Temperature trends prediction of the lithium-ion battery: A neural network based on signal model decomposition

Key Points

• The prediction of temperature trends can enhance the lifespan of lithium-ion batteries, impacting performance.
• A neural network approach achieved a prediction accuracy of 92% in validating temperature changes.
• Analysis utilized signal model decomposition to refine data input for improved forecasting accuracy.
• Further studies may enable broader applications in energy storage systems for increased efficiency.