Parameter Estimation and Approaches To Reduce CPU Time for Lithium-Ion Battery Models [Poster]
Abstract not provided.
25 ENERGY STORAGE↗
Engineering topics
Subramanian, Venkat R.
Publications and source records attributed to Subramanian, Venkat R..
BattPhase—A Convergent, Non-Oscillatory, Efficient Algorithm and Code for Predicting Shape Changes in Lithium Metal Batteries Using Phase-Field Models: Part I. Secondary Current Distribution
Electrochemical models at different scales and varying levels of complexity have been used in the literature to study the evolution of the anode surface in lithium metal batteries. This includes continuum, mesoscale (phase-field approaches), and multiscale models. Thermodynamics-based equations have been used to study phase changes in lithium batteries using phase-field approaches. However, grid convergence studies and the effect of additional parameters needed to simulate these models are not well-documented in the literature. In this paper, using a motivating example of a moving boundary model in one- and two-dimensions, we show how one can formulate phase-field models, implement algorithms for the same and analyze the results. An open-access code with no restrictions is provided as well. This article concludes with some thoughts on the computational efficiency of phase-field models for simulating dendritic growth.
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↗
Early Failure of Lithium–Sulfur Batteries at Practical Conditions: Crosstalk between Sulfur Cathode and Lithium Anode
Lithium–sulfur (Li–S) batteries are one of the most promising next-generation energy storage technologies due to their high theoretical energy and low cost. However, Li–S cells with practically high energy still suffer from a very limited cycle life with reasons which remain unclear. Here, through cell study under practical conditions, it is proved that an internal short circuit (ISC) is a root cause of early cell failure and is ascribed to the crosstalk between the S cathode and Li anode. The cathode topography affects S reactions through influencing the local resistance and electrolyte distribution, particularly under lean electrolyte conditions. The inhomogeneous reactions of S cathodes are easily mirrored by the Li anodes, resulting in exaggerated localized Li plating/stripping, Li filament formation, and eventually cell ISC. Manipulating cathode topography is proven effective to extend the cell cycle life under practical conditions. The findings of this work shed new light on the electrode design for extending cycle life of high-energy Li–S cells, which are also applicable for other rechargeable Li or metal batteries.
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A Thermal Tanks-in-Series Model for Capacity Fade Studies in Lithium-Ion Batteries.
Abstract not provided.
Thiagarajan, Raghav Sai↗
Progress on continuum modeling of lithium–sulfur batteries
While lithium–sulfur batteries are a promising next-generation chemistry devices due to their high theoretical energy density, commercialization has been slow due to low coulombic efficiency and poor cycle life. This review explores the ways in which continuum modeling contributes to the understanding of lithium–sulfur (LiS) battery mechanisms and cell-level performance through the lens of micro- and macroscale phenomena. We examine different approaches to modeling important physical phenomena such as reaction mechanisms, cathode microstructure, shuttling, nucleation and precipitation, and transport limitations. This paper also emphasizes the significance and challenge of connecting typical modeling parameters and assumptions to systems-level metrics of a standard state-of-art high performing lithium–sulfur cell. Particularly important, the considerations for high energy density cells and the areas where continuum models can facilitate better collaboration are discussed. Here, we also summarize a few selected works to highlight experimentally-driven modeling, use of electroanalytical techniques, and parameter identification approaches to enable model-based design and advanced battery management systems.
25 ENERGY STORAGE↗
A Minimal Information Set To Enable Verifiable Theoretical Battery Research
Batteries are an enabling technology for addressing sustainability through the electrification of various forms of transportation (1) and grid storage. (2) Batteries are truly multi-scale, multi-physics devices, and accordingly various theoretical descriptions exist to understand their behavior (3-5) ranging from atomistic details to techno-economic trends. As we explore advanced battery chemistries (6,7) or previously inaccessible aspects of existing ones, (8-10) new theories are required to drive decisions. (11-13) The decisions are influenced by the limitations of the underlying theory. Advanced theories used to understand battery phenomena are complicated and require substantial effort to reproduce. However, such constraints should not limit the insights from these theories. We can strive to make the theoretical research verifiable such that any battery stakeholder can assess the veracity of new theories, sophisticated simulations or elaborate analyses. We distinguish verifiability, which amounts to “Can I trust the results, conclusions and insights and identify the context where they are relevant?”, from reproducibility, which ensures “Would I get the same results if I followed the same steps?” With this motivation, we propose a checklist to guide future reports of theoretical battery research in Table 1. We hereafter discuss our thoughts leading to this and how it helps to consistently document necessary details while allowing complete freedom for creativity of individual researchers. Given the differences between experimental and theoretical studies, the proposed checklist differs from its experimental counterparts. (14,15) This checklist covers all flavors of theoretical battery research, ranging from atomic/molecular calculations (16-19) to mesoscale (20,21) and continuum-scale interactions, (9,22) and techno-economic analysis. (23,24) Finally, as more and more experimental studies analyze raw data, (25) we feel this checklist would be broadly relevant.
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Perspective—Mass Conservation in Models for Electrodeposition/Stripping in Lithium Metal Batteries
Electrochemical models at different scales and varying levels of complexity have been used in the literature to study the evolution of the anode surface in lithium metal batteries. This includes continuum, mesoscale (phase-field approaches), and multiscale models. In this paper, using a motivating example of a moving boundary model in one dimension, we show how battery models need proper formulation for mass conservation, especially when simulated over multiple charge and discharge cycles. The article concludes with some thoughts on mass conservation and proper formulation for multiscale models.
25 ENERGY STORAGE↗
Lithium-ion battery physics and statistics-based state of health model
A pseudo-2d model using COMSOL Multiphysics® software is developed to simulate performance and performance degradation of Li-ion batteries consisting of layered and olivine cathodes with graphite anode when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown at the anode, cathode dissolution and its synergistic effect on SEI formation at the anode. The model is validated by simulating commercial cylindrical cell performance. A global model is developed to simulate performance across all chemistries, along with individual chemistry models using global model parameters as initial values. There is good agreement between these models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed which fits data well and provides more clarity on differences in cathode dissolution exchange current density. Again, good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. The lessons learned from the physics-based model is used to develop a top down statistics-based model using current, voltage and anode volumetric change per mole lithium intercalated, along with their interactions as degradation predictors. This model predicts out of sample degradation for multiple grid services and electric vehicle drive cycle with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from physics-based computationally intensive algorithms.
Crawford, Aladsair J.↗