Research On State Estimation Of Power Li-ion Batteries For Electric Vehicles

With the increasing resource consumption and environmental pollution problems,especially the frequent fog and haze,electric vehicles(EVs)have been at the forefront of current research topics.The power batteries are the key factors to the development of EVs.Because of the advantages of high-energy density,long cycle life,high security and low self-discharge rate,the Li-ion batteries have become the main power source of EVs.However,the electrochemical reactions inside the the battery are complex during charge and discharge process,and the harsh working conditions of EVs are full of strong interference.Accurate real-time management of the battery,protecting the safety of the battery and prolonging the battery service life under the complicated operating conditions are of great significance to the development and promotion of EVs.How to suppress the serious interference during charge and discharge process and realize the accurate modeling and state estimation of Li-ion batteries have been the difficulty and the emphasis of the battery management technologies.The main aspects of our research work in this thesis are the modeling and estimation of the available capacity of the battery,state of charge(SOC),state of energy(SOE)through the analysis the effect of the temperature,dynamic load,and drift current on the state estimation.The main contents of this thesis are listed as below:1)Aiming at the fact that the available capacity of Li-ion batteries is dependent on battery temperatures,an extended Peukert equation is propoed for available capacity estimation for electric vehicle batteries at various temperatures.The relationship between the parameters of the traditional Peukert equation and the temperature are established through the analysis of experimental data.Then,the battery temperature is taken as an input variable into the Peukert equation.The comparison of the estimated and the actual battery available capacity indicates that the proposed algorithm can provide a reliable and accurate estimation of the available capacity for Li-ion batteries at various temperatures.2)The SOC is a critical parameter of power Li-ion batteries for EVs.To suppress the interference of the temperature and the drift noise in current measurement,we present a temperature-compensated model with a dual-particle-filter estimator for SOC estimation of power Li-ion batteries in EVs.Aiming at parameter perturbations caused by the temperature and noise interference caused by the drift current,the temperature effect on the internal resistance and voltage is described in the new model,and the drift current is taken as undetermined static parameter in the battery model.A dual particle filter estimator is designed for simultaneous SOC and drift current estimation based on the new model.A further current detection precision can be obtained by the drift current estimation,and the battery model is improved through analyzing the temperature effect.The experimental and simulation results indicate that that the accuracy and robustness of the SOC estimation are improved by this method.3)The SOE of power Li-ion batteries is an important index for energy optimization and management.In the applied battery system,the fact that the discharge current and temperature change due to the dynamic load will result in errors in the estimation of the battery residual energy.To address this problem,a new method based on the Back-Propagation Neural Network(BPNN)is presented for the SOE estimation.In the proposed approach,in order to take into account the energy loss on the internal resistance,the electrochemical reactions and the decrease of the OCV,the SOE is introduced to replace the SOC.Additionally,the discharge current and temperature are taken as the training inputs of the BPNN to overcome their interference on the SOE estimation.The simulation experiments on LiFePO4 batteries indicate that the proposed method based on the BPNN can estimate the SOE much more reliably and accurately.4)To ensure the driving safety of EVs,we have carried out a research on the structure design of the battery management system(BMS),data collection technology and charge-discharge control strategy.A BMS scheme with the central-distributed structure is designed for power Li-ion batteries.The data acquisition circuit with high precision and high reliability is developed for the battery voltage,current and temperature.According to the different applications of the battery,a proper charge-discharge control strategy is proposd to work together with the vehicle control unit and the charger.The security and reliability of BMS is improved by these work.The developed BMS with these techniques has been widely used in a variety of EVs.