Pith. sign in

REVIEW 2 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2504.05728 v1 pith:AB3QSE2P submitted 2025-04-08 cs.AI cs.LG

Tianqi Ding , Dawei Xiang , Tianyao Sun , YiJiashum Qi , Zunduo Zhao This is my paper · ORCID
classification cs.AIcs.LG
keywords ai-drivenbatteriesbilstmcomprehensivehealthlstmpredictionprognostics
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Machine Learning-Based Study on the Synergistic Optimization of Supply Chain Management and Financial Supply Chains from an Economic Perspective

    cs.LG 2025-09 reject novelty 4.0 of 10

    A DML regression on roughly 40,000 firm-year observations reports positive effects of data element marketization on five supply chain resilience indicators, but the abstract's promised operational improvements are not...

  2. Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks

    cs.IR 2025-06 reject novelty 2.0 of 10

    A hybrid LLM-plus-GNN recommender is claimed to beat collaborative filtering, LLM-only, and GNN-only baselines on financial product ranking, with NDCG@10 of 0.372.

Pith tools