Study On SOC Estimation Of Fractional Order Multi-innovations Extended Kalman Filtering Algorithm For Lithium Ion Batteries

With the continuous development and progress of the society,the energy storage device represented by lithium ion battery has become a research hotspot at home and abroad.In practical engineering applications,lithium ion batteries,as the core energy storage device in pure electric vehicles,have a very close energy interaction with smart grid.Battery charge estimation is a key technical problem of lithium-ion batteries in pure electric vehicles.In this paper,the battery charge estimation problem is studied and analyzed from three aspects.First of all,this paper has set up a platform battery characteristics test,and the battery model is a research and analysis,the second-order RC equivalent circuit model combined with fractional order calculus theory to build the lithium ion battery fractional order equivalent circuit model,and USES the parameter identification method based on genetic algorithm is compared with the second order RC equivalent circuit model is verified,The fractional equivalent circuit of lithium-ion battery has higher model precision.Second,in the light of the new type lithium ion battery equivalent circuit model parameter identification method is a research and analysis,in order to solve the parameter identification method based on genetic algorithm defects existing in the identification accuracy is not high,puts forward the parameter identification method based on adaptive genetic algorithm and the parameter identification method based on ant colony algorithm,The simulation results show that the proposed two new parameter identification methods have higher parameter identification accuracy.Finally,this paper studies and analyzes the new battery charge estimation method for lithium-ion batteries,and proposes to combine the multi-innovation theory with the fractional-order extended Kalman filter algorithm to form the fractional-order multi-innovation extended Kalman filter algorithm.The validity and feasibility of the proposed algorithm for battery charge estimation are verified by simulation experiments.At the end of this paper,the content of the study is summarized,and based on the current research,the work that can be continued in the future is prospected.