Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter

Qinghe Liu, Shouzhi Liu, Haiwei Liu, Hao Qi, Conggan Ma and Lijun Zhao
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Qinghe Liu: School of Automotive Engineering, Harbin Institute of Technolgy; Weihai 264209, China
Shouzhi Liu: School of Automotive Engineering, Harbin Institute of Technolgy; Weihai 264209, China
Haiwei Liu: School of Automotive Engineering, Harbin Institute of Technolgy; Weihai 264209, China
Hao Qi: School of Automotive Engineering, Harbin Institute of Technolgy; Weihai 264209, China
Conggan Ma: School of Automotive Engineering, Harbin Institute of Technolgy; Weihai 264209, China
Lijun Zhao: School of Automotive Engineering, Harbin Institute of Technolgy; Weihai 264209, China

Energies, 2019, vol. 12, issue 11, 1-13

Abstract: State of charge (SOC) estimation of lithium batteries is one of the most important unresolved problems in the field of electric vehicles. Due to the changeable working environment and numerous interference sources on vehicles, it is more difficult to estimate the SOC of batteries. Particle filter is not restricted by the Gaussian distribution of process noise and observation noise, so it is more suitable for the application of SOC estimation. Three main works are completed in this paper by taken LFP (lithium iron phosphate) battery as the research object. Firstly, the first-order equivalent circuit model is adapted in order to reduce the computational complexity of the algorithm. The accuracy of the model is improved by identifying the parameters of the models under different SOC and minimum quadratic fitting of the identification results. The simulation on MATLAB/Simulink shows that the average voltage error between the model simulation and test data was less than 24.3 mV. Secondly, the standard particle filter algorithm based on SIR (sequential importance resampling) is combined with the battery model on the MATLAB platform, and the estimating formula in recursive form is deduced. The test data show that the error of the standard particle filter algorithm is less than 4% and RMSE (root mean square error) is 0.0254. Thirdly, in order to improve estimation accuracy, the auxiliary particle filter algorithm is developed by redesigning the importance density function. The comparative experimental results of the same condition show that the maximum error can be reduced to less than 3.5% and RMSE is decreased to 0.0163, which shows that the auxiliary particle filter algorithm has higher estimation accuracy.

Keywords: SOC estimation; lithium iron phosphate; auxiliary particle filter; Kalman filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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