrated into the innovation process in an important technology for a net-zero future: lithium-ion batteries (LIBs), based on a qualitative case study using extant literature and an elite interview campaign with key inventors in the LIB field and R&D/industry experts. We identify the breakthrough innovations in LIBs, discuss the extent to which breakthrough innovations—plus a few others—have resulted from spillovers, and identify different mechanisms and enablers underlying these spillovers, which can be leveraged by policymakers and R&D managers who are interested in facilitating spillovers in LIBs and other clean-energy technologies.

Improving the understanding of electric vehicle technology and policy diffusion across countries

The transport sector is particularly difficult to decarbonize. Use of electric vehicles (EV)—a potentially transformative and sustainable transport technology—can reduce greenhouse gas emissions, domestic fossil fuel demand, energy import dependency, and air pollution. Policies play an important role in the diffusion of new technologies, such as EVs, principally in their formative stage as they compete with an incumbent technology. However, great discrepancies exist across countries regarding EV support and uptake.EV diffusion is conceptualized as an outcome of policy diffusion based on national characteristics and international mechanisms. This study aims to explain the variation in EV policy diffusion across countries, by conducting an event history analysis on EV diffusion (EVs > 1% market share) between 2010 and 2017, using a sample of 60 countries. It identifies characteristics and mechanisms relevant to the novel technology's “formative phase”, focusing on the formation of state goals, international diffusion, and local technology adoption and deployment. The empirical contribution lies in identifying and validating socioeconomic and political factors and the international mechanisms influencing a country's position on the diffusion curve. This can help improve scenarios via better reflecting EV diffusion.

Spatial dimensions of electric mobility—Scenarios for efficient and fair diffusion of electric vehicles in the Milan Urban Region

The spread of Electric Vehicles (EVs) and the diffusion of the digital sharing mobility service are conditions capable of producing significant impacts on the urban environment and mobility practices. Literature mainly focuses on the technical, safety, regulatory and commercial aspects of Electric Vehicles; less attention has been paid to the contextual conditions (settlements, endowments, mobility practices), individual preferences, lifestyle and attitudinal factors, able to guarantee an efficient and fair diffusion of electric mobility in terms of use of the energy and urban resources, as well as access to urban opportunities. Based on this, the paper investigates the spatial pattern of potential demand for electric vehicles in the Milan Urban Region (North Italy), and its relevance in defining diversified and site-based EV policies for promoting a fair transition towards low carbon mobility. For analysing the relationships between electric mobility, local socioeconomic and settlement features and mobility practices, a multicriteria analysis has been carried out, processing variables that describe the contextual conditions and the propensity towards the adoption of EVs. The empirical application offers four scenarios for identifying different intensities, modes and speeds in the diffusion of EVs, in order to orient policy-making processes to support a sustainable mobility transition.

Fast Modelling and Identification of Hydraulic Brake Plants for Automotive Applications

Diffusion of electric and hybrid vehicles is accelerating the development of innovative braking technologies. Calibration of accurate models of a hydraulic brake plant involves availability of large amount of data whose acquisition is expensive and time consuming. Also, for some applications, such as vehicle simulators and hardware in the loop test rig, a real-time implementation is required. To avoid excessive computational loads, usage of simplified parametric models is almost mandatory. In this work, authors propose a simplified functional approach to identify and simulate the response of a generic hydraulic plant with a limited number of experimental tests. To reproduce complex nonlinear behaviours that are difficult to be reproduced with simplified models, piecewise transfer functions with scheduled poles are proposed. This innovative solution has been successfully applied for the identification of the brake plant of an existing vehicle, a Siemens prototype of instrumented vehicle called SimRod, demonstrating the feasibility of proposed method.

Economic and environmental sustainability of Dynamic Wireless Power Transfer for electric vehicles supporting reduction of local air pollutant emissions

In recent years, Dynamic Wireless Power Transfer (DWPT) technology has gained interest within the market of charging systems for electric vehicles. This technology can potentially boost the diffusion of electric vehicles (EVs) and consequently contribute in reducing the local air pollution and the related externalities.In this context, an environmental and economic analysis has been performed to estimate the social costs saving derived by the decarbonization of the passenger cars mobility. The benefits evaluated in the analysis are compared to the investment and maintenance costs necessary to install and operate a DWPT infrastructure. This under the basic assumption of the present work that considers the widespread adoption of electric vehicles achievable only in presence of the parallel integration of DWPT systems in the motorway infrastructure. At the same time, the work takes into account the possible variations of the energy mix and the effects related to the increase of the electric energy demand related to the increase of the circulating electric vehicles.The analysis is carried out for the six most populous European countries (i.e. Italy, France, Germany, Poland, Spain and UK) and it indicates as the estimated social costs savings are capable to sustain the investment for a wide integration of DWPT in almost all these countries.

Home charging and electric vehicle diffusion: Agent-based simulation using choice-based conjoint data

Electric vehicles offer the potential of climate-friendly driving and have received more and more attention in diffusion research recently. However, the effect of the availability of home charging options on the market potential of electric vehicles has not been targeted in detail in the diffusion process so far, although it represents a beneficial differentiation to conventional vehicles. Therefore, we present an agent-based simulation to address the buying behavior of German consumers regarding electric and plug-in hybrid electric vehicles using empirical data primarily derived from a choice-based conjoint study. We explicitly consider individual possibilities of home charging in our model, which appears to have an important influence on the diffusion of both, electric and plug-in hybrid electric vehicles. However, its importance decreases with faster charging times at public charging stations. Furthermore, our findings reveal that in the longer term, technological progress in charging time, range, and charging station density of electric vehicles is presumably cannibalizing plug-in hybrid electric vehicles’ market shares more than that of conventional vehicles. However, with still lower technological capabilities of electric vehicles, a governmental subsidy can initially promote plug-in hybrid electric vehicles, but electric vehicles will benefit later from that promotional effect.

Diffusion of electric vehicles in Brazil from the stakeholders' perspective

A major challenge for humankind is to reduce global carbon dioxide (CO2) emissions to avoid the most harmful impacts of climate change. The transport sector is responsible for almost 1/4 of the world’s energy-related emissions, with road transportation representing around 1/5 of the fuel consumption. Electric vehicles (EVs) may help to reduce CO2 emissions, but their diffusion is uncertain due to market barriers. This paper investigates the electric vehicle (EV), which has a smaller ecological footprint, as an alternative capable of meeting the needs of personal transport in Brazil. To carry out this evaluation, the study considers socio-techno-economic and political-environmental-innovation aspects to assess the challenges and opportunities facing the adoption of electric vehicles. The analysis was carried out based on a survey administered to a wide range of stakeholders. A SWOT analysis was performed to understand the most critical factors affecting the future of EVs in Brazil, which is a top-ten automotive manufacturer worldwide and a leader in sugar cane ethanol production. The results suggest light-duty electric vehicles (LDEV) as a first option for low-carbon passengers’ mobility and that EVs expansion will require market regulation, incentive policies and adequate charging infrastructure. Both consumers and society in general will benefit most from the expansion of EVs due to low emissions and total cost of ownership (TCO). The results show where decision-makers should focus their attention.

The Planning Elements of the Sea Port City Regeneration Applying Smart

Purpose: The Smart City Project aims to secure the sustainability of cities with many social problems and to improve the lives of urban residents. However, the implementation tasks and methods for realizing a smart city may vary depending on the situation of each city. If you can determine the priority and appropriate methods of each project in the smart city project, you can increase the chances of smart city implementation success. In this paper, we proposed a decision model based on AHP (Analytical Hierarchy Process) that can identify the priority of each tasks that can be included in the smart city project and the appropriate methods for driving forward each task. The proposed decision model was applied in consideration of the situation of Jeju Smart City and suggested implications through expert surveys and analysis. Research design, data and methodology: The proposed decision model is designed to apply the AHP and has a structure including three-tier factors. In the proposed decision model, the first tier corresponds to the driving area of the smart city, the second tier corresponds to the driving task, and the third tier corresponds to the deriving method. The proposed decision model was applied to the Jeju Smart City project. In the Jeju Smart City decision model, evaluation factors in each tier were derived through literature review and expert brainstorming. As an example of applying the decision model for Jeju Smart City, tourism, resident life, and industry were derived as the promotion areas included in the first tier. The promotion tasks included in the 2nd tier include electric vehicle diffusion, energy development, MaaS, safety, and garbage. Big data platform and block chain special zone were derived. As the promotion methods for each project, the government-led, private enterprise-led, join-up, and living labs were drawn. The proposed decision model can be customized for each city s situation. Conclusions: The most important driving area in Jeju Smart City was the area of resident life. The tasks of high importance were in the field of safety and garbage. As for the method of deriving tasks in the project, the proportion of the government (public) lead was high. The decision model proposed in this paper can be customized for other smart city projects and used.

Electric Vehicles Plug-In Duration Forecasting Using Machine Learning for Battery Optimization

The aging of rechargeable batteries, with its associated replacement costs, is one of the main issues limiting the diffusion of electric vehicles (EVs) as the future transportation infrastructure. An effective way to mitigate battery aging is to act on its charge cycles, more controllable than discharge ones, implementing so-called battery-aware charging protocols. Since one of the main factors affecting battery aging is its average state of charge (SOC), these protocols try to minimize the standby time, i.e., the time interval between the end of the actual charge and the moment when the EV is unplugged from the charging station. Doing so while still ensuring that the EV is fully charged when needed (in order to achieve a satisfying user experience) requires a “just-in-time” charging protocol, which completes exactly at the plug-out time. This type of protocol can only be achieved if an estimate of the expected plug-in duration is available. While many previous works have stressed the importance of having this estimate, they have either used straightforward forecasting methods, or assumed that the plug-in duration was directly indicated by the user, which could lead to sub-optimal results. In this paper, we evaluate the effectiveness of a more advanced forecasting based on machine learning (ML). With experiments on a public dataset containing data from domestic EV charge points, we show that a simple tree-based ML model, trained on each charge station based on its users’ behaviour, can reduce the forecasting error by up to 4× compared to the simple predictors used in previous works. This, in turn, leads to an improvement of up to 50% in a combined aging-quality of service metric.

Scenario structuring methodology for computer-aided scenario design: An application to envisioning sustainable futures

A number of thematic scenarios have been developed for envisioning sustainable futures in order to support strategic decision-making for government and industry. However, methodologies for designing scenarios have generally been under-theorized. For a better understanding and communication among stakeholders involved, this paper aims to propose the concept of computer-aided scenario design whose key characteristic is to provide a graphical representation of scenarios. That is, the paper proposes a method for representing the logical structure of a scenario by drawing the idea of graph theory. Based on this representation method, the paper dynamically connects a scenario with its associated simulators for undertaking what-if analysis in a quantitative manner. In an illustrative case study, the proposed method is applied to an extant scenario for hybrid electric vehicle diffusion. The results show that the method clarified the logical structure of the scenario and the rationales for deriving the scenario's conclusions. It was also demonstrated that the method enabled to create scenario variants when the original assumptions were changed. The proposed method would facilitate the iterative process of scenario design, thereby encouraging scientific dialog between researchers and stakeholders.

Agent-Based Modeling of Feeder-Level Electric Vehicle Diffusion for Distribution Planning

Faced with a rapidly growing electric vehicle (EV) load, distribution planners need a methodology to forecast when and where these loads will likely appear on specific distribution feeders. This paper proposes a new diffusion forecasting approach for residential EV and charging stations using agent-based modeling. Residential EV adoption is treated as a multi-criteria decision making problem modeled via analytic hierarchy process (AHP). The customer adoption model CANE is developed considering customer characteristics including car age, EV attractiveness valued using logistic regression, neighbor influences and customer economics. Distribution feeder topology is combined with property geographic information system (GIS) parcels and household travel survey data to determine geographic and electric locations for EV and charging stations. A Monte Carlo method is utilized to obtain the most likely outcome of the stochastic vehicle decision-making process and perform model calibration. The proposed diffusion forecasting approach is demonstrated using actual distribution feeder data. Using the diffusion results, EV impact on system annual peak, energy, losses, transformer aging and feeder upgrades is evaluated using quasi-static time-series power flow analysis. Case analyses are presented that examine the effect of EV price and charging station placement on EV diffusion and distribution feeder impact.

Public charging infrastructure and the market diffusion of electric vehicles

A comprehensive roll-out of public charging infrastructure will be costly. However, its impact on the diffusion of electric vehicles (EVs) is not clear. Our study aims at estimating the extent to which an increasing availability of public charging infrastructure promotes consumers decisions to switch to EVs. We make use of a German data set including monthly registrations of new cars at the ZIP-code level between 2012 and 2017 and match it with the official registry of charging stations. We measure charging infrastructure by its quantity, capacity and abundance in order to estimate its impact on EV adoption. A dynamic heterogeneous panel approach is deployed in order to identify the structural long-run relationship between charging infrastructure and monthly EV registrations. We find evidence of a positive long-run relationship (and also of a causal link) but on a rather low scale. We conclude that consumers do not necessarily react to the mere number of public chargers but attach more importance to charging speed and are to a large extent influenced by unobservable common factors.

Many Miles to Paris: A Sectoral Innovation System Analysis of the Transport Sector in Norway and Canada in Light of the Paris Agreement

Transport is associated with high amounts of energy consumed and greenhouse gases emitted. Most transport means operate using fossil fuels, creating the urgent need for a rapid transformation of the sector. In this research, we examine the transport systems of Norway and Canada, two countries with similar shares of greenhouse gas emissions from transport and powerful oil industries operating within their boundaries. Our socio-technical analysis, based on the Sectoral Innovation Systems approach, attempts to identify the elements enabling Norway to become one of the leaders in the diffusion of electric vehicles, as well as the differences pacing down progress in Canada. By utilising the System Failure framework to compare the two systems, bottlenecks hindering the decarbonisation of the two transport systems are identified. Results indicate that the effectiveness of Norway’s policy is exaggerated and has led to recent spillover effects towards green shipping. The activity of oil companies, regional and federal legislative disputes in Canada and the lack of sincere efforts from system actors to address challenges lead to non-drastic greenhouse gas emission reductions, despite significant policy efforts from both countries. Insights into the effectiveness of previously implemented policies and the evolution of the two sectoral systems can help draw lessons towards sustainable transport.

Application of Solid-State Transformers in a Novel Architecture of Hybrid AC/DC House Power Systems

The ongoing diffusion of solid-state DC/DC converters makes possible a partial migration of electric power systems from the present AC paradigm to a future DC scenario. In addition, the power demand in the domestic environment is expected to grow considerably, for example, due to the progressive diffusion of electric vehicles, induction cooking and heat pumps. To face this evolution, the paper introduces a novel electric topology for a hybrid AC/DC smart house, based on the solid-state transformer technology. The electric scheme, voltage levels and converters types are thoroughly discussed to better integrate the spread of electric appliances, which are frequently based on internal DC buses, within the present AC distribution networks. Voltage levels are determined to guarantee high safety zones with negligible electric risk in the most exposed areas of the house. At the same time, the developed control schemes assure high power quality (voltage stability in the case of both load variations and network perturbations), manage power flows and local resources according to ancillary services requirements and increase the domestic network overall efficiency. Dynamic simulations are performed, making use of DIgSILENT PowerFactory software, to demonstrate the feasibility of the proposed distribution scheme for next-generation smart houses under different operating conditions.