State of charge (SOC) estimation of lithium batteries is one of the most important\nunresolved problems in the field of electric vehicles. Due to the changeable working environment\nand numerous interference sources on vehicles, it is more difficult to estimate the SOC of batteries.\nParticle filter is not restricted by the Gaussian distribution of process noise and observation noise,\nso it is more suitable for the application of SOC estimation. Three main works are completed in this\npaper by taken LFP (lithium iron phosphate) battery as the research object. Firstly, the first-order\nequivalent circuit model is adapted in order to reduce the computational complexity of the\nalgorithm. The accuracy of the model is improved by identifying the parameters of the models\nunder different SOC and minimum quadratic fitting of the identification results. The simulation on\nMATLAB/Simulink shows that the average voltage error between the model simulation and test\ndata was less than 24.3 mV. Secondly, the standard particle filter algorithm based on SIR (sequential\nimportance resampling) is combined with the battery model on the MATLAB platform, and the\nestimating formula in recursive form is deduced. The test data show that the error of the standard\nparticle filter algorithm is less than 4% and RMSE (root mean square error) is 0.0254. Thirdly, in\norder to improve estimation accuracy, the auxiliary particle filter algorithm is developed by\nredesigning the importance density function. The comparative experimental results of the same\ncondition show that the maximum error can be reduced to less than 3.5% and RMSE is decreased to\n0.0163, which shows that the auxiliary particle filter algorithm has higher estimation accuracy.
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