LITHIUM-ION BATTERY PROGNOSTIC AND HEALTH MONITORING, A HYBRID APPROACH

Abstract

In this thesis, we propose a series of hybrid methods to solve the challenging problems in the field of Lithium-ion Battery Prognostics and Health Monitoring. Firstly, a B-spline function based model is proposed to model the Open Circuit Voltage (OCV) curve. Secondly, a Principle Differential Analysis (PDA) based battery model is proposed to identify the states from the working data of current and voltage. A two-phases identification routine is proposed to identify the OCV curve and the capacity sequentially, which reduces the dependency on off-line measurement. Next, the capacity recovery is modelled using a recovery shock-damping model, where the shock is triggered by resting and the magnitude depends on the state of degradation and the duration of rest. Lastly, we propose an integrative degradation algorithm for capacity prediction considering the recovery effect. When jointly applied, the prediction accuracy is improved. The computational load is manageable due to its parametric nature.