Designing Data-Driven Battery Prognostic Approaches for Variable Loading Profiles: Some Lessons Learned

Abhinav Saxena, Jose R. Celaya, Indranil Roychoudhury, Bhaskar Saha, Sankalita Saha, and Kai Goebel
Submission Type: 
Full Paper
Supporting Agencies (optional): 
NASA
AttachmentSizeTimestamp
phmce_12_001.pdf902.52 KBJune 20, 2012 - 9:32am

Among various approaches for implementing prognostic algorithms data-driven algorithms are popular in the industry due to their intuitive nature and relatively fast developmental cycle. However, no matter how easy it may seem, there are several pitfalls that one must watch out for while developing a data-driven prognostic algorithm. One such pitfall is the uncertainty inherent in the system. At each processing step uncertainties get compounded and can grow beyond control in predictions if not carefully managed during the various steps of the algorithms. This paper presents analysis from our preliminary development of data-driven algorithm for predicting end of discharge of Li-ion batteries using constant load experiment data and challenges faced when applying these algorithms to randomized variable loading profile as is the case in realistic applications. Lessons learned during the development phase are presented.

Publication Year: 
2012
Publication Volume: 
3
Publication Control Number: 
001
Page Count: 
10
Submission Keywords: 
Challenges in Prognostics
Data-driven prognostics
applications
lithium-ion batteries
data-driven
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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