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

2025/04/08 by Tianqi Ding, Dawei Xiang, Ding, Tianqi +5 · 2 citations
Engineering · #Advanced Battery Technologies Research #Advancements in Battery Materials #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2504.05728

openalex publication_date 2025/04/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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