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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Comparison and Application of Battery Modeling Methods for Conceptual Electric Aircraft

Growing interest in design and optimization of electrified aircraft propulsion concepts prompts the need for accurate, flexible, and efficient methods to model battery systems. Presented in this paper are three battery modeling methods that have been used at NASA’s Glenn Research Center, each representing different mathematical or electrical approaches. Thévenin equivalent circuit, normalization, and curve-fitting methods are compared against battery cell test data for the X-57 Maxwell electric aircraft technology demonstrator. The methods are then applied in a simple multidisciplinary optimization context using NASA’s Six-Passenger Electric Quadrotor concept to determine their applicability and performance. The normalization method achieves the highest accuracy for steady and unsteady discharge rates with a voltage mean error percentage of 0.423% and 1.186%, respectively. Optimal quadrotor mission range between the models varies up to 0.5 nmi, identifying current battery modeling methods as a potentially significant contributor to mission analysis error. A set of relevant tools and techniques for conceptual battery modeling are identified in this paper, with conclusions made on the utility of each modeling approach for various design challenges.

power↗
DOE OSTI2025

How to Model Batteries (with PV, Stand-Alone, or Hybrids) in SAM and PySAM

This tutorial will be a deep dive into considerations for battery modeling and demonstrating how to model them in SAM, including battery chemistry, thermal modeling, degradation/lifetime, dispatch, interconnection limits and curtailment, and their associated impacts on project profits and battery lifetime. By the end of the tutorial attendees will know how to size and model both behind-the-meter and front-of-meter battery systems, including financial analysis and pairing with other PV models (including pvlib) via PySAM.

25 ENERGY STORAGE↗
NASA NTRS1979

An averaging battery model for a lead-acid battery operating in an electric car

A battery model is developed based on time averaging the current or power, and is shown to be an effective means of predicting the performance of a lead acid battery. The effectiveness of this battery model was tested on battery discharge profiles expected during the operation of an electric vehicle following the various SAE J227a driving schedules. The averaging model predicts the performance of a battery that is periodically charged (regenerated) if the regeneration energy is assumed to be converted to retrievable electrochemical energy on a one-to-one basis.

Bozek, J. M.↗
DOE OSTI2025

Economic Analysis of Battery Energy Storage Systems Incorporating Uncertain Battery Model

A high-fidelity battery model is essential for precise economic analysis of battery energy storage systems (BESSs), but these models are computationally intensive. Heuristic models offer computational efficiency but compromise the accuracy of economic analysis results. We assess the impact of errors in heuristic battery models on economic analysis by utilizing open-circuit voltage (OCV) measurements from battery experiments.

Choi, Hyungjin [Sandia National Laboratories (SNL-↗
NASA NTRS1986

Modeling Ni-Cd performance. Planned alterations to the Goddard battery model

The Goddard Space Flight Center (GSFC) currently has a preliminary computer model to simulate a Nickel Cadmium (Ni-Cd) performance. The basic methodology of the model was described in the paper entitled Fundamental Algorithms of the Goddard Battery Model. At present, the model is undergoing alterations to increase its efficiency, accuracy, and generality. A review of the present battery model is given, and the planned charges of the model are described.

Jagielski, J. M.↗
NASA NTRS1996

First principles nickel-cadmium and nickel hydrogen spacecraft battery models

The principles of Nickel-Cadmium and Nickel-Hydrogen spacecraft battery models are discussed. The Ni-Cd battery model includes two phase positive electrode and its predictions are very close to actual data. But the Ni-H2 battery model predictions (without the two phase positive electrode) are unacceptable even though the model is operational. Both models run on UNIX and Macintosh computers.

Timmerman, P.↗
DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part I: Implementation and multi-fidelity hierarchies for the single-particle model

To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. Here, this study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models - such as the single-particle model (SPM) and the pseudo-2D (P2D) model - with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/PINNSTRIPES). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

25 ENERGY STORAGE↗
NASA NTRS2013

Electrochemistry-based Battery Modeling for Prognostics

Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

battery↗
NASA NTRS2022

Exploration of an Adaptive Routine for Battery Modeling

The purpose of this document is to explore the use of adaptive routines in battery modeling. The adaptive routines consist of real-time state estimators combined with battery parameter model components that are adjusted in real-time as battery data becomes available. Several aspects are explored. It is shown that model parameter identification is possible for simple battery models using available input/output data measurements. The online system identification used is recursive least squares. Model identification may be combined with a state observer such as the extended Kalman filter or the unscented Kalman filter to form an adaptive model combined with state estimation. However, such a combination is found to be problematic due to uncertainty, observability and stability issues. This paper is organized as follows. Section 1 introduces adaptive routines and possible roles they play in battery modeling. In Section 2 real-time parameter identification is described with results based on battery data. Section 3 reviews various state estimators and results using a simple battery model. In Section 4 parameter identification and state estimation are combined to form an adaptive routine. Finally, in Section 5 conclusions are drawn and future work is suggested.

Adaptive↗
DOE OSTI2026

An immersed interface method for microstructure-scale electrochemical battery models: numerical formulation and performance portable implementation

We present the numerical formulation, verification, and performance portable implementation of an immersed interface method for microstructure scale electrochemical modeling of batteries. The innovation in this approach is the resolution of chemical species and electrostatic potential discontinuities at active interfaces without the use of interface conforming unstructured grids. A unified formulation on Cartesian grids for all domains (electrodes and electrolyte) is used with interfacial flux conditions applied using volume fraction or “color” function gradients. We have developed one dimensional and two dimensional test cases with analytic solutions for electrochemical modeling using which we verified the consistency and accuracy of our scheme. Our solver is also validated against solutions from a macroscale model and an unstructured multi-subdomain solver for a full lithium ion cell. We then demonstrated the utility of our solver on an image-based complex battery electrode microstructure at high charging rate. Our technique also exhibits good scalability on distributed memory architectures using central processing units (CPU), with problem sizes up to 1.8 billion degrees of freedom and with 5400 ranks. Initial performance studies of our open-source performance portable solver showed about 70 times speed up using a graphics processing unit (GPU) compared to single compute core for a problem with 4 million cells.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

A Parametric Battery Model for the Conceptual Design of Electric Aircraft

Electric aircraft conceptual designs usually have an assumed specific power and specific energy for the aircraft’s energy storage system that dictates the total capacity and the peak power available. The specific energy and power are inherently linked to the state-of-charge and discharge rate from which they were derived; therefore, they will not directly correspond to the diverse circumstances encountered in various aircraft missions and flight segments. These parameters turn the battery system into a black box, disregard potential electrical restrictions, and disallow the aircraft and battery to be optimized as a system. Peering into this box, this study highlights the importance of incorporating a parametric battery model into the conceptual design workflow by splitting high-level terms such as power into voltage and current and investigating their variability during the discharge process. Through the modeling of these more detailed parameters, this methodology shows the feasibility of using low states-of-charge for contingency operations, including the end of the reserve mission, expanding the amount of usable capacity for electric aircraft. Stark differences in aircraft capabilities can arise between varying fidelity battery models due to late-mission, high-power flight operations. This parametric battery model effectively captures these differences by evaluating the limitations that arise within the individual battery cells and the aircraft powertrain. This paper shows that the unusable charge of a battery is set by the balked landing power requirement and can realistically range from 5% to 44% based on assumptions. This sets the analog to unusable fuel capacity in aircraft with liquid fuel systems. Needing only aircraft- and mission-level inputs and only seconds of run time, this model is a prime fit for the fast, accurate exploration of the electric aircraft conceptual design space.

Battery↗
DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model

Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or multiple replicate cells. To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates. A physics-informed neural network (PINN) is developed as a surrogate for the pseudo-2D (P2D) battery model calibration. For the P2D surrogate, additional training regularization was needed as compared to the PINN single-particle model (SPM) developed in Part I. Both the PINN SPM and P2D surrogate models are exercised for parameter inference and compared to data obtained from a direct numerical solution of the governing equations. A parameter inference study highlights the ability to use these PINNs to calibrate scaling parameters for the cathode Li diffusion and the anode exchange current density. By realizing computational speed-ups of ~2250x for the P2D model, as compared to using standard integrating methods, the PINN surrogates enable rapid state-of-health diagnostics. Finally, in the low-data availability scenario, the testing error was estimated to ~2 mV for the SPM surrogate and ~10 mV for the P2D surrogate which could be mitigated with additional data.

25 ENERGY STORAGE↗
DOE OSTI2025

Microstructure Scale Lithium-Ion Battery Modeling: Part I. On Through-Plane Heterogeneity, Impact of Mesh Representation, and Differences between Macro- and Microscale Models

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.

25 ENERGY STORAGE↗
DOE OSTI2026

Gaps in PV-Coupled Battery Modeling

With the rapid increase in battery attachment rates for PV systems and the continued qualification of storage for the ITC under OBBB, accurately characterizing PV-coupled batteries is becoming increasingly critical for the PV industry. This presentation will explore the key gaps in current best practices for modeling PV-coupled battery storage, including challenges such as battery thermal conditioning, the influence of dispatch strategies on battery value, the limitations of oversimplified degradation and performance models, and the absence of a centralized database containing sufficient battery parameters for accurate modeling. For behind-the-meter applications, we will also address issues like mismatched solar resource and load profiles, as well as uncertainties in future utility rates. Where applicable, we will quantify the impacts of these gaps on the accuracy and uncertainty of modeling efforts and highlight ongoing research aimed at addressing these challenges.

14 SOLAR ENERGY↗
NASA NTRS1983

Battery model for electrical power system energy balance

A model to simulate nickel-cadmium battery performance and response in a spacecraft electrical power system energy balance calculation was developed. The voltage of the battery is given as a function of temperature, operating depth-of-charge (DOD), and battery state-of-charge. Also accounted for is charge inefficiency. A battery is modeled by analysis of the results of a multiparameter battery cycling test at various temperatures and DOD's.

Hafen, D. P.↗
DOE OSTI2025

Microstructure Scale Lithium-Ion Battery Modeling, Part IV: The Representativity of Microstructure Parameters and Electrochemical Response

Lithium-ion battery electrochemical models require an accurate description of the electrodes microstructures to be predictive, that can be achieved through nanoscale imaging. Such observations are however limited by their field of view (FOV), as they provide only a subset of the whole electrode volume that does not necessarily represent the whole electrode microstructure heterogeneity, and therefore can bias the analysis. A representativity analysis has been performed on the microstructure parameters and, in a novel way, on the full cell electrochemical response to evaluate the predictions representativeness, and thus relevance, of a microstructure scale electrochemical model. The microstructure parameter deviation propagations to the electrochemical response have been quantified for different charge rates. This defines a threshold for the microstructure parameters FOV for a desired maximum deviation of the electrochemical response. Electrochemical model shows cell representative section areas are increasing with C-rate, due to higher in-plane heterogeneities, indicating larger FOVs are required specifically for fast charge modeling. Representativity analysis determines a cell FOV of 144.4 × 154.4 μm 2 is large enough to establish a convergence on the representative section areas for low-intermediate C-rate (≤2.5 C), therefore positively concludes on the model representativeness for these rates, but is not large enough to conclude for higher rates.

25 ENERGY STORAGE↗
NASA NTRS2021

Mathematical Characterization of Battery Models

The purpose of this document is to demonstrate the use of the Extended Kalman Filter as a tool for battery state estimation and the estimation of battery state of charge. The mathematical details based on the equivalent circuit model are presented followed by an electrochemical engineering model. A simplified first-order model is used to demonstrate the procedure followed by second and third-order models. Next a simplified electrochemistry model is presented along with observer development. State observability is calculated for the simpler equivalent circuit models and the simplified electrochemistry model. An outline of the battery model parameter identification method is presented, and model performance based on experimental and flight data is demonstrated.

Battery↗