Early-Stage State-of-Health Prediction of Lithium Batteries for Wireless Sensor Networks Using LSTM and a Single...

Area of Science:

• Electrical Engineering
• Computer Science
• Materials Science

Background:

• Lithium batteries are critical for wireless sensor networks and smart grids.
• State-of-Health (SOH) prediction is vital for grid reliability, energy management, and battery longevity.
• Accurate SOH estimation supports renewable energy integration and reduces maintenance costs.

Purpose of the Study:

• To present a solution for predicting the SOH and Remaining Useful Life (RUL) of lithium batteries.
• To evaluate the effectiveness of Long Short-Term Memory (LSTM) networks for battery SOH prediction.
• To determine the optimal data requirements for accurate SOH estimation using LSTMs.

Main Methods:

• Utilized two datasets: NASA's raw battery data and curve-fitted data using a single exponential model.
• Trained LSTM networks on data representing 30%, 50%, and 65% battery cycle consumption.
• Explored various LSTM architectures and hyperparameters to optimize performance.

Main Results:

• An LSTM model trained with only 50 records (30% usage) achieved a Mean Squared Error (MSE) of 1.68×10-4 and Root Mean Squared Error (RMSE) of 1.30×10-2.
• The best-performing LSTM model, trained with 110 records, yielded an MSE of 2.51×10-5 and an RMSE of 5.01×10-3.
• Demonstrated that accurate SOH prediction is possible even with a limited amount of battery usage data.

Conclusions:

• LSTM networks offer a robust solution for lithium battery SOH and RUL prediction in smart grids.
• Effective SOH estimation can be achieved with significantly less data than previously assumed.
• The findings support improved battery management strategies, enhancing the reliability and efficiency of smart grid operations.