Method and apparatus for diagnosing state of battery using neural network model
AssigneeSamsung SDI Co., Ltd.
InventorKwanghum Park
Life of this patent
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Abstract
A method and apparatus for diagnosing a state of a battery includes measuring electrochemical feature data of all of frequency regions of the battery, training a neural network model that estimates the state of the battery in the measured electrochemical feature data, identifying a frequency region having a high contribution to a prediction of the state of the battery based on a feature map by using the trained neural network model, reducing a data dimension of the frequency region by extracting the electrochemical feature data of the identified frequency region, retraining the neural network model by using electrochemical feature data of the reduced frequency region, and diagnosing the state of the battery by using the retrained neural network model.
Claims20
A method of diagnosing a state of a battery using an apparatus for diagnosing the state of the battery, the method comprising:
- measuring electrochemical feature data of all of frequency regions of the battery;
- training a neural network model that estimates the state of the battery in the measured electrochemical feature data;
- identifying a frequency region having a high contribution to a prediction of the state of the battery based on a feature map by using the trained neural network model;
- reducing a data dimension of the frequency region by extracting the electrochemical feature data of the identified frequency region;
- retraining the neural network model by using electrochemical feature data of the reduced frequency region;
- and
- diagnosing the state of the battery by using the retrained neural network model.
An apparatus for diagnosing a state of a battery, comprising:
- an electrochemical feature data measurement module configured to measure EIS data of all of frequency regions of the battery;
- a model training module configured to train a neural network model that estimates the state of the battery in the measured electrochemical feature data;
- a frequency identification module configured to identify a frequency region having a high contribution to a prediction of the state of the battery based on a feature map by using the trained neural network model;
- a data dimension reduction:
- module configured to reduce a data dimension of the frequency region by extracting the electrochemical feature data of the identified frequency region;
- a model retraining module configured to retrain the neural network model by using electrochemical feature data of the reduced frequency region;
- and
- an SOH diagnosis module configured to diagnose the state of the battery by using the retrained neural network model.
Dependencies are inferred from the claim text.