Quan1995417 / Prediction-of-Remaining-useful-life-in-Li-ion-batteries

Using LSTM-RNN model with Transfer Learning

Prediction of Remaining Useful Life (RUL) of Lithium ion (Li-ion) Batteries

Predict the remaining useful life (RUL) of lithium ion (Li-ion) batteries using an LSTM RNN model.

In predictive maintenance, data regarding the health of the equipment is collected, and then accordingly maintenance of the device can be scheduled.

Predictive maintenance in batteries are used for their health monitoring, which includes: Prediction of State of health (SoH) Prediction of Remaining Useful Life.

Here, an LSTM RNN model is used to learn the long-term dependency of the degradation data of capacities and predict the lithium-ion battery’s RUL and perform transfer learning to attain the best result.