Combined Prediction Of State Of Charge And State Of Health Of Lithium-ion Batteries For Vehicles

In the context of global energy shortage and China’s strategic efforts to develop new energy industry,new energy equipment represented by energy storage system and new energy Battery Electric Vehicle(BEV)has become an indispensable part of China’s current energy network.Among them,the related battery management technology is not only the focus of research but also the difficulty of current scientific research.Due to the comprehensive influence of battery manufacturing process and materials,the production cost of power lithium-ion battery is often high,and the improvement and breakthrough of battery technology are faced with many difficulties.Therefore,in order to avoid battery damage or equipment failure caused by improper battery management,the State Of Charge(SOC)and State Of Health(SOH)Of the battery must be accurately predicted.Real-time monitoring and forecasting of Battery running state is the key to prolong Battery life and improve operating efficiency in Battery Management System(BMS).The introduction mainly focuses on the research background and significance of this paper,as well as the research status of battery SOC and SOH at home and abroad,and finally leads to the main content of this paper.(1)Relying on university-enterprise cooperation projects,structures,the lithium ion power battery experiment platform,the design of the lithium ion battery basic performance tests,including the available capacity test,different discharge ratio test,different temperature,discharge test,open circuit voltage,battery internal resistance testing,through the experiment analyzed the influence factors of SOC and SOH,and the recession mechanism of lithium ion power battery is analyzed.(2)Through the analysis of the advantages and disadvantages of different lithium ion power battery model,considering the influences of environmental temperature is established a new second-order RC lithium ion power battery model,the parameters for the identification,and use the HPPC experimental model accuracy is verified,the results show that HPPC pulse discharge validation experiments between an average error of 0.01 V,the maximum error of0.06 V,the maximum error percentage are controlled within 2% accuracy,shows that the model precision is higher;After the model validation,using EKF algorithm for SOC estimation,the results show that the two models of simulation with trend of SOC curves and real curves are consistent,as the discharge reaction,SOC are falling,ordinary second order RC 0.04 within the scope of the error of the equivalent circuit model was basically,and considering the effects of environmental temperature second-order RC 0.02 within the scope of the error of the equivalent circuit model was basically,shows that considering the effects of environmental temperature second-order RC equivalent circuit model of SOC curve is closer to the real value,higher precision,the model has good reliability.(3)In single discharge,for example,this paper analyze the battery capacity and internal resistance,battery factory ohm internal resistance of 2 m Ω,rated capacity 32 ah,the initial SOC is 0.99,the sampling period T = 1 s.In the working condition of UDDS and DST conditions using DEKF algorithm to estimate the SOC and SOH synergy,and get the ohm internal resistance estimate,capacity estimation and SOC estimation,the results show that the ohm internal resistance has a process of decline,perhaps because lithium ion battery charging and discharging temperature increases,lead to internal resistance is reduced,the back as the reaction progresses,lithium ion battery ohm internal resistance increases gradually,the discharge end,resistance increases significantly,this is because the chemical reactions inside the cell,cause the SEI film thickening,ion motion resistance increases,thus make the battery internal resistance increases,At the same time,it can be seen that DEKF algorithm can get accurate estimation of ohmic internal resistance;For SOC,along with the discharge reaction,SOC constantly decreases,and both would reflect this trend,but in DEKF algorithm and the simulation value and real value more consistent,closer,the curve fit error only within the scope of 0.015 and 0.01,significantly less than the EKF algorithm 0.05 within the scope of the error,under the working condition of UDDS and DST proved the joint estimation SOC and SOH DEKF algorithm is reliable,high precision.