Loading...

Developing an Artificial Intelligence Algorithm for Diagnosis and Prognosis of Failures

Chenariyan Nakhaee, Muhammad | 2017

823 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 49457 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Houshmand, Mahmood; Fattahi, Omid
  7. Abstract:
  8. Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for capacity estimation. First, new features are extracted from cyclic charge/discharge cycles and used as health indicators. Three algorithms are used to characterize the relationship between extracted features and battery capacity. Decision tree, random forest and boosting algorithms are trained using a public dataset and selected features to estimate capacity. The overall estimation process comprises offline and online stages. A supervised learning step is established for model verification to ensure the generalizability of our proposed models for real application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed algorithms for online capacity estimation of lithium-ion batteries
  9. Keywords:
  10. Artificial Intelligence ; Random Forest Algorithm ; Decision Tree Learning ; Machine Learning ; Lithium Ion Batteries ; Diagnosis ; Prognostics Failure

 Digital Object List

 Bookmark

No TOC