Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing

• URL: http://arxiv.org/abs/2307.05521v1
• Date: Fri, 7 Jul 2023 13:48:50 GMT
• Title: Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing
• Authors: Marc Duquesnoy, Chaoyue Liu, Vishank Kumar, Elixabete Ayerbe, Alejandro A. Franco
• Abstract summary: In this study, we tackle the issue of high-performance electrodes for desired battery application conditions. We propose a powerful data-driven approach supported by a deterministic machine learning (ML)-assisted pipeline for bi-objective optimization of the electrochemical performance. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes.
• Score: 61.27691515336054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The optimization of the electrode manufacturing process is important for upscaling the application of Lithium Ion Batteries (LIBs) to cater for growing energy demand. In particular, LIB manufacturing is very important to be optimized because it determines the practical performance of the cells when the latter are being used in applications such as electric vehicles. In this study, we tackled the issue of high-performance electrodes for desired battery application conditions by proposing a powerful data-driven approach supported by a deterministic machine learning (ML)-assisted pipeline for bi-objective optimization of the electrochemical performance. This ML pipeline allows the inverse design of the process parameters to adopt in order to manufacture electrodes for energy or power applications. The latter work is an analogy to our previous work that supported the optimization of the electrode microstructures for kinetic, ionic, and electronic transport properties improvement. An electrochemical pseudo-two-dimensional model is fed with the electrode properties characterizing the electrode microstructures generated by manufacturing simulations and used to simulate the electrochemical performances. Secondly, the resulting dataset was used to train a deterministic ML model to implement fast bi-objective optimizations to identify optimal electrodes. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes.

Related papers

• Improving Electrolyte Performance for Target Cathode Loading Using Interpretable Data-Driven Approach [1.6333646796408297]
  In this work, a data-driven approach is leveraged to find a high-performing electrolyte formulation for a novel interhalogen battery. The experimental dataset with variable electrolyte compositions and active cathode loading, is used to train a graph-based deep learning model. The trained model is used to further optimize the electrolyte formulation compositions for enhancing the battery capacity at a target cathode loading.
  arXiv  Detail & Related papers  (2024-09-03T15:33:25Z)
• Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design [20.855235423913285]
  We introduce Uni-ELF, a novel multi-level representation learning framework to advance electrolyte design. Our approach involves two-stage pretraining: reconstructing three-dimensional molecular structures at the molecular level using the Uni-Mol model, and predicting statistical structural properties. Through this comprehensive pretraining, Uni-ELF is able to capture intricate molecular and mixture-level information, which significantly enhances its predictive capability. We believe this innovative framework will pave the way for automated AI-based electrolyte design and engineering.
  arXiv  Detail & Related papers  (2024-07-08T17:26:49Z)
• Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model [4.301136099065666]
  We introduce a novel approach leveraging a transformer-based molecular representation model to effectively and efficiently capture the representation of electrolyte formulations. The performance of the proposed approach is evaluated on two battery property prediction tasks and the results show superior performance compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-28T09:55:29Z)
• Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization [5.637250168164636]
  Gradient-based and metaheuristic optimization techniques are limited by their lack of robustness, high computational costs, and susceptibility to local minima. In this study, Bayesian Optimization is used for tuning the dynamic parameters of an electrochemical equivalent circuit battery model (E-ECM) for a nickel-manganese-cobalt (NMC)-graphite cell.
  arXiv  Detail & Related papers  (2024-05-17T12:59:15Z)
• Orbital-Free Density Functional Theory with Continuous Normalizing Flows [54.710176363763296]
  Orbital-free density functional theory (OF-DFT) provides an alternative approach for calculating the molecular electronic energy. Our model successfully replicates the electronic density for a diverse range of chemical systems.
  arXiv  Detail & Related papers  (2023-11-22T16:42:59Z)
• Crystal-GFN: sampling crystals with desirable properties and constraints [103.79058968784163]
  We introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials. In this paper, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.
  arXiv  Detail & Related papers  (2023-10-07T21:36:55Z)
• Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning [0.3141085922386211]
  We develop a differentiable geometric deep learning model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation. We show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. With a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps.
  arXiv  Detail & Related papers  (2023-10-03T22:26:38Z)
• Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization [59.45414406974091]
  We introduce a framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks. We study the flexibility of NSGA-II, which we extend by two variants: 1) varying goals, that optimize solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and 2) active-inactive genotype, that accommodates different possibilities that can be activated or deactivated. Results show that adaption with standard NSGA-II greatly reduces the number of evaluations required for optimization to a target goal, while the proposed variants further improve the adaption costs.
  arXiv  Detail & Related papers  (2023-05-31T12:07:50Z)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)
• Universal Battery Performance and Degradation Model for Electric Aircraft [52.77024349608834]
  Design, analysis, and operation of electric vertical takeoff and landing aircraft (eVTOLs) requires fast and accurate prediction of Li-ion battery performance. We generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning.
  arXiv  Detail & Related papers  (2020-07-06T16:10:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.