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Flying cars, essentially vertical takeoff and landing aircraft (VTOL), are an emerging, disruptive technology that is expected to reshape future transportation. VTOLs can be powered by battery electric, fuel cell, or internal combustion engine, which point to entirely different needs for industry expertise, research & development, supply chain, and infrastructure supports. A pre-analysis of the propulsion technology competition is crucial to avoid potential wrong directions of research, investment, and policy making efforts. In this study, we comprehensively examined the cost competitiveness of the three propulsion technologies. Here we show that battery electric has already become the lowest-cost option for below-200-km VTOL applications, covering intra-city and short-range inter-city travels. This cost advantage can be robustly strengthened in the long term under various technology development scenarios. Battery energy density improvement is the key to reducing cost. In particular, a 600 Wh/kg battery energy density provides battery electric with all-range cost advantage, and promises high return in business. Fuel cell and internal combustion engine, under certain technology development scenarios, can obtain cost advantage in long-range applications, but face intense competition from ground transportation such as high-speed rail. The findings suggest a battery-electric-prioritized VTOL development strategy, and the necessity of developing VTOL-customized high-energy-density batteries.

In this study, 98 regression models were specified for easily estimating shortest distances based on great circle distances along the U.S. interstate highways nationwide and for each of the continental 48 states. This allows transportation professionals to quickly generate distance, or even distance matrix, without expending significant efforts on complicated shortest path calculations. For simple usage by all professionals, all models are present in the simple linear regression form. Only one explanatory variable, the great circle distance, is considered to calculate the route distance. For each geographic scope (i.e., the national or one of the states), two different models were considered, with and without the intercept. Based on the adjusted R-squared, it was observed that models without intercepts generally have better fitness. All these models generally have good fitness with the linear regression relationship between the great circle distance and route distance. At the state level, significant variations in the slope coefficients between the state-level models were also observed. Furthermore, a preliminary analysis of the effect of highway density on this variation was conducted.

Recently in the July 6, 2021 issue of Environmental Science & Technology, Tong et al. estimated health and climate impacts from large-scale long-haul truck electrification. The monetized damages range from a 47%–54% increase to a 77%–88% reduction. However, with adjustments to the marginal emission and static assumptions, impacts are found to be almost certainly positive, a 7%–94% reduction in damages.

Air taxis are currently being demonstrated. Few studies have quantified their external effects in reducing on-road vehicle fuel consumption. The hypothesis of this paper is that air taxis may divert some drivers away from congested traffic corridors, improve traffic speed and fuel economy, and reduce congestion-induced energy consumption. A model is developed that links several key components: mode choice, the relationship between travel demand and traffic speeds, the relationship between traffic speeds and fuel economies, and the heterogenous value of travel time. It is applied to the route from downtown Los Angeles to Los Angeles International Airport, where at peak hours 38,200 vehicles attempt to use the route that has an hourly capacity of 17,200 vehicles. The model estimates that, with conservative assumptions and near-term technologies, diverting 3.2% of the traffic to air taxis could produce a 15% reduction in traffic vehicle fuel use. With optimistic assumptions and mature technologies, the study estimates that diverting 20% of traffic could reduce the traffic vehicle fuel use by about 74%. The key insight is that if a small share of congested travelers switched to air taxis, motivated by private benefits of time savings, significant external benefits for other road travelers (time savings and fuel savings) and to society (reduced energy use and emissions), would ensue creating a win-win-win outcome. These estimates (which are not intended as predictions because of the stated limitations) strongly suggest the need to consider the external energy effect in future cost-benefit analyses of air taxi technologies.

Incentives can influence travelers' mobility decisions and thus be used to indirectly redistribute traffic demands in space and in time to reduce congestion. This study aims to understand travelers' mobility decisions in response to customer incentives. An online questionnaire survey was conducted to obtain travelers' opinions and preferences on customer incentives. The results suggest that incentive types and travelers’ personal and social-economic attributes are coupled to affect their acceptance and rejection decisions. This study sheds light on using incentives to switch near-by travelers to potential business customers during peak hours, which ultimately contributes to congestion relief and economic prosperity.

This study explored the national hydrogen refueling infrastructure requirement along major United States (US) interstate highway corridors to support the deployment of fuel cell electric trucks (FCETs) for the national long-haul trucking fleet. Given the long-haul trucking shipment demand in 2025 projected by the Freight Analysis Framework, locations and capacities of hydrogen stations were identified for inter-zone freight flows, and the total daily refueling demand was estimated for intra-zone flows for each FAF zone. Based on the infrastructure deployment results, we conducted an economic feasibility analysis of FCETs by evaluating the total ownership cost. We found that when the FCET penetration is relatively high (e.g., 10% penetration), FCETs become more competitive in terms of fuel cost and idling cost and could be economic viable if the incremental vehicle cost is reduced to meet the near-term FCET technology cost targets and the liquefaction cost is reduced to an optimal case. We also observed that the station cost depends on regional factors, particularly regional demand, which is used to determine station capacity. Thus, one possible strategy for station roll-out is to have early investment in target regions where station costs are expected to be relatively low such as the Pacific and West South Central regions.

Range limitation is a significant obstacle to market acceptance of battery electric vehicles (BEVs). Range anxiety is exacerbated when drivers could not reliably predict the remaining battery range...

Given the explosive growth of the passenger vehicle market and energy demands in China, research on vehicle-use intensity and driver-travel patterns is critical for better assessing travel demand and its implications for alternative fuel vehicles, energy security, and environmental policies. This study attempts to estimate annual vehicle kilometers traveled (AVKT) per privately-owned passenger vehicle and their daily distance patterns by vehicle class and geographic region. The data sample from a survey consists of 169,292 privately owned passenger vehicles, made by 177 car manufacturers during 2003–2018, running in 82 cities from 27 provincial regions. The log-transformed average AVKT is estimated to be 12,377 km with 95% probability ranging from 5490 to 28,579 km. The investigation reveals that vehicles from South China have the highest AVKT at 13,320 km. Generally, vehicles in small cities have higher AVKT than in big cities, except AVKT of tier 1 cities being higher. Another trend is that more expensive or larger vehicles tend to be driven more. A model is fitted for estimating AVKT based on region, city type, automaker, price range, and certain vehicle features including class and age. Data of daily commuting distances in recent years are also analyzed. The average daily commuting distances typically range from 21 to 28 km. Using the validated Gamma distribution method, daily distance distributions are specified for different regions. It is found that 99% of the daily driving distance is no more than 88.0–112.0 km, depending on region. Utility factors of plug-in electric vehicles are also estimated to be much higher than those based on driving data in the USA. These findings suggest global mitigations strategies on vehicle fuel use, electrification, and greenhouse gases should consider vehicle-use intensity at the regional level.

The fast-growing light-duty plug-in electric vehicle (PEV) market in China has important implications for both the global vehicle market and energy policies. From the perspective of demand pull-supply push, this study examines China's PEV market by reviewing sales, product performance, and government policies from the last decade; and by comparing it with the market in the United States, the study attempts to generate market dynamics insights to the Chinese PEV market. On the demand side, inexpensive, small-sized PEVs have contributed the most to the recent rapid increase in sales, but this trend is diminishing. The majority of Chinese PEV sales in recent years occurred in economically prosperous regions or cities that are able to supply lucrative monetary incentives and otherwise PEV purchasing and driving privileges to consumers. On the supply side, expansion of the PEV market, incentivized by government policies, enable young, privately-owned automakers to compete with the established automakers. Overall, the PEV market concentration ratio is still small: hundreds of PEV models and new PEV automakers are competing in China; but when it comes to some major local markets, PEV sales are commonly dominated by the local automakers. In the U.S. market, except for Tesla, the vast majority of PEV models are manufactured by established manufacturers of conventional vehicles. Demand-pull policies (e.g. direct monetary purchase incentives) are being transitioned to supply-push policies (e.g. special fuel economy credits for PEVs and PEV sales mandate) which will be major government measures to further stimulate PEV sales with less fiscal burden.

Emerging technologies play important roles in shaping future mobility systems and impacting sustainability performance of the transportation sector in major economies, such as the United States of America (USA) and China. This study applies a life cycle framework to demonstrate and evaluate the synergies of the following four emerging transportation system technologies both qualitatively and quantitatively: (1) wireless charging; (2) shared mobility services; (3) autonomous driving; and (4) battery electric vehicles (BEV). The new concept of a wireless charging and shared autonomous battery electric vehicle (W+SABEV) system is introduced and modeled. First, an analytical framework is presented to assess the pros and cons of the W+SABEV system vs. a conventional plug-in charging BEV system, adhering to the principles of sustainable mobility and highlighting the impacts and dynamics of the disruptive technologies on the key parameters that define sustainable mobility. Second, a quantitative analysis presents the synergies of the four technologies by modeling a W+SABEV system and demonstrates that the combination of the four technologies can shorten the payback time of greenhouse gas (GHG) emission burdens for infrastructure and vehicles. Compared to a plug-in charging BEV system, a W+SABEV system pays back the additional GHG emission burdens of wireless charging infrastructure deployment within 5 years if the wireless charging utility factor (ratio of en route charging time vs. trip time) is above 19%.

This study quantified the significance of assuming heterogeneity in consumer valuation of fuel economy on the estimated compliance with fuel economy standards, with two approaches. One is mathematical derivation on how the heterogeneity affects the estimated market acceptance of high-efficiency vehicles. The other is a consumer choice based simulation method to analyze the heterogeneity impacts on the compliance with the fuel economy standards for the entire light duty vehicle fleet in the United States. Our findings suggest that the direction of the impact of heterogeneity assumption depends on market positions of high-efficiency vehicles: (1) with the “Likely-Accept” condition, where high-efficiency vehicles sell well and higher achieved fuel economy is expected, the heterogeneity assumption can add additional risks to the compliance; and (2) with the “Likely-Reject” condition where the fuel economy standards may have difficulty in compliance, the heterogeneity can reduce the risks and improve the compliance outcome. It is concluded that consideration of fuel economy valuation heterogeneity is important in fuel economy standard analysis. It is hoped that such consideration may provide a framework to reconcile the different views on consumer valuation of fuel economy.

This paper explores the feasibility of further research into providing free public charging services. Charging behavior research has noted some increased usage of free charging facilities, but inferential analysis has not studied whether this increased usage is a result of decreased price solely or free is a special price. Recent research in other fields, such as behavioral economics and marketing, has analyzed whether people’s decision process varies between when a product’s price is “free” and when it has a positive price. Laboratory and field experiments have found that people treat zero as a special price and obtain additional benefit from purchasing a free product or obtaining a free product in a bundle. Because of a lack of data and study in the electric vehicle area, evaluating the effectiveness of free charging programs is currently difficult. This paper explores potential impacts of free charging on plug-in electric vehicle adoption by integrating empirical findings in marketing research with consumer choice modeling. A behavioral framework is proposed to represent the perceived benefit from free charging events. The Market Acceptance of Advanced Automotive Technologies (MA3T) model was modified to incorporate a value of free from free charging events. Results show that a short-term free public charging program could possibly increase plug-in electric vehicle sales, decrease oil consumption, and decrease greenhouse gas emissions at an efficient cost–benefit rate. This exploratory work motivates further study and the paper concludes with a discussion of research programs that could be used to determine a valuation for free charging.

The behavior of drivers in charging a battery electric vehicle (BEV) can be influenced by psychological factors such as personality and risk preference. This paper proposes a cumulative prospect theory (CPT) based modeling framework to describe the charging behavior of BEV drivers. CPT captures an individual’s attitude and preference toward risk in the decision-making process. A BEV mass-market scenario is constructed using the 2017 National Household Travel Survey (NHTS) data. This paper applies the CPT-based charging behavior model to study the battery state-of-charge (SOC) when drivers decide to charge their vehicles, charging timing and location choices, and charging power demand profile under the mass-market scenario. In addition, sensitivity analyses are used to examine the drivers’ risk attitudes and public charger network coverage. BEV drivers who display a higher degree of risk-seeking tend to charge vehicles at a lower SOC. Some home charging shifts to workplace and public charging as the public charger network expands, but home charging still plays the most significant role in BEV use. The power demand from public chargers increases significantly with BEV expansion and has a larger impact on the power grid. The time-of-use (TOU) electricity rate can shift peak power demand to off-peak periods from midnight to early morning.

Dynamic wireless power transfer (DWPT), or dynamic wireless charging technology, enables charging-while-driving and offers opportunities for eliminating range anxiety, stimulating market penetration of electric vehicles (EVs), and enhancing the sustainability performance of electrified transportation. However, the deployment of wireless charging lanes on highways and urban road networks can be costly and resource-intensive. A life cycle assessment (LCA) is conducted to compare the sustainability performance of DWPT applied in a network of highways and urban roads for charging electric passenger cars. The assessment compares DWPT to stationary wireless charging and to conventional plug-in charging using a case study of Washtenaw County in Michigan, USA over 20 years. The LCA is based on three key sustainability metrics: costs, greenhouse gas (GHG) emissions, and energy burdens, encompassing not only the use-phase burdens from electricity and fuel, but also the upfront deployment burdens of DWPT infrastructure. A genetic algorithm is applied to optimize the rollout of DWPT infrastructure both spatially and temporally in order to minimize life cycle costs, GHG, and energy burdens: (1) spatial optimization selects road segments to deploy DWPT considering traffic volume, speed, and pavement remaining service life (RSL); (2) temporal optimization determines in which year to deploy DWPT on a particular road segment considering EV market share growth as a function of DWPT coverage rate, future DWPT cost reduction, and charging efficiency improvement. Results indicate that optimal deployment of DWPT electrifying up to about 3% of total roadway lane-miles reduces life cycle GHG emissions and energy by up to 9.0% and 6.8%, respectively, and enables downsizing of the EV battery capacity by up to 48%, compared to the non-DWPT scenarios. Roadside solar panels and storage batteries are essential to significantly reduce life cycle energy and GHG burdens but bring additional costs. Breakeven analysis indicates a breakeven year for solar charging benefits to pay back the DWPT infrastructure burdens can be less than 20 years for GHG and energy burdens but longer than 20 years for costs. A monetization of carbon emissions of at least $250 per metric tonne of CO2 is required to shift the optimal “pro-cost” deployment to the optimal “pro-GHG” deployment. A roadway segment with volume greater than about 26,000 vehicle counts per day, speed slower than 55 miles per hour (1 mile ≈ 1.609 km), and pavement RSL shorter than 3 years should be given a high priority for early-stage DWPT deployment.

Using GPS travel survey data from three metropolitan areas, this paper estimates the relationship between plug-in electric vehicle (PEV) public charging infrastructure coverage and charging opportunity. The former is defined as the percentage of public areas with charging stations installed and the latter represents the drivers probability of accessing a public charger at their trip stops (stop-based) or during at least one stop of their travel day (trip chain-based). Understanding this relationship is important to PEV manufacturers and electric vehicle supply equipment (EVSE) providers, as well as government agencies, for evaluating different levels of EVSE deployment and estimating the impact of its availability on PEV adoption. Heuristic procedures are developed to estimate stop- and trip chain-based charging opportunities. Charging opportunity reflects the visibility of EVSE as the incremental security that charging stations enable, while evaluating the potential of a driver to access EVSE at travel stops or during the day, for different levels of EVSE deployment on the transportation network. The metrics proposed do not assess the probability of completing trips with electric vehicles of a given range. The analysis results indicate that drivers’ trip destinations concentrate in a few popular places and EVSE deployment in these areas could achieve relatively high levels of charging opportunity. This finding is also robust to regional variations.

For over ten years, China has been the largest vehicle market in the world. In order to address energy security and air quality concerns, China issued the Dual Credit policy to improve vehicle efficiency and accelerate New Energy Vehicle adoption. In this paper, a market-penetration model is combined with a vehicle fleet model to assess implications on greenhouse gas (GHG) emissions and energy demand. Here we use this integrated modeling framework to study several scenarios, including hypothetical policy tweaks, oil price, battery cost and charging infrastructure for the Chinese passenger vehicle fleet. The model shows that the total GHGs of the Chinese passenger vehicle fleet are expected to peak in 2032 under the Dual Credit policy. A significant reduction in GHG emissions is possible if more efficient internal combustion engines continue to be part of the technology mix in the short term with more New Energy Vehicle penetration in the long term., China issued the Dual Credit policy to improve vehicle efficiency and accelerate new energy vehicle adoption. Here the authors show that the total Greenhouse gas emissions (GHGs) of the Chinese passenger vehicle fleet are expected to peak in 2032 and a significant reduction in GHG emissions is possible by optimizing the Dual Credit policy.

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people’s resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.

A full-scale understanding of the dynamics of the Chinese vehicle market can benefit stakeholders with respect to rational decision-making and effective long-term investment. This study attempts to discover the common vehicle pricing patterns in the Chinese market by quantifying statistical correlations among critical vehicle features from intrinsic powertrain systems to extrinsic market positioning. The data samples involve almost all passenger vehicle models sold in 2013 to 2019. After comparing multiple statistical methodologies, a log-transformation variant of the multinomial linear regression model was found to be the best one, and the goodness of fit shows that this model can offer stable estimates, which were validated using 2019 market data. The insights achieved are: (1) The price and major performance features of SUVs/crossovers are similar to those of sedans; (2) If all other explicit features remain the same, the price of a Japanese midsize sedan is 62% higher than that of a Chinese midsize sedan, and European midsize vehicles have the highest prices overall. (3) The incremental price of fuel consumption varies by vehicle class and fuel economy. For example, from 30 to 50 MPG, the vehicle price increases by $119 for a Chinese brand sedan vehicle, by $69 for a Chinese brand SUV.

The plug-in electric vehicle (PEV) is recognized as a pivotal component in transportation energy security and technology innovation in China. With government incentives and substantial capital investments, the Chinese PEV market has become one of the largest in the world. However, is this market boom ephemeral or will it be continuing? This chapter provides a general review of the Chinese PEV market mainly from the consumer perspective, by summarizing the characteristics of the vehicle policies and consumer concerns in the PEV market in recent years. Moreover, it introduces detailed examples on how the policies, technologies, and infrastructure shape the market. In addition to employing large amounts of reliable data from the China Automotive Technology and Research Center for the review, the authors also adopted a quantitative analysis tool—the New Energy and Oil Consumption Credits model—to project the potential industrial impacts of the government policies in multiple scenarios. Based on the findings, it seems that the prosperity of the PEV market, incentivized by the government, will continue in the short run. The direct subsidies to PEV from the government will gradually phase out, and the supply-side push policies such as the dual-credit policy and other charging infrastructure incentives will work as the invisible hand to force the automakers to invest more in the PEV segment. However, in the long run, innovation in PEV technology and business models, together with consumer acceptance will be the major factors that can sustain further market growth.

The Passenger Cars Corporate Average Fuel Consumption and New Energy Vehicle Credit Regulation (dual-credit policy) was enacted by the Chinese government in 2017 to stimulate the fuel-efficient and electrification technologies in the China's passenger vehicle market. This study summarizes the dual-credit policy and develops the New Energy and Oil Consumption Credits Model to quantify the impacts of this policy on consumer choices and industry profits, where internal subsidies as decision variables are used to represent industry responses to the policy. Scenarios in 2016–2020 are simulated and discussed. Key findings from the model results include: (1) the Corporate Average Fuel Consumption rules alone may stimulate more plug-in electric vehicle (PEV) sales than the dual-credit policy; however, (2) the dual-credit policy could stimulate more battery electric vehicles (BEVs) in market, compared to other policy scenarios; (3) the industry could “lose” approximately $2122/vehicle by 2020 under the dual-credit policy; (4) battery electric sedans with a range greater than 250 km and plug-in hybrid SUVs could be popular under the dual-credit policy; (5) credit allocations for BEVs in the dual-credit policy can influence the PEV production; and (6) reduction of the fuel-efficient technology costs helps to minimize profit losses impacted by the policy.

The market diffusion of plug-in electric vehicles (PEVs) is a research topic which is often addressed, yet PEV market diffusion models differ in their approaches, the factors they include and results. Here, 40 market diffusion models for PEVs are compared in their scope, approach and findings to point out similarities or differences and make recommendations for future improvements in modeling in this field. Important input factors for the US are the purchase price and operating costs, while for Germany energy prices and the charging infrastructure are mentioned more often. Furthermore, larger sales shares of plug-in hybrid electric vehicles than battery electric vehicles are often found in the short term results (until 2030) while the picture is not so clear for the medium- to long-term. Future market penetration models should include specific PEV features like the limited range of battery electric vehicles or access to charging infrastructure, which are currently not covered by many models. Also, the integration of current policy regulations and, if possible, indirect policy incentives would enhance research in this field.

This paper presents a wide-ranging analysis of Class 3-8 commercial vehicle electrification by means of developing a framework tool which uses a quantitative method of estimating electric vehicle energy consumption and appropriate charging considerations. The Fleet DNA composite statistics on real-world driving behavior is used to evaluate feasible or market-ready battery electric vehicle (BEV) technologies in medium- and heavy-duty (MD/HD) applications. In the paper, ten representative Class 3-8 commercial vehicle electrifications have been evaluated as a function of various service coverages, including applications in port drayage tractors, refuse trucks, delivery trucks, buses, and bucket trucks. The results indicate significant energy savings and fuel cost savings across all MD/HD vehicle electrifications. The majority of MD BEVs, with the exception of Class 3 bucket trucks, achieve better than a 5-year payback with 50–75% service coverage. For HD BEVs, with the exception of the Class 8 port drayage tractors, the 90% service coverage results in a 10-year or longer payback time, while the 50% service coverage yields a 7–8 year payback. Class 8 port drayage tractors should achieve payback in no more than a 3.5 years with 50–75% service coverage. Thus, the analysis indicates a highly feasible potential for Class 3-6 MD vehicles to be electrified, and feasible opportunities for electrification in Class 7-8 HD short-distance applications.

China has become the world's largest plug-in electric vehicle (PEV) market. One major barrier to greater consumer acceptance of PEVs is the lack of home parking spaces for charging outlets. This study developed a methodology to estimate the residential parking ratios (parking spaces vs household numbers) and project the residential community-weighted parking availabilities (home parking availabilities) in China, by area and by province, through data mining from several major real estate trading network platforms. The results show that the home parking availabilities from 2015 to 2050 vary by geographic areas and building life expectancy. A method was developed to quantify the shadow values of home parking impacting on PEV ownership costs and combined with Monte Carlo simulation to address estimation uncertainty. Depending on the PEV type and all-electric range, the value of home parking space to a PEV owner, measured by the reduced vehicle ownership cost, ranges from $2399 USD to $10,802 USD. The total incremental shadow value, relative to the 2015 situation, of the home parking availability for PEV owners increases over time due to both improvement in home parking availability and increase in the PEV population, and is estimated to reach over $2.51 billion USD by 2025 (U.S. dollars in 2015 level).

Fuel cell electric vehicles (FCEVs) are considered an important part of a portfolio of options to address challenges in the transportation sector, including energy security and pollution reduction. The market success of FCEVs depends on standardization of key vehicle and infrastructure parameters, including the delivered hydrogen pressure (DHP). This study developed and utilized the Hydrogen Optimal Pressure (HOP) model to systematically identify the optimal DHP among 350, 500, and 700 bar toward the lowest total consumer cost and analyze how the optimal DHP may be affected by attributes of drivers, vehicles, and hydrogen refueling stations. The DHP of 700 bar a robustly better choice than 350 bar or 500 bar for Region Strategy, regardless of fuel availability, FCEV adoption, driver types, time values, and fuel economies. A DHP of 300 or 500 bar can the winner in Cluster Strategy if combined with certain assumptions of driving patterns and time value. the optimal pressure is found to be very sensitive to fuel availability, fuel economy, driving pattern and time value. The appeal of a higher DHP such as 700 bar (or even higher) is more obvious during the early market stages, when the number of hydrogen stations is limited and early FCEV consumers likely have higher time value, and thus may be willing to pay more for the increased range with higher DHP. Future research on mixed DHPs within a station and across stations is suggested.

Electric taxis have the potential to improve urban air quality and save driver’s energy expenditure. Although battery electric vehicles (BEVs) have drawbacks such as the limited range and charging inconvenience, technological progress has been presenting promising potential for electric taxis. Many cities around the world including New York City, USA are taking initiatives to replace gasoline taxis with plug-in electric vehicles. This paper extracts ten variables from the trip data of the New York City yellow taxis to represent their spatial-temporal travel patterns in terms of driver-shift, travel demand and dwell, and examines the implications of these driving patterns on the BEV taxi feasibility. The BEV feasibility of a taxi is quantified as the percentage of occupied trips that can be completed by BEVs of a given driving range during a year. It is found that the currently deployed 280 public charging stations in New York City are far from sufficient to support a large BEV taxi fleet. However, adding merely 372 new charging stations at various locations where taxis frequently dwell can potentially make BEVs with 200- and 300-mile ranges feasible for more than half of the taxi fleet. The results also show that taxis with certain characteristics are more suitable for switching to BEV-200 or BEV-300, such as fewer daily shifts, fewer drivers assigned to the taxi, shorter daily driving distance, fewer daily dwells but longer dwelling time, and higher likelihood to dwell at the borough of Manhattan.

This study introduces a multistage chance-constrained stochastic model for strategic planning of battery electric vehicle (BEV) inter-city fast charging infrastructure. A mixed integer programming model is developed to determine where and when charging stations are opened, and how many chargers are required for each station to meet the growing BEV inter-city demand. The model is applied to a case study in California and solved by genetic algorithm. This study showed that investment in inter-city charging infrastructure is vital to alleviate the range anxiety. Also, planning decisions depend on many factors, such as the design level of service and vehicle range.

Summary The promotion of plug-in electric vehicles (PEVs) is pivotal to China's carbon neutrality strategy. Therefore, it is important to understand the vehicle market evolution and its impacts in terms of costs, sales, industry fuel economy, and PEV's battery material demand. By examining vehicle technologies, cost, policy incentives, infrastructure, and driver behavior, this study quantitatively projects the dynamics of China's passenger vehicle market from 2020 to 2050 under multiple technology evolution scenarios. By 2050, battery electric vehicles could gain significant market share—as much as 30.4%–64.6%; and the industry's sales-weighted average fuel consumption could reach 1.81–3.11 L/100 km. Cumulative battery demand from PEVs could soar to over 700 GWh by 2050, whereas battery recycling alone could satisfy about 60% of the demand by 2050. The key metal supplies—lithium, cobalt, and nickel—for China's PEV market are projected, and nickel should be concerned more over the coming decades., Graphical abstract, Highlights • The dual-credit policy is critical to China's vehicle electrification before 2035 • Market share of battery electric vehicles could reach 30.4%–64.6% by 2050 • Industry weighted average fuel consumption could reach 1.81–3.11 L/100 km by 2050 • Battery recycling alone could satisfy 60% of the vehicle market battery demand by 2050, Electrochemical energy storage; Energy policy; Energy Resources; Energy transportation; Energy flexibility; Energy Systems

This study introduces an integrated modeling framework to evaluate long-term national corridor charging infrastructure requirements in the United States to support the growing inter-city charging demand with the rapid growth in the battery electric vehicle (BEV) market. The core model is an optimization model that considers spatial and temporal dimensions and models heterogeneous behaviors between travelers. The model also introduces the travelers’ inconvenience cost function by linking travelers’ acceptance of the charging infrastructure with exogenous technology and social factors. The inconvenience cost function simulates mode choice between BEVs and alternative modes by heterogenous travelers. We applied the framework to assess the inter-regional charging infrastructure requirements for the entire U.S. mainland interstate highway network. We evaluated impacts on the infrastructure design and its public acceptance with changes in policy, technology, and demographic characteristics, and we also quantified the importance of modeling full-scale inter-regional charging infrastructure requirements compared to the conventional regional level analyses.

This study explored factors that affect market-driven compliance with both Corporate Average Fuel Economy (CAFE) and greenhouse gas (GHG) standards (together called the National Program) in the United States for phase I 2012–2016 and phase II 2017–2025. We considered a consumer-choice-based simulation approach, using the MA3T model, to estimate the market acceptance of fuel efficiency (FE) technologies and alternative fuel technologies as reflected by new sales of light-duty vehicle (LDV). Because both full and extremely low FE valuations are common in the literature, we use a moderate assumption of a 10-year perceived vehicle lifetime at a 7% annual discount rate in the baseline and include both extreme views (5 years and 15 years) in the sensitivity analysis. The study focuses on market-driven compliance and therefore excludes manufacturers’ cross-subsidization. The model results suggest that the LDV industry is able to comply with both standards even without cross-subsidization and with projected high technology cost, mainly thanks to the multiple credit programs and technology advancements. The compliance robustness, while encouraging, however is based on moderate market assumptions, such as Annual Energy Outlook 2016 Reference oil price projection and moderate FE consumer valuation. Sensitivity analysis results reveal two significant risk factors for compliance: low oil prices and consumers’ FE undervaluation.

In this study, a framework is proposed for minimizing the societal cost of replacing gas-powered household passenger cars with battery electric ones (BEVs). The societal cost consists of operational costs of heterogeneous driving patterns' cars, government investments for charging deployment, and monetized environmental externalities. The optimization framework determines the timeframe needed for conventional vehicles to be replaced with BEVs. It also determines the BEVs driving range during the planning timeframe, as well as the density of public chargers deployed on a linear transportation network over time. We leverage data sets that represent US household driving patterns, as well as the automobile and the energy markets, to apply the model. Results indicate that it takes 8 years for 80% of our conventional vehicle sample to be replaced with electric vehicles, under the base case scenario. The socially optimal all-electric driving range is 204 miles, with chargers placed every 172 miles on a l...