We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
State of health prediction of medical lithium batteries based on multi-scale decomposition and deep learning.
- Authors
Liu, Chang Chun; Wu, Tao; He, Cheng
- Abstract
To guarantee rescue time and reduce medical accidents, a health degradation prediction model of medical lithium-ion batteries based on multi-scale deep neural network was proposed aiming at the problems of poor model adaptability and inaccurate prediction in current state of health prediction methods. The collected energy data of medical lithium-ion batteries were decomposed into main trend data and fluctuation data by ensemble empirical mode decomposition and correlation analysis. Then, deep Boltzmann machines and long short-term memory were used to model the main trend and fluctuation data, respectively. The predicting outcomes of deep Boltzmann machines and long short-term memory were effectively integrated to obtain the health predicted results of medical lithium-ion battery. The experimental results show that the method can effectively fit the health trend of medical lithium-ion batteries and obtain accurate state of health prediction results. The performance of the method is better than other typical prediction methods.
- Subjects
LITHIUM cells; FORECASTING; DEEP learning; HILBERT-Huang transform; BOLTZMANN machine; MEDICAL supplies; MEDICAL imaging systems
- Publication
Advances in Mechanical Engineering (Sage Publications Inc.), 2020, Vol 12, Issue 5, p1
- ISSN
1687-8132
- Publication type
Article
- DOI
10.1177/1687814020923202