Fractional variable resistance-capacitance modeling and state-of-charge estimation of lithium-ion batteries
Accurate estimation of the state of charge(SOC)of Lithium-ion(Li-ion)batteries is crucial for the reliable and safe operation of new energy electric vehicles,and a high-precision battery model is fundamental for SOC estimation.Traditional resistance-capacitance(RC)models of Li-ion batteries face several challenges,including the complex structure of the high-order models,the low approximation of the low-order models,and difficulties in accurately estimating the SOC due to abrupt changes in the Li-ion states.This study proposes a fractional variable resistance-capacitance model of Li-ion based on fractional calculus theory.Using the Akachi Information criterion,the optimal order of the fractional RC model was determined for various SOC levels,and a time-varying RC battery model was developed,which was adapted to different SOCs.A strong tracking fractional extended Kalman filter algorithm was constructed by incorporating an attenuation factor,and the SOC of Li-ion was estimated to address the influence of historical data on the current estimated value.The model's performance was validated using the fractional extended Kalman filter algorithm under three different working conditions,including urban road cycle conditions.The findings indicate that the average absolute error(AAE)of the model decreased from 0.0197 to 0.0160 V under pulse discharge conditions,the voltage errors are all less than 50 mV,and the prediction accuracy is relatively improved by 18.8%.The AAE and root mean square error are reduced by the improved approach,which not only verifies the effectiveness of the proposed approach but also provides a new insight for improving the accuracy of SOC estimation and the computation efficiency of Li-ion batteries.
Akachi information criterionvariable resistance-capacitance modelfractional orderstrong tracking
万方数据
维普
