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A lithium battery service life prediction method based on a physical neural network

PendingCN122283479ABreaking through the limitations of "black box"Preserve Feature Extraction CapabilityElectrical batteryPhysical neural network
This invention provides a method for predicting the lifespan of lithium batteries based on physical neural networks, comprising: constructing a coupled ordinary differential equation of lithium battery charge state and thermodynamic evolution, and establishing a continuous-time dynamic model of lithium battery charge change; constructing a PI-LSTM hybrid analysis framework based on physical information neural networks, and incorporating the coupled ordinary differential equation as a physical constraint term into the loss function of the PI-LSTM hybrid analysis framework; collecting time-series sensing data of lithium battery operation, training the PI-LSTM hybrid analysis framework, and completing the inversion identification of lithium battery physical parameters; based on the trained PI-LSTM hybrid analysis framework, outputting the state of charge and time to depletion of the lithium battery under the target operating condition, and completing the prediction of lithium battery lifespan; this invention aims to achieve accurate and interpretable prediction of lithium battery SOC and TTE in multiple devices, providing support for battery management of electrical equipment such as drones.
Owner:HUNAN NORMAL UNIVERSITY