Unsupervised Adaptive Fleet Battery Pack Fault Detection With Concept Drift Under Evolving Environment
Author:
Affiliation:
1. MathWorks, Natick, MA, USA
2. General Motors Research and Development Center, Warren, MI, USA
3. Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA
Funder
a collaborative agreement between General Motors Company and Northeastern University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/8856/10631719/10436628.pdf?arnumber=10436628
Reference42 articles.
1. A survey of methods for monitoring and detecting thermal runaway of lithium-ion batteries
2. Data-driven prediction of battery cycle life before capacity degradation
3. Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures
4. Model-Based Lithium-Ion Battery Resistance Estimation From Electric Vehicle Operating Data
5. A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature
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