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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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This help sheet provides information on how battery energy storage systems can support electric vehicle (EV) fast charging infrastructure. It is an informative resource that may help states, communities, and other stakeholders plan for EV infrastructure deployment, but it is not intended to be used as guidance, set policy, or establish or replace any standards under state or federal law.
In photoactive metal complexes the localization of photoexcited charges dictates the site of chemical reactivity, but few studies measure the charge redistribution in these systems with spatial precision. Herein, we track the inter- and intra-ligand charge transfer processes that underpin light-driven charge separation in the well-studied “molecular lightswitch” [Ru(bpy) 2 dppz] 2+ (aqueous [Ruthenium II (2,2′-bipyridine)2(dipyrido[3,2-a:2′,3′-c]phenazine)] 2+ [Cl − ] 2 ) by probing the electronic structure of ligand nitrogen atoms in real-time using ultrafast X-ray absorption spectroscopy and first principles calculations. We confirm the localization of excited electron density on the phenazine N atoms of dppz and we newly identify two parallel electron transfer pathways to populate this state. Sub-70 fs electron transfer to the phenazine portion of dppz is observed and attributed to intra-ligand electron transfer following Ru-to-dppz metal-to-ligand charge transfer (MLCT) excitation. This fast charge transfer was not reported in prior ultrafast studies. The slower (ca. 2 ps) charge transfer reported extensively in time-resolved optical absorption and emission studies is reassigned here to inter-ligand electron “hopping” between nearly isoenergetic ligand moieties following Ru-to-bpy MLCT excitation. In conclusion, the results demonstrate much faster charge separation than previously identified in this well-studied system, highlighting how extended azaacene ligand motifs promote the competitive charge transfer processes needed to drive light-driven electron transfer chemistry.
Li-ion battery performance and degradation are strongly correlated with the electrode microstructures and can be modeled at different scales, each with their own limitations. Herein, we compare predictions achieved with a macro- and a micro-scale model, that is, respectively, neglecting or considering the microstructural heterogeneity of the composite electrodes, on virtual numerically generated and real microstructures. While both models are in relative agreement at the low charge rates, differences arise for fast charging scenarios and especially for the real, highly heterogenous, microstructures. The microscale model predicts that electrolyte concentration saturation and depletion, respectively, at the back of the cathode and of the anode are exacerbated, and that lithium plating occurs earlier for real microstructures. The present work also indicates that the mesh representation significantly impacts the microscale model predictions, and consequently that microscale models should add surface area as a parameter to consider explicitly surface roughness. This article is the first of a series, with subsequent entries further investigating in-plane heterogeneities, lithium plating, and the impact of microstructure representativity on model predictions.
Electrification of heavy-duty (HD) vehicles marks an important milestone and technical challenge in the electric vehicle (EV) industry and the public grid. However, implementing EV charging at this scale will necessitate that traditional truck stops be updated with EV charging infrastructure that could represent 10's of MW in electricity consumption. Furthermore, as the transportation sector is represented as critical infrastructure, supporting resiliency considerations in EV charging infrastructure will be critical. This paper proposes an optimization-based approach for optimally designing a MW-scale microgrid charging network. This approach transforms conventional designed truck stops into a reliable HDEV charging stations capable of overnight slow charging and 30-minute to 1 hour fast charging. Using a mixed-integer linear program formulation blending capacity planning and reliability constraints, an optimal network configuration can be solved for a proposed EV charging station that includes photovoltaic and battery energy storage capabilities.
Despite advances in lithium-ion battery technology, critical challenges remain that must be addressed to accelerate electric vehicle (EV) adoption and global energy transformation. Significantly improved battery thermal management (BTM) is key to overcoming these challenges. BTM approaches focus on increasing heat transfer coefficients via air, liquid, or refrigerant cooling, but less attention is given to reducing the battery's thermal resistance, a major bottleneck for heat transfer. This work introduces a novel approach to reduce battery thermal resistance by integrating in-plane heat transfer with optimized cell geometry, minimized thermal resistances, and reduced interfacial resistances, representing a departure from previous methods. The standard prismatic can cell incorporating this technology is referred to as the high heat transfer (HHT) battery. An equivalent resistance battery thermal model is developed for speed and accuracy, validated against experimental data in the literature, demonstrating strong correlation and ensuring reliable predictions for real-world performance. Thermal performance metrics of the conventional and HHT batteries are compared using a parametric study with air, liquid, and refrigerant boundary conditions across a range of aspect ratios. The HHT battery shows a heat removal rate up to 20 times higher than a conventional battery. These findings suggest that HHT technology could be transformative for EV battery performance, enabling fast charging, mitigating thermal runaway, extending battery life, reducing cold-weather power loss, increasing reliability, lowering costs, and enabling higher energy density, all critical for EV adoption and energy transformation. Future work will focus on prototyping and real-world testing to refine these findings for commercial-scale applications.
The USABC Advanced Battery development plan had the following three focus areas: 1. Existing technology validation, implementation, and cost reduction 2. Identification of the next viable technology with emphasis on the potential to meet USABC cost and energy density goals. 3. Support high-risk, high-reward battery technology R&D The primary aim of this project was focused on the barriers that most impede wider public adoption of electrification in vehicles-cost, energy density, low-temp performance, calendar life, and improvements in abuse tolerance. The specific objectives are as follows: Cost Reduction: drive to significantly reduce battery cost to be in-line with the $\$$100/kWh or less (by the end of calendar year 2020) DOE cost goals. Additionally, goals of $\$$75/KWh were set for 2023 aligned with fast charge and some offset of energy density. Energy Density Increase: focus on improving the cell-level specific energy and energy densities to >350 watt hours per kilogram (Wh/kg) and >750Wh/L. Low Temperature Performance Increase: strive toward developments that improve the discharge power and eliminate or dramatically reduce the life limiting lithium plating associated with regenerative braking at low temperatures, during the program. Calendar Life Increase: drive to achieve a 15-year calendar life. Abuse Tolerance Improvement: strive to develop improvements in Li-ion abuse tolerance and/or development of electrochemical energy storage technologies with inherently better response to abuse circumstances. Emerging Areas: initiate new programs that address emerging technologies that arise during the contract period.
This work demonstrates integration of an ultrafast laser onto a roll-to-roll machine, the laser structuring of a double-sided, 700 m long roll of graphite battery anode and its subsequent manufacture into 27 Ah prismatic cells. The electrode was ablated with a novel hybrid-microstructure composed of both hexagonally arranged pores for enhanced rate performance and channels for fast electrolyte wetting. Subsequently, this anode and a non-ablated baseline anode are paired with an NMC111 cathode for cell building and electrochemical characterization. Compared to the baseline, laser ablated cells demonstrated a reduction in soaking time of at least 60%, an improvement in fast charge capability with >30% more capacity accepted during 6C charging, and an extension of cycle life of >40% during 0.5C cycling. Further, a perspective is provided on scaling ultrafast laser ablation of battery electrodes to industrial throughputs. Additionally, lessons learned from this pilot-scale demonstration are provided in regards to optical architecture, debris removal, and system control. A techno-economic analysis is used to demonstrate that laser ablation can be integrated into existing electrode manufacturing facilities with only ≈$\$$1.3 per kWh increase (≈2%) in manufacturing cost. Preemptive electrode design for laser ablation is discussed as a further method for enhancing performance. Finally, an analysis of available laser systems and beam-scanning architectures is used to determine design requirements to scale process throughput to a state-of-the-art speed of 50 m min −1 . This analysis demonstrates that laser ablating Li-ion battery electrodes has multiple benefits to manufacturing and battery performance, that the technology already exists to achieve high laser-ablation throughputs, and that integrating ultrafast laser ablation to electrode manufacturing will not create a cost or processing bottleneck.
The quest for efficient solar-driven water splitting, a promising avenue for clean fuel production, faces challenges due to limited solar energy conversion efficiency. Traditional approaches study the overall water splitting as two spatially separate half reactions on two unrelated sites, hindering full utilization of photogenerated charge and water molecules. To overcome these limitations, an integrated cluster heterostructure catalyst on InGaN/GaN semiconductor nanowires is proposed for the effective utilization of photogenerated charge carriers and water molecules on the same redox localization. By establishing the fast charge extraction kinetics based on InGaN/GaN nanowires, the integration of Rh and MoRhOx clusters on the nanowire surface enables simultaneous and fast hydrogen/oxygen evolution reactions at the cluster heterostructure. Furthermore, the integrated strategy can enhance the charge redistribution across the heterostructure between the two clusters, further optimizing adsorption of reaction intermediates on each cluster for boosted photocatalytic water splitting activity. Consequently, the integrated heterostructure triggers a 40-fold increased hydrogen production efficiency in an artificial leaf system. This study provides valuable insights for the rational design of advanced heterostructured photocatalysts for water splitting and beyond.
Ethyl acetate (EA) was identified as a promising electrolyte solvent for sodium-ion batteries (SIBs), exhibiting low viscosity, cost-effectiveness, and low toxicity. Despite a significant portion of aggregation being linked to the weak solvation of Na + /EA as revealed by molecular dynamics (MD) simulations, pulsed-field gradient nuclear magnetic resonance (pfg-NMR) analysis identified a noteworthy Na+ diffusion coefficient of 3.95×10 -10 m 2 s -1 at 25°C in the presence of 1 m NaPF 6 salt. Employing fluoroethylene carbonate (FEC) as a film-forming additive to create electrode-electrolyte interphase, this electrolyte surprisingly made ~210 mAh Na 0.97 Ca 0.03 [Mn 0.39 Fe 0.31 Ni 0.22 Zn 0.08 ]O 2 (NCMFNZO)/hard carbon (HC) pouch cells achieve a lengthy cycling lifetime of 250 cycles with ~80 % capacity retention, cycled up to 4.0 V at 40°C. X-ray photoelectron spectroscopy (XPS) revealed increasing interphasial organic species over cycling, augmenting charge transfer resistance on both cathode and anode, particularly during fast charging or low temperatures (<10°C), promoting Na plating. Finally, gas chromatography-mass spectrometry combined with density functional theory identified CO 2 as the major gas generated from charged cathode/electrolyte interactions, exhibiting temperature/voltage dependence.
In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. Further, the results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.
This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.
This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.
This report examines the mechanical forces that direct current fast charging (DCFC) electric vehicle supply equipment (EVSE) connectors and electric vehicle (EV) inlet ports experience during normal usage. As we witness the fast and growing variety of EVs, charge port locations, and bigger and more powerful EVSE configurations including the SAE J3400 North American Charging Standard (NACS), which has increased the use of adapters with J3400 and J1772 compatibility. These new conditions contribute to increased mechanical forces due to their size, weight and lever arm effect, which creates the need to study and compare these forces to the limits on UL2251 and IEC 62196-1 standards. This report focuses solely on high power DC charging EVSE connectors, EV inlets and adapters for the specific compatibility cases on J3400 with J1772. We first describe the details of the 100N and the 750N side-load evaluations as described in the standards, then present the data obtained by replicating these tests to finally go into more detail on the findings and our recommendations.
In spite of the high energy efficiency and environmental benefits of electric vehicles (EVs), adoption rates are increasing at a relatively slow pace, primarily because of EVs’ limited range and long charging durations. To reduce EV charging times to be comparable to refueling times of conventional vehicles, extreme fast charging systems are required. Such charging systems would reduce drivers’ range anxiety and enable long-distance interstate travel with EVs. Here, this substantial target in charging rates with 15–20 minutes of recharging times, requires research and development from grid to batteries with advanced charging systems. Wireless power transfer systems for EV charging applications are flexible, convenient, and highly efficient, and they allow automated charging. This paper reviews the power electronics and winding and resonant tuning network configurations for polyphase wireless power transfer systems for high-power wireless charging applications.
Federal regulations are driving the adoption of electrification technologies to reduce carbon dioxide equivalent (CO2e) emissions, a metric that quantifies the global warming potential of various greenhouse gases in terms of carbon dioxide (CO2). Although no specific CO2 regulations exist for heavy-duty off-road machines, future reductions are likely, given stricter emissions standards for on-road vehicles. The heavy-duty off-road sector offers significant fuel-saving potential, as its focus has traditionally been on reliability and performance rather than fuel efficiency. This dissertation examines fuel and CO2e savings opportunities on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i, from stock configuration to simple modifications to a complete teardown and reconfiguration of the machine with a plug-in series hybrid architecture using electrified hydraulics. The study begins by modeling the baseline machine’s fuel and energy consumption, calibrating with experimental data from custom operating cycles. An energy analysis identifies key areas for fuel savings. Two simple powertrain modifications result in a combined 16.2% fuel savings. Next, a Pugh-style analysis narrows a list of electrified architectures, leading to high-fidelity models that evaluate total lifetime CO2e and costs. Higher electrification levels reduce CO2e emissions but increase costs, and electricity grid emissions significantly impact CO2e for plug-in architectures. A plug-in series hybrid is chosen for the project. In its base control form, 49% fuel and 29% CO2e savings are expected from the plug-in series hybrid compared to the baseline machine. Further savings are pursued through regenerative braking (6.3%) and load-following hydraulic control (17.8%), totaling 24.1% fuel savings, and leading to a total of 61% fuel and 41% CO2e savings compared to the baseline. Battery chemistries and charging strategies are also analyzed for cost and CO2e impacts, finding LFP batteries as superior due to longevity, and overnight level 2 charging usually at a lower cost but resulting in higher emissions than opportunity DC fast-charging (DCFC). DCFC emissions are highly dependent on grid emissions, and DCFC cost is highly dependent on grid demand charges. Finally, artificial intelligence is applied to operating cycle recognition. Neural network accuracy ranges from 81% to 99%, with applications to worksite efficiency and safety improvements.
With current and future regulations continuing to drive reductions in carbon dioxide equivalent (CO 2 e) emissions in the on-road industry, the off-road industry is also likely to be regulated for fuel and CO 2 e savings. This work focuses on converting a heavy-duty off-road material handler from a conventional diesel powertrain to a plug-in series hybrid, achieving a 49% fuel reduction and 29% CO 2 e reduction via simulation. Control strategies were refined for energy savings, including a regenerative braking strategy to increase regenerative braking and a load-following hydraulic strategy to decrease electrical energy consumption. The load-following hydraulic control shuts off the hydraulic electric machine when it is not needed—an approach not previously seen in a load-sensing, pressure-compensated system. Furthermore, these strategies achieved a 24.1% fuel savings, resulting in total savings of 61% in fuel and 41% in CO 2 e in the plug-in series compared to the conventional machine. Beyond control strategies, this study evaluated battery chemistry and charging strategy refinements for total cost of ownership (TCO) and lifetime CO 2 e. LFP batteries emerged as the most cost-effective and least emitting due to their longer lifespan, which reduced replacement frequency. Charging comparisons showed that Level 2 charging (L2C) typically resulted in lower TCO but higher lifetime CO 2 e than DC fast charging (DCFC). DCFC costs were heavily influenced by local demand charges, and DCFC emissions were heavily influenced by local grid emissions.
The Excel-based CHECT tool estimates the LCOC ($/kWh) by charger type (Level 1, Level 2, and DC fast charging) for a given charging hub scenario. CHECT requires users to input certain charging hub scenario parameters, including the number of chargers, daily utilization, and charger replacement frequency by charger type. Additional inputs such as the charging schedule, local utility rates, capital/operational costs, and financial inputs can be customized by the user, or the tool can generate results using appropriate default values from literature for the charging hub scenario and service location(s). Using the above inputs, CHECT performs a robust techno-economic analysis to generate the LCOC by charger type, broken down by cost category (e.g., capital, operational, utility, taxes) for various combinations of charging hub types (multiunit dwelling or public) and ownership models (residential, utility, or private company). It also outputs the annual discounted cash flows and determines the most sensitive input variables. In addition, the tool allows users to easily compare the LCOC across various ownership models or across different states.
Medium- and heavy-duty freight rely on service plazas and truck stops to refuel their vehicles and meet their obligations. Creating a reliable fast-charging network across the Northeast is critical to provide security for fleets to electrify.