Estimation Of State Of Power And State Of Energy In Lithium-ion Batteries Based On Particle Filter

In recent years,in order to implement the support for energy conservation and emission reduction,the state and local finance office have introduced preferential subsidy for new energy vehicles,leading to the rapid development of new energy vehicles.As the core component and main power source of new energy vehicles,power battery is very important for better monitoring and management.State of Power(SOP)is the maximum power that the lithium battery can release or absorb during a certain time interval which can evaluate the maximum charge and discharge capacity of the power battery;State of Energy(SOE)represents the remaining energy of the battery,which helps to estimate the residual range,optimize and manage the battery energy.It is necessary to accurately evaluate the SOP and SOE of the lithium battery as important parameters in the battery management system.For lithium battery is a complex nonlinear time-varying system,the capacity,voltage and internal resistance changes under different currents.Focus on accurate estimation of SOP and SOE in lithium battery,the main research contents include the following aspects:In this paper,charging and discharging experiments of 18650 type lithium battery are carried out under different currents in constant temperature.Using those current and voltage data analyze the influence of charging and discharging current on battery capacity,internal resistance and polarization characteristics.The results show that the battery parameters change with charge and discharge rate.Therefore,based on the polarization phenomena,current rate characteristics and the analysis of classical models,a dynamic polarization model time-varying parameters is established.Then identify the parameters of the model through the charging and discharging experiments,and the functions relationship between the parameters and the current are fitted.The accuracy of the model is verified under dynamic conditions compared with the classical model without time-varying parameters.The results show that the dynamic polarization model based has high accuracy.SOE estimation method based on particle filter is proposed.For the battery is a complex and nonlinear system,Particle Filter(PF)is used for SOE estimation to reduce the influence of the uncertainty caused by the noise.Based on the Monte Carlo theory,the SOE of the battery is expressed by probability.In order to improve the accuracy of SOE estimation,the joint estimation of SOC and SOE based on the Dual Particle Filter(DPF)is carried out to modify the estimation results of SOE using SOC estimation results.The estimation results are verified under dynamic conditions.The results show that the SOE estimation under joint estimation has higher accuracy.A dynamic SOP estimation method based on multiple constraints is proposed.To meet the acceleration,regenerative braking and gradient climbing power requirements without fear of over-charging or over-discharging and violating the safe operating area,the limiting factors of SOP are analyzed.The peak currents under charging and discharging cutoff voltage and SOC are calculated respectively by the iterative algorithm.Combining with designed current,estimate the battery terminal voltage and calculate the peak power within the limit of SOE,and the battery SOP is finally obtained.According to the peak current under the limits of the current,voltage,SOC and the peak power of multiple constraints,the mode of action of the limit conditions under the dynamic conditions and the effect of duration on SOP are analyzed.The accuracy of the SOP estimation is verified under the constant power discharge.The results show that the SOP estimation method based on multiple constraints can be used to estimate the charge and discharge capacity of the battery under dynamic conditions.