A Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction

When classical particle filter (PF) techniques are used for dynamic system state estimation, they have some limitations: for example, when the weights of simulated samples are not sufficiently large, these classical PFs may suffer from sample impoverishment. In addition, the degraded diversity in sampling particles will limit the estimation accuracy, since the particles cannot capture the entire probability density function (pdf) effectively. To tackle these problems, a mutated PF (MPF) technique is proposed in this paper to approximate the posterior pdf of system states. In the MPF, a novel mutation approach is proposed to search extended areas of the prior pdf using mutated particles to make more comprehensive exploration of the posterior pdf. In addition, a particle selection scheme is suggested in the MPF to detect and process low-weight particles so as to explore the high-likelihood area of the posterior pdf more thoroughly. The effectiveness of the developed MPF technique is verified by simulation tests using a benchmark test model. It is implemented for predicting remaining useful life of batteries. Test results show that the developed MPF can capture a system's dynamics effectively and track system characteristics accurately.