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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
NASA NTRSDate not supplied

Battery Cell-to-Pack Scaling Trends for Electric Aircraft

Battery pack gravimetric energy density is one of the most important, yet often miss- estimated design parameters for sizing all-electric aircraft. Proper accounting for thermal, structural, and operational safety margins are frequently lost when extrapolating performance from the cell level to the aircraft level. This paper summarizes the relevant engineering and certification details needed to better account for the penalties associated when assembling battery packs. The relationship between the cell and pack energy density is not linear, as is often assumed. Furthermore, the relationship varies depending on pack requirements, cell chemistry, and architecture. Parametric, high-fidelity models are used to determine optimal battery pack sizes over a range of conditions to better quantify technology scaling effects.

Battery Electric Aircraft↗
NASA NTRS1996

Electrolyte Management Considerations in Modern Nickel Hydrogen and Nickel Cadmium Cell and Battery Designs

This paper reviews three general areas where the potassium ion content can impact the performance and life of nickel hydrogen and nickel cadmium cells. Sample calculations of the concentration or volume changes that can take place within operating cells are presented. With the aid of an accurate model of an operating cell or battery, the impact of changes of potassium ion content within a potential cell design can be estimated. All three of these areas are directly related to the volume tolerance and pore size engineering aspects of the components used in the cell or battery design.

Thaller, Lawrence H.↗
DOE OSTI2020

Joint optimal scheduling for electric vehicle battery swapping-charging system based on wind farms

Insufficiencies in charging facilities limit the broad application of electric vehicles (EVs). In addition, EV can hardly represent a green option if its electricity primarily depends on fossil energy. Considering these two problems, this paper studies a battery swapping-charging system based on wind farms (hereinafter referred to as W-BSCS). In a W-BSCS, the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station (CCS), which can centrally charge EV batteries and then distribute them to multiple battery swapping stations (BSSs). The operational framework of the W-BSCS is analyzed, and some preprocessing technologies are developed to reduce complexity in modeling. Then, a joint optimal scheduling model involving a wind power generation plan, battery swapping demand, battery charging and discharging, and a vehicle routing problem (VRP) is established. Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem. Numerical results verify the effectiveness of the joint optimal scheduling model, and they also show that the W-BSCS has great potential to promote EVs and wind power.

17 WIND ENERGY↗
NASA NTRS2019

Constraint-Based Off-Nominal Behavior Modeling for Europa Clipper

The risk analysis for the Europa Clipper mission evaluates the probability of mission failure based on the failure rates of individual components and dependencies among them. The probabilities are calculated by integrating over the intervals of time within which a fault occurs, accounting for an infinite number of cases. The response of the spacecraft to different faults can result in different schedules of activities, changing the intervals of integration. Europa currently uses models of spacecraft systems and components to simulate individual flight scenarios. The goal is to develop a framework for integrating, automating, and improving this modeling process. We describe an approach to generating the schedules for the different fault cases and determining the intervals for faults. It is not enough to just simulate individual cases because we are working with continuous variables that generate an infinite number of possible futures. Instead, we determine time windows within which certain faults can occur and use these time windows as bounds for integration. We found that determining these time windows is a constraint optimization problem. In order to represent these problems, we employ a language based on ontologies of behavior and scenarios. The language enables us to specify constraints in a simple, declarative syntax. A constraint-based analysis engine uses the declarative specification to identify bounds on system parameters and fill in details of behavior. For example, we created a detailed model of power generation, power use, and the corresponding effects on the battery in order to determine when an undervoltage fault can occur. An undervoltage during a trajectory correction maneuver requires that thrusting be interrupted for just enough time to recharge the battery such that the maneuver can be completed within battery limits. This behavior is generated based on the model to minimize the interruption time. For certain scenarios the constraint optimization problems were simple enough to be solved by hand, but the framework made the process substantially faster. It also produced solutions to other problems that we could not solve by hand or with existing tools and allowed us to generate and run many scenarios at once. The scenario language and engine greatly simplified the process of identifying time bounds and separating cases.

Everline, Chester J.↗
DOE OSTI2021

SPECs Early-Stage Decision Model: User Manual

The SPECs Early-Stage Decision (ESD) model is central to the SPECs procurement solutions toolkit. The ESD model is an Excel-based spreadsheet model, which provides information about the economic and strategic value of a proposed battery-storage project or solar-plus-storage (solar-plus) project. The model can be used to explore combinations of storage-related project value streams in order to define a potential project, while educating co-op decision-makers about project benefits and costs. A sensitivity analysis function speeds the development of "what-if" scenarios. A gap analysis function solves for top-priority metrics and supports the inclusion of hard-to-monetize strategic values, such as the value of storage to defer costly system upgrades in light of increasing distributed solar and other distributed energy resources (DERs). Model outputs include the utility data, assumptions, and use-case scenarios that are recommended content for the requests for proposals (RFPs). The model may also provide an initial "sanity check" for RFP responses, supporting further discussions among utility staff, vendors and stakeholders. The ESD is not a "finance-grade" modeling tool, and users are cautioned to be mindful of its limitations, but the model has been reviewed by users, who recommend it as a way to drive faster and better project design and planning, as well as to facilitate better communications with vendors, grid partners, and stakeholders.

14 SOLAR ENERGY↗
DOE OSTI2021

U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2020 [PowerPoint]

NREL has been modeling U.S. photovoltaic (PV) system costs since 2009. This report benchmarks costs of U.S. solar PV for residential, commercial, and utility-scale systems, with and without storage, built in the first quarter of 2020 (Q1 2020). Our methodology includes bottom-up accounting for all system and project-development costs incurred when installing residential, commercial, and utility-scale systems, and it models the capital costs for such systems.

14 SOLAR ENERGY↗
DOE OSTI2017

Li + Defects in a Solid-State Li Ion Battery: Theoretical Insights with a Li 3 OCl Electrolyte

In a solid-state Li ion battery, the solid-state electrolyte exits principally in regions of high externally applied potentials, and this varies rapidly at the interfaces with electrodes due to the formation of electrochemical double layers. Here, we investigate the implications of these for a model solid-state Li ion battery Li|Li 3 OCl|C, where C is simply a metallic intercalation cathode. We use DFT to calculate the potential dependence of the formation energies of the Li + charge carriers in superionic Li 3 OCl. We find that Li+ vacancies are the dominant species at the cathode while Li+ interstitials dominate at the anode. With typical Mg aliovalent doping of Li 3 OCl, Li + vacancies dominate the bulk of the electrolyte as well, with freely mobile vacancies only ~ 10 -4 of the Mg doping density at room temperature. We study the repulsive interaction between Li+ vacancies and find that this is extremely short range, typically only one lattice constant due to local structural relaxation around the vacancy and this is significantly shorter than pure electrostatic screening. We model a Li 3 OCl- cathode interface by treating the cathode as a nearly ideal metal using a polarizable continuum model with an ε r = 1000. There is a large interface segregation free energy of ~ - 1 eV per Li + vacancy. Combined with the short range for repulsive interactions of the vacancies, this means that very large vacancy concentrations will build up in a single layer of Li 3 OCl at the cathode interface to form a compact double layer. The calculated potential drop across the interface is ~ 3 V for a nearly full concentration of vacancies at the surface. This suggests that nearly all the cathode potential drop in Li 3 OCl occurs at the Helmholtz plane rather than in a diffuse space-charge region. We suggest that the conclusions found here will be general to other superionic conductors as well.

25 ENERGY STORAGE↗
DOE OSTI2025

Understanding LIB Battery Electrodes Through Classical Electrochemical Interface Theory

Classical models of the electrochemical interface can be applied to complex and dynamic electrodes to understand important mechanisms that are relevant to device-level phenomena like calendar aging or cycle life. Here, we show that negative alloy electrodes with wide electrochemical windows cannot be assumed to have static interfaces throughout all states of charge. Under high states of charge, the interface -including the solid electrolyte interphase - has characteristics that resemble a highly polarized electrode far from the point of zero charge. Under these conditions, the interface can be understood through the Helmholtz model. At lower states of charge, the interface capacitance decreases and the space charge layer length increases. This transformation resembles a metal electrode approaching the point of zero charge which is understood through the Gouy-Chapman-Stern-Grahame model of the electrochemical interface. This transformation happens at both silicon and carbon-coated silicon interfaces. From voltage-limited cycling experiments, in the limit of the diffuse double layer, the silicon electrode impedance rises significantly in the first few cycles. This rise is related to the continuous electrochemical reduction of electrolyte components from an interface that is not electronically screened from the electrolyte. In other words, an electrode interface for batteries should resemble a 'Helmholtz-like' structure.

anode↗
DOE OSTI2024

Batteries (2023 Annual Progress Report)

This document summarizes the progress of VTO battery R&D projects supported during the fiscal year 2023. Its R&D focus was on the development of high-energy batteries for PEVs. The electrochemical energy storage roadmap describes ongoing and planned efforts to develop electrochemical storage technologies for EVs. To advance battery technology, which can in turn improve market penetration of PEVs, the program investigates various battery chemistries to overcome specific technical barriers, e.g., battery cost, performance, life (both the calendar life and the cycle life), its tolerance to abusive conditions, and its recyclability/sustainability.

25 ENERGY STORAGE↗
DOE OSTI2024

Control and Optimization of Energy Storage System in Power Distribution System

The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1985

Nickel-cadmium Battery Cell Reversal from Resistive Network Effects

During the individual cell short-down procedures often used for storing or reconditioning nickel-cadmium (Ni-Cd) batteries, it is possible for significant reversal of the lowest capacity cells to occur. The reversal is caused by the finite resistance of the common current-carrying leads in the resistive network that is generally used during short-down. A model is developed to evaluate the extent of such a reversal in any specific battery, and the model is verified by means of data from the short-down of a f-cell, 3.5-Ah battery. Computer simulations of short-down on a variety of battery configurations indicate the desirability of controlling capacity imbalances arising from cell configuration and battery management, limiting variability in the short-down resistors, minimizing lead resistances, and optimizing lead configurations.

Zimmerman, A. H.↗
DOE OSTI2020

Quantification of Load Flexibility in Residential Buildings Using Home Energy Management Systems

With increasing penetration of renewable energy resources, the flexibility of operating behind-the-meter (BTM) resources plays a key role in enhancing grid reliability and resilience. Residential buildings with home energy management systems (HEMS) can provide desired flexibility for the distribution system operator (DSO) while considering customer comfort and preferences. This paper discusses a methodology to quantify the flexibility of BTM resources of residential buildings using HEMS. First, we propose a model predictive control framework to formulate the flexibility band comprising nominal, upper, and lower demand profiles. Second, the paper proposes a dispatch method for HEMS to compute the control signals for each BTM resource (e.g., air conditioner, water heater, home battery system) upon receiving a flexibility service request from the DSO. The case study provides insight into the flexibility provided at the whole-home level with different user preferences and seasons. The results demonstrate that HEMS is capable of providing flexibility service at the request of the DSO while delivering primary services to the building occupants.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRSDate not supplied

Battery State-of-Health Aware Path Planning for a Mars Rover

A rover mission consists of visiting waypoints to gather scientific samples based on set requirements. However, rovers face operational uncertainties during the mission, affecting the performance of its electrical and mechanical components and overall mission success. Hence, it is critical to have a decision-making framework that is aware of the health state of the components when planning the path of the vehicle. In particular, battery degradation, and consequently the battery State of Health (SOH), can affect the optimality of decisions made by the autonomous system in the long term. This paper presents a decision-making system that incorporates information on the energy drawn from the battery (based on the vehicle’s velocity), terrain conditions, and model-based prognostic modules to assess the impact on the battery’s state of charge (SoC). The decision-making system was formulated as a Markov Decision Process (MDP) to reach the goal destination by sending commands in a determined amount of time while maintaining the battery SoC within the policy stated. The MDP problem was programmed using the open-source framework POMDPs.jl, which has a variety of online and offline solvers. To solve the MDP problem online, we used Monte Carlo Tree Search (MCTS). Results from simulations demonstrate the effect that battery degradation and charging plans have on decision-making.

Prognostics↗
DOE OSTI2020

Feasibility of Operating a Heavy-Duty Battery Electric Truck Fleet for Drayage Applications

Vehicle fleet electrification is regarded as one major pathway toward achieving energy independence and reducing air pollution and greenhouse gas emissions. Compared to light-duty and medium-duty vehicles, electrification of heavy-duty vehicles, especially Class 8 trucks, is more challenging owing to the battery size required to attain the driving range necessary for their operating goals. As drayage trucks generally have a limited daily mileage, return to a home base every night, and spend a large amount of time creeping and idling, drayage operation has been the first targeted application for Class 8 electric trucks. The feasibility of operating battery electric drayage trucks at the individual vehicle level has recently been demonstrated. However, questions remain as to whether these trucks are capable of meeting the needs of typical drayage operation at the fleet level. Here we present a feasibility analysis of operating an electric truck fleet based on real-world operation data of a diesel drayage operator in Southern California. Second-by-second activity data collected from 20 trucks in the fleet were used to estimate the corresponding electric energy consumption and the state of charge of the battery using a microscopic electric energy consumption model. An algorithm for generating tours of drayage activity from the collected data was developed and implemented. Multiple scenarios with different battery charging and truck scheduling assumptions were analyzed. The results show that 85% of the tours could be served by electric trucks if there is opportunity for charging at the home base during the time gap between consecutive tours.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Economic Dispatch for DC-Connected Battery Systems on Large PV Plants

Falling costs of energy storage combined with an increasing need to mitigate variable generation integration issues is leading to more PV projects that include batteries. Large PV plants that include batteries face challenges optimizing the value of the power they produce, especially when the batteries are DC-coupled and otherwise clipped power (due to high DC/AC ratio) is available. Multiple charging times and sources must be considered, including: grid charging, charging from PV that would otherwise be exported to the grid, and charging from clipped PV power. Then discharge schedules must be optimized against time-of-day pricing. The National Renewable Energy Laboratory in partnership with Southern Company has developed a heuristic algorithm in the System Advisor Model (SAM) which strategically charges the battery from PV and discharges the battery depending on the time-of-day prices of the PPA, the forecast PV production, and the estimated wear cost of the battery. The algorithm and case study results from a generic plant will be presented with recommendations for future work.

25 ENERGY STORAGE↗