STATISTICAL MODELS AND INFERENCE FOR LI-ION BATTERY PROGNOSTICS

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. 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. The effectiveness and promising features are demonstrated by practical case studies.

Author dc:creator

• XU XIN

• Prognostics, Degradation model, Data-Driven method, Equivalent circuit model, End of discharge, Remaining useful cycles