Skip to main navigation
Skip to search
Skip to main content
The University of Brighton Home
Search content at The University of Brighton
Home
Profiles
Research units
Equipment
Projects
Research output
Activities
Student theses
Remaining Life Prediction of Li-Ion Batteries Considering Sufficiency of Historical Data
Zilong Xin
, Xugang Zhang
, Qingshan Gong
, Feng Ma
,
Yan Wang
School of Arch, Tech and Eng
Communication and Creative Ecologies Research Excellence Group
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Remaining Life Prediction of Li-Ion Batteries Considering Sufficiency of Historical Data'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Global-local
100%
Historical Data
100%
Root Mean Square Error
100%
Broad Learning System
100%
Remaining Life Prediction
100%
Global Components
100%
Local Component
100%
History Data
100%
Li-ion Battery
100%
Dropout
50%
Hybrid Model
50%
Pearson Correlation Coefficient
50%
Remaining Useful Life
50%
Intrinsic Mode Function
50%
Dropout Technique
50%
Overfitting
50%
Capacity Curve
50%
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)
50%
Decomposition Algorithm
50%
Noising Method
50%
Capacity Regeneration Phenomena
50%
Dropout Model
50%
Engineering
Life Prediction
100%
Remaining Life
100%
Root Mean Square Error
100%
Historical Data
100%
Learning System
100%
Lithium Ion Battery
100%
Pearsons Linear Correlation Coefficient
50%
Predicted Value
50%
Empirical-Mode Decomposition
50%
Raw Data
50%
Intrinsic Mode Function
50%
Hybrid Model
50%
Chemical Engineering
Learning System
100%