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.