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 delivered.
AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation
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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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cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
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A Machine Learning-Based Study on the Synergistic Optimization of Supply Chain Management and Financial Supply Chains from an Economic Perspective
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 delivered.