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

arXiv: https://arxiv.org/abs/2504.05728 · HTML: https://mlsanity.com/paper/2504.05728/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey](https://mlsanity.com/paper/2412.20367/) (2024)
- [SCORE: Story Coherence and Retrieval Enhancement for AI Narratives](https://mlsanity.com/paper/2503.23512/) (2025)
- [Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion](https://mlsanity.com/paper/2501.04606/) (2025)
- [Optimized Coordination Strategy for Multi-Aerospace Systems in Pick-and-Place Tasks By Deep Neural Network](https://mlsanity.com/paper/2412.09877/) (2024)
- [GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2504.00946/) (2025)

## Built on by (most cited first, 5 of 5)

- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs](https://mlsanity.com/paper/2506.12182/) (2025)
- [Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports](https://mlsanity.com/paper/2508.15845/) (2025)
- [A Machine Learning-Based Study on the Synergistic Optimization of Supply Chain Management and Financial Supply Chains from an Economic Perspective](https://mlsanity.com/paper/2509.03673/) (2025)
- [Gradient Boosting Decision Tree with LSTM for Investment Prediction](https://mlsanity.com/paper/2505.23084/) (2025)

## Abstract (arXiv)

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.