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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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At least 73 records · Page 4

Ligand-Assisted Separation of Rare Earth Elements via Capillary Electrophoresis

Rare earth elements (REEs) are a class of critical materials vital in applications such as EVs, batteries, and defense weapons systems. These elements are found primarily in ores, and due to their similar chemical behavior, the challenge remains to find an effective means of separation. The addition of ligands of varying charge, size, denticity, etc. can help to increase separation, yet the mechanistic behavior and factors influencing separation are still poorly understood. Here, iminodiacetic acid (IDA) is used to exploit and maximize differences in electrophoretic mobilities between adjacent REEs. Mobilities and diffusion coefficients of REEs in aqueous media are measured via CE and are compared to MD and FEM simulations.

36 MATERIALS SCIENCE↗

Automation for Electric Vehicle Battery Pack Disassembly

Battery‐electric vehicles (EVs) are growing exponentially. The demand for these batteries is expected to increase sevenfold by 2035. The EV batteries reach their end of life when the capacity fades to 70%–80% of new, with some being removed from the primary applications with even lower levels of degradation. These batteries can be used in less demanding applications. The disassembly process is currently manual, slow, unsafe, and expensive. Automation is needed to increase the throughput. EV battery packs feature various continually changing designs and form factors, which limit the usefulness of deterministically programmed robotic solutions. The conceptual robotic disassembly of EV batteries has attracted the attention of researchers. However, while many approaches have been proposed, practical implementations are lacking. Here, we review proposed concepts for EV battery disassembly and describe the selected approach, with elements of partial solutions validated in a laboratory setting, including the selection of commercial solutions, the development of custom end effectors, and methodologies for detection, localization, and classification of fasteners. The computer vision tasks employed an overhead 2D camera to detect the type of battery pack and approximate localization of fasteners, and a 3D camera mounted on the robotic arm for precise localization (position and tilt) and classification.

Islam, Abu [Rochester Inst. of Technology, Rochest↗

Development and Validation of a Simulation Tool to Predict the Combined Structural, Electrical, Electrochemical and Thermal Responses of Automotive Batteries

Battery safety is one of the most important design factors of electrified vehicles (EVs) since battery failure may lead to a catastrophic consequence. To improve the safety of battery systems in EVs, battery behaviors under various abuse conditions should be well understood. One way to examine battery response under extreme conditions is to conduct abuse tests in different scenarios. They can provide first-hand pass/fail information on battery safety, but they are usually expensive and time-consuming, which is inconvenient during design iterations and optimization. On the other hand, computational modeling can predict the battery behaviors in a more efficient and cost-effective way, which becomes an important tool to evaluate battery safety. Modeling battery responses and failure is challenging since it involves multiple coupled physical processes, needs to consider complex material properties and is computationally intensive. This project aims to develop a practical simulation tool to predict the combined structural, electrical, electrochemical, and thermal responses of automotive batteries to crash-induced crush and short circuit and validate it for conditions relevant to automotive crash. Advanced material constitutive models will be utilized to capture the mechanical response of cell components. Moreover, methods will be developed to reduce computational complexity of the model and allow battery safety simulations to extend from cell-level to module/pack level with affordable computational resources.

25 ENERGY STORAGE↗

Procedure for Assessing the Suitability of Battery Second Life Applications after EV First Life

Using batteries after their first life in an Electric Vehicle (EV) represents an opportunity to reduce the environmental impact and increase the economic benefits before recycling the battery. Many different second life applications have been proposed, each with multiple criteria that have to be taken into consideration when deciding the most suitable course of action. In this article, a battery assessment procedure is proposed that consolidates and expands upon the approaches in the literature, and facilitates the decision-making process for a battery after it has reached the end of its first life. The procedure is composed of three stages, including an evaluation of the state of the battery, an evaluation of the technical viability and an economic evaluation. Options for battery configurations are explored (pack direct use, stack of battery packs, module direct use, pack refurbish with modules, pack refurbish with cells). By comparing these configurations with the technical requirements for second life applications, a reader can rapidly understand the tradeoffs and practical strategies for how best to implement second life batteries for their specific application. Lastly, an economic evaluation process is developed to determine the cost of implementing various second life battery configurations and the revenue for different end use applications. An example of the battery assessment procedure is included to demonstrate how it could be carried out.

25 ENERGY STORAGE↗

A method for modeling battery-temperature-aware EV power profiles utilizing Next-Gen Profile data

With the expected increase in the number of electric vehicles (EVs) on the road in the coming years, it is important that analysis tools are capable of modeling and predicting the expected load on the power grid due to both individual EV charging sessions as well as large populations of vehicles. To do this accurately, the power profile of an EV charge session must be accurately modeled, including for scenarios where the temperature is above or below the ideal, and also take into account the nuances of manufacturer charging preferences. This paper introduces a method that utilizes the data in the Next-Gen Profile (NGP) data collection project to build a model of EV charging that takes into account the variations in charging power that occur due to off-nominal battery temperature and manufacturer preferences that limit power due to cold temperatures or high battery state-of-charge.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

Comparative Assessment of Battery Carbon Footprint Calculation Frameworks to Support U.S. Battery Manufacturing

A “battery passport” is a digital record that provides comprehensive information about an individual battery across its life cycle. This concept has been introduced by Battery Regulation (EU) 2023/1542 [1], which applies to batteries for electric vehicles (EVs), industrial batteries with a capacities greater than 2 kWh, and batteries for light means of transport (LMT) greater than 2kWh sold in the European Union (EU) market – impacting manufacturers and exporters across multiple jurisdictions globally. Among its reporting mandates, a key feature of battery passports is the requirement for a carbon footprint (CF) calculation methodology supported by enhanced data granularity to enable traceable and verifiable CF results. While no other jurisdictions have yet adopted formal battery passport requirements like the EU’s, some are developing CF calculation methods to comply with the EU Battery Regulation or are creating CF-related regulations that could evolve in a similar direction, reflecting the growing importance of battery CF guidelines for manufacturers seeking to remain competitive in global markets.

Zhang, Jingyi [Argonne National Laboratory (ANL), ↗

U.S. Federal Policy Considerations for the Management of Retired Large-Format Batteries

The global demand for large-format batteries used in electric vehicles (EV) and battery energy storage is expected to continue as governments call for zero emission policies. Total installed large-scale stationary battery energy storage is expected to increase almost 20-fold in the coming years - from 17 GW installed globally in 2020 to 358 GW projected in 2030. Similarly, light duty EVs sales globally are expected to increase more than 8-fold from 2020 to 2030 - from 3 million units to 25 million units. The expected demand for large-format batteries brings supply chain concerns, and economic opportunities. Domestic reuse and recycling is one potential circular economy solution for large-format batteries. This presentation discusses current U.S. law and regulatory landscape for the reuse and recycling of large-format batteries, and how certain policy frameworks impact reuse and end-of-life management decisions for large-format battery materials.

battery↗

Grid Impact Analysis of Heavy-Duty Electric Vehicle Charging Stations

This paper presents a grid impact analysis of heavy-duty electric vehicle (EV) charging stations. In this work, heavy-duty EVs have battery capacities high enough to provide a range of 250-500 miles on a single charge, such as long-haul trucks. Heavy-duty EVs will require extremely fast charging rates to reduce charging time and will induce very high charging loads (at the multiple-megawatt scale) if several vehicles charge at the same time. Therefore, analysis is needed to understand the impact of charging station loads on the electric power grid and set the baseline for developing mitigation plans and necessary system upgrades. We develop a systematic procedure to analyze the potential impact of the placement of charging stations on the grid. Charging load is modeled using a DC fast-charging station model. A voltage load sensitivity matrix approach is leveraged to investigate the challenges of placing charging stations on the feeder. Given the charging load profiles and suggested charging station locations, time-series simulations are performed on various connection points on the feeder to understand the impact. The analysis is performed on both the IEEE 34-bus system and a realistic feeder from California. Initial mitigation solutions are developed based on insights from this analysis.

47 OTHER INSTRUMENTATION↗

A Software/Hardware Framework for Efficient and Safe Emergency Response in Post-Crash Scenarios of Battery Electric Vehicles

The adoption rate of battery electric vehicles (EVs) is rapidly increasing. Electric vehicles differ significantly from conventional internal combustion engine vehicles and vary widely across different manufacturers. Emergency responders (ERs) and recovery personnel may have less experience with EVs and lack timely access to critical information such as the extent of the stranded energy present, high-voltage safety hazards, and post-crash handling procedures in a user-friendly manner. This paper presents a software/hardware interactive tool named Electric Vehicle Information for Incident Response Solutions (EVIRS) to aid in the quick access to emergency response and recovery information. The current prototype of EVIRS identifies EVs using the VIN or Make, Model, and Year, and offers several useful features for ERs and recovery personnel. These features include integration and easy access to emergency response procedures tailored to an identified EV, vehicle structural schematics, the quick identification of battery pack specifications, and more. For EVs that are not severely damaged, EVIRS can perform calculations to estimate stranded energy in the EV’s battery and discharge time for various power loads using either EV dashboard information or operational data accessed through the CAN interface. Knowledge of this information may be helpful in the post-crash handling, management, and storage of an EV. The functionality and accuracy of EVIRS were demonstrated through laboratory tests using a 2021 Ford Mach-E and associated data acquisition system. The results indicated that when the remaining driving range was used as an input, EVIRS was able to estimate the pack voltage with an error of less than 3 V. Conversely, when pack voltage was used as an input, the estimated state of charge (SOC) error was less than 5% within the range of 30–90% SOC. Additionally, other features, such as retrieving emergency response guides for identified EVs and accessing lessons learned from archived incidents, have been successfully demonstrated through EVIRS for quick access. EVIRS can be a valuable tool for emergency responders and recovery personnel, both in action and during offline training, by providing crucial information related to assessing EV/battery safety risks, appropriate handling, de-energizing, transport, and storage in an integrated and user-friendly manner.

25 ENERGY STORAGE↗

Electric and hybrid vehicles charge efficiency tests of ESB EV-106 lead acid batteries

Charge efficiencies were determined by measurements made under widely differing conditions of temperature, charge procedure, and battery age. The measurements were used to optimize charge procedures and to evaluate the concept of a modified, coulometric state of charge indicator. Charge efficiency determinations were made by measuring gassing rates and oxygen fractions. A novel, positive displacement gas flow meter which proved to be both simple and highly accurate is described and illustrated.

Rowlette, J. J.↗

Large Local Currents in a Lithium-Ion Battery during Rest after Fast Charging

Increasing electric vehicle (EV) adoption requires lithium-ion batteries that can be charged quickly and safely. Some EV batteries have caught on fire despite being neither charged nor discharged. While the lithium that plates on graphite during fast charging affects battery safety, so do the internal ionic currents that can occur when the battery is at rest after charging. These currents are difficult to quantify; the external current that can readily be measured is zero. Here we study a graphite electrode at rest after 6C fast charging using operando X-ray microtomography. We quantify spatially resolved current density distributions that originate at plated lithium and end in underlithiated graphite particles. The average current densities decrease from 1.5 to 0.5 mA cm -2 in about 20 min after charging is stopped. Surprisingly, the range of the stripping current density is independent of time, with outliers above 20 mA cm -2 . In conclusion, the persistence of outliers provides a clue as to the origin of catastrophic failure in batteries at rest.

25 ENERGY STORAGE↗

Adaptive Algebraic Derivative Estimation for Battery Electric Buses Energy Consumption Forecasting

The limited service life of onboard batteries for EVs is a challenge, underscoring the need for real-time battery usage prediction. This paper proposes an adaptive Algebraic Derivative Estimation (ADE) approach for forecasting the energy consumption of battery electric buses. By dynamically adjusting the sliding window length, the adaptive ADE retains the fixed-length ADE’s key advantage—namely, operating online without reliance on extensive historical datasets—while substantially bolstering forecast accuracy by actively trading estimation bias off estimation variance. Comparative experiments against both the conventional ADE with a fixed length and a representative machine learning algorithm, XGBoost, were conducted, with performance evaluated via root mean square error, mean absolute error, and the coefficient of determination. The results demonstrate that the proposed approach significantly outperforms baseline methods.

Cui, Tianyang [The University of Texas at Dallas]↗

2023 Critical Materials Strategy

The global effort to curb carbon emissions is accelerating demand for clean energy technologies and the materials they rely on. Demand for these materials will only continue to grow, especially as some nations aim to achieve net zero emissions by 2050. While some major materials like steel, copper, and aluminum are already powering the fossil fuel economy, others are more minor materials with potential supply risks. These risks could jeopardize the ability to reduce greenhouse gas emissions within the desirable timeframe to avoid significant climate change. In some cases, it may be necessary to take action to improve the resilience of material supply chains and mitigate supply risks. Understanding the importance of individual materials to clean energy and the supply risks associated with them is necessary to identify which materials may serve as potential roadblocks to a clean energy future. The U.S. Department of Energy (DOE) issued a series of 13 supply chain deep dive assessment reports on various energy technologies in 2022 in response to President Biden’s Executive Order on America’s Supply Chains (E.O. 14017). These reports emphasized that supply chain bottlenecks can occur at any stage of the value chain from mining and refining to component and even sub-system manufacturing. The bottlenecks are a combination of factors such as material availability, equipment availability, work force availability and quality, logistics, regulatory framework, and market conditions. These bottlenecks were worsened during the global Covid-19 pandemic. Its lingering impacts have hindered capacity expansion for material supply chains and prevented product lead-time recovery. One approach to reduce supply chain risks for the United States is to have a strong domestic manufacturing sector with a diverse set of producers. Boosting responsible domestic production would require leveraging the latest science not only in material extraction but also in developing substitutes, recycling, reuse, and remanufacturing. This report is an updated analysis of previous Critical Materials Strategy (CMS) reports published by the DOE in 2010, 2011, and 2019 based on national and global priorities, technology advancement, and technology adoption trends. Like the CMS reports, this analysis presents the results of a formal material criticality assessment to identify which materials are critical to the continued deployment of clean energy technologies globally. The analysis in this report leveraged the DOE supply chain deep dive assessments to develop the initial list of materials to evaluate. This DOE Critical Materials Assessment (CMA) is conducted independently of criticality assessments performed by other U.S. government agencies, such as that conducted by the U.S. Geological Survey (USGS). This analysis complements the USGS critical minerals determination in three aspects. First, the DOE assessment is performed from a global perspective, while the USGS analysis focusses on the importance of minerals to the U.S. economy. Second, this report focuses on the importance of materials to clean energy technologies, rather than to the economy in general. Lastly, this study is forward looking to 2035 based on clean energy deployment scenarios, whereas the USGS assessment is retrospective. Materials evaluated in this report that do not appear in the USGS Critical Minerals List include copper, uranium, electrical steel, and SiC. A draft version of this report received ~80 public comments related to supporting data and methodological improvement. Those comments have been incorporated as much as possible where appropriate. Highlights of findings from this 2023 CMA include: Rare earth materials (neodymium, praseodymium, dysprosium, and terbium) used in magnets in electric vehicle (EV) motors and wind turbine generators continue to be critical. While dysprosium (Dy) and terbium (Tb) are both heavy rare earth elements that serve the same function in magnets, the criticality of Tb is slightly lower than that for Dy in the short term due to the widespread use of Dy in high-grade magnets and Tb’s present role as a substitute. Similarly, praseodymium (Pr) is critical in the medium term but only near critical in the short term because it is more substitutable in magnets than neodymium (Nd); Materials used in batteries for EVs and stationary storage are now considered to be critical. While cobalt (Co) was found to be critical in this and previous reports, lithium (Li) becomes critical in the medium term due to its broader use in various battery chemistries and the rampant growth of the EV industry. Natural graphite is a new addition in this assessment and is also found to be critical; Platinum group metals used in hydrogen electrolyzers, such as platinum (Pr) and iridium (Ir), are critical due to an increased focus on hydrogen technologies to achieve net zero carbon emissions, while those used in catalytic converters, such as rhodium (Rh) and palladium (Pd), were screened out due to the decreased importance of catalytic converters in the medium term; Gallium (Ga) continues to be critical due to its use in light-emitting diodes (LEDs). In addition, the use of Ga has increased in magnet manufacturing and in semiconductor in forms such as gallium arsenide (GaAs) or gallium nitride (GaN); Major materials like Aluminum (Al), copper (Cu), nickel (Ni), and silicon (Si) move from noncritical in the short term to near critical in the medium term due to their importance in electrification; Electrical steel is near critical due to its use in transformers for the grid and electric motors in EVs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Projecting Recent Advancements in Battery Technology to Next–Generation Electric Vehicles

Electric vehicles (EVs) have seen rapid growth in adoption over the last several years. Advancements to increase battery life and performance, policy shifts, and high charging rate are expected to further accelerate the development of next generation of EVs. Battery improvements continue to emerge, enabling increased driving range, total distance driven over the life of vehicles, and ability to charge at high rates. Herein, an analysis framework to provide insights into inclusive design metrics, such as specific energy of batteries, energy consumption of vehicles, and charging power infrastructure development, is developed. Various cell-level fast charge protocols to realistic battery designs to understand the infrastructure needs associated with achieving range replacement of 32.25 km min -1 (20 mi min -1 ) are also scaled. Additionally, by calculating scaled power and peak to average power ratio, it is found that there needs to be more distinct alignment between the research efforts focused at the cell level and what is being developed for EV charging infrastructure needs. Finally, impact of high direct current voltage architecture in next-generation EVs is discussed. The findings in this work provide an insight into recent advancements in battery technology to next-generation EVs.

20 mi/min↗

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.

25 ENERGY STORAGE↗

EMOS (Energy Management Optimization System) [SWR-21-46]

EMOS software performs a real-time hierarchal optimal control for energy systems like multi-port electric vehicles charging site with distributed energy resources (DERs) and energy storage systems (ESSs). It gathers information in real-time from electric vehicles, power grid, and DERs, solves a multi-objective energy management optimization problem, and output setpoint for chargers, ESS converters, DERs converters, and grid converters. The software incorporates a novel integration of two control tasks: a) An Energy Management Optimization (EMO), which is the brain of EMOS controller that gathers information in real-time from EVs [e.g., battery size, state-of-charge (SOC), desired SOC, and charge acceptance curve], grid (e.g., electricity price, allowed feeder capacity, ramp rate limit, and reactive power), and DERs (e.g., prediction for solar generation for PV systems). It solves a control optimization problem in real-time to find optimal setpoint for ESSs power dispatch, EVs charging rate, and grid inverters. The objectives are to (1) minimize the charging cost considering grid energy, demand charges, and battery energy, (2) minimize charging time to meet fast charging criterion, (3) keep high energy level on ESSs at the end of an operating period, while satisfying constraints related to grid, EVs, and power converters. b) Real-Time Energy Management System (RT-EMS) is a rule-based algorithm that has faster response than EMO. It receives optimal setpoint from EMO and actual measurements from the system and modify the setpoint to compensate for any fast disturbance in the system, until a new optimum solution is received. Fast disturbances may include vehicle connect/disconnect, unpredicted variation in DERs profiles, variation in grid voltage, errors in PV generation prediction, and others. In addition, RT-EMS regulates voltage at point of common coupling (PCC) by managing reactive power of grid converters.

Mohamed, Ahmed↗