A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries

• Published in: Energies (Basel) Vol. 12; no. 19; p. 3678
• Main Authors: Sun, Tianfei, Xia, Bizhong, Liu, Yifan, Lai, Yongzhi, Zheng, Weiwei, Wang, Huawen, Wang, Wei, Wang, Mingwang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 26.09.2019
• Subjects: Accuracy; Algorithms; Artificial intelligence; Bayesian analysis; Design of experiments; extreme learning machine (elm); Kalman filters; Lithium; Lithium-ion batteries; Machine learning; Methods; Neural networks; Optimization; Parameter estimation; particle filter (pf); Principal components analysis; remaining useful life (rul); Useful life; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12193678

The prognosis of lithium-ion batteries for their remaining useful life is an essential technology in prognostics and health management (PHM). In this paper, we propose a novel hybrid prediction method based on particle filter (PF) and extreme learning machine (ELM). First, we use ELM to simulate the battery capacity degradation trend. Second, PF is applied to update the random parameters of the ELM in real-time. An extreme learning machine prognosis model, based on particle filter (PFELM), is established. In order to verify the validity of this method, our proposed approach is compared with the standard ELM, the multi-layer perceptron prediction model, based on PF (PFMLP), as well as the neural network prediction model, based on bat-particle filter (BATPFNN), using the batteries testing datasets of the National Aeronautics and Space Administration (NASA) Ames Research Center. The results show that our proposed approach has better ability to simulate battery capacity degradation trends, better robustness, and higher Remaining Useful Life (RUL) prognosis accuracy than the standard ELM, the PFMLP, and the BATPFNN under the same conditions.