PATENT CLAIM ANALYSIS

Application Number: 15960767
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-10
Patent Classification: ["320", "107000"]

Abstract:
The present invention relates to a method for predicting the state of health of a battery based on numerical simulation data. A method for predicting the state of health of a battery, which is performed by a battery management system, according to an embodiment of the present invention includes: a step of obtaining a verified numerical simulation database, into which solution data of the battery is extracted and stored, when a numerical analysis result is verified by an experimental result using electrical and chemical analysis of the battery; a step of counting the number of charges or discharges when a deviation between reference data read from the verified numerical simulation database and measurement data read from the battery is within a preset range and battery capacity satisfies a preset condition; and a step of predicting a state of health of the battery using the number of charges or discharges and a classifier based on a learned machine learning algorithm.

Claim (Index 12):
The method for predicting the state of health of the battery based on numerical simulation data of  claim 8 ,\n wherein the step of predicting uses a machine learning algorithm, wherein the machine learning algorithm is Support Vector Machine, Bayes Classifiers, Artificial Neural Networks, or Decision Tree.

Metadata:
- Claim Count in Document: 32.0
- Percentile: 91.0
- Lexical Diversity: 2.33333
- Patent Class: 320.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['12423922', '13269406', '15359099', '14645893', '15372836']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7170678387541365
- 35 USC 102 Novelty (BERT): 0.5122513030535554
- Combined Prediction Score: 0.6965861851840784
- Mean Citation Score: 199.59588
- Max Citation Score: 210.01872000000003
- Similarity Product: 115.25081099109651

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test