Batteries

Apply predictive maintenance development tools to battery systems that range from individual cells to battery packs

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.

Functions

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

Featured Examples

Nonlinear State Estimation of a Degrading Battery System

Estimate the states of a nonlinear system using an unscented Kalman filter in Simulink.

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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.

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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.

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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.

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