DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis

Introduction

Battery degradation remains a critical challenge in the pursuit of green technologies and sustainable energy solutions. Despite significant research efforts, predicting battery capacity loss accurately remains a formidable task due to its complex nature, influenced by both aging and cycling behaviors. To address this challenge, we introduce DiffBatt, a novel general-purpose model for battery degradation prediction and synthesis. Discover how DiffBatt leverages GenAI to enhance battery technology in our paper!

Project Description

DiffBatt is a novel general-purpose model for battery degradation prediction and synthesis. It leverages diffusion models with classifier-free guidance and transformer architecture to achieve high expressivity and scalability. DiffBatt operates as a probabilistic model to capture uncertainty in aging behaviors and a generative model to simulate battery degradation.

Features

• Classifier-Free Guidance: DiffBatt uses classifier-free guidance, which combines conditional and unconditional diffusion models, to generate high-quality degradation curves, making it a powerful tool for predicting battery capacity loss.
• Probabilistic Modeling: DiffBatt captures uncertainty in aging behaviors, allowing it to provide accurate predictions even in situations with high levels of variability.
• Generative Modeling: DiffBatt can simulate battery degradation, enabling the generation of synthetic degradation curves that can be used for enhanced model training and data augmentation.
• Transformer Architecture: The model uses transformer architecture for attention-based processing of the input capacity matrix.
• Superior Generalizability: DiffBatt demonstrated superior generalizability in the remaining useful life prediction task, outperforming all other models with a mean RMSE of 196 across all datasets.
• Flexibility as a Foundation Model: Once trained on large datasets, DiffBatt can serve as a foundation model that can be fine-tuned for specific tasks or domains, making it a versatile tool for various battery degradation prediction and synthesis applications.

Model Architecture

The DiffBatt model is built using a customized U-Net architecture, which consists of several key components:

Down-sampling Blocks

The down-sampling blocks are a series of encoding layers that progressively reduce the spatial resolution of the input data while increasing its depth. Each down-sampling block consists of two residual blocks with attention and pooling operations. The architecture can be broken down as follows:

• Two residual blocks: These blocks take the output from the previous layer and perform transformations using ResidualBlock_Down. This is followed by a normalization operation using GroupNormalization.
• Attention block: If specified, an attention block is applied to the output of the residual blocks. This allows the model to focus on specific features or patterns in the data.
• Down-sampling: The output from the previous layer is down-scaled using DownSample to reduce the spatial resolution.

Middle Block

The middle block serves as an intermediate processing stage between the down-scaling and up-scaling operations. Its architecture consists of two residual blocks with attention. These blocks process the output from the previous layer, applying residual transformations with attention.

Up-sampling Blocks

The up-sampling blocks are responsible for up-scaling the feature maps and performing refinement operations. Each up-sampling block consists of:

• Three residual blocks with attention: These blocks process the output from the previous layer, applying residual transformations with attention.
• Up-sampling: The output is up-scaled using UpSample to increase the spatial resolution with transposed convolutions.
• Skips and Concatenation: In each up-sampling block, the output from the corresponding down-sampling block is retrieved and concatenated with the current feature map, and then passed through a residual block.

Additional Components

The model utilizes several other components:

• TimeMLP: Processes time embedding using a multilayer perceptron (MLP).
• TransformerEncoder: Embeds the capacity matrix using a transformer encoder.
• PositionalEncoding1D: Adds information about cycle life to the diffusion process using positional encoding.

Residual Blocks for Down-sampling

The ResidualBlock_Down function defines a residual block architecture specifically designed for down-scaling operations. Its primary goal is to progressively reduce the spatial resolution of the input data while increasing its depth. Here's a breakdown of its components:

1. Normalization and activation: The input undergoes group normalization followed by application of an activation function.
2. Convolutional layer: A convolutional layer with kernel size 3, zero padding and width equal to the specified width is applied.
3. Temporal embedding processing and conditioning: The temporal embedding ($\mathbf{c}_t$) is processed by applying an activation function followed by a dense layer with output shape (width,). This processed $\mathbf{c}_t$ is then added to the convolutional output $\mathbf{x}$ $$ \mathbf{y} = \mathbf{x} + \mathbf{c}_t. $$
4. Normalization and activation: The resulting output from the concatenation undergoes group normalization, application of an activation function, and another convolutional layer with kernel size 3, zero padding and width equal to the specified width.
5. Shortcut connection creation: A shortcut connection (residual) is created by adding the output from the last convolutional layer with the residual (input) after it has passed through a convolutional layer with kernel size 1.

Residual Blocks for Up-sampling

The ResidualBlock_Up function defines a residual block architecture specifically designed for up-scaling operations. Its primary goal is to progressively increase the spatial resolution of the input data while decreasing its depth. Here's a breakdown of its components:

1. Normalization and activation: The input undergoes group normalization followed by application of an activation function.
2. Convolutional layer: A convolutional layer with kernel size 3, zero padding and width equal to the specified width is applied.
3. Temporal embedding processing: The temporal embedding ($\mathbf{c}_t$) is processed by applying an activation function followed by a dense layer with output shape (width,). This processed $\mathbf{c}_t$ is then concatenated with the convolutional output using an additive operation (keras.layers.Add()).
4. Capacity-matrix embedding processing: The capacity-matrix embedding ($\mathbf{c}_q$) is also processed in the same way as the temporal embedding.
5. Conditioning and normalization: This step is performed by multiplying the capacity-matrix embedding with the convolutional output $\mathbf{x}$ and adding the time embedding $\mathbf{c}_t$, followed by group normalization and application of an activation function. $$ \mathbf{y} = \mathbf{c}_q \times \mathbf{x} + \mathbf{c}_t. $$
6. Normalization and activation: The resulting output from the previous layer undergoes group normalization and application of an activation function, followed by another convolutional layer with kernel size 3, zero padding and width equal to the specified width.
7. Shortcut connection creation: A shortcut connection (residual) is created by adding the output from the last convolutional layer with the residual (input) after it has passed through a convolutional layer with kernel size 1.

The primary differences between these two residual blocks lie in how they are conditioned with temporal and capacity-matrix embeddings.

Transformer Encoder

The TransformerEncoder is a critical component of the DiffBatt model, which is used to encode the capacity matrix. This encoder uses a combination of attention mechanisms and dense layers to capture complex relationships between different features in the capacity matrix.

Here's a detailed breakdown of the TransformerEncoder architecture:

• Dense Layer: The input data is passed through a dense layer with units number of neurons.
• Attention Block: The output of the dense layer is then passed through an attention block, which selectively updates certain features based on their relevance. This block uses a weighted sum of the input features to compute the attention weights.
• Global Average Pooling: The output of the attention block is then passed through a global average pooling layer to reduce the spatial dimensions of the feature maps.
• Final Dense Layer: The output of the previous layers is then passed through another dense layer with units number of neurons.

The TransformerEncoder architecture can be represented as follows:

Transformer Encoder:
  +-----------+
  |  Dense    |
  +-----------+
           |
           |  Attention Block
           v
  +-----------+-----------+
  | Global Avg Pooling    |
  +-----------+-----------+
           |
           |  Final Dense 
           v
  +-----------+
  | Output    |
  +-----------+

This architecture allows the model to capture complex relationships between different features in the capacity matrix, which is essential for making accurate predictions.

File and Folder Structure

The project consists of the following files and folders:

• unet.py: This file contains the implementation of the customized DDPM architecture.
• gaussian_diffusion.py and diffusion_model.py: These files contain the implementations of the Gaussian diffusion process and the diffusion model, respectively.
• testing.py: This file contains a script for testing the trained model on a separate test set.
• trained_models/: This folder contains pre-trained models for various battery datasets.
• data/: This folder contains the pre-processed dataset used for training and testing the models.

Getting Started

To get started with the project, follow these steps:

1. Clone the repository: git clone https://github.com/HamidrezaEiv/DiffBatt.git
2. Install required dependencies: pip install -r requirements.txt
3. Train the model using the provided script (e.g., python training.py)

Requirements

• NumPy 1.*
• TensorFlow 2.14.0

Citation

If you find our work relevant to your research, please cite:

@inproceedings{diffbatt,
title={DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis},
author={Hamidreza Eivazi and Andr{\'e} Hebenbrock and Raphael Ginster and Steffen Bl{\"o}meke and Stefan Wittek and Christoph Hermann and Thomas S. Spengler and Thomas Turek and Andreas Rausch},
booktitle={Neurips 2024 Workshop Foundation Models for Science: Progress, Opportunities, and Challenges},
year={2024},
url={https://openreview.net/forum?id=EW9sDAuPAS}
}

Contributing

We welcome contributions to the project! If you'd like to contribute, please fork the repository and submit a pull request with your changes.