AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation
By: Tianqi Ding , Dawei Xiang , Tianyao Sun and more
Potential Business Impact:
Predicts battery life accurately for longer use.
This paper presents a comprehensive review of AI-driven prognostics for State of Health (SoH) prediction in lithium-ion batteries. We compare the effectiveness of various AI algorithms, including FFNN, LSTM, and BiLSTM, across multiple datasets (CALCE, NASA, UDDS) and scenarios (e.g., varying temperatures and driving conditions). Additionally, we analyze the factors influencing SoH fluctuations, such as temperature and charge-discharge rates, and validate our findings through simulations. The results demonstrate that BiLSTM achieves the highest accuracy, with an average RMSE reduction of 15% compared to LSTM, highlighting its robustness in real-world applications.
Similar Papers
A novel Neural-ODE model for the state of health estimation of lithium-ion battery using charging curve
Machine Learning (CS)
**Predicts how long electric car batteries will last.**
SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring
Machine Learning (CS)
Helps electric car batteries last much longer.
Prognosis Of Lithium-Ion Battery Health with Hybrid EKF-CNN+LSTM Model Using Differential Capacity
Machine Learning (CS)
Helps batteries last longer by testing them faster.