An integrated probabilistic approach to lithium-ion battery remaining useful life estimation

Estimating lithium-ion battery remaining useful life (RUL) is a key issue in an intelligent battery management system. This paper presents an integrated prognostic approach that unifies two types of health indices (HIs), battery capacity and time interval of equal discharging voltage difference series, to perform direct and indirect RUL estimation for lithium-ion battery. To satisfy different practical requirements, a data-driven monotonic echo state networks (MONESNs) algorithm is adopted to track the nonlinear patterns of battery degradation. The main contributions of this paper are: 1) to enhance the predictive capability of each HI and identify its failure threshold by implementing an HI correlation model and cycle life threshold transformation and 2) to increase the computational stability of the proposed approach through the ensemble of MONESN submodels that can also describe the prognostic uncertainty. Essentially, this approach constitutes a probabilistic integration and data-driven prognostic framework with uncertainty management capability. Two sets of industrial lithium-ion battery data are used to show the capability of the proposed approach. It is expected that this approach can be broadly applied to other application areas, where data-driven prognostic approaches are needed.