Battery SoC Prognosis

State of Capacity (SoC), State of health (SoH) forecasts and RUL prediction are increasingly important in battery prognostics as they are key parameters of appropriate battery management strategy to avoid catastrophic failure, to enhance battery durability and to optimize cost. Also, due to the widespread applications of Lithium-ion Batteries (LiB) in many industrial sectors, research of SoH, SoC prediction holds great academic value and economic impact. In general, SoH is defined as a performance index to describe the degree of degradation of battery, and SoC denotes capacity of battery in compared to the capacity in its fully charged state. In the present study, SoC, SoH, and RUL prediction mainly predicts future battery capacity, which helps battery to be used to designed potential and maximum life expectancy before failed.

To present an efficient, adaptive, and accurate solution for battery capacity prediction in a multi-cell setting, this study aims to build up an online battery capacity prognosis solution. The solution demonstrates an efficient yet effective way to exploit the cross-trajectory correlations without adding many computation complexities to the standard GPR model, but taking advantages of the historical available data.