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Addressing Sensor Data Heterogeneity and Sample Imbalance: A Transformer-Based Approach for Battery Degradation

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Summary

This study introduces a Transformer-based model to improve electric vehicle battery health monitoring. It enhances remaining useful life (RUL) prediction accuracy by addressing data heterogeneity and sample imbalance.

Keywords:
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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Data Science

Background:

  • Electric vehicle battery health monitoring faces challenges with diverse data and imbalanced samples.
  • Accurate remaining useful life (RUL) estimation is crucial for battery management systems.

Purpose of the Study:

  • To develop a novel Transformer-based approach for enhanced battery health monitoring and RUL estimation.
  • To address data heterogeneity and sample imbalance in electric vehicle battery datasets.

Main Methods:

  • Utilized the NASA lithium-ion battery cycling dataset.
  • Implemented a multimodal feature fusion strategy for heterogeneous data integration.
  • Employed adaptive resampling and a hierarchical attention mechanism to handle imbalanced samples.
  • Leveraged Transformer architecture's self-attention for long-term dependency capture.

Main Results:

  • Achieved a 21.3% increase in accuracy for heterogeneous data processing.
  • Improved prediction capability for imbalanced samples by 24.5% compared to traditional methods.
  • Demonstrated significant enhancements in battery degradation prediction accuracy.

Conclusions:

  • The proposed Transformer-based model effectively addresses data heterogeneity and sample imbalance in battery health monitoring.
  • The method provides reliable data support for preventive maintenance and replacement decisions in electric vehicle battery management systems.
  • Enhanced reliability and economic efficiency of electric vehicle battery management systems.