Validation of RUL estimation method for battery prognostic under different fast-charging conditions
Author:
Affiliation:
1. University of Florence,Dept. of Information Engineering,Florence,IT,50139
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9806445/9806446/09806707.pdf?arnumber=9806707
Reference27 articles.
1. A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter
2. Particle Learning Framework for Estimating the Remaining Useful Life of Lithium-Ion Batteries
3. Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model
4. Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter
5. An optimized Relevance Vector Machine with incremental learning strategy for lithium-ion battery remaining useful life estimation
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