Research And Implementation Of SOC Estimation Algorithm For AGV Battery

Automatic guided vehicle(AGV)is an automated handling equipment,general power provided by the battery,can be in accordance with the established route of material handling.AGV is essential for modern industrial production line handling tool.It is difficult to estimate the battery SOC value accurately and accurately in the condition of AGV car.The accuracy of current integration method is low in AGV vehicle under special working conditions,not meeting the requirements for industrial production.In this paper,an improved extended Kalman filter algorithm is proposed,which can be used to estimate the SOC value of the AGV battery,which can control the error within 5%.Specific research contents are as follows:Firstly,analyze the actual operating conditions of the AGV car in detail,clear the charging current: the discharge current is small,the charging time is short,the charging frequency is high.Secondly,establish the equivalent model of Thevenin battery,estimate the battery SOC by using the Extended Kalman filter.The estimation accuracy greatly improved compared to current integration method,but the traditional extended Kalman filter in AGV car special condition tracking effect is poor,the resulting is high.Thirdly,In view of the traditional extended Kalman filter method to estimate the AGV car battery SOC value tracking problems,proposed improved extended Kalman filter,the filter gain extended Kalman filter method is improved to dynamically adjust the filter gain improved extended Kalman filter method in special conditions of the tracking effect.Finally,By reading the actual running data of the AGV car to simulate the working conditions,and then analyzing the effect of the extended filtering method to estimate the SOC value of the AGV battery,and verifying the validity of the improved extended Kalman filter algorithm.Experiments show that the extended Kalman filter method relative to current time integral method estimation accuracy,the filter gain dynamic correction improves the estimation process tracking effect,solves the problem of inaccurate estimation of residual capacity of battery AGV vehicles under special working conditions,the car battery AGV SOC error control in less than 5%.At the same time,the system hardware and software are designed.In view of the AGV vehicle and electric vehicle has many similarities,especially with the development of electric vehicles in the condition of more and more complex,how to improve the complex conditions of battery SOC estimation accuracy has larger significance,so the research result of this paper has certain promotion significance.