Jieqi Wei
Research Subjects
Research Interests
Jieqi Wei has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Chaolong Zhang
Jinling Institute of Technology
1 shared publication - Jiajia Shao
College of Engineering, Huzhou University, Huzhou 313000, China
1 shared publication - Kaixin Cheng
College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, China
1 shared publication - Pengyu Jiang
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
1 shared publication - Runrun Zhang
Institute of Advanced Materials, Nanjing Tech University, Nanjing 210023, China
1 shared publication - Shi Chen
College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, China
1 shared publication
Latest Publications
- Open Access
A Hybrid RUL Prediction Framework for Lithium-Ion Batteries Based on EEMD and KAN-LSTM
Accurately estimating the remaining useful life (RUL) of lithium-ion batteries in energy storage systems is critical for ensuring both the safety and reliability of the power grid. To address the complex nonlinear degradation behavior associated with battery aging, this study proposes a novel RUL prediction framework that integrates ensemble empirical mode decomposition (EEMD) with an ensemble learning algorithm. The approach first applies EEMD to decompose aging data into a residual component and several intrinsic mode functions (IMFs). The residual component is then modeled using a long short-term memory (LSTM) network, while the Kolmogorov–Arnold network (KAN) focuses on learning from the IMF components. These individual predictions are subsequently combined to reconstruct the overall capacity degradation trajectory. Experimental validation on real lithium-ion battery aging datasets demonstrates that the proposed method provides highly accurate RUL predictions, exhibits strong robustness, and effectively captures nonlinear characteristics under varying operating conditions. Specifically, the method achieves R2 above 0.96 with absolute RUL errors within 2–3 cycles on NASA datasets, and maintains R2 values above 0.91 with errors within 7–15 cycles on CALCE datasets. Furthermore, the optimal KAN hyperparameters for different IMF components are identified, offering valuable insights for multi-scale modeling and future model optimization.
Open AccessA Hybrid RUL Prediction Framework for Lithium-Ion Batteries Based on EEMD and KAN-LSTM
Accurately estimating the remaining useful life (RUL) of lithium-ion batteries in energy storage systems is critical for ensuring both the safety and reliability of the power grid. To address the complex nonlinear degradation behavior associated with battery aging, this study proposes a novel RUL prediction framework that integrates ensemble empirical mode decomposition (EEMD) with an ensemble learning algorithm. The approach first applies EEMD to decompose aging data into a residual component and several intrinsic mode functions (IMFs). The residual component is then modeled using a long short-term memory (LSTM) network, while the Kolmogorov–Arnold network (KAN) focuses on learning from the IMF components. These individual predictions are subsequently combined to reconstruct the overall capacity degradation trajectory. Experimental validation on real lithium-ion battery aging datasets demonstrates that the proposed method provides highly accurate RUL predictions, exhibits strong robustness, and effectively captures nonlinear characteristics under varying operating conditions. Specifically, the method achieves R2 above 0.96 with absolute RUL errors within 2–3 cycles on NASA datasets, and maintains R2 values above 0.91 with errors within 7–15 cycles on CALCE datasets. Furthermore, the optimal KAN hyperparameters for different IMF components are identified, offering valuable insights for multi-scale modeling and future model optimization.