HumanLight: Incentivizing ridesharing via human-centric deep reinforcement learning in traffic signal control
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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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Heuristic solutions to the single depot electric vehicle scheduling problem with next day operability constraints
This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming, we generate bus blocks that accommodate electric vehicles (EVs), ensuring successful completion of each block while considering recharging requirements between blocks and during off-hours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP, and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints. Further, a case study conducted reveals that nearly 100% electrification for Chicago, IL and Austin, TX transit buses is viable yet requires 1.6 EVs at 150-mile range per diesel vehicle.
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Simulation–optimization model for location of a public electric vehicle charging infrastructure
The paper develops a simulation–optimization model that determines where to locate electric vehicle chargers to maximize their use by privately owned electric vehicles. Applying this model to the central-Ohio region, we demonstrate that a combination of level-one and -two chargers is preferable to level-two chargers only. We further explore interactions between the optimization criterion used and the budget available. Lastly, we finally show that although the optimal location is sensitive to the specific optimization criterion considered, overall service levels are less sensitive to the optimization strategy.
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Socially optimal electric driving range of plug-in hybrid electric vehicles
Our study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of 3.19 per day when exclusively charging at home, compared to 3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. Moreover, when workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Finally, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.
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Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices
A large body of transport sector-focused research recognizes the complexity of human behavior in relation to mobility. Yet, global integrated assessment models (IAMs), which are widely used to evaluate the costs, potentials, and consequences of different greenhouse gas emission trajectories over the medium-to-long term, typically represent behavior and the end use of energy as a simple rational choice between available alternatives, even though abundant empirical evidence shows that real-world decision making is more complex and less routinely rational. This paper demonstrates the value of incorporating certain features of consumer behavior in IAMs, focusing on light-duty vehicle (LDV) purchase decisions. An innovative model formulation is developed to represent heterogeneous consumer groups with varying preferences for vehicle novelty, range, refueling/recharging availability, and variety. The formulation is then implemented in the transport module of MESSAGE-Transport, a global IAM, although it also has the generic flexibility to be applied in energy-economy models with varying set-ups. Comparison of conventional and behaviorally-realistic model runs with respect to vehicle purchase decisions shows that consumer preferences may slow down the transition to alternative fuel (low-carbon) vehicles. Consequently, stronger price-based incentives and/or non-price based measures may be needed to transform the global fleet of passenger vehicles, at least in the initial market phases of novel alternatives. Otherwise, the mitigation burden borne by other transport sub-sectors and other energy sectors could be higher than previously estimated. Moreover, capturing behavioral features of energy consumers in global IAMs increases their usefulness to policy makers by allowing a more realistic assessment of a more diverse suite of policies.
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A stochastic flow-capturing model to optimize the location of fast-charging stations with uncertain electric vehicle flows
Here, we develop a model to optimize the location of public fast charging stations for electric vehicles (EVs). A difficulty in planning the placement of charging stations is uncertainty in where EV charging demands appear. For this reason, we use a stochastic flow-capturing location model (SFCLM). A sample-average approximation method and an averaged two-replication procedure are used to solve the problem and estimate the solution quality. We demonstrate the use of the SFCLM using a Central-Ohio based case study. We find that most of the stations built are concentrated around the urban core of the region. As the number of stations built increases, some appear on the outskirts of the region to provide an extended charging network. We find that the sets of optimal charging station locations as a function of the number of stations built are approximately nested. We demonstrate the benefits of the charging-station network in terms of how many EVs are able to complete their daily trips by charging midday—six public charging stations allow at least 60% of EVs that would otherwise not be able to complete their daily tours without the stations to do so. We finally compare the SFCLM to a deterministic model, in which EV flows are set equal to their expected values. We show that if a limited number of charging stations are to be built, the SFCLM outperforms the deterministic model. As the number of stations to be built increases, the SFCLM and deterministic model select very similar station locations.
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Comparison of the greenhouse gas and criteria pollutant emissions from the SF-BREEZE high-speed fuel-cell ferry with a diesel ferry
A theoretical comparison is made of the “well to waves” (WTW) greenhouse gas (GHG) and criteria pollutant emissions from the SF-BREEZE high-speed hydrogen PEM fuel cell ferry and the VALLEJO ferry powered by traditional diesel engine technology but constrained to Tier 4 emissions standards. The emissions were calculated for a common maritime mission, the current ferry route between Vallejo CA and San Francisco CA. Calculations are made of the energy required for the SF-BREEZE and VALLEJO to perform the mission route profile. The SF-BREEZE requires 10.1% more fuel energy than the VALLEJO, primarily due to the SF-BREEZE being heavier. Estimates are made for the SF-BREEZE GHG emissions associated with five LH2 fuel production pathways including renewable and non-renewable (fossil-fuel based) methods. Estimates are also made for GHG emissions associated with fossil-diesel production and delivery as well as those for biodiesel, which can be considered a renewable “drop-in” fuel replacement for conventional diesel fuel. We find that the GHG emissions for the SF-BREEZE using non-renewable LH 2 are significantly higher than for the Tier 4 diesel-fueled VALLEJO on a per passenger basis. However, using renewable LH 2 , the GHG emissions for the SF-BREEZE ferry are reduced 75.8% compared to the diesel-fueled VALLEJO operating at Tier 4 emissions standards. We also compare the criteria pollutant emissions (NO x , HC, PM 10 ) for the SF-BREEZE to that of the VALLEJO held to Tier 4 emissions standards fueled by diesel fuel or biodiesel. Hydrogen PEM fuel cell technology dramatically reduces NO x and HC emissions below the most advanced Tier 4 criteria pollutant emissions requirements regardless of whether the LH 2 is made by NG reforming or via water electrolysis using 70% renewable energy. Renewable LH 2 made with greater than 84% renewable process energy is needed to also drop the SF-BREEZE PM 10 emissions below that of Tier 4 for high-speed fuel cell ferry transportation. Overall, the results show that operating a hydrogen fuel cell ferry on nearly 100% renewable hydrogen provides the dramatic reduction in GHG and criteria pollutant emissions commensurate with the problems of global climate change and maritime air pollution worldwide.
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Impact of public electric vehicle charging infrastructure
Our work uses market analysis and simulation to explore the potential of public charging infrastructure to spur US battery electric vehicle (BEV) sales, increase national electrified mileage, and lower greenhouse gas (GHG) emissions. By employing both scenario and parametric analysis for policy driven injection of public charging stations we find the following: (1) For large deployments of public chargers, DC fast chargers are more effective than level 2 chargers at increasing BEV sales, increasing electrified mileage, and lowering GHG emissions, even if only one DC fast charging station can be built for every ten level 2 charging stations. (2) A national initiative to build DC fast charging infrastructure will see diminishing returns on investment at approximately 30,000 stations. (3) Some infrastructure deployment costs can be defrayed by passing them back to electric vehicle consumers, but once those costs to the consumer reach the equivalent of approximately 12¢/kWh for all miles driven, almost all gains to BEV sales and GHG emissions reductions from infrastructure construction are lost.
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A stochastic operational model for controlling electric vehicle charging to provide frequency regulation
The charging of battery electric vehicles (BEVs) is a potential source of flexibility and ancillary services to power systems. Herein this paper proposes a two-stage stochastic problem that can be used to optimize the charging of BEVs in a public charging station to provide frequency regulation and energy arbitrage. The model also co-optimizes the use of distributed energy resources, including battery energy storage and photovoltaic solar panels. We demonstrate the performance of the proposed model using a case study based on the Central-Ohio region. The case study shows that proper management of flexibility in BEV charging can provide high-quality frequency regulation services, which is also of significant financial value to the station operator. As such, the modeling methodology that we propose here can further accelerate the adoption of BEVs. This is because the value streams generated by the provision of frequency regulation can reduce the cost of BEV ownership and the net cost of owning and operating a public BEV-charging station.
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Public charging infrastructure for plug-in electric vehicles: What is it worth?
A lack of charging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Public charging infrastructure has tangible and intangible value, such as reducing range anxiety or building confidence in the future of the PEV market. Quantifying the value of public charging infrastructure can inform analysis of investment decisions and can help predict the impact of charging infrastructure on future PEV sales. Estimates of willingness to pay (WTP) based on stated preference surveys are limited by consumers' lack of familiarity with PEVs. As an alternative, we focus on quantifying the tangible value of public PEV chargers in terms of their ability to displace gasoline use for PHEVs and to enable additional electric (e-) vehicle miles for BEVs, thereby mitigating the limitations of shorter range and longer recharging time. Simulation studies provide data that can be used to quantify e-miles enabled by public chargers and the value of additional e-miles can be inferred from econometric estimates of WTP for increased vehicle range. Functions are synthesized that estimate the WTP for public charging infrastructure by plug-in hybrid and battery electric vehicles, conditional on vehicle range, annual vehicle travel, pre-existing charging infrastructure, energy prices, vehicle efficiency, and household income. A case study based on California's public charging network in 2017 indicates that, to the purchaser of a new BEV with a 100-mile range and home recharging, existing public fast chargers are worth about $1500 for intraregional travel, and fast chargers along intercity routes are valued at over $6500.
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Optimizing workplace charging facility deployment and smart charging strategies
This study introduces a workplace charging (WPC) optimization model that maximizes the total satisfied electric miles of employees’ plug-in electric vehicles, subject to a given annual budget. The model optimizes both planning decisions of charger number and power levels and operation decisions of charging spot assignment and charging schedule for the given temporal distribution of charging demands and varied electricity prices. Results of experiments based on national average travel data indicate that the actual WPC strategy varies by budget level. Through optimization, the strategy could reduce impacts of the varied electricity price by shifting charging schedules to periods when electricity prices are low. Also, the model is expanded to study the trade-off between providing WPC and addressing consequence of degraded charging service by including the per-mile shadow cost of unsatisfied charging demand. Finally, we observe that their relative competitiveness mainly depends on the actual shadow cost of WPC.
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Hybrid electric buses fuel consumption prediction based on real-world driving data
Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. Here, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.
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Characterization of commercial vehicles’ start-up operations from in-use data
Diesel engines produce disproportionate levels of emissions when the engine and after-treatment systems are operating at low temperatures. This situation arises most commonly when the vehicle is first started after overnight. To quantify emissions attributable to vehicle starts, a sizable collection of on-road commercial vehicle operating data is analyzed to identify start-up events and inspect associated emissions. Data was obtained from the National Renewable Energy Laboratory’s (NREL’s) Fleet DNA and from the Center for Environmental Research & Technology (CE-CERT). Included are 500 + diesel vehicles with more than 42,000 recorded days, drawn from 25 vocational categories across the United States. Analysis shows that vehicle behavior, in terms of engine cold- and warm-operation, starts per day, soak time, and warm-up duration, differs significantly between vehicle vocations. Also, weighting factors for cold- and hot-starts currently used in the U.S. Environmental Protection Agency’s Federal Test Procedure (FTP) for heavy-duty emissions certification accurately represent real-world operations. Although the FTP includes a comparable fraction of cold operation, the hot fraction is much shorter than real-world operation due to limited test duration. The investigation also revealed that real-world engines operate for a significant amount of time when the engine coolant is in the “hot-stabilized” region, but the selective catalytic reduction (SCR) temperature is below its effective operating temperature of 200 °C. Of the vehicles under investigation, almost 20% of their operational time is within this condition. Therefore, novel approaches to raise and maintain SCR temperature are highly required to further reduce engine emissions.
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Development of heavy-duty vehicle representative driving cycles via decision tree regression
Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.
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Exploring the future energy-mobility nexus: The transportation energy & mobility pathway options (TEMPO) model
Not Available
Muratori, Matteo↗
Techno-economic comparison of electrification for heavy-duty trucks in China by 2040
We show that electrification of heavy-duty trucks (HDTs) is critical of achieving sustainability and carbon neutrality in road freight. Based on the total cost of ownership and the life-cycle emissions, the study compared the traditional diesel internal combustion engine vehicle technologies with potential electrification options for HDTs in China, including battery electric vehicle, plug-in hybrid electric vehicle, hydrogen fuel cell vehicle, and battery electric catenary vehicle. Scenario and sensitivity analysis were explored the life-cycle effects of these electrification options in China from 2020 to 2040. It is found that green electricity and green hydrogen are the most important factors influencing the life-cycle emissions of CO 2 , NO X , and PM 2.5 for different HDTs’ electrification options. The plug-in hybrid electric vehicle of HDTs has shown good performance in terms of total cost and carbon emissions, mainly due to the reduced battery capacity and the current grid mix in China.
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Developing a heavy-duty vehicle activity database to estimate start and idle emissions
Heavy-duty vehicle start and idling activities were characterized from two datasets to improve emission estimates in the MOtor Vehicle Emission Simulator (MOVES): 1. Fleet DNA from the National Renewable Energy Laboratory (NREL) and 2. A dataset collected by the University of California, Riverside for the California Air Resources Board. Furthermore, the combined dataset includes 564 commercial vehicles, over 23,000 vehicle days of operation and covers seven of the nine heavy-duty source types defined by MOVES. The start and idle activities are characterized and illustrated across MOVES source types, vocations, fleets, days, and hours. This study provides the most comprehensive analysis yet made publicly available to characterize start and idle activity for heavy-duty vehicles within the United States. The results also show there is significant uncertainty in the average heavy-duty idle and start activity due to the large variation in activity across fleets and vocations, and sparsity of nation-wide vehicle population data by vocation.
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Optimal mass evacuation planning for electric vehicles before natural disasters
The electric vehicle (EV) market has significantly expanded because EVs have lower operational costs while leaving less environmental footprint than internal combustion engine vehicles. However, EVs also come with drawbacks, including long charging time and short operational ranges. With these drawbacks and limited charging facilities, efficient long-distance EV evacuation management is challenging and has not been properly addressed. Without an efficient evacuation plan, serious congestion could happen at charging facilities, the evacuation process would be excessively long, and thus human lives may be put at risk. This study is motivated to investigate the optimal mass evacuation planning for EVs considering limited charging facilities. A three-stage method is proposed to efficiently approach this problem. A case study of Florida hurricane evacuation is conducted. Here, the method's effectiveness is verified by comparing it with a benchmark. Management insights and policy indications are drawn through sensitivity analysis of key parameters.
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