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Novel Carbon (C)-Boron (B)-Nitrogen (N)-Containing H2 Storage Materials

The following summarizes the research conducted for DOE project DE-EE0005658 “Novel Carbon(C)-Boron(B)-Nitrogen(N)-Containing H2 Storage Materials”. This work focused in part on the continued study of two materials identified from the preceding project DE-FG360GO18143 (“Hydrogen Storage by Novel CBN Heterocycle Materials”) as lead candidates to meet the DOE technical targets for either vehicular or non-automotive hydrogen storage applications. Specifically, a room-temperature liquid, 3-methyl-1,2-cyclopentane (B), and a high H2 capacity solid, 1,2-BN-cyclohexane (J), were selected for further characterization and performance optimization. In addition to these compounds, the current project also aimed to prepare several new materials predicted to be disposed towards direct reversibility of H2 release and uptake, a feature deemed critical to achieving efficient recycling of spent fuel end products. To assist in the rational design of these and other next-generation materials, this project undertook to investigate the mechanism of hydrogen release from established compounds (mainly B and J) using a combined experimental/computational approach. Among this project’s signature accomplishments, the preliminary synthetic route to B was optimized for production on decagram scale. With such quantities of material available, its performance in powering an actual 30 W proton exchange membrane (PEM) fuel cell stack was tested and found to be identical to that of facility H2. Despite this positive proof-of-concept achievement, however, further consideration of neat B as a potential hydrogen storage material was abandoned due to evidence of thermal instability. Specifically, mass spectrometry-coupled thermogravimetric analysis (TGA-MS) revealed significant H2 release from B to initiate at 50 °C, well below the 60 °C minimum threshold set by the DOE. This result prompted a more extensive investigation in the decomposition mechanism of B vis-à-vis that of J, which exhibited in neat form a substantially higher onset temperature for spontaneous H2 release (70 °C). Solution-phase kinetic experiments using ReactIR established a second-order dependence for the initial loss of H2 from both B and J; Arrhenius analysis, however, revealed the activation barrier for this reaction was lower for B than for J, which presumably contributes to the diminished thermal stability of the former. On the basis of these and other experimental results, extensive computational efforts yielded a reasonable mechanistic model for the dehydrogenation of 1,2-BN-cycloalkane materials. While the prospect of neat B as a suitable hydrogen storage material was discarded, it was proposed that the combination of B with more thermally stable amine-borane-based materials might afford mixtures with improved properties. Indeed, when B was combined with ammonia borane (AB) in a 2:1 molar ratio, the two materials formed a liquid. More significantly, this mixture remained liquid even after complete dehydrogenation, thus establishing the potential for a single-phase fuel cycle. (In contrast, the dehydrogenation product of neat B is a low melting solid (mp = 28-30 °C).) Another advantage conferred by the blend formulation was a dramatic reduction in the amount of borazine produced by AB. Borazine is a well-known contaminant of H2 produced by the thermal decomposition of neat AB, and exerts deleterious effects on fuel cell performance. Residual gas analysis (RGA) of the gas stream generated from the B-AB blend, however, detected just 0.01% borazine content when a Pt-Ni nanoparticle dehydrogenation catalyst was used. In all ii then, the 2:1 B-AB blend marks a major achievement in the effort to develop a suitable liquid amine-borane hydrogen storage material, and merits further investigation into the optimization for practical adoption. Similar realization of the potential of J as a high % wt. H2 material required a method to dehydrogenate the carbonaceous components of the molecule without the use of a sacrificial hydrogen acceptor, as had been reported in the previous project. Ultimately, this reaction was achieved for a B,N-disubstituted BN-cyclohexene model substrate using a gas flow system with a fixed Pd/C catalyst bed. Considerable work remains, however, to translate these initial results into a general protocol for complete dehydrogenation of fully saturated BN-cycloalkane materials such as J. With concrete confirmation of the possibility to perform both BN and CC dehydrogenation on a single theoretical substrate, COMSOL modeling was used to evaluate the effects of thermodynamically coupling the two reactions. It was hypothesized that the heat generated from exothermic BN dehydrogenation would partially drive the endothermic CC dehydrogenation reaction; this additional heat consumption was expected to in turn confer the benefit of lowering the maximum reactor temperature. A two-dimensional model of an axisymmetric reactor including experimental kinetic and calculated thermodynamic parameters for both reactions did indeed predict these outcomes. The extent to which the effects of thermodynamic coupling actually manifested, however, were also revealed to depend strongly on the relative rates of the two reactions, as well as the magnitude of the equilibrium constant governing the progress of the endothermic process. Given the evident complexity of attaining high effective % wt. H2 capacity with J, alternative systems were investigated for greater facility of extensive H2 release. Among those studied, 1,2,4,5-bis-BN-cyclohexane (H) demonstrated the most favorable properties, particularly with respect to thermal stability: rather than decompose, a neat sample instead sublimed when heated above 150 °C. Nevertheless, two commercially available catalytic systems were identified to effect release of two H2 equivalents from H. Release of further equivalents were apparently impeded by the formation of either polymeric material or one of two dimeric cage compounds depending on the catalyst used. Notably, a method to regenerate H from these product mixtures remains to be developed. Thus, while H may prove useful for certain long-term energy storage needs, it is currently less suited applications involving frequent fuel consumption. Similar difficulties were also encountered in attempts to realize the complete fuel cycle of 1,3-BN-cyclohexane (E) and B,N-substituted derivatives thereof. It had been initially proposed that E would provide for readily reversible BN dehydrogenation through a measure of frustrated Lewis pair-type character. Indeed, computations predicted this reaction would be essentially thermoneutral in solution. In the course of attempts to fully hydrogenate the spent fuel, however, dimeric species formed and proved resistant to further BN reduction. While a number of monomeric cyclic compounds were also successfully synthesized as formal boron-nitrogen frustrated Lewis pairs, none demonstrated any capacity to split H2 across the BN unit. The challenge of developing a practical amine-borane-based material for readily reversible hydrogen storage thus remains unresolved at this time. As such, it deserves consideration as a major objective of any future work.

08 HYDROGEN↗

Organic Electrochemical Transistor Channel Materials: Copolymerization Versus Physical Mixing of Glycolated and Alkoxylated Polymers

Organic electrochemical transistors (OECTs) feature a polymer channel capable of conducting both ions and electronic charges. The choice of the channel material is critical for OECT performance. Many efforts have focused on improving performance via the chemical tunability of conjugated polymers – through backbone, side chain, and molar mass engineering – leading to useful design principles for accumulation-mode OECT materials. However, tuning the chemical structure of conjugated polymers often requires time-consuming optimization of the synthesis route. Meanwhile, variations in molar mass, dispersity, structural defects, and metal content present challenges when attempting to analyze the detailed effects of structural modifications, as multiple performance-determining factors are often (unintentionally) changed at the same time. Therefore, this study explores blended channel materials obtained by physically mixing glycolated and alkoxylated polymers in different ratios, and compares their OECT performance with the corresponding statistical copolymers. It is shown that mixing two well-performing materials creates blends that enable rational tuning of the transistor properties without compromising on performance. Thus, channels based on blends of alkoxylated and glycolated polymers hold promise for OECT technology with tailored response, as only two materials are needed to achieve any desired side chain ratio, simplifying the optimization of OECT characteristics.

copolymerization↗

In-route inductive versus stationary conductive charging for shared automated electric vehicles: A university shuttle service

In-route inductive charging technology, as applied to automated electric vehicles, can help realize a fully automated system of both vehicles and chargers. This study presents a planning optimization analysis for fixed-route automated shuttles supported by in-route inductive charging technology. A techno-economic feasibility of inductive charging was assessed in comparison with stationary charging, including Level 2 AC chargers, and DC fast chargers (DCFCs). This analysis considered both present-day and future vehicle operations and overall system costs. A real project with two circulator Navya Arma shared automated electric vehicles (SAEVs) at the University of Michigan was investigated using real-world collected energy and travel data. The outcomes show that the proper design of quasi-dynamic inductive chargers at designated stops allows SAEVs to realize unlimited driving range and be cost-competitive to DCFC technology. Considering present-day costs and vehicles, low-speed SAEVs can realize charge-sustaining operation at a minimum cost either by implementing a 50-kW inductive charger at two stops with one segment per position and a 29-kWh onboard battery, or by installing a 100-kW inductive charger at one stop with one segment per position and a 28-kWh onboard battery. Overall, considering future costs and vehicles, either a 40-kW charger at one stop with a 29-kWh battery or a 50-kW charger at the north stop with a 14-kWh battery would enable charge-sustaining operation. In addition, quasi-dynamic inductive solution can reduce the onboard battery by about 15% while providing unlimited driving range, but stationary scenarios require about 112% additional battery capacity to support a 12-h driving range.

33 ADVANCED PROPULSION SYSTEMS↗

Three-dimensional structural dynamics of DNA origami Bennett linkages using individual-particle electron tomography

Scaffolded DNA origami has proven to be a powerful and efficient technique to fabricate functional nanomachines by programming the folding of a single-stranded DNA template strand into three-dimensional (3D) nanostructures, designed to be precisely motion-controlled. Although two-dimensional (2D) imaging of DNA nanomachines using transmission electron microscopy and atomic force microscopy suggested these nanomachines are dynamic in 3D, geometric analysis based on 2D imaging was insufficient to uncover the exact motion in 3D. In this paper, we use the individual-particle electron tomography method and reconstruct 129 density maps from 129 individual DNA origami Bennett linkage mechanisms at ~6-14 nm resolution. The statistical analyses of these conformations lead to understanding the 3D structural dynamics of Bennett linkage mechanisms. Moreover, our effort provides experimental verification of a theoretical kinematics model of DNA origami, which can be used as feedback to improve the design and control of motion via optimized DNA sequences and routing.

59 BASIC BIOLOGICAL SCIENCES↗

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. Here, in this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

High-Throughput Discovery of Robust Metal-Organic Frameworks for CO 2 Capture (Final Report)

The goal of the project is to develop and utilize high-throughput instrumentation for the discovery of robust, cost-effective metal-organic frameworks for the efficient capture of CO 2 from power plant flue gas. To this end, we have used highly-parallel and automated synthesize methods to accelerate the discovery of new MOF structures and their optimized synthesis and activation routes. To be able to rapidly screen these new materials for their potential CO 2 capture performance, we have developed high-throughput structural characterization instruments and gas adsorption instruments. To guide the materials choice and design, we have also developed computational tools to analyze the life cycle and sorbent performance of existing and hypothetical materials.

36 MATERIALS SCIENCE↗

Production of Radiohalogens: Bromine and Astatine for Imaging and Therapy (Final Report)

The UAB team focused our efforts on Project 1: Production and Characterization of 76,77 Br and has completed all milestones. We have conducted irradiations using novel cobalt selenide targets prepared in house and by our collaborators at UWisc. A distillation apparatus for the purification of the produced radiobromine has been constructed and radiobromine is in routine production at UAB. The gas target route is under final optimization for routine production. A chemistry graduate student, Samuel Ferran, led the project for both the gas and solid target experiments this work will be included in his thesis project. Two manuscripts are in preparation based on this work. Additionally, an undergraduate student Retta El Sayed assisted with this project and is now enrolled in graduate school. Monthly collaborative meetings took place as well as several in person team meetings with all participating sites.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Energy Consumption and Cost Reduction of Future Light-Duty Vehicles through Advanced Vehicle Technologies: A Modeling Simulation Study Through 2050

The U.S. Department of Energy’s (DOE’s) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) aim to develop sustainable, affordable and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate vehicle-level fuel savings potential is critical for understanding DOE’s impact. In this work, we simulated technologies funded by VTO and HFTO for light duty vehicles. The simulations were performed across: Multiple powertrain configurations (i.e., conventional, power-split, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles), Vehicle classes (i.e., compact car, midsize car, small sport utility vehicle [SUV], midsize SUV, and pickup trucks); and Fuels (i.e., gasoline, diesel, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015, 2020, 2025, 2030, and 2045. A delay of 5 years is assumed between laboratory year and model year (year technology is introduced into production). Finally, uncertainties are included for both technology performance and cost aspects by considering two cases: Low case, aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on regulations, business as usual; and High case, aligned with aggressive technology advancements based on R&D targets developed through support by VTO & HFTO. These scenarios are not intended as predictions of future performances. The energy and cost impact of different technologies were estimated using Autonomie (www.autonomie.net), Argonne vehicle system simulation tool. Autonomie is a state-of-the-art vehicle system simulation tool used to assess the energy consumption, performance and cost of multiple advanced vehicle technologies across classes (from light to heavy duty), powertrains (from conventional to HEVs, FCEVs, PHEVs and BEVs), components and control strategies. Autonomie is packaged with a complete set of vehicle models for a wide range of vehicle classes, powertrain configurations and component technologies, including vehicle level and component level controls. These controls were developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies including analyzing various component technologies, sizing powertrains components for different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route based vehicle energy control and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions and estimates the vehicle-level energy consumption benefits and associated technology costs for the various types of light duty vehicles. All details of vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

33 ADVANCED PROPULSION SYSTEMS↗

Data Driven Fuel Efficient Driving Behavior Feedback for Fleet Vehicles

Dependency of the transport sector on fossil fuels is encouraging a significant amount of research in to improving fuel efficiency in vehicles. Three primary techniques are identified for vehicle fuel efficiency improvement: 1) vehicle technology improvements such as drivetrain improvements, 2) traffic infrastructure improvements such as traffic flow management and route selection, and 3) driver behavior changes such as acceleration and deceleration profiles. Out of the 3 techniques, driver behavior changing has the least implementation cost and is able to provide immediate results. Thus, this paper presents a fuel efficient driving behavior identification and feedback architecture that is specific to fleet vehicles. The presented method utilizes historical data from fleet drivers on specific routes and generates fuel optimal velocity profiles that do not affect travel time. The identified velocity profile is the prompted to the driver via a low-cost plug-and-play style un-obstructive display. The display uses an intuitive and easily understandable visualization to prompt drivers on fuel efficient velocity. The presented architecture was tested on the Idaho National Laboratory (INL) bus fleet in real-world driving conditions and was shown to be able to increase the fuel economy by 9% and 20% in two different driving scenarios.

99 GENERAL AND MISCELLANEOUS↗

Selective recovery of silver from waste low-temperature co-fired ceramic and valorization through silver nanoparticle synthesis

Highlights: • Silver rich LTCC an e-waste has been recycled and valorized. • Selectively, Ag was leached and recovered as nanopowder. • The process can circulate the Ag within the industry in a close loop. • Brings back the material to production stream and address the circular economy. • Can be part of cradle-to-cradle technology and lower the futuristic carbon economy. - Abstract: Considering the value of silver metal and silver nanoparticles, the waste generated during manufacturing of low temperature co-fired ceramic (LTCC) were recycled through the simple yet cost effective process by chemical-metallurgy. Followed by leaching optimization, silver was selectively recovered through precipitation. The precipitated silver chloride was valorized though silver nanoparticle synthesis by a simple one-pot greener synthesis route. Through leaching-precipitation optimization, quantitative selective recovery of silver chloride was achieved, followed by homogeneous pure silver nanoparticle about 100 nm size were synthesized. The reported recycling process is a simple process, versatile, easy to implement, requires minimum facilities and no specialty chemicals, through which semiconductor manufacturing industry can treat the waste generated during manufacturing of LTCC and reutilize the valorized silver nanoparticles in manufacturing in a close loop process. Our reported process can address issues like; (i) waste disposal, as well as value-added silver recovery, (ii) brings back the material to production stream and address the circular economy, and (iii) can be part of lower the futuristic carbon economy and cradle-to-cradle technology management, simultaneously.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Capturing Travel Mode Adoption in Designing On-Demand Multimodal Transit Systems

This paper studies how to integrate rider mode preferences into the design of on-demand multimodal transit systems (ODMTSs). It is motivated by a common worry in transit agencies that an ODMTS may be poorly designed if the latent demand, that is, new riders adopting the system, is not captured. This paper proposes a bilevel optimization model to address this challenge, in which the leader problem determines the ODMTS design, and the follower problems identify the most cost efficient and convenient route for riders under the chosen design. The leader model contains a choice model for every potential rider that determines whether the rider adopts the ODMTS given her proposed route. To solve the bilevel optimization model, the paper proposes an exact decomposition method that includes Benders optimal cuts and no-good cuts to ensure the consistency of the rider choices in the leader and follower problems. Moreover, to improve computational efficiency, the paper proposes upper and lower bounds on trip durations for the follower problems, valid inequalities that strengthen the no-good cuts, and approaches to reduce the problem size with problem-specific preprocessing techniques. The proposed method is validated using an extensive computational study on a real data set from the Ann Arbor Area Transportation Authority, the transit agency for the broader Ann Arbor and Ypsilanti region in Michigan. The study considers the impact of a number of factors, including the price of on-demand shuttles, the number of hubs, and access to transit systems criteria. The designed ODMTSs feature high adoption rates and significantly shorter trip durations compared with the existing transit system and highlight the benefits of ensuring access for low-income riders. Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude. Funding: This research was partly supported by National Science Foundation [Leap HI Proposal NSF-1854684] and the Department of Energy [Research Award 7F-30154].

Operations Research & Management Science↗

Connectivity-enhanced route selection and adaptive control for the Chevrolet Volt

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. Furthermore, these represent substantial opportunities considering that they only require software adjustments to implement.

30 DIRECT ENERGY CONVERSION↗

Connectivity-Enhanced Route Selection and Adaptive Control for the Chevrolet Volt: Preprint

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3% respectively. These represent substantial opportunities considering that they only require software adjustments to implement.

33 ADVANCED PROPULSION SYSTEMS↗

Engine-in-the-Loop Study of a Hierarchical Predictive Online Controller for Connected and Automated Heavy-Duty Vehicles

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing. This work is experimentally experimentally validated via a sequence of EIL studies intended for evaluating the computational costs and fuel savings associated with these algorithms. These EIL studies involve the closed-loop validation of the proposed control strategies, both individually and combined. These studies show that this hierarchy of algorithms is capable of running online, with the round-trip communication delay inherent in EIL simulation being one of the key factors affecting the EIL results. Moreover, the EIL studies are encouraging, both in terms of the successful hierarchical integration of the underlying algorithms and also in the resulting fuel savings seen in the EIL tests. In particular, the EIL results suggest that an aggressive overall goal of reducing vehicle fuel consumption by 15-20% or more is potentially achievable, especially in urban/suburban scenarios.

33 ADVANCED PROPULSION SYSTEMS↗

A predictive machine learning approach for microstructure optimization and materials design

This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. In conclusion, experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.

36 MATERIALS SCIENCE↗

Techno-economic optimization of shale gas to dimethyl ether production processes via direct and indirect synthesis routes

The shale gas to dimethyl ether process via direct synthesis route with a syngas H2/CO=1 has a higher dimethyl ether yield than that via indirect synthesis route. However, the direct synthesis route involves various energy and capital-intensive processes, such as the complicated dimethyl ether separation process, and the CO2 recycle and comprssion process. In this study, a techno-economic analysis of the shale gas to dimethyl ether process via direct and indirect synthesis routes is performed using Aspen Process Economic Analyzer based on the rigorous process model developed in Aspen Plus. Effects of different CO2 removal technologies and dimethyl ether synthesis technologies from syngas are evaluated. Both, direct and indirect plants are optimized in Aspen Plus equation-oriented environment using the rigorous process models. The effects of key design parameters (i.e. H2/CO in the syngas) as well as investment parameters (i.e. prices of raw materials, products, and utilities, plant scale) on the process economics as been evaluated. The direct dimethyl ether synthesis route processing a syngas with H2/CO =1 and using the Rectisol process for CO2 capture has the highest overall thermal efficiency on the lower heat value basis is found to be much lower than the current U.S. market diesel price indicating the feasibility of the process.

Mevawala, Chirag↗

Integrated Routing and Traffic Signal Control for CAVs via Reinforcement Learning Approach

Incorporating Connected and Automated Vehicles (CAVs) into urban traffic networks presents opportunities and challenges for traffic management systems. This paper aims to develop an integrated routing and traffic signal control system designed explicitly for CAVs, utilizing a Reinforcement Learning (RL) approach. The objective is to enhance traffic flow and improve overall transportation efficiency in the controlled areas. We propose an innovative framework that employs the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Q-network (DQN), to dynamically adjust the number of vehicles in the routes and the duration of traffic signals. Our simulation results demonstrate that a DQN agent successfully optimizes the number of vehicles in the routes and traffic signal timings of traffic signal controllers, eventually reducing total travel time. The study illustrates the potential usage of RL-based systems in managing routing and traffic signals for CAVs, offering a promising opportunity for future urban traffic management strategies.

Park, Jiho [New York University]↗