Parameter Identification And State Of Charge Estimation Of Power Lithium Battery Model

In order to alleviate the energy crisis and a series of problems caused by environmental pollution,countries around the world have paid great attention to the research and development of new energy vehicles.As an important component of new energy vehicles,the power lithium battery has rapidly become a hot spot for research in the world,and how to accurately estimate the State of Charge(SOC)of the battery is an important issue in the research and development of new energy vehicles.In this paper,we mainly study the algorithms related to the identification of model parameters and SOC estimation of new energy vehicle batteries,and do the following research work.(1)Experiments are conducted on a ternary lithium battery to obtain its actual battery capacity,and relevant experiments are carried out under different cycle operating conditions.These experimental data lay the data foundation for the next battery model parameter identification and SOC estimation.(2)By introducing the common types of battery models,the second-order RC equivalent circuit model is chosen as the battery model for this paper on the basis of balancing the calculation volume and accuracy,and this is used to study the method of model parameter identification in depth.Firstly,the Least Square(LS)and optimisation algorithms of the traditional offline parameter identification algorithms are investigated,and the accuracy of the models identified is analysed under pulsed discharge conditions.Then,to address the problems of offline parameter identification,online parameter identification is investigated,mainly in two aspects:firstly,online parameter identification by Recursive Least Square(RLS)series and secondly,online parameter identification by Kalman Filter(KF)series.Finally,the errors of these two models were investigated under the Urban Dynamometer Driving Schedule(UDDS),and the Unscented Kalman Filter(UKF)was chosen as the parameter identification algorithm in this paper.(3)Combined estimation of model parameter identification and SOC.First,the traditional single Extended Kalman Filter(EKF)algorithm,which is not combined with online parameter identification,is analysed,and the AEKF algorithm with improved adaptive filter is proposed to achieve noise adaption in the SOC estimation process in view of the errors generated by this algorithm due to noise fixation.Then,the single SOC estimation algorithm is fused with the previously studied online parameter identification UKF algorithm,and the multi-timescale theory is introduced to propose the UKF-AEKF algorithm under multiple timescales,and the accuracy and robustness of the algorithm are verified experimentally under UDDS conditions.Finally,the adaptability of the UKF-AEKF algorithm to different working conditions is further illustrated by the Federal Urban Driving Schedule(FUDS)experiments.(4)The effects of different initial values of SOC,different time scales and different temperatures on the algorithm are analysed in turn to address the possible influencing factors of the UKF-AEKF algorithm proposed in this paper.The analysis of the experimental results confirms the superiority of this algorithm,while the analysis is done for the unsatisfactory cases,and further ideas for the next validation and improvement are proposed.