Abstarct: Abstract Since the measurement of the state of charge cannot be done directly, the goal is to estimate the state of charge. In this research, the "improved particle filter with particle swarm optimization algorithm combined with jump operator" method is used to estimate the state of charge of the battery. This method is model oriented. Therefore, the parameters of the system model are identified by the regression least squares method .In order to check and evaluate the performance of the proposed method, estimation of the state of charge is also done with three other methods. The first method, i.e. the standard particle filter, has weaknesses such as: dependence on the number of primary particles, deterioration of particles, deviation from the real value, etc. In the next method; In the particle filter sampling stage, the particle swarm optimization algorithm is used, which to some extent improves the problems of dependence on the number of primary particles, particle deterioration, and low accuracy, but it has its own weaknesses. Such as: convergence to the local optimal value and low speed. In the next method, estimation is done with particle swarm optimization algorithm. There are problems with this method. Such as: low speed and accuracy as well as divergence during estimation. As a result, in order to avoid these problems, estimation is done using the proposed method. In this method, in the particle filter sampling stage, the particle swarm optimization algorithm combined with the mutation operator is used. The mutation operator selects a number of particles and mutates them. As a result, it prevents premature convergence to the local optimal value and also prevents the phenomenon of particle poverty. In addition, this method solves the problem of particle destruction, divergence, reduction of accuracy and also the low speed of the optimization algorithm to an acceptable extent. In the continuation of the research, the estimation using these four methods is simulated on Panasonic battery data, and the results are compared with each other, and as a result, the proposed method is selected as the best method.