Study On Combined Online Estimation Of SOC And SOH Based On MIUKF Algorithm

At present,my country’s energy and environmental issues are becoming increasingly prominent,and electric vehicles have received widespread attention from social and automotive industry research and development departments with their advantages of low pollution and high efficiency.Battery Management System(BMS)is widely used to manage,observe,and protect batteries to ensure the safe and reliable operation of vehicles.The estimation of state of charge(SOC)and state of health(SOH)of lithium batteries is a key part of battery management systems,which can control the energy balance of the battery in real-time.This paper takes the 18650 lithium battery as an experimental object,establishes an equivalent circuit model and carries out internal parameter identification.Based on the improved algorithm,the joint online prediction of SOC and SOH of the lithium battery is realized.First of all,this article introduces the internal structure and working principles of lithium batteries in detail and analyzes their basic performance indicators at the same time.It focuses on analyzing the voltage characteristics,resistance characteristics,capacity characteristics,and charging and discharge rate characteristics of lithium batteries.Considering the complexity and accuracy of the model,this paper considers using a second-order RC equivalent circuit model.The relationship curve of OCV-SOC is obtained by polynomial fitting the experimental data,and the proposed adaptive genetic algorithm(AGA)is used to identify the internal parameters of lithium batteries,greatly improving the accuracy of identification.Then,this article briefly introduces a variety of SOC estimation methods.Based on the identified model parameters,an improved unscheduled Kalman filter(UKF)algorithm is introduced,which introduces multiple innovation theory to optimize the traditional unscented Kalman filter.The residual scalar of the original system is extended to a residual matrix,thereby expanding the Kalman gain to a Kalman gain matrix,then update and iterate all new and old data to complete the online estimation of lithium battery SOC under cycle conditions.Under the UDDS testing condition,the improved algorithm estimation value is compared with the original algorithm estimation value to verify the effectiveness of the improved algorithm and high accuracy.Finally,based on a brief introduction to SOH estimation methods,this article uses extended Kalman filter(EKF)algorithm to estimate the capacity of lithium batteries,and then uses the proposed AGA-MIUKF-EKF algorithm to jointly estimate the SOC and SOH of lithium batteries.Under the UDDS testing conditions,a detailed comparison is made between the AGA-MIUKF-EKF algorithm,the AGA-UKF-EKF algorithm,and the estimated values of the AGA-MIUKF algorithm,the maximum errors of the joint algorithm for SOC and SOH estimation are 0.84% and 0.99%,indicating high estimation accuracy.