Design Of Li-ion Battery State Of Charge And Health Estimating Algorithm Based On Sliding-mode Observer

Nowadays,due to the shortage of petroleum resources and the increasingly serious environmental problems,traditional fossil energy vehicles are gradually replaced by electric vehicles.As a vehicle energy storage component,power battery accounts for nearly half of the cost of electric vehicle production and development,which is the bottleneck restricting the development of electric vehicles.The State of Charge(So C)and the health state(State of Health,So H)are the basis of the battery management system.The accurate estimation value is directly related to the efficiency and safety of the vehicle operation.However,due to the nonlinear characteristics of battery reaction and the interference of many environmental factors,it is very difficult to estimate battery state accurately.In this paper,an online robust battery So C and So H estimation algorithm based on the sliding-mode observer were proposed.Firstly,the working principle of lithium battery is explained,and the definition of battery state of charge and health is given.Based on the support the battery test project of CALCE(Center for Advanced Life Cycle Engineering)battery research center of University of Maryland,the test scheme and results of the power battery were expounded,and the important characteristics of the battery's temperature characteristics and hysteresis characteristics were analyzed according to the experimental data.Next,comparing the existing battery models and the first order Thevenin equivalent circuit model is selected.The recursive least square method is used to identify the battery parameters,including the function of the open circuit voltage to the So C,the internal resistance of the battery,the polarization resistance and the polarization capacitance of the battery.After this,the model is verified by comparison experiment with different battery test schemes,which proves the validity of the model.On the basis of the model,a full order terminal sliding mode observer is designed based on the sliding mode observer to estimate the battery So C.This method is an online estimation algorithm,which inherits the advantage of robustness of the sliding mode observer,and avoids the current So C estimation algorithm shortage that they either only perform offline or require high modelling accuracy.Compared to the traditional lineal sliding-mode observer,it converges in finite time and attenuates chattering.The test results of two algorithms are compared through two standard vehicle driving scheme validation.The results show that the algorithm can estimate the battery So C with high accuracy.Compared with the linear sliding mode state tracking,the estimation accuracy of the proposed algorithm is higher.Until recently,there have been still few studies on online estimation algorithms for battery So H.According to the common application of the automobile,the internal resistance and capacity of the battery are identified as the So H of the battery.The battery double sliding mode observer system is constructed by using the slowly changing characteristics of battery state parameters.The test results of two algorithms are compared through two standard vehicle working conditions experiments.Experiment results show that the algorithm can observe So H in real time with high precision,showing optimistic prospect in application.