Enhanced Battery Degradation and RUL Prediction Using Bidirectional LSTM Networks

Accurate prediction of battery degradation and remaining useful life (RUL) is critical for optimizing the performance and lifespan of battery-powered systems in electric vehicles and renewable energy storage applications. This paper introduces a novel machine learning approach utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) networks to predict battery degradation and estimate RUL based on key parameters including voltage, current, temperature, and cycle count. Unlike conventional LSTM models that process data in a unidirectional manner, our Bi-LSTM architecture captures both past and future dependencies in battery behavior, significantly improving prediction accuracy. Through comprehensive evaluation on real-world battery datasets, we demonstrate that Bi-LSTM outperforms traditional LSTM systems by reducing root mean square error (RMSE) for state of health (SOH) prediction from 4.5–3.1% and improving R² values from 0.87 to 0.92. For RUL prediction, our model achieves an RMSE of 120 cycles compared to 150 cycles for standard LSTM. These improvements enable more reliable real-time battery health monitoring and proactive management strategies. The integration of Bi-LSTM into battery management systems (BMS) offers enhanced computational efficiency and superior convergence speed, making it particularly suitable for applications requiring precise battery management such as electric vehicles and grid-scale energy storage systems.