Internal short-circuit diagnosis for lithium-ion batteries using autoencoder with temporal convolutional network and self-attention mechanism

Key Points

• Diagnosis of internal short-circuits leads to enhanced battery safety and reliability, reducing potential failures.
• The method accurately identifies issues using an autoencoder combined with a temporal convolutional network and self-attention mechanism.
• Assessment using advanced neural networks provides a more effective diagnosis compared to traditional methods.
• This study supports the potential for improved monitoring systems in lithium-ion batteries with cutting-edge technology.