Interactive data-model hybrid method based on unscented Kalman filter and bidirectional long short-term memory network with self-attention for lithium-ion battery remaining useful life prediction

Key Points

• The hybrid method predicts remaining useful life, improving accuracy in performance assessments.
• Key metrics indicate high prediction reliability with reduced error rates of up to 15%.
• Analysis employs an interactive data-model hybrid using unscented Kalman filter and LSTM techniques.
• Enhanced forecasting implies better resource management and sustainability in battery usage.