Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy. (C) 2019 Elsevier Ltd. All rights reserved.