Xgboost-based remaining useful life estimation model with extended kalman particle filter for lithium-ion batteries

HIGHLIGHTS

  • who: Sadiqa Jafari and Yung-Cheol Byun from the Department of Electronic Engineering, Institute of Information Science and, National University, Republic of Korea have published the research: XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries, in the Journal: Sensors 2022, 22, 9522. of /2022/
  • what: The empirical model, which includes exponential, linear, polynomial, and Verhulst models, focuses on determining the underlying mathematical connection of the capacity degradation trajectory. In the paper`s final portion, the authors compare the proposed method with the recent work on the XGBoost . . .

     

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