Lithium-ion battery state-of-health estimation and remaining useful life prediction

Lithium-ion batteries are important power sources of the electronic devices. A method based on the SOH (state-of-health) parameters was proposed to estimate the lithium-ion battery SOH and RUL (remaining useful life). The improvement was made towards the defect that the current research works do not update the probability density during the RUL prediction process. Moreover, the SVR-PF (support vector regression-particle filter) algorithm was applied to improve the degeneracy phenomenon of the standard particle filter. The simulation results show that the proposed parameters estimate the battery SOH well, and accurately predict the RUL; the SVR-PF has good smoothing and prediction capability.