Remaining Useful Life Prediction of Lithium-ion Batteries using Particle Filter Algorithm in Communication Power Supply

Abstract

With the development of communication technologies, the technology of power support, particularly the technology of lithium-ion batteries plays an important role in the communication area. The evaluation for the remaining useful life of lithium-ion batteries is a hot issue, to provide the maintenance plans and ensure the reliability and safety of system. In this paper, a method using particle filter algorithm for the remaining useful life prediction of lithium-ion batteries in communication power supply is carried out, and the comparisons of prediction effect with the least square method are investigated. From the results, it is clearly seen that the proposed particle filter algorithm presents a better prediction accuracy and stability than the least square method. In addition , the proposed particle filter algorithm presents a weak data dependence than the least square method. For a better prediction result, the number of data to be predicted by the proposed particle filter algorithm accounts about 55% of the total number of the cycle time, which is smaller than that accounts 70% by the least square method. This paper provides a technology support for the remaining useful life prediction of lithium-ion batteries.

Technology in community development: A comparative review of USA and African Projects

“…The current trends in ICTs for community development encompass a wide range of applications, including the use of communication technology in adolescent relationships and identity development, continuous-variable quantum secret sharing in wireless links, and the development of future specialists' communicative competence (Cyr et al, 2014;Liu et al, 2021;Ponomarenko et al, 2023). Furthermore, the comprehensive review of lithium-ion batteries modeling and state of health prediction reflects the ongoing advancements in battery technologies, which have implications for communication power supply and emerging businesses (Wu et al, 2023;Elmahallawy et al, 2022).…”