RECENT RESEARCH ON BATTERY DIAGNOSTICS, PROGNOSTICS, AND UNCERTAINTY MANAGEMENT

This chapter provides a state-of-the-art review for Li-ion battery diagnostics, prognostics, and uncertainty management. It illustrates battery models used for battery state-of-charge (SOC) and state-of-health (SOH) estimation and reviews various estimation algorithms. The chapter elaborates data-driven prognostics for predicting the remaining useful life (RUL) of battery SOC and SOH. In particular, a Copula-based sampling method is explained in detail for predicting the RUL of the capacity fade. The chapter describes various uncertainties in battery diagnostics and prognostics and a proposed framework is illustrated for managing the battery model parameter uncertainty and model uncertainty in a systematic manner. Battery models can be classified into two groups: electrochemical models and equivalent circuit models (ECMs). Five types of uncertainty play a key role for reliable estimation of the battery performances of interest and they can be classified as measurement uncertainty, algorithm uncertainty, environmental uncertainty, model parameter uncertainty, and model uncertainty.