Modeling Electrochemical Decomposition of Fluoroethylene Carbonate on Silicon Anode Surfaces in Lithium Ion Batteries
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bio-inspired, energy storage (including batteries ↗
SEARCH · Engineering Papers
Results for “battery modeling”
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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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↗
Modeling Battery Performance Due to Intercalation Driven Volume Change in Porous Electrodes
Simulations are presented that result from incorporating dimensional and porosity changes in porous electrodes caused by volume changes in the active material during intercalation into a detailed lithium-ion battery model. Porosity and dimensional changes in an electrode can significantly affect the resistance of the battery during cycling, which in turn alters the reaction distributions in the porous electrodes. In addition, volume changes generate stresses in the electrode which can lead to premature failure of the battery. Material conservation equations are coupled with the mechanical properties of porous electrodes to link dimensional and porosity changes to stresses and the resulting resistances that occur during the intercalation processes. Through the use of porous rock mechanics, porosity and strain gradients can be predicted based on state of discharge and discharge rate. Several different battery casings and discharge rates are examined and operating curves are predicted.
25 ENERGY STORAGE↗
A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models
Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.
25 ENERGY STORAGE↗
Coupled Thermal-Electrochemical Full Battery Modeling of LiSi/FeS2 Batteries
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Voskuilen, Tyler↗
Coupled Thermal-Electrochemical Full Battery Modeling of LiSi/FeS2 Batteries
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Voskuilen, Tyler↗
Recent Improvements in PV+Battery Modeling in NREL's System Advisor Model
This poster covers recent updates to the NREL System Advisor Model's battery model that can be coupled to the PV model to add value to both front of meter and behind the meter systems. Topics include new dispatch algorithms focusing on smoothing the output of a PV plant to meet ramp rate requirements and responding to price signals to maximize system revenue, validated battery lifetime models, grid outage simulations and resiliency metrics, and the new levelized cost of storage (LCOS) metric. We will also share preliminary results from NREL analysis projects using these features.
battery↗
Modeling Extreme Deformations in Lithium Ion Batteries
A simultaneously coupled modeling approach to study the electrochemical and thermal behavior of lithium-ion batteries under large mechanical deformation has been developed. The thermo-electrochemical pseudo-2D (P2D) battery model is coupled with a mechanical material model. Mechanical, thermal, and electrochemical models are implemented as user-defined sub-routines in the commercial multi-physics code LS-DYNA. The mechanical strain experienced by anode, cathode and separator results in thickness and porosity changes in each layer which in turn influences electrochemical behavior. The evolution of concentration profiles and cell potential are studied under different mechanical loading conditions. Internal short-circuits caused by mechanical deformation and corresponding physical behaviors are also elucidated. We discuss the competing effects of improved transport at higher temperature due to the internal short-circuit versus a drop in the effective ionic conductivity and electrolyte diffusivity due to mechanical deformation.
47 OTHER INSTRUMENTATION↗
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-↗
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↗
Evaluation of Equivalent Battery Model Representations for Thermostatically Controlled Loads in Commercial Buildings
Models for thermostatically controlled loads in commercial buildings often include many parameters and variables compared to residential buildings. As such, it is beneficial to use reduced-order models to represent these resources. A classic example of such a model is the Virtual Battery or Equivalent Battery Model (EBM). In this paper, the typical EBM is extended to higher-order commercial Heating, Ventilation, and Air-conditioning (HVAC) models and adapted for electric water heaters. Finally, we compare the performance of EBMs with detailed thermal models using three classic optimization problems - energy maximization, energy minimization, and power reference tracking. Our results show that the EBM-constrained and detailed thermal model-constrained problems produce similar outcomes in terms of temperature, power, and total energy consumption.
commercial buildings↗
A Robust Numerical Treatment of Solid-Phase Diffusion in Pseudo Two-Dimensional Lithium-Ion Battery Models
Solid-phase diffusion in active materials of lithium-ion batteries significantly affects charging and safety-related behavior of lithium-ion batteries. Therefore, it is essential to develop an efficient and robust numerical algorithm for solving solid-phase diffusion equations in physics-based battery models. In this work, we discuss the origins of numerical instabilities that can occur when solving the solid-phase diffusion equations using iterative methods. Then, in order to resolve such issues, we propose a simple numerical treatment to the surface flux term of discretized solid-phase diffusion equations. To demonstrate its numerical robustness, the proposed method is implemented into a pseudo two-dimensional (P2D) physics-based battery model and simulations are conducted at wide ranges of operating conditions. Even with extremely poor initial guesses for the Li+ concentrations of the active materials, computations using the proposed method do not diverge and the their computational speeds are comparable to those with conventional initial guesses. Comprehensive tests of the proposed method are also performed with a dynamic current profile based on US06 driving profile and a multi-stage charging profile with very high initial C-rate (12C).
battery modeling↗
A Unified Virtual Battery Model for Responsive Assets
We propose a unified virtual battery model for the flexibility of various responsive assets including batteries, thermostatically controllable loads (TCLs), deferrable loads, shiftable loads, and photovoltaics. Such a unified model lays a foundation to apply transactive control to responsive assets for ancillary service provision. We generalize the virtual battery model to shiftable loads and PVs. In the unified model, we select a set of parameters to represent the flexibility of different responsive assets to provide ancillary services related to active power. The set of parameters includes an energy state, an energy upper bound, an energy lower bound, a power upper bound, and a power lower bound. These parameters are selected to be as few as possible to represent the ancillary service provision flexibility of different responsive assets. The meaning of these parameters are explained to those responsive assets. Simulation examples are provided to show the efficacy of the general model to deal with various responsive assets in a unified way.
battery, photovoltaic↗
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↗
Efficient Conservative Numerical Schemes for 1D Nonlinear Spherical Diffusion Equations with Applications in Battery Modeling
Mathematical models of batteries which make use of the intercalation of a species into a solid phase need to solve the corresponding mass transfer problem. Because solving this equation can significantly add to the computational cost of a model, various methods have been devised to reduce the computational time. In this paper we focus on a comparison of the formulation, accuracy, and order of the accuracy for two numerical methods of solving the spherical diffusion problem with a constant or non-constant diffusion coefficient: the finite volume method and the control volume method. Both methods provide perfect mass conservation and second order accuracy in mesh spacing, but the control volume method provides the surface concentration directly, has a higher accuracy for a given numbers of mesh points and can also be easily extended to variable mesh spacing. Variable mesh spacing can significantly reduce the number of points that are required to achieve a given degree of accuracy in the surface concentration (which is typically coupled to the other battery equations) by locating more points where the concentration gradients are highest. (C) 2013 The Electrochemical Society. All rights reserved.
Zeng, Y↗
Battery Model Parameterization Using Manufacturer Datasheet and Field Measurement for Real-Time HIL Applications
Here, this paper presents a novel battery model parameterization method using actual field measurement and manufacturer datasheet for real-time hardware-in-the-loop (HIL) applications. It is critical that real-time HIL models can accurately reproduce field test results so that tests can be conducted on HIL testbeds instead of in the field. In the past, numerical heuristic optimization algorithms were often used to derive parameters for battery models. However, the deterministic algorithms often reach a locally optimal solution and stochastic heuristic searching strategies suffer from low searching efficiency. Therefore, in this paper, we propose a global-local searching enhanced genetic algorithm (GL-SEGA). By applying the generalized opposition-based learning mechanism, GL-SEGA can efficiently explore the global solution space. By using the trust-region-reflective method to perform the local search, the GL-SEGA can improve the accuracy and convergence in its local exploitations. Field measurements and manufactory datasheets are used to test and validate the accuracy and robustness of the GL-SEGA algorithm.
25 ENERGY STORAGE↗
Impacts of Lock-off Time on Virtual Battery Model from Thermostatically Controlled Loads
We test through simulation the influence of lock-off time on the aggregated flexibility of thermostatically controllable loads (TCLs), which is modeled by virtual battery (VB) models. The VB model provides a simple but effective way to quantify the aggregated flexibility of TCLs, but its efficacy is not clear when there is lock-off time for TCLs. TCLs cannot be turned on during the lock-off period, and so lock-off time imposes inevitable restrictions on not only the flexibility of individual TCLs but also the aggregated flexibility of a group of TCLs. We first test the influence of lock-off time on the efficacy of VB models with two artificial signals. One of the signal is of low frequency while the other is of high frequency. We then use real-world data to test the influence. One set of data is from a duck curve from CAISO while the other set of data is from AGC signals from PJM. The duck curve changes slowly while the AGC signals change rapidly over time. From both the artificial signals and real-word data, we find that lock-off time has a more significant impact on rapid signals than on slow signals.
Wang, Peng↗
Battery models, systems, and methods using robust fail-safe iteration free approach for solving differential algebraic equations
Battery models using robust fail-safe iteration free approach for solving Differential Algebraic Equations, and associated systems and methods are disclosed. In one embodiment, a method includes generating a model of the rechargeable battery; determining one or more initial conditions for one or more algebraic variables of the model using a solver; holding differential variables of the model static by a switch function while determining the one or more initial conditions; applying the initial conditions to the model by the switch function; and determining one or more parameters for the rechargeable battery by solving the algebraic and differential equations.
25 ENERGY STORAGE↗