Model Parameter Online Identification Based SOC Estimation Method

doi:10.12068/j.issn.1005-3026.2020.11.004

Abstract

Abstract: In view of the problems that genetic algorithm (GA) has slow convergence speed， be easy to fall into local optimum and difficult to realize online application， and that the identification background of power battery equivalent circuit model is with strong nonlinearity and high real-time requirements. An optimized identification framework is proposed that can quickly reduce the search space and effectively avoid falling into the local optimum for online fast search， thus realizing the online fast identification of the parameters of the equivalent circuit model of the electric vehicle power battery， and expanding the application range of the global search optimization algorithm. Further， the proposed algorithm is applied to the state of charge (SOC) estimation， based on the improved GA unscented partical filter (IGA-UPF) is proposed. The SOC estimation method is compared with the SOC estimation method based on least square-unscented partical filter (LS-UPF)， which proves that the online fast parameter identification framework proposed has better model parameter identification accuracy.

Key words: parameter online identification, genetic algorithm, unscented particle filter (UPF) algorithm, state of charge (SOC), electric vehicles

CLC Number:

TP335.58

LIU Fang， MA Jie， SU Wei-xing， HE Mao-wei. Model Parameter Online Identification Based SOC Estimation Method[J]. Journal of Northeastern University Natural Science, 2020, 41(11): 1543-1549.

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