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A generic model-free approach for lithium-ion battery health management
Guangxing Bai,
Pingfeng Wang
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peer-review
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Dive into the research topics of 'A generic model-free approach for lithium-ion battery health management'. Together they form a unique fingerprint.
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Keyphrases
Lithium-ion Battery
100%
Generic Model
100%
Model-free Methods
100%
Artificial Neural Network
100%
Battery Management
100%
Dual Extended Kalman Filter
100%
State of Charge
75%
Physical Model
75%
Health Diagnosis
50%
Extended Kalman Filter Algorithm
50%
Accurate Estimation
25%
Health Status
25%
Battery Design
25%
Energy Storage Devices
25%
Voltage Output
25%
Service Life
25%
Reliable Operation
25%
Data-driven Approach
25%
Safe Operation
25%
Model Output
25%
Generic Data
25%
Electronic Equipment
25%
Unexpected Failures
25%
Terminal Voltage
25%
State-space Equations
25%
Battery Model
25%
Battery Manufacturing
25%
Battery Operation
25%
Physical Battery
25%
Battery Discharging
25%
Engineering
Battery (Electrochemical Energy Engineering)
100%
Lithium-Ion Batteries
100%
Generic Model
100%
Extended Kalman Filter
44%
Artificial Neural Network
44%
State of Charge
33%
Physical Model
33%
State of Health
33%
Filtering Algorithm
22%
Experimental Result
11%
Battery Model
11%
Output Voltage
11%
Energy Storage
11%
Reliable Operation
11%
Terminal Voltage
11%
Safe Operation
11%
Generic Data
11%
Space Equation
11%
Filter State
11%
Physics
Lithium-Ion Batteries
100%
Artificial Neural Network
100%
Energy Storage
25%
Electronic Equipment
25%
Chemical Engineering
Neural Network
100%