Batteries
Apply predictive maintenance development tools to lithium-ion battery cells and battery packs. Specialized features and algorithms use knowledge of charge, discharge profiles, and cycle-to-cycle and cell-to-cell variations to enable detection of anomalies and to identify remaining cycle life. You can analyze battery data both from lab test data and from actual in-use data.
Functions
Topics
- 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.
- Compute Battery Differential Curves for a Single Phase
Compute the differential curves for battery measurements that collected from a single constant current (CC) discharging phase.
Featured Examples
Nonlinear State Estimation of a Degrading Battery System
Estimate the states of a nonlinear system using an unscented Kalman filter in Simulink.
Battery Cycle Life Prediction from Initial Operation Data
Predict the remaining cycle-life of a fast charging Li-ion battery using a supervised machine learning algorithm.
Battery Cycle Life Prediction Using Deep Learning
Predict the remaining cycle-life of a fast charging Li-ion battery by training a deep neural network.
Battery Second-Life Application State of Health (SoH) Estimation of Li-Ion Batteries
Estimate battery state of health (SoH) for second-life application using measurements from first life and terminal voltage during second life.
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.
Automatic Data Segmentation and Feature Extraction for Reference Performance Test in Lab-Measured Battery Aging Data
Reference performance tests (RPT) are included in battery aging test design when there is no unified measurement available for obtaining remaining capacity or when additional measurements (slow rate cycles, pulses, EIS, etc.) are needed to characterize the battery's health. This example illustrates the use of a neural network for separating current pulses from regular charge-discharge steps and the extraction of features, that serve as health indicators, from the identified pulses.
