Research On State Of Charge Estimation Of Electric Vehicle Power Battery Pac

In order to solve the energy crisis and environmental pollution problems,the research and application of electric vehicles have developed rapidly.In order to ensure the safe application of the vehicle power battery,the BMS can monitor the battery status in real time and undertake the function of protecting the battery.SOC is an important parameter of BMS.Accurate estimation of SOC can ensure that the battery is in normal working state and ensure the safe and stable operation of electric vehicles.At present,the estimation accuracy of SOC is affected by factors such as battery model error,algorithm and operating conditions.For this reason,this paper proposes a SOC estimation method based on intelligent algorithm to optimize the PF.Firstly,according to the influencing factors of battery capacity,the discharge experiments of lithium iron phosphate batteries at different temperatures and discharge rates are carried out.The second-order RC equivalent circuit model is selected,and the SOC state model based on the correction factor is proposed.Aiming at the problem that the forgetting factor in FFRLS lacks reasonable constraints,VFFRLS is proposed.Online parameter identification is carried out through DST experiment,and the model accuracy is verified by comparing the offline identification results and the FFRLS identification results under DST conditions.Secondly,in view of the particle depletion problem caused by the traditional PF algorithm in SOC estimation,an improved ACO algorithm optimized PF algorithm is proposed for battery SOC estimation.And in view of the problem that the ACO algorithm is easy to fall into the local optimum,it is proposed to adjust the pheromone concentration and heuristic relative influence parameters and improve the pheromone volatilization rate to improve the ACO algorithm.In the IACO-PF algorithm,ants will replace the particles and reposition them before the update step to solve the particle depletion problem by increasing the diversity of the particles.The accuracy of the IACO-PF algorithm is preliminarily verified in the MATLAB simulation environment.Finally,the IACO-PF algorithm is brought into the established battery state space model for simulation experiments.The experimental results show that the lithium-ion battery SOC result based on the IACO-PF algorithm is more effective and accurate than other algorithms,and the maximum estimation error does not exceed 2%.The battery pack experimental platform was built and the IACO-PF algorithm was written into the BMS,and the error between the estimated SOC value and the real SOC value in different intervals was compared.The results showed that the maximum error of the IACO-PF algorithm was4.08%,which met the actual accuracy requirements.