Research On Modeling,State Estimation And Management Strategy Of Power Lithium-ion Batteries

Environmental pollution and energy crisis are two major challenges facing the world today.The electric vehicle as a kind of zero-emission transportations is a good solution for energy and environment issues.Aiming at the domestic and foreign research hotspots of the power battery system,this thesis studies the modeling,state estimation and management strategies of the power lithium-ion batteries based on modeling and analysis methods of complex system and modern filtering techniques.The main propose of this thesis is to build up a theory and method system which contains system modeling,estimation and control,and provide reference for fine,safe and efficient management of energy storage systems.The main work of this thesis can be summarized as follows:(1)An accurate battery model is beneficial to the description and analysis of battery behavior,which is also the basis and premise of battery state estimation.The lithium-ion battery is a complex electrochemical system with strong non-linear and time-varying characteristics,while the parameters of the battery are susceptible to external environment and other factors.Therefore,it is difficult for battery modeling in a random environment.Based on the analysis of the battery internal mechanism and external behavior,a data-driven battery modeling and parameter identification method is proposed in this thesis.(2)This thesis proposes a multi-model switching state-of-charge(SOC)estimation method.The improved interpretative structural modeling method is introduced to implement model switching of four typical electrochemistry battery models.The influence of temperature to the coulomb efficiency is analyzed in the thesis,and a battery capacity retention rate(CRR)model is established.A CRR model based SOC estimation approach is proposed and the influence of the drift noise to the estimation accuracy is analyzed in the validation test.In order to solve the divergence phenomenon which produced by the conventional Kalman filter method when observing the state variables in non-linear and non-Gaussian systems,a Bayesian estimation theory based SOC and state-of-energy(SOE)estimation approach is proposed.The experiments under dynamic working conditions are performed to verify the accuracy and robustness of the proposed method.(3)The circuit topology and control strategy of passive equalization and active equalization are studied.Aiming at the drawbacks of passive equalization,an active equalization circuit based on a bidirectional DC/DC is proposed.Based on the research of battery model and state estimation method,an active equalization control strategy based on SOC is proposed,and the performance of different equalization strategies is compared by experiments.(4)The modeling and state estimation methods of the lithium-ion battery can be extended to battery/ultracapacitor hybrid energy storage system.Therefore,a novel parameter and state co-estimator based on a dual-filter is proposed,where the extended Kalman filter and unscented Kalman filter are employed for parameter updating and SOC estimation of lithium-ion battery and ultracapacitor,respectively.To reduce the convergence time of the model parameters,the recursive least square algorithm is used to provide initial values with small deviation.By comparing the accuracy of state estimation of lithium-ion battery and ultracapacitor under dynamic discharge profile,the estimation strategy proposed in this thesis is verified.(5)By integrating the technologies include battery modeling,state estimation and balance management to the applications of battery management systems,the management degree of refinement and intelligence can be improved,and the battery life can be extended.The optimization of battery modeling,state estimation and management strategies can be implemented by analyzing the battery data accumulated in battery management systems.This thesis introduces the software and hardware frames of the battery management system.The case study of the battery management system for electric vehicles is analyzed.