Key Points:

• • Battery health monitoring requires accurate state-of-health estimation to prevent failures and optimize performance
• • DLinear neural networks provide superior SOH prediction accuracy (R² of 91.91%) compared to traditional approaches
• • The method combines trend decomposition with linear networks for efficient processing of battery sensor data
• • Implementation requires minimal computational resources while maintaining high prediction accuracy (MAE of 0.15%)