AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation
It addresses battery health prediction for applications like electric vehicles, but it is incremental as it reviews and compares existing methods.
This paper tackles the problem of predicting State of Health in lithium-ion batteries by comparing AI algorithms like FFNN, LSTM, and BiLSTM across multiple datasets and scenarios, finding that BiLSTM achieves the highest accuracy with an average RMSE reduction of 15% compared to LSTM.
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.