State Of Charge Estimation For Automotive Power Lithium Battery Packs Based On Migration Model At Wide Temperature Environment And Full Life Cycle

To combat global climate change and achieve environmental sustainability by reducing emissions,countries all over the world are actively researching and developing the new energy vehicle industry represented by electric vehicles.The power battery serves as the primary source of power for electric vehicles,significantly impacting their range,performance,and safety.Therefore,accurately estimating and monitoring the internal state of the power battery,including state of charge(SOC)and state of health,is critical in ensuring its safe and efficient operation.SOC is a crucial internal state parameter,indicating the amount of charge remaining in the battery’s current state.Precise SOC estimation facilitates more accurate control of charge and discharge currents to prevent overcharging and overdischarging,enhance battery efficiency,and extend battery life.However,Li-ion batteries’ electrochemical reaction process is highly non-linear and susceptible to coupling effects from external temperature changes,continuous battery aging,and battery pack inconsistencies,all of which present challenges to accurately and efficiently estimating the SOC of power batteries.Hence,this paper concentrates on quickly and precisely estimating the SOC of automotive power lithium batteries in complex temperature environments throughout their lifetime and conducts research on accurate SOC estimation across a wide temperature range based on a migration model.The main work accomplished is as follows:A migration model-based battery modeling method is proposed to reduce the workload and improve the efficiency of battery modeling in complex environments.Firstly,the conventional second-order RC equivalent circuit model is chosen as the base model for building the migration model,and the parameters are identified by the Adaptive Forgetting Factor Recursive Least Square(AFFRLS)method,and then the relationship curves between the model parameters and SOC are obtained by polynomial fitting.Finally,the migration model was built according to the obtained relationship curve.To address the problem that it is difficult to accurately estimate the SOC of a single lithium-ion battery over a wide temperature full-life range,a migration modelbased SOC estimation method for single lithium-ion batteries over a wide temperature full-life range is proposed.Firstly,the migration factor is determined online based on the weight selection particle filtering algorithm to achieve the online migration of Liion battery model parameters under different temperatures and aging states,so as to obtain the information of model parameters under the influence of temperature and aging states,and the online correction of the relationship curve between model parameters and SOC,and then achieve the fast and accurate estimation of SOC of single-cell Li-ion battery over a wide temperature and whole life range.Finally,the available capacity estimation is achieved using capacity backpropagation based on the SOC estimation results.The results show that the proposed method has the obvious advantages of small computational effort and high accuracy in estimating SOC and available capacity in the full-life wide temperature environment,and the maximum error of SOC estimation is less than 3%,and the maximum error of capacity estimation is less than 2%.A migration model-based method is proposed for estimating the SOC of series Li-ion battery packs over a wide temperature range,which is difficult to estimate accurately and efficiently.Firstly,the migration model is constructed to cope with the influence of temperature variation on the model parameters and reduce the modeling effort required,and by defining the battery pack SOC efficiently,the influence of battery inconsistency is fully considered and the complexity of battery pack SOC estimation is reduced.Finally,the battery pack SOC estimation is validated based on experimental data,and the results show that the proposed method has the advantages of high computational accuracy,low complexity,and safety and reliability.