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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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Profitable Recycling of Low-Cobalt Lithium-Ion Batteries Will Depend on New Process Developments
The rapidly-growing fleet of electric vehicles contributes to transforming transport, but presents challenges for managing spent lithium-ion batteries in coming decades. In Joule, Chen et al. review the advantages and limitations of existing lithium-ion battery recycling processes. To scale rapidly, recycling must be profitable, even for low-cobalt batteries.
The influence of electrochemical cycling protocols on capacity loss in nickel-rich lithium-ion batteries
The transition towards electric vehicles and more sustainable transportation is dependent on lithium-ion battery (LIB) performance. Ni-rich layered transition metal oxides, such as NMC811 (LiNi 0.8 Mn 0.1 Co 0.1 O 2 ), are promising cathode candidates for LIBs due to their higher specific capacity and lower cost compared with lower Ni content materials. However, complex degradation mechanisms inhibit their use. In this work, tailored aging protocols are employed to decouple the effect of electrochemical stimuli on the degradation mechanisms in graphite/NMC811 full cells. Using these protocols, impedance measurements, and differential voltage analysis, the primary drivers for capacity fade and impedance rise are shown to be large state of charge changes combined with high upper cut-off voltage. Focused ion beam-scanning electron microscopy highlights that extensive microscale NMC particle cracking, caused by electrode manufacturing and calendering, is present prior to aging and not immediately detrimental to the gravimetric capacity and impedance. Scanning transmission electron microscopy electron energy loss spectroscopy reveals a correlation between impedance rise and the level of transition metal reduction at the surfaces of aged NMC811. The present study provides insight into the leading causes for LIB performance fading, and highlights the defining role played by the evolving properties of the cathode particle surface layer.
Applications of Digital Twin System in a Smart City System with Multi-Energy
With the increasing number of electric vehicles (EV), the smart transportation system is becoming more closely related to the smart energy system in a smart city. However, because of the high complexity, dynamics, and large-scale of these two systems, it is challenging to study their operation problems systematically, especially in severe conditions, e.g., the 2021 winter Texas power traffic crisis. With artificial intelligence, federated learning, edge computing, and automatic control, a digital twin based smart city is proposed in this paper and it focuses on two applications: smart transportation and smart energy grid. Both systems are presented in a structured design manner with several components in the digital twin system, which also contains many real-implementations of different scenarios. The proposed digital twin of a smart city provides a potential clue for the problems mentioned above.
Pool Fire Analysis Methodology for Assessing Damage to Waste Containers
This document provides guidelines, whereby the physical parameters associated with postulated flammable/combustible liquid pool fires are determined (thermal analysis) in order to estimate the number of waste containers impacted. Pool fire damage to waste containers may occur due to a container being fully engulfed (within the periphery of the pool), partially engulfed, or within the critical heat flux distance. The methodology described here for estimating the size of the pool fire is developed using spill characteristics outlined in Chapter 65 of the SFPE Handbook (Ref. 7) and testing done by Hansen (Ref. 25). For exposures, the critical flux height is determined using the detailed method of Shokri and Beyler (Ref. 8) from the flame height, calculated as described by Heskestad (Ref. 7) and the average black body emissive power, determined using the method of Shokri and Beyler (Ref. 8). Three types of liquid pool fires should be considered; unconfined crash/instantaneous spill fires, unconfined continuously flowing (also called metered leak) spill fires, and confined spill fires. All fuel sources should be considered consistent with the facility Fire Hazard Analysis (FHA). However, the most likely liquid fuel sources present in TRU waste facilities are those present on TRU waste handling vehicles (forklifts, pallet movers, transport trucks, etc.). This method can also be used for assessing damage to items other than waste containers or other methods of analysis may be used with appropriate justification.
Nano- and Earth-Science-inspired Electric Vehicle Battery Recycling to Secure Battery Supply Chain
With the expected growth of electric vehicles (EV) in the transportation sector, recycling or upcycling spent battery materials will be required to reduce EV costs, lessen waste, and ease critical material supply chain pressure for EV battery applications. “Direct” recycling or upcycling is an alternative to current industrial approaches. Many challenges remain for viable direct battery upcycling, including upgrading the transition metal ratio in the costliest component (cathode oxide); separation of transition metal ions in the exit stream; battery disassembly; improving cathode exfoliation which can lead to a new way of direct recycling; and lithium metal anode recycling -- including safety concerns. We devise and deploy a suite of methods to deal with these challenges. The results pave the way to future work on battery recycling, in areas of technological/commercial readiness level ranging from basic research to large-scale commercialization.
SERA: A Hydrogen Infrastructure Capacity Expansion Model
The Scenario Evaluation and Regionalization Analysis (SERA) model is an infrastructure planning optimization model that can guide hydrogen production, delivery, and end-use investment decisions and accelerate the adoption of low-cost hydrogen at scale, whether for fuel cell electric vehicles or non-transportation applications. In this talk, we will review the SERA model objective function as well as the data inputs and outputs. We will also look at a SERA case study identifying potential dispensed costs of hydrogen along major refueling corridors throughout the United States. In addition to the SERA model, Justin will also discuss his recent work for the Office of Manufacturing and Energy Supply Chains on electrolyzer supply chain readiness, and his work for the Hydrogen Fuel Cell Technologies Office and Environmental Protection Agency on the levelized cost of dispensed hydrogen for heavy-duty trucking.
Development of an Energy Efficient and Cost Effective Autonomous Vehicle Research Platform
Commercialization of autonomous vehicle technology is a major goal of the automotive industry, thus research in this space is rapidly expanding across the world. However, despite this high level of research activity, literature detailing a straightforward and cost-effective approach to the development of an AV research platform is sparse. To address this need, we present the methodology and results regarding the AV instrumentation and controls of a 2019 Kia Niro which was developed for a local AV pilot program. This platform includes a drive-by-wire actuation kit, Aptiv electronically scanning radar, stereo camera, MobilEye computer vision system, LiDAR, inertial measurement unit, two global positioning system receivers to provide heading information, and an in-vehicle computer for driving environment perception and path planning. Robotic Operating System software is used as the system middleware between the instruments and the autonomous application algorithms. After selection, installation, and integration of these components, our results show successful utilization of all sensors, drive-by-wire functionality, a total additional power* consumption of 242.8 Watts (*Typical), and an overall cost of $118,189 USD, which is a significant saving compared to other commercially available systems with similar functionality. This vehicle continues to serve as our primary AV research and development platform.
At a Glance: Electric Vehicles
Electric vehicles (EVs) include all-electric vehicles - also referred to as battery electric vehicles (BEVs) - and plug-in hybrid electric vehicles (PHEVs).
Freight Electrification, Moving Goods While Managing Grid Impacts
Analyzing freight data from the Salt Lake City inland port demonstrates significant potential forintegrating battery-electric freight vehicles (BEVs) into key transportation corridors. Our partnership with freight operators like RSD and MWCS, and other companies moving freight around the port, provided valuable fleet telemetry and operationalinsights, highlighting critical corridors on and around I-15 and I-80.
Quantum Computing in Next-Generation Transportation Optimization
We explore how quantum computing (QC) can advance transportation optimization, with a focus on two high-impact areas: traffic signal control and vehicle electrification with grid integration. As transportation systems grow in complexity, classical optimization methods increasingly struggle to deliver scalable and efficient solutions, particularly for real-time, data-rich environments. This work identifies key challenges within these two domains where QC may offer advantages, particularly in handling combinatorial decision spaces and dynamic constraints. We begin by outlining the limitations of classical approaches for traffic signal control optimization and electric vehicle charging coordination, highlighting where computational limitations arise. Previous quantum formulations are presented and new formulations are proposed to demonstrate how emerging quantum algorithms, including quantum annealing and the Quantum Approximation Optimization Algorithm, could be leveraged to reformulate and address these problems. We also evaluate the suitability of current quantum hardware and discuss recent trends that indicate when QC may become a viable tool for transportation applications. While acknowledging the present limitations of QC technologies, this poster emphasizes the importance of preparing quantum-compatible models today. By reviewing and establishing formulations that align with the strengths of quantum algorithms, researchers and practitioners can better position themselves to take advantage of QC advancements as they occur. This work aims to provide a practical, forward-looking perspective on the near-term potential of quantum computing in transportation optimization.
Evaluate the System-Level Impact of Connected and Automated Vehicles Coupled with Shared Mobility: An Agent-based Simulation Approach
With the rapid growth of information and communication technologies, Connected and Automated Vehicles (CAVs) are deemed to be disruptive with the potential to significantly improve overall transportation system efficiency, however, may bring Vehicle Miles Traveled (VMT) increase or other issues. Further, shared mobility systems are another disruptive force that is reshaping our travel patterns. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance and energy efficiency, we develop a mesoscopic simulation-based framework for mobility and energy efficiency evaluation considering the disruptive transportation technologies. Under this framework, we develop novel models for energy intensity and modal activity, and evaluated a variety of energy scenarios for different combinations of CAV applications, various levels of automation, roadway characteristics, and traffic conditions, while also varying different vehicle types and fuel/powertrain technologies. Based applying this modeling suite to a calibrated BEAM simulation network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes.
Projecting Electric Vehicle Electricity Demands and Charging Loads
After over a century of petroleum dominance, electric vehicles are rapidly disrupting the transportation energy landscape. At the same time, the electric power systems are undergoing profound changes as variable renewables replace dispatchable fossil generators. It is critically important to understand how transportation electrification will impact electricity demand, including changes in the load shapes that characterize the system, and the value of flexible electric vehicle charging to better balance electricity demand and supply. This talk focuses on methods to project electricity demand for EV charging with high spatiotemporal fidelity to enable electricity system modeling and analysis and summarizes results from recent NREL studies in this area.
Off-Road Vehicle Energy-Saving Potential
The U.S. Department of Energy’s (DOE’s) Vehicle Technology Office (VTO) has played a critical role in enabling electrification and reducing fuel consumption in the automotive sector. However, the off-road vehicle sector accounts for 8% of transportation fuel, and that share is likely to increase over time as onroad vehicles move towards electrification. DOE can again play a key role in the off-road vehicle market by providing tools that can be used by academia and the industry to evaluate the impact of advanced technologies. The need for a pre-competitive simulation tool was also identified during a 2019 DOE-led workshop at Argonne National Laboratory that included OEMs and suppliers to this market. Autonomie, a powerful and robust system simulation tool for vehicle energy consumption and performance analysis, was developed by Argonne National Laboratory in collaboration with General Motors. Its application covers energy consumption and performance analysis throughout the entire vehicle development process by leveraging its plug-and-play powertrain and vehicle model architecture and development environment
Large-scale scenarios of electric vehicle charging with a data-driven model of control
Transportation electrification is forecast to bring millions of new electric vehicles to roads worldwide this decade. Planning to support those vehicles depends on detailed scenarios of their electricity demand in both uncontrolled and controlled or smart charging scenarios. In this work, we present a novel modeling approach to enable rapid generation of demand estimates that represent the impact of controlled charging for large-scale scenarios with millions of individual drivers. To model the effect of load modulation control on aggregate charging profiles, we propose a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control under a range of electricity rate schedules, achieving small errors (2.5%–4.5%) while accelerating computations by more than 4000 times. To generate the uncontrolled charging demand for scenarios with residential, workplace, and public charging we use statistical representations of a large data set of real charging sessions. We demonstrate the methodology by generating diverse sets of scenarios for California's charging demand in 2030 which consider multiple charging segments and controls, each run locally in under 50 s. We further demonstrate support for rate design by modeling the large-scale impact of a new, custom rate schedule for workplace charging.
LA100 Equity Strategies. Chapter 10: Household Transportation Electrification
The LA100 Equity Strategies project integrates community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. This chapter focuses on residential electric vehicle (EV) incentive programs and multimodal electrified transportation services as means to increase equity in household transportation electrification. Specifically, NREL modeled EV adoption and affordability under business-as-usual and enhanced low-income incentives scenarios and transportation-related energy burdens under multimodal electric travel scenarios, including shared EVs, e-bikes, and improved transit services. Based on our analysis and community guidance, we identified strategies for 1)?increasing equity in new and used light-duty EV adoption and EV charging infrastructure distribution, focused on household used EV ownership and home charging access and 2) affordable, time-efficient, and equitable multimodal electrified transportation options, specifically considering the non-vehicle-owning population. Research was guided by input from the community engagement process, and associated equity strategies are presented in alignment with that guidance.
Analysis of Automated Transit Network Systems with Battery-Electric Vehicles in Automated Mobility Districts: Preprint
The paper provides an overview of the insights and findings of the National Renewable Energy Laboratory's (NREL) ongoing research on the implementation of automated mobility districts (AMDs). AMD is a term coined by NREL to describe a geographically defined district or major activity center located in a dense urban setting with mobility applications provided by automated/autonomous vehicle (AV) systems spanning internal circulation and first-mile/last-mile connections to regional transportation hubs. Research over the past five years has focused on understanding the evolution of AMDs, beginning with demonstrations of automated shuttles prior to the pandemic, to more integrated on-demand mobility systems currently in initial stages of deployment. Initial insights and findings from earlier studies include the need for designation of a "jurisdiction having authority," a clear vision of a complete system to provide end-to-end mobility services, and the requisite intelligent infrastructure to complement AV technology. NREL's most recent Phase III research investigates station boarding/alighting (curb) issues, the full electrification of fleets, and the need for a systems engineering methodology (SEM) to properly analyze the complexities resulting from the convergence of automation, on-demand mobility, and electrification of the transit systems within the AMD. The paper reviews the findings of AMD research conducted during Phase I, II, and III, with special emphasis on Phase III results with respect to a descriptive example of the proposed SEM when a "digital twin" analytical model is used to simulate the transport fleet's battery-electric vehicle miles of travel and associated duty cycles through a rigorous analytical assessment with a comprehensive modeling process.
Towards an Energy Future with Ubiquitous Electric Vehicles: Barriers and Opportunities
The electrification of personal transportation holds great potential for lowering greenhouse gas emissions and reducing climate change. The promise of electric vehicles (EVs) to serve these goals has resulted in a broad range of supporting policies aimed at encouraging widespread EV adoption at both the state and federal levels in the United States and around the world. While the EV revolution and prospects of a world with ubiquitous EVs are impacting various industries and many aspects of daily life, strategic interactions between the power grid and EVs are crucial for a successful energy transition. However, managing the interplay between EVs and the power grid remains a challenge. Motivated by that tension, this paper surveys a variety of solutions, policies, and incentives that are focused on effectively managing EV charging behaviors. The paper’s objective is to explore these tools to ensure that EV owners have ultimate control over their personal vehicles while simultaneously allowing the power grid to mitigate adverse network impacts. Furthermore, this paper examines the role of charging infrastructure technology and its strategic placement in facilitating the seamless integration of EVs into the grid. Additionally, the paper highlights financial mechanisms associated with EV integration and discusses the consequences of these mechanisms.