NMC Particle Detection and Segmentation in X-ray Nano-tomography Images of Lithium-Ion Battery Cathodes

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow, adapted from matterport/Mask_RCNN , for the instance segmentation of Ni0.33Mn0.33Co0.33 (NMC) particles in Lithium-ion battery cathodes. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.

Abstract

The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles’ evolving (de)attachment with the conductive matrix. Herein, we tackle this issue with a unique combination of multiscale experimental approaches, machine-learning-assisted statistical analysis, and experiment-informed mathematical modeling. Our results suggest that the degree of particle detachment is positively correlated with the charging rate and that smaller particles exhibit a higher degree of uncertainty in their detachment from the carbon/binder matrix. We further explore the feasibility and limitation of utilizing the reconstructed electron density as a proxy for the state-of-charge. Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode’s microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity.

The repository includes:

• Pre-trained weights
• Training code for new datasets
• Jupyter notebooks to visualize the detection pipeline at every step

New model considering the shape characteristics of NMC particles is coming, stay tune.

Installation

1. Clone this repository via git clone https://github.com/hijizhou/LIBNet.git
2. Install dependencies and current repo

    pip install -r requirements.txt

3. Run setup from the repository root directory

    python3 setup.py install

4. From the Releases page, download mask_rcnn_particles.h5 from the section Pretrained Mask R-CNN model and example data. Save it in the model directory of the repo.
5. (Optional) Download example_data.zip. Unzip it such that it's in the path data/example/.

Run Jupyter notebooks

Quick demo

Open the quick_demo_particle.ipynb. You can use the example data to see the detection and segmentation results by the pre-trained model.

Inspection of training data

Open the inspect_training_data_particle.ipynb. You can use these notebooks to explore the dataset and run through the detection pipeline step by step.

Inspection of pre-trained model

Open the inspect_pretrained_model_particle.ipynb. This notebook goes in depth into the steps performed to detect and segment particles. It provides visualizations of every step of the pipeline.

Performance evaluation

Training on your own dataset

I used VGG Image Annotator (VIA) for the labeling, see this blog for the detailed instruction with an example.

Train a new model starting from pre-trained weights

python3 particles.py train --dataset=/path/to/your/dataset --weights=model/mask_rcnn_particle.h5

Train a new model starting from ImageNet weights

python3 particles.py train --dataset=/path/to/your/dataset --weights=imagenet

Train a new model starting from COCO weights

python3 particles.py train --dataset=/path/to/your/dataset --weights=coco

Citation

Use this bibtex to cite this repository:

@article{jiang_lib_segmentation2020,
  title={Machine-Learning-Revealed Statistics of the Particle-Carbon/Binder Detachment in Li-Ion Battery Cathodes},
  author={Z. Jiang, J. Li, Y.Yang, L. Mu, C. Wei, X. Yu, P. Pianetta, K. Zhao, P. Cloetens, F. Lin and Y. Liu},
  journal={Nature Communications},
  year={2020},
  volume={11},
  number={2310},
  doi={10.1038/s41467-020-16233-5},
  publisher={Nature Publishing Group}
}

Contributing

Contributions to this repository are always welcome. Examples of things you can contribute:

• Accuracy Improvements. A more accurate model based on the shape characteristic is coming.
• Training on your own data and release the trained models.
• Visualizations and examples.

Requirements

Python 3.6, TensorFlow 1.3, Keras 2.0.8 and other common packages listed in requirements.txt.