Data-Driven Based Battery Health Prognosis with Diagnosis Uncertainties and Insufficient Training Data Sets

Abstract

This paper investigates data-driven based battery prognosis with diagnosis uncertainties and insufficient training data sets. Four types of data-driven prognosis methods are investigated including the neural network, similarity-based approach, relevance vector machine, and a recently developed copula-based approach. The remaining useful life (RUL) predictions of lithium-ion battery capacity are compared with capacity estimation error due to the fact that onboard lithium-ion battery capacity estimation is difficult and almost always contains estimation errors. Thus, robustness of each prognosis methods can be studied for real time capacity RUL estimation. Furthermore, collection of sufficient run-to-failure training data sets for lithium-ion batteries is almost impossible even though it is desirable for all data-driven based methods. Therefore, robustness of these methods in terms of the insufficient training data sets is also studied. These insightful results will helpdesigners choose appropriate prognosis algorithms in designing battery management systems (BMS) for lithium-ion batteries.