Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model
Muhammad Umair Ali,
Karam Dad Kallu,
Haris Masood,
Kamran Ali Khan Niazi,
Muhammad Junaid Alvi,
Usman Ghafoor,
Amad Zafar
Affiliations
Muhammad Umair Ali
Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, South Korea
Karam Dad Kallu
Department of Robotics and Intelligent Machine Engineering (RIME), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST) H-12, Islamabad 44000, Pakistan
Haris Masood
Department of Electrical Engineering, University of Wah, Wah Cantt 47040, Pakistan
Kamran Ali Khan Niazi
Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark
Muhammad Junaid Alvi
Department of Electrical Engineering, NFC Institute of Engineering and Fertilizer Research, Faisalabad 38090, Pakistan
Usman Ghafoor
Department of Mechanical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan
Amad Zafar
Department of Electrical Engineering, University of Lahore, Islamabad Campus, Islamabad 44000, Pakistan; Department of Electrical Engineering, The Ibadat International University, Islamabad 44000, Pakistan; Corresponding author
Summary: A data-driven approach is developed to predict the future capacity of lithium-ion batteries (LIBs) in this work. The empirical mode decomposition (EMD), kernel recursive least square tracker (KRLST), and long short-term memory (LSTM) are used to derive the proposed approach. First, the LIB capacity data is split into local regeneration and monotonic global degradation using the EMD approach. Next, the KRLST is used to track the decomposed intrinsic mode functions, and the residual signal is predicted using the LSTM sub-model. Finally, all the predicted intrinsic mode functions and the residual are ensembled to get the future capacity. The experimental and comparative analysis validates the high accuracy (RMSE of 0.00103) of the proposed ensemble approach compared to Gaussian process regression and LSTM fused model. Furthermore, two times lesser error than other fused models makes this approach an efficient tool for battery health prognostics.
