What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models
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SEARCH · Engineering Papers
Results for “Advanced Propulsion”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Light-duty plug-in electric vehicles in China: An overview on the market and its comparisons to the United States
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Electricity rates for electric vehicle direct current fast charging in the United States
While several efforts are promoting a widespread and convenient network of direct current fast charging (DCFC) stations to support electric vehicles, there is limited understanding of the magnitude and variability of the cost of electricity for these applications. This information gap may hinder optimal investing and planning for charging station placement and in turn affect electric vehicle adoption and usage. Here, we assess the electricity cost for different scenarios of DCFC station size and use based on over 7500 commercial and industrial electricity rates available for 2017 across the United States. Results show that the cost of electricity for DCFC varies dramatically, ranging from less than 0.10 cents to over 2 dollars per kilowatt-hour, depending on station design and high uncertainty in use. The main driver of cost is low utilization, which results from a combination of few charging events and limited energy recharged during each event. Low utilization leads to significantly higher electricity cost, particularly for rates with demand charges; however, cost decreases rapidly as utilization increases. For high-utilization stations, selecting rates with demand charges can actually reduce electricity costs compared to non-demand-charge rates. Furthermore, significant opportunities for cost savings based on existing rates include preferential charging during off-peak hours and limiting multi-plug station power so that not all plugs can be used simultaneously at maximum power.
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Transportation and electricity systems integration via electric vehicle charging-as-a-service: A review of techno-economic and societal benefits
Here, in support of global decarbonization efforts, the adoption of electric vehicles is proceeding rapidly across transportation sectors, population groups, and regions. However, access to electric vehicle charging infrastructure remains sparse and inequitable, and technical, economic, and procedural challenges have encumbered its expansion. Charging-as-a-service, which can mitigate cost and effort risks of charging equipment ownership, adds a means of meeting many of these challenges. As charging-as-a-service has not been analyzed or addressed in the scholarly literature, we synthesize media, marketing, and analogous scholarly publications to categorize and describe the functions asserted by existing charging-as-a-service vendors. We demonstrate and quantify the benefits of many of these functions via four example business cases, sited at a detached residence, a multi-unit dwelling, a consumer-facing business, and a commercial fleet depot. Outcomes show that savings and revenues realized via charging-as-a-service can reduce charging costs, beneficially shape grid loads, and make charging installation economical where it otherwise would not be. Discussion highlights the need to deploy charging-as-a-service in cooperation with initiatives promoting societal benefits, including charging access equity.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
The electric vehicles-solar photovoltaics Nexus: Driving cross-sectoral adoption of sustainable technologies
Residential and transportation energy consumption account for more than one-half of the overall energy consumption in the United States. Adoption of electric vehicles (EVs) can play a key role in decarbonizing the transportation sector, while the adoption of renewable energy sources (e.g., solar photovoltaics [PVs]) could bring similar benefits to the residential energy sector and in turn support transport electrification. Although the market shares for both EVs and PVs continue to grow, both of these emerging technologies are deployed rather disjointly, without considering the existence of potential similarities among users who own (or aspire to own) these technologies. This might be due to lack of understanding of the behavioral interdependence in consumer preferences toward these technologies. To fill this gap in knowledge, this study utilizes data from the 2018 WholeTraveler Transportation Behavior Study to develop an integrated model system that explores interactive EV and PV adoption behaviors. A structural equation model is employed that incorporates direct effects as well as error correlations among the adoption behaviors for EVs and PVs. Model results indicate that the adoption behavior for both these technologies is indeed interconnected and significantly influenced by attitudes, values, and personality traits. Findings from this research suggest that incentives (e.g., subsidies) that drive bundled adoption of EV-PV systems could accelerate the adoption of both of these sustainable technologies. In conclusion, this study highlights the need to consider transport and building energy-efficient technology adoption behavior in a single integrated structure.
14 SOLAR ENERGY↗
Charging needs for electric semi-trailer trucks
Battery-electric vehicles provide a pathway to decarbonize heavy-duty trucking, but the market for heavy-duty battery-electric semi-trailer trucks is nascent, and specific charging requirements remain uncertain. We leverage large-scale vehicle telematics data (>205 million miles of driving) to estimate the charging behaviors and infrastructure requirements for U.S. battery-electric semi-trailer trucks within three operating segments: local, regional, and long-haul. We model two types of charging - mid-shift (fast) and off-shift (slow) - and show that off-shift charging at speeds compatible with current light-duty charging infrastructure (i.e., =350 kW) can supply 35% to 77% of total energy demand for local and regional trucks with =300-mile range. Megawatt-level speeds are required for mid-shift charging, which make up 44% to 57% of energy demand for long-haul trucks with =500-mile range. However, demand shifts from mid-shift to off-shift charging as the range for battery-electric trucks increases and when off-shift charging is widely available. Finally, we observe geographic trends in charging demand, finding that local trucks have greater demand within urban areas, whereas long-haul trucks have more demand along rural interstate corridors. As the range for battery-electric trucks increases, we show that charging demand shifts from rural to urban locations due to observed vehicle dwell tendencies.
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Vehicle-cycle and life-cycle analysis of medium-duty and heavy-duty trucks in the United States
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POINT: Partially Observable Imitation Network for Traffic Signal Control
Smart traffic signals bring together transportation infrastructure and advance technologies to improve the mobility and efficiency of urban transportation network. Adaptive traffic signal control studies can be categorized into modeling-based approaches and learning-based approaches. In order to take advantages of these two systems, this study developed an offline-online combined Partial Observable Imitation Network for Traffic signal control (POINT). In the offline system, the traffic signal timing optimization problem was formulated as a Mixed Integer Nonlinear Programming (MINLP) given complete traffic information, i.e., second-by-second speeds and locations of all vehicles. Furthermore, the objective of MINLP is to minimize total travel delays considering individual vehicle trajectories under Connected Vehicle (CV) environment. The calculated optimal solutions under various traffic conditions were considered as the ”expert” decisions. In the online system, an imitation neural network model was developed to learn the ”expert” signal plans generated from offline system. Given partial observable traffic conditions in real time, e.g., the aggregate-level of traffic volume, the POINT model can compute the signal timing parameters in the online system. The numerical results demonstrated that the proposed method outperformed other state-of-the-art signal control method under high and unbalanced traffic demand levels in terms of reducing travel delays and queue length.
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Grid impact analysis using controller-hardware-in-the-loop for high-power vehicle charging stations
A controller-hardware-in-the-loop (CHIL) architecture for the evaluation of grid impacts arising from high-power vehicle charging stations is presented in this paper. Unlike simulation-based studies, the proposed method can be used to capture the interactions of the grid and the charging load along with charger controllers in real time. The proposed method can be used to evaluate the impact of charging load on the grid in terms of voltage variations and line congestion. The proposed CHIL platform allows for de-risking the vehicle charging station deployment by simulating the complex interactions among all the components of a vehicle charging station - i.e., the grid, vehicle, and charger controller - in a realistic manner before using the charging station in a grid. Further, the proposed CHIL approach can be used to evaluate the voltage regulation causalities of the vehicle charging station. Experimental results are presented in the paper to illustrate the applicability of the proposed method in a laboratory environment.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Design and development of a probe-based multiplexed multi-species absorption spectroscopy sensor for characterizing transient gas-parameter distributions in the intake systems of I.C. engines
We designed and developed a 4-probe multiplexed multi-species absorption spectroscopy sensor system for gas property measurements on the intake side of commercial multi-cylinder internal-combustion (I.C.) engines; the resulting cycle- and cylinder-resolved concentration, temperature and pressure measurements are applicable for assessing spatial and temporal variations in the recirculated exhaust gas (EGR) distribution at various locations along the intake gas path, which in turn is relevant to assessing cylinder charge uniformity, control strategies, and CFD models. Furthermore, the diagnostic is based on absorption spectroscopy and includes an H 2 O absorption system (utilizing a 1.39 m distributed feedback (DFB) diode laser) for measuring gas temperature, pressure, and H 2 O concentration, and a CO 2 absorption system (utilizing a 2.7 m DFB laser) for measuring CO 2 concentration. The various lasers, optical components and detectors were housed in an instrument box, and the 1.39- m and 2.7- m lasers were guided to and from the engine-mounted probes via optical fibers and hollow waveguides, respectively. The 5kHz measurement bandwidth allows for near-crank angle resolved measurements, with a resolution of 1.2 crank angle degrees at 1000 RPM. Our use of compact stainless steel measurement probes enables simultaneous multi-point measurements at various locations on the engine with minimal changes to the base engine hardware; in addition to resolving large-scale spatial variations via simultaneous multi-probe measurements, local spatial gradients can be resolved by translating individual probes. Along with details of various sensor design features and performance, we also demonstrate validation of the spectral parameters of the associated CO 2 absorption transitions using both a multi-pass heated cell and the sensor probes.
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Quantitative decoding of the response a ceramic mixed potential sensor array for engine emissions control and diagnostics
The development of on-board sensors for emissions monitoring is necessary for continuous monitoring of the performance of catalytic systems in automobiles. We have fabricated mixed potential electrochemical gas sensing devices with Pt, La 0.8 Sr 0.2 CrO 3 (LSCO), and Au/Pd alloy electrodes and a porous yttria-stabilized zirconia electrolyte. The three-electrode design takes advantage of the preferential selectivity of the Pt + Au/Pd and Pt + LSCO pairs towards different species of gases and has additional tunable selectivity achieved by applying a current bias to the latter pair. Voltages were recorded in single, binary, and ternary gas streams of NO, NO 2 , C 3 H 8 , and CO. We have also trained artificial neural networks to examine the voltage output from sensors in biased and unbiased modes to both identify which single test gas or binary mixture of two test gases is present in a gas stream as well as extract concentration values. We were then able to identify single and binary mixtures of these gases with accuracy of at least 98%. For determining concentration, the peak in the error distribution for binary mixtures was 5% and 80% of test data fell under <12% error. The sensor stability was also evaluated over the course of over 100 days and the ability to retrain ANNs with a small dataset was demonstrated.
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The future of automotive lithium-ion battery recycling: Charting a sustainable course
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How do perceptions of safety and car ownership importance affect autonomous vehicle adoption?
In this study, we explicitly modeled how individuals' perceptions of automated vehicle (AV) safety and the importance they place on car ownership affect mode choices involving conventional and automated vehicles in the context of privately owned cars and ridehailing services. We adopted psychometric questions to capture these two latent variables and designed a stated preference survey based on the participants' actual travel patterns. Then, we quantified the impact of these latent variables on mode choices using an integrated choice and latent variable (ICLV) model. We found that both latent variables have a statistically significant effect on mode choices. The results show that car ownership importance has the most potent effect on privately owned cars (conventional car and self-driving car), followed by driverless ridehailing and conventional ridehailing. We also found that changes in safety perception are equivalent to sizable changes in price. In addition, we further investigated the impact of improvements in safety perception through four scenarios. The scenario testing results show that as the distribution of perceived safety is compressed toward positive safety perception, the market share of AVs spikes and dominates regular cars. Our results demonstrate that based on our respondents' current understanding of AVs, even if AV prices were comparable to regular cars, we cannot expect widespread use of AVs. However, improvements in AVs' safety and, consequently, consumer safety perception can considerably expand AVs' market share, and may offset the high cost of using the technology.
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CO2 emissions associated with electric vehicle charging: The impact of electricity generation mix, charging infrastructure availability and vehicle type
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McLaren, Joyce (ORCID:0000000309600283)↗
Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach
Autonomous vehicles are drawing significant attention from governments, manufacturers and consumers. Experts predict them to be the primary means of transportation by the middle of this century. Recent literature shows that vehicle automation has the potential to alter traffic patterns, vehicle ownership, and land use, which may affect fuel consumption from the transportation sector. In this paper, we developed a data-rich analytical framework to quantify system-wide fuel impacts of automation in the United States by integrating (1) a dynamic vehicle sales, stock, and usage model, (2) an historical transportation network-level vehicle miles traveled (VMT)/vehicle activity database, and (3) estimates of automation's impacts on fuel efficiency and travel demand. The vehicle model considers dynamics in vehicle fleet turnover and fuel efficiency improvements of conventional and advanced vehicle fleet. The network activity database contains VMT, free-flow speeds, and historical speeds of road links that can help us accurately identify fuel-savings opportunities of automation. Based on the model setup and assumptions, we found that the impacts of automation on fuel consumption are quite wide-ranging - with the potential to reduce fuel consumption by 45% in our 'Optimistic' case or increase it by 30% in our 'Pessimistic' case. Second, implementing automation on urban roads could potentially result in larger fuel savings compared with highway automation because of the driving features of urban roads. Lastly, through scenario analysis, we showed that the proposed framework can be used for refined assessments as better data on vehicle-level fuel efficiency and travel demand impacts of automation become available.
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On the evaluation and selection of network-level traffic control policies: Perimeter control, TUC, and their combination
Perimeter control (PC) of urban traffic networks can be effective in increasing network-wide efficiency. PC operates on the border of a protected region of a traffic network. Most studies thus far considered fixed-time plans for the inner part of these regions. A few studies have shown that combining PC with locally actuated or decentralized traffic control systems may have positive effects on traffic performance, including better-defined Network Macroscopic Fundamental Diagrams (NMFDs), increased network throughput, and reduced delays. The Traffic-responsive Urban Control (TUC) is a real-time network-wide traffic control system with particular design characteristics, such as the balancing of link's occupancies and an inherent gating feature. These characteristics suggest that TUC may enhance the traffic network performance when combined with PC whilst improving the resulting NMFDs and network throughput and delays. Here, in this work, we investigate the effect of feedback perimeter control (FPC), TUC, and their combination on the NMFD and on the traffic conditions of general traffic and public transport in the microsimulation of a realistic model of the Christchurch Central Business District in New Zealand. We perform a thorough investigation of practical aspects of both control strategies and their combination, including parameter tuning and infrastructure requirements, and how they may affect the control system choice. Results show higher throughput and less hysteresis on the NMFDs, particularly when TUC is involved. PC provides benefits concentrated in the protected region which can greatly benefit public transportation if there is an overlap with the transit network. The combination of TUC and FPC boosts network-wide throughput.
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Estimation of vehicle home parking availability in China and quantification of its potential impacts on plug-in electric vehicle ownership cost
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 paper 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. Finally, 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).
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Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels
Activity-travel choices of individuals are influenced by spatial dependency effects. As individuals interact and exchange information with, or observe the behaviors of, those in close proximity of themselves, they are likely to shape their behavioral choices accordingly. For this reason, econometric choice models that account for spatial dependency effects have been developed and applied in a number of fields, including transportation. However, spatial dependence models to date have largely defined the strength of association across behavioral units based on spatial or geographic proximity. In the current context of social media platforms and ubiquitous internet and mobile connectivity, the strength of associations among individuals is no longer solely dependent on spatial proximity. Rather, the strength of associations among individuals may be based on shared attitudes and preferences as well. In other words, behavioral choice models may benefit from defining dependency effects based on attitudinal constructs in addition to geographical constructs. In this paper, frequency of usage of car-sharing and ride-hailing services is modeled using a generalized heterogeneous data model (GHDM) framework that incorporates multi-dimensional dependencies among decision-makers. The model system is estimated on the 2014-2015 Puget Sound Regional Travel Study survey sample, with proximity in latent attitudinal constructs defined by a number of personality trait variables. Model estimation results show that social dependency effects arising from similarities in attitudes and preferences are significant in explaining shared mobility service usage. Ignoring such effects may lead to erroneous estimates of the adoption and usage of future transportation technologies and mobility services.
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