Capacity Degradation Estimation of Lithium Battery Based on Intelligent Particle Filter

This is the authors' abstract. We don't add key points for this paper.

With the booming development of the UAV industry, lithium-ion batteries are gradually being commonly used. For lithium-ion batteries, accurately estimating their battery life degradation can calculate the remaining useful life (RUL) of lithium-ion batteries. Thereby, improving the reliability of lithium-ion battery-driven products and effectively reducing internal and external failure risks. As the current electric charge status of lithium-ion batteries cannot be directly obtained, their capacity degradation model has strong nonlinearity and non-Gaussian characteristics. In order to enhance the accuracy of capacity degradation estimation for lithium-ion batteries, this paper analyses and models the capacity degradation process of lithium-ion batteries using an improved intelligent MH particle filtering algorithm, and compares the estimation results of the proposed new method (MHPF) with those of classical PF and AGPF. The results prove that MHPF can obtain more reliable capacity degradation estimation results for lithium-ion batteries.