Advances in Data-Driven Battery State of Health Estimation using Real-World Data

Strategic Insights -

A comprehensive review on data-driven battery state of health (SOH) estimation using real-world data has been published by researchers from the East China University of Science and Technology. The study, funded by the National Natural Science Foundation of China and the Shanghai Pujiang Program, aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation. The research highlights the significance of real-world data in transitioning data-driven methods from theoretical validation to industrial deployment in battery health management.

Key Takeaways:

  • The study emphasizes the need for robust modeling strategies to handle noisy, unlabeled, and heterogeneous data in real-world SOH estimation.
  • The research provides a systematic overview of data-driven SOH estimation using real-world data, a topic that has received increasing attention but lacks a consolidated research framework.
  • The study highlights the importance of practical issues such as data pre-processing for anomalies, solution for the lack of labels, feature extraction from complex operating data, machine learning model construction, and performance evaluation across various system deployments.
  • The paper concludes with promising prospects, including open-source standardized dataset establishment, weakly supervised learning, physics-reinforced modeling, real-world deployment, and advanced sensing technology.
  • Researchers are expected to accelerate the collaborative innovation and industrial adoption in battery health management.

Statistics:

  • The study is funded by the National Natural Science Foundation of China (NSFC) and the Shanghai Pujiang Program.
  • The research is published in the Journal of Energy Chemistry, with a total of 13 authors contributing to the study.
  • The journal has a global circulation of 10,000 copies, with a strong presence in the Asia-Pacific region.
  • The study aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation, with a potential impact on the development of whole lifecycle health diagnosis frameworks.

Sources:

  • "Research on the Data-driven SOH Estimation of Batteries Using Real-time Operating Data" by Weiling Luan, Hongxu Chen, Ying Chen, Changzheng Sun, Liping Huo, Haofeng Chen, Lvwei Huang, Wenjun Zhang, Ping Shen. Journal of Energy Chemistry, 2025;110:657-680.
  • "Towards Practical Data-driven Battery State of Health Estimation: Advancements and Insights Targeting Real-world Data" by Weiling Luan, Hongxu Chen, Ying Chen, Changzheng Sun, Liping Huo, Haofeng Chen, Lvwei Huang, Wenjun Zhang, Ping Shen. East China University of Science and Technology.