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Flexible Parallel Neural Network Architecture Model for Early Prediction of Lithium Battery Life

Lidang Jiang, Zhuoxiang Li, Changyan Hu, Qingsong Huang, Ge He

TL;DR

The paper addresses early prediction of battery life (EPBL) for lithium batteries under varying data distributions. It proposes Flexible Parallel Neural Network (FPNN), integrating 3D CNN, 2D CNN, InceptionBlock, and a dual-stream layout, with an automatically learned Number of InceptionBlocks (NOI) to adapt to task complexity. On the MIT dataset, FPNN achieves outstanding predictive accuracy with MAPEs as low as 0.88% when 40 charging cycles are used, and demonstrates robustness across data volumes. The approach also provides interpretability through weight visualizations and ablation studies, offering a practical, scalable solution for battery health monitoring.

Abstract

The early prediction of battery life (EPBL) is vital for enhancing the efficiency and extending the lifespan of lithium batteries. Traditional models with fixed architectures often encounter underfitting or overfitting issues due to the diverse data distributions in different EPBL tasks. An interpretable deep learning model of flexible parallel neural network (FPNN) is proposed, which includes an InceptionBlock, a 3D convolutional neural network (CNN), a 2D CNN, and a dual-stream network. The proposed model effectively extracts electrochemical features from video-like formatted data using the 3D CNN and achieves advanced multi-scale feature abstraction through the InceptionBlock. The FPNN can adaptively adjust the number of InceptionBlocks to flexibly handle tasks of varying complexity in EPBL. The test on the MIT dataset shows that the FPNN model achieves outstanding predictive accuracy in EPBL tasks, with MAPEs of 2.47%, 1.29%, 1.08%, and 0.88% when the input cyclic data volumes are 10, 20, 30, and 40, respectively. The interpretability of the FPNN is mainly reflected in its flexible unit structure and parameter selection: its diverse branching structure enables the model to capture features at different scales, thus allowing the machine to learn informative features. The approach presented herein provides an accurate, adaptable, and comprehensible solution for early life prediction of lithium batteries, opening new possibilities in the field of battery health monitoring.

Flexible Parallel Neural Network Architecture Model for Early Prediction of Lithium Battery Life

TL;DR

The paper addresses early prediction of battery life (EPBL) for lithium batteries under varying data distributions. It proposes Flexible Parallel Neural Network (FPNN), integrating 3D CNN, 2D CNN, InceptionBlock, and a dual-stream layout, with an automatically learned Number of InceptionBlocks (NOI) to adapt to task complexity. On the MIT dataset, FPNN achieves outstanding predictive accuracy with MAPEs as low as 0.88% when 40 charging cycles are used, and demonstrates robustness across data volumes. The approach also provides interpretability through weight visualizations and ablation studies, offering a practical, scalable solution for battery health monitoring.

Abstract

The early prediction of battery life (EPBL) is vital for enhancing the efficiency and extending the lifespan of lithium batteries. Traditional models with fixed architectures often encounter underfitting or overfitting issues due to the diverse data distributions in different EPBL tasks. An interpretable deep learning model of flexible parallel neural network (FPNN) is proposed, which includes an InceptionBlock, a 3D convolutional neural network (CNN), a 2D CNN, and a dual-stream network. The proposed model effectively extracts electrochemical features from video-like formatted data using the 3D CNN and achieves advanced multi-scale feature abstraction through the InceptionBlock. The FPNN can adaptively adjust the number of InceptionBlocks to flexibly handle tasks of varying complexity in EPBL. The test on the MIT dataset shows that the FPNN model achieves outstanding predictive accuracy in EPBL tasks, with MAPEs of 2.47%, 1.29%, 1.08%, and 0.88% when the input cyclic data volumes are 10, 20, 30, and 40, respectively. The interpretability of the FPNN is mainly reflected in its flexible unit structure and parameter selection: its diverse branching structure enables the model to capture features at different scales, thus allowing the machine to learn informative features. The approach presented herein provides an accurate, adaptable, and comprehensible solution for early life prediction of lithium batteries, opening new possibilities in the field of battery health monitoring.
Paper Structure (12 sections, 8 equations, 6 figures, 3 tables)

This paper contains 12 sections, 8 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Dataset
  3. Methods
  4. Data Preprocessing
  5. FPNN (Flexible Parallel Neural Network)
  6. FPNN Architecture
  7. FPNN Units in FPNN
  8. Results and Discussion
  9. Evaluation of FPNN Model Predictive Performance
  10. Ablation Experiments
  11. Model Interpretability Analysis
  12. Conclusion

Figures (6)

  • Figure 1: provides a comprehensive analysis of lithium-ion battery performance: (a) Based on the MIT dataset, it shows the trend of lithium-ion battery discharge capacity decay over cycles, with an inset graph revealing the statistical distribution of battery life. (b) Displays the voltage changes of the "b1c23" battery across various charging cycles. The areas of voltage rise and fall are marked with black circles in the graph, highlighting the fluctuation characteristics of voltage during the charging process. (c) Describes the temperature variation trend of the "b1c23" battery during the charging process, where the temperature changes reflect the thermal management state at different charging stages.
  • Figure 2: Schematic Diagram of the EPBL Technology Route Based on FPNN
  • Figure 3: describes the detailed architecture and components of FPNN: ① 3D convolutional layer, using 3×3 convolutional kernels, with 64 channels; ② InceptionBlocks module; ③ 2D convolutional layer, with a kernel size of 7×7 and 64 channels; ④ Max pooling layer, with a pooling kernel size of 3×3; ⑤ InceptionBlock flexible unit; ⑥ 2D convolutional layer, with a kernel size of 1×1 and 16 or 24 channels (used as the target channel number for residual connections in other cases); ⑦ Average pooling layer, with a pooling kernel size of 3×3; ⑧ 2D convolutional layer, with a kernel size of 3×3 and 16 or 24 channels. The figure also shows: (a) FPNN video-like data after preprocessing; (b) The overall architecture of FPNN; (c) Detailed structure of the flexible module InceptionBlocks; (d) Specific details of the flexible unit InceptionBlock.
  • Figure 4: comprehensively demonstrates the performance of FPNN in EPBL tasks: (a) shows the EPBL results of the FPNN trained with the first 10 cycles of data, incorporating a subplot that displays a histogram of residual frequencies; (b) is a heatmap illustrating the impact of different cyclic data volumes and NOI settings on the FPNN's prediction MAPE; (c) a box plot, further revealing the distribution of prediction errors; (d) shows the MAE and RMSE of FPNN predictions at different cyclic data volumes; (e) displays the MAE performance for specific batteries 'b1c3' and 'b2c20'.
  • Figure 5: Results of the FPNN Ablation Experiments: (a) MAPE; (b) MAE; (c) RMSE.
  • ...and 1 more figures