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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

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↗
DOE OSTI2021

A Rechargeable Carbon-Oxygen Battery for Efficient, Low-Cost Energy Storage

As part of the Cyclotron Road program, Noon Energy Inc investigated the feasibility of a new class of rechargeable flow battery for long-duration energy storage at the 100+ hour storage capacities needed to make intermittent solar/wind power available 24/7 year-round. Whereas conventional batteries store energy in relatively expensive metals like cobalt, lithium, and vanadium, the new battery stores energy in abundant, ultra-low-cost carbon and oxygen, storage media that cost well below $1 per kWh capacity (less than the cost of their containers). It also can operate with a high roundtrip energy efficiency of ~80% and has 10x higher energy density than conventional aqueous flow batteries, even higher than lithium ion batteries at system level. These properties make it suitable for a variety of potential stationary energy storage applications as well as for electric transportation vehicles (long-haul marine shipping and trucking), so determining the best product-market fit and market entry was a key outcome of Noon’s Cyclotron Road fellowship. Noon had demonstrated initial materials development and proof-of-principle testing before the fellowship. Overall, within the fellowship Noon carried out key initial technology development in market applications, system design, risk assessment and prototyping.

25 ENERGY STORAGE↗
DOE OSTI2021

Institutional and Policy Landscape for Solar-Plus-Storage Deployment by Electric Cooperatives

As co-location with battery storage has become a significant solar project-design trend across the United States, policies at all levels have started to adjust. Generally, these policies address battery storage development in major, urban markets, served by large investor-owned utilities (IOUs). Yet all kinds of utilities nationwide are seeking ways to tap into the fast-growing battery storage market, and they are all stakeholders in the federal, state, and local policies that impact the storage market's development. The electric cooperative sector, which includes some 830 distribution utilities and 63 generating and transmission (G&T) utilities across a mostly rural landscape, is no exception. It is impacted by federal, state, and local government policy requirements and incentives, as well as by policies that are controlled by the sector's own institutions. The objective of this paper is to identify and discuss the various policy and institutional factors that are relevant to the electric cooperative sector, and especially to local, distribution-level electric cooperatives (co-ops) that may be interested in procuring energy storage. Given this, the paper is largely written from the local co-op perspective. It also may be useful to wholesale G&T cooperatives. Other parties that may find the paper useful include storage developers, who are often unfamiliar with co-ops; local and state policymakers addressing policy options for this sector, and local renewable or storage stakeholders, who wish to understand the policy landscape affecting their local utility. Although co-op energy storage programs may tap customer-side batteries, this examination is focused on utility-side "front of the meter" projects and the policy issues they raise.

14 SOLAR ENERGY↗
DOE OSTI2022

Optimization Models For Drone Deployment

Model that supports drone deployment. Analysis on speed, package weight, energy consumption, # of drones, and battery replacements. This software developed tools for drone deployment optimization for direct delivery by introducing a new model that presents new insights addressing real-life issues. Specifically, this developed a new mixed-integer programming model with both time windows and battery replacements.

Roni, MohammadS↗
DOE OSTI2019

Batteries 2018 Annual Progress Report

This document summarizes the progress of VTO battery R&D projects supported during the fiscal year 2018 (FY 2018). In FY 2018, the DOE VTO battery R&D funding was approximately $101 million. Its R&D focus was on the development of high-energy batteries for PEVs and very high-power devices for hybrid vehicles. The electrochemical energy storage roadmap describes ongoing and planned efforts to develop electrochemical storage technologies for PEVs. To advance battery technology to improve the market penetration of PEVs and hybrid vehicles, the program focuses on overcoming specific technical barriers typically related to the battery’s cost, performance, life (both calendar life and cycle life), its tolerance to abusive conditions, and its recyclability/sustainability. Various battery chemistries are being investigated with that aim.

25 ENERGY STORAGE↗
DOE OSTI2020

An Optimization-Based Planning Tool for On-Demand Mobility Service Operations

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2021

Decomposing Loosely Coupled Mixed-Integer Programs for Optimal Microgrid Design

Microgrids are frequently employed in remote regions, in part because access to a larger electric grid is impossible, difficult, or compromises reliability and independence. Although small microgrids often employ spot generation, in which a diesel generator is attached directly to a load, microgrids that combine these individual loads and augment generators with photovoltaic cells and batteries as a distributed energy system are emerging as a safer, less costly alternative. In this work, we present a model that seeks the minimum-cost microgrid design and ideal dispatched power to support a small remote site for one year with hourly fidelity under a detailed battery model; this mixed-integer nonlinear program (MINLP) is intractable with commercial solvers but loosely coupled with respect to time. A mixed-integer linear program (MIP) approximates the model, and a partitioning scheme linearizes the bilinear terms. We introduce a novel policy for loosely coupled MIPs in which the system reverts to equivalent conditions at regular time intervals; this separates the problem into subproblems that we solve in parallel. We obtain solutions within 5% of optimality in at most six minutes across 14 MIP instances from the literature and solutions within 5% of optimality to the MINLP instances within 20 minutes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deployment of Multifidelity Uncertainty Quantification for Thermal Battery Assessment Part I: Algorithms and Single Cell Results

This report documents the results of an FY22 ASC V&V level 2 milestone demonstrating new algorithms for multifidelity uncertainty quantification. Part I of the report describes the algorithms, studies their performance on a simple model problem, and then deploys the methods to a thermal battery example from the open literature. Part II (restricted distribution) applies the multifidelity UQ methods to specific thermal batteries of interest to the NNSA/ASC program.

25 ENERGY STORAGE↗
DOE OSTI2023

State of battery health estimation based on swelling characteristics

There is disclosed an electrical device including a battery, and a battery management system. The battery management system includes a controller in electrical communication with a pressure sensor to monitor the state of health of the battery. The controller applies a method for determining the state of health that uses a non-electrical (mechanical) signal of force measurements combined with incremental capacity analysis to estimate the capacity fading and other health indicators of the battery with better precision than existing methods. The pressure sensor may provide the force measurement signal to the controller, which may determine which incremental capacity curve based on force to use for the particular battery. The controller then executes a program utilizing the data from the pressure sensor and the stored incremental capacity curves based on force to estimate the capacity fading and signal a user with the state of health percentage.

Stefanopoulou, Anna G.↗
DOE OSTI2024

Energy storage system

A system, a method, and a computer program product for providing heterogeneous unifying battery storage. A state-of-health value of a battery is determined. The state-of-health value of the battery is less than an original capacity value of the battery. The battery is connected to an electrical power source for re-conditioning. A target state-of-health value for the battery and a number of cycles required to achieve the target state-of-health value of the battery are determined. Each cycle in the number of cycles includes at least one of: a charging the battery and a discharging the battery. The battery is re-conditioned by cycling the battery using the determined number of cycles. Cycling includes drawing electrical power from the electrical power source.

Tong, Shijie↗
DOE OSTI2026

Low-Temperature Plasma-Based Metrology of Lithium-Ion Battery Electrode Materials (CRADA Final Report)

As part of the Cyclotron Road program, SirenOpt Inc. evaluated its low-temperature plasma-based metrology sensor prototype for measuring multiple critical properties of lithium-ion battery electrode materials in parallel and in real-time. Cost-effective, minimal-waste manufacturing of high-performance battery electrode materials will be vital for achieving society’s net-zero carbon emission goals. Because existing electrode metrology sensors cannot operate within most sections of manufacturing lines, manufacturers often complete hundreds of processing steps before they can test their products and detect problems. When manufacturers perform these offline tests, they typically only test a small portion of the manufactured products. Current electrode manufacturing thus often yields many low-quality products, or off-spec products that must be thrown away all together. For example, at least 6% of the total lithium-ion battery manufacturing cost (i.e., over $250 million/year for the average gigafactory) is devoted to processing defective electrodes that are not scrapped until performance tests are failed during late-stage quality control checks. Electrode variability also leads manufacturers to build extra cells into battery packs to reduce the risk of poor performance. For example, many electric vehicle (EV) manufacturers include up to 10% more cells than needed, which substantially increases the cost and weight of the final EV product. The SirenOpt sensor can potentially enable early detection of poorly manufactured electrodes and allow them to be removed earlier from manufacturing lines, which can save battery manufacturers (hundreds of) millions of dollars per year. The sensor can further be used to improve product quality by accelerating R&D and process optimization, improving quality control, and enabling real-time process control. Overall, a real-time, in-situ metrology strategy can create unprecedented opportunities for implementation of smart manufacturing practices and advanced quality and process control solutions to realize higher battery electrode throughput and performance.

25 ENERGY STORAGE↗
DOE OSTI2023

DP-Based Optimization of BESS to Substitute RICE Reserves for Improved Economic Benefits

In regions with a significant proportion of renewable energy sources, reciprocating internal combustion engines (RICE or IC) are frequently employed as grid reserve solutions. This study suggests a scheduling technique for battery energy storage systems (BESS) that utilizes dynamic programming (DP) to replace IC units, adding versatility to the total reserve capacity and resulting in higher BESS earnings compared to traditional scheduling approaches. The capacity sweeping approach to optimize BESS’s revenue sometimes leads to operation which is counter intuitive to conventional battery sizing approaches.

25 ENERGY STORAGE↗
DOE OSTI2014

Office of Electricity Delivery and Energy Reliability (OE) National Energy Technology Laboratory (NETL) American Recovery and Reinvestment Act 2009 United States Department of Energy

The purpose of this project was for Seeo to deliver the first ever large-scale or grid-scale prototype of a new class of advanced lithium-ion rechargeable batteries. The technology combines unprecedented energy density, lifetime, safety, and cost. The goal was to demonstrate Seeo’s entirely new class of lithium-based batteries based on Seeo’s proprietary nanostructured polymer electrolyte. This technology can enable the widespread deployment in Smart Grid applications and was demonstrated through the development and testing of a 10 kilowatt-hour (kWh) prototype battery system. This development effort, supported by the United States Department of Energy (DOE) enabled Seeo to pursue and validate the transformational performance advantages of its technology for use in grid-tied energy storage applications. The focus of this project and Seeo’s goal as demonstrated through the efforts made under this project is to address the utility market needs for energy storage systems applications, especially for residential and commercial customers tied to solar photovoltaic installations. In addition to grid energy storage opportunities Seeo’s technology has been tested with automotive drive cycles and is seen as equally applicable for battery packs for electric vehicles. The goals of the project were outlined and achieved through a series of specific tasks, which encompassed materials development, scaling up of cells, demonstrating the performance of the cells, designing, building and demonstrating a pack prototype, and providing an economic and environmental assessment. Nearly all of the tasks were achieved over the duration of the program, with only the full demonstration of the battery system and a complete economic and environmental analysis not able to be fully completed. A timeline over the duration of the program is shown in figure 1.

25 ENERGY STORAGE↗
DOE OSTI2014

FY2013 Energy Storage R&D Progress Report

The FY 2013 Progress Report for Energy Storage R&D focuses on advancing the development of batteries to enable a large market penetration of hybrid and electric vehicles. Program targets focus on overcoming technical barriers to enable market success including: (1) significantly reducing battery cost, (2) increasing battery performance (power, energy, durability), (3) reducing battery weight & volume, and (4) increasing battery tolerance to abusive conditions such as short circuit, overcharge, and crush.

battery development↗
DOE OSTI2021

Delivery drone route planning over a battery swapping network

Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.

drone battery swapping↗
DOE OSTI2016

Development of a Hydrogen Energy System as a Grid Frequency Management Tool

The Hawai‘i Natural Energy Institute (HNEI) is conducting research to assess the technical potential of using an electrolyzer-based hydrogen (H2) production and storage system as a grid demand response tool using battery data from a 200 MW grid to show the kind of response required. The hydrogen produced by the electrolyzer is used for transportation. A 65 kg/day hydrogen energy system (HES) consisting of a PEM electrolyzer, 35 bar buffer tank, 450 bar compressor, and associated chiller systems was purchased and installed at the Hawaii Natural Energy Laboratory Hawaii Authority (NELHA) to demonstrate long-term durability of the electrolyzer under cyclic operation required for frequency regulation on an island grid system. The excess hydrogen was stored for use by three fuel-cell buses to be operated at Hawai‘i Volcanoes National Park (HAVO) and by the County of Hawai‘i Mass Transit Agency (MTA). This paper describes the site selection and equipment commissioning, plus a comprehensive test plan that was developed to characterize the performance and durability of the electrolyzer under dynamic load conditions. The controls were modified for the operating envelope and dynamic limits of the electrolyzer. While the data showed these modifications significantly improved the system response time, it is not fast enough to match a BESS response time for grid frequency management. The electrolyzer can only be used for slower acting changes (1 to 0.5 Hz). A potential solution is to design an electrolyzer/BESS hybrid system and develop a modeling program to find the optimum mix of battery and electrolyzer to provide the maximum grid regulation services at minimum cost.

08 HYDROGEN↗
DOE OSTI2022

BLAST aka: BLAST-Py see also BLAST-Lite (Battery Lifetime Analysis and Simulation Tool Suite - Python) [SWR-22-69]

Battery Lifetime Analysis and Simulation Tool Suite (BLAST or BLAST-Py) developed in the Python programming language. BLAST-Py predicts the evolution of lithium-ion battery performance metrics over their lifetime, using models trained on lab-based accelerated aging data to predict battery performance in dynamic, real-world use. BLAST-Py contains existing models for a variety of lithium-ion battery chemistries (NMC/Gr and LFP/Gr). See also the open-source version of this software tool known as "BLAST-Lite" at: https://github.com/NREL/BLAST-Lite

Smith, Kandler↗