A novel genetic marginalized particle filter method for state of charge and state of energy estimation adaptive to multi-temperature conditions of lithium-ion batteries

Power lithium-ion batteries are widely used in various fields, the battery management system (BMS) is the main object of battery energy management and safety monitoring, so the accurate collaboration of state of charge (SoC) and state of energy (SoE) is estimated to be essential for the BMS system. In this work, a novel genetic marginal particle filter (GMPF) algorithm to estimate SoC and SoE accurately. The forgetting factor recursive least square (FFRLS) algorithm is used to identify the second-order Thevenin equivalent model parameter, and the genetic algorithm is used to improve the re-sampling process of the traditional particle filtering (PF) algorithm, according to the Rao-Blackwell theory in statistical science, the marginalization of part of the linear state variables during the calculation of particle filtering, the distribution of the post-test is similar to a single Gaussian distribution. The GMPF algorithm is verified under the conditions of the hybrid pulse power characteristic (HPPC) and the Beijing bus dynamic stress test (BBDST) with 15 °C, 25 °C, and 35 °C respectively, and experimental results show that the improved GMPF algorithm can effectively realize the collaborative estimation of the SoC and SoE of power lithium-ion batteries. The mean absolute error of SoC and SoE estimation is always less than 1.56 %, the root-mean-square error is always less than 1.58 %. And the GMPF algorithm is suitable for temperature environments of 15 °C to 35 °C.