Work

AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation

Year: 2025

Type: conference-paper

Abstract: This paper presents a comprehensive review of AIdriven prognostics for State of Health (SoH) prediction in lithiumion 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.

DOI: 10.1109/icetac65964.2025.11144279

Authors: Tianqi Ding, Dawei Xiang, Tianyao Sun, Yijiashun Qi, Zunduo Zhao

Institutions: Baylor University, University of Connecticut, University of Michigan, New York University

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Good health and well-being

Open Access status: green

OpenAlex ID: W4414010105

FWCI: 26.28

Cites: 36

Cited by: 16

Related to: 10