Research On Life-cycle State Of Charge Estimation Method For Lithium-ion Batteries Based On Dualadaptive Extended Kalman Filter Algorithm

In recent years,electric vehicles(EVs)have been developed rapidly around the world,the battery management(BMS)is very important to EVs,which has major impact on the energy management and safty protection of EVs.The state of charge(SOC)estimation is one of the core functions of BMS,which make the SOC estimation accuracy one of the key indicators of BMS.There is a common drawback on current SOC estimation algorithm: The aging of battery is not considered,the estimation accuracy of those algorithm drop gradually with the degradation of battery.This project proposed a novel life-cycle SOC estimation algorithm for lithium-ion battery based on Improved Single Particle Model(SP+)and Dual Adaptive Extended Kalman Filter(DAEKF).which has higher SOC estimation accuracy during the whole life-cycle of battery compared with the traditional algorithms.The detailed work of this project is as follows:Firstly,the battery degradation,which has a major impact on SOC estimation,was not been considered in most SOC estimation research recently.Therefore this paper carried out an aging experiment of lithium battery to explore the variation of battery parameter during the degradation.Then,this project conducted SP+ model parameter sensitivity analysis to explore the impact of battery parameter variation on its SOC estimation.Based on the result of analysis,several SP+ model parameters which has strong impact on SOC estimation was selected.The scheme of parameter modification in SOC estimation algorithm was established.Secondly,aiming at the fact the accuracy of traditional SOC estimation methods drops with the degradation of battery,a cycle-life SOC estimation algorithm was proposed in this paper based on SP+ model and DAEKF algorithm.Then the validation experiment based on the degradation data of battery was carried out to validate the accuracy of the SOC estimation algorithm during the whole life-cycle of battery.Finally,aiming at the fact that the engineering validation was absent on most SOC estimation research,this paper conducted the engineering realization and validation of SOC estimation algorithm: a validation system for life-cycle SOC estimation algorithm to simulate the working process of BMS was established,The life-cycle SOC estimation algorithm was transplanted to the system.Then an engineering validation experimentfor life-cycle algorithm was conducted to validate its accuracy and efficiency under embedded platform.