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Evaluation of Cell Inconsistency in Lithium-Ion Battery Pack Using the Autoencoder Network Model
Shyr Long Jeng
*
, Wei Hua Chieng
*
此作品的通信作者
機械工程學系
研究成果
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引文 斯高帕斯（Scopus）
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Keyphrases
Network Model
100%
Long Short-term Memory Model
100%
Autoencoder Network
100%
Lithium-ion Battery Pack
100%
Cell Inconsistency
100%
Autoencoder
80%
Battery Energy Storage System
40%
Convolutional Long Short-term Memory (ConvLSTM)
40%
Fully Connected Model
40%
In Situ
20%
Optimal Performance
20%
Different Voltage
20%
Battery Pack
20%
Reconstruction Error
20%
Convolutional Neural Network
20%
Battery Life
20%
Abnormal Cells
20%
High Capability
20%
Time-varying Data
20%
Charging Process
20%
Connected in Series
20%
Telemetry
20%
Convolutional Neural Network Model
20%
Electric Vehicle
20%
Nominal Capacity
20%
Battery Condition
20%
Hybrid Convolutional Neural Network
20%
Electric Boat
20%
Complex Working Conditions
20%
Lithium-ion Battery Cell
20%
Engineering
Battery Pack
100%
Network Model
100%
Lithium-Ion Batteries
100%
Long Short-Term Memory
100%
Autoencoder
100%
Convolutional Neural Network
80%
Battery Energy Storage
40%
Optimal Performance
20%
Battery Life
20%
Electric Vehicle
20%
Nominal Capacity
20%
Charging Process
20%
Chemical Engineering
Long Short-Term Memory
100%
Neural Network
80%
Earth and Planetary Sciences
Energy Storage
100%
Real Time
50%