Li-ion battery SOC estimation based on RTS-IEKPF algorithm
Compared with traditional fuel vehicles,electric vehicles have the advantages of saving energy resources and environmental protection.Therefore,the electric vehicles have been widely studied and promoted.As the main energy source for electric vehicles,the power battery can realise reliable management and control of the battery pack.It can provide the required power for the vehicle while achieving the best performance.The state of charge(SOC)of the battery is one of the core parameters of the battery management system(BMS).The accuracy of the SOC may directly affect the cycle life of the battery and the performance of the BMS.The instantaneous high-current charge and discharge of the battery will increase the nonlinearity of the battery while working.To estimate the SOC of the lithium battery by using the Kalman filter method will engender fairly great error.The particle filter method is not limited by the system model or noise distribution,and it has high practicability.However,it can affect the filtering that the particle itself may face degradation and depletion.In order to obtain higher precision of SOC estimation,a new estimation method is proposed here.It is called RTS-IEKPF algorithm,in combination with the IEKPF algorithm and RTS optimal smoothing,which uses an iterative EKF algorithm to generate a particle filter recommended distribution(IEKPF algorithm),and applies the RTS fixed interval optimal smoothing algorithm to a nonlinear system.RTS smoothing has good inhibition to suppress observation noise.It can stabilise the filtering result in the environment with large observation noise,accelerate the convergence speed of the algorithm and improve the precision of estimation.According to the experimental results,the battery model has a higher precision in estimation,and the RTS-IEKPF algorithm has better tracking filtering performance as well as higher estimation accuracy when estimating SOC.
SOC estimation particle filter optimal smoothing
Tiezhou WU Xinyu DU Fuchao XIANG
Hubei Key Laboratory for High-efficiency Utilisation of Solar Energy and Operation Control of Energy Storage System,Hubei University of Technology,430068,China
国际会议
武汉
英文
442-450
2018-08-21（万方平台首次上网日期，不代表论文的发表时间）