High-Risk Electric Vehicle Identification Based on Logistic Regression
Author:
Affiliation:
1. Beijing Institute of Technology,National Engineering Lab of Electrical Vehicle & Collaborative Innovation Center for Electric Vehicle,Beijing,China,100081
2. Elkhorn South High School,Omaha,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10441787/10441797/10442516.pdf?arnumber=10442516
Reference9 articles.
1. Hybrid Embedded Power Supply Combining High-Energy Density and Ultra-High Power Lithium-ion Batteries For Electric Vehicle Applications
2. Mechanism, modeling, detection, and prevention of the internal short circuit in lithium-ion batteries: Recent advances and perspectives
3. Model-Based Battery Thermal Fault Diagnostics: Algorithms, Analysis, and Experiments
4. Safety Risk Identification of Lithium-ion Battery Based on Kolmogorov Complexity
5. Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data
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