{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/134922"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/134922","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"STATISTICAL MODELS AND INFERENCE FOR LI-ION BATTERY PROGNOSTICS","abstract":"Developing prognostics and health management methods for Li-ion batteries has received increasing attention in recent years. This thesis proposes three statistical models and inference for the Li-ion battery prognostics based on the easy to measure operational profiles. The three models include a Bayesian hierarchical model which is good at long term predictions of battery degradation state, a state space based model which is appropriate for short term predictions, and a hybrid model which combines a physical and statistical model. 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With the developed models, we can take full use of battery operation profiles, implement battery in-cycle operation management and remaining useful life prediction in one framework and update prognostic results with real time observation. 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