배터리

개별 셀부터 배터리 팩에 이르는 배터리 시스템에 예측 정비 개발 툴 적용

리튬 이온 배터리 셀과 배터리 팩에 예측 정비 개발 툴을 적용합니다. 특화된 특징과 알고리즘이 충전과 방전 프로파일, 사이클 간의 변동과 셀 간의 변동에 대한 지식을 바탕으로 이상을 감지하고 잔여 사이클 수명을 판별합니다.

함수

`meanDifferenceModel`	Identify most degraded cell in serially connected lithium-ion battery pack (R2022b 이후)
`adjacentPairCorrelationModel`	Identify worst cell relative to other cells in serially connected lithium-ion battery pack (R2023a 이후)

`batteryTestDataParser`	Convert data produced by battery lab testing into a format that is compatible with analysis and feature extraction (R2024b 이후)
`segmentData`	Classify and organize raw battery measurement data by identifying cycling phase, cycling mode, and data validity (R2024b 이후)
`batteryTestFeatureExtractor`	Specify feature set to extract from parsed battery cycling data (R2024b 이후)
`extract`	Extract selected battery features from parsed and segmented battery test data (R2024b 이후)
`computeDifferentialCurves`	Compute incremental capacity, differential voltage, and differential temperature curves that can be used to analyze battery behavior under constant-current conditions (R2024b 이후)

Characterize Battery Health Using Cycler-Based Test Data
Use specialized battery data parsing and feature extraction tools to organize test measurements and extract features from Lithium-Ion battery test data to evaluate battery health status.
Data Analysis and Feature Extraction for Battery Raw Cycling Data
Extract features from raw battery data using a workflow that includes data preprocessing, data organization and analysis, and feature extraction and visualization.