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Data-Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects.

Shanling Ji1, Jianxiong Zhu1,2, Yaxin Yang1

  • 1School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China.

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Summary
This summary is machine-generated.

Data-driven artificial intelligence enhances battery characterization and prognosis for safer operation. This review highlights advanced methods and the role of interpretable learning in accelerating next-generation battery development.

Keywords:
battery characterizationbattery prognosisdata‐driven methodsexplainable artificial intelligencephysics‐informed learning

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Artificial Intelligence

Background:

  • Battery characterization and prognosis are crucial for understanding electrochemical mechanisms and ensuring operational safety.
  • Data-driven artificial intelligence (AI) systems offer advanced capabilities for battery analysis.
  • Recent progress has focused on integrating AI with various characterization techniques.

Purpose of the Study:

  • To provide a comprehensive review of data-driven methods for battery characterization and prognosis.
  • To highlight the role of physics-informed interpretable learning in accelerating energy device development.
  • To discuss challenges and future prospects in the field.

Main Methods:

  • Summarizing recent advances in informative image characterization and impedance spectroscopy.
  • Reviewing high-throughput screening approaches for revealing electrochemical mechanisms.
  • Comparing various physics-informed modeling strategies for battery prognosis.

Main Results:

  • Data-driven AI significantly enhances the ability to characterize and predict battery performance.
  • Physics-informed interpretable learning is key to unlocking insights from large battery datasets.
  • Advanced methods improve electrochemical transparency and generalization in battery models.

Conclusions:

  • Data-driven approaches, particularly physics-informed interpretable learning, are vital for next-generation battery development.
  • Enhanced electrochemical transparency and generalization are achievable through these advanced methods.
  • This review offers insights for future research in battery technology.