Particle Filter-based Degradation Modeling and SOH Prediction for Lithium-ion Batteries of Autonomous Systems
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Lithium-Ion Battery, State of Health (SOH), Remaining Useful Life (RUL), Particle Filter, Degradation Modeling
Zhang, M., Yang, D., Du, J., Sun, H., Li, L., Wang, L., & Wang, K. (2023). A review of SOH prediction of Li-ion batteries based on data-driven algorithms. Energies, 16(7), 3167.
Lu, J., Xiong, R., Tian, J., Wang, C., & Sun, F. (2023). Deep learning to estimate lithium-ion battery state of health without additional degradation experiments. Nature Communications, 14(1), 2760.
Seo, D., & Shin, J. (2025). State-of-Health (SOH)-Based Diagnosis System for Lithium-Ion Batteries Using DNN With Residual Connection and Statistical Feature. International Journal of Energy Research, 2025(1), 4046189.
Lee, J. D., Seo, D., Shin, J., & Bang, H. (2025). Fast real-time state-of-health estimation method for lithium-ion battery using sparse identification of nonlinear dynamics. Journal of Intelligent & Robotic Systems, 111(3), 93.
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