Remaining Useful Life Prediction of Lithium-ion Battery Based on Double Exponential Empirical Particle Filter
Author:
Affiliation:
1. Zhishan Honors School, Jiangnan University,Wuxi,Jiangsu,China,214122
2. Jiangnan University,Engineering Research Center of Internet of Things Technology and Applications (Ministry of Education),Wuxi,Jiangsu,China,214122
Funder
National Key Research and Development Program of China
Natural Science Foundation of Jiangsu Province
Jiangsu Science and Technology Association Young Science and Technology Talents Lifting Project
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10239690/10239711/10240861.pdf?arnumber=10240861
Reference17 articles.
1. Remaining useful performance analysis of batteries
2. Comparison of prognostic algorithms for estimating remaining useful life of batteries
3. Remaining useful life prediction of lithium-ion battery based on improved particle filtering;chen;China Measurement & Test,2021
4. Interacting multiple model particle filter for prognostics of lithium-ion batteries
5. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life
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