Optimized Battery Model Based Adaptive Sigma Kalman Filter for State of Charge Estimation
Liu Yi1, Tan Guojun1, He Xiaoqun1, 2
1. School of Information and Electrical Engineering China University of Mining and Technology Xuzhou 221008 China; 2. Kailuan ( Group) Co. Ltd Tangshan 063000 China
Abstract The nonlinear model was applied to describe the lithium iron phosphate battery by mathematical model method, and the status model and observation model were optimized. Take into consideration the influences of charge-discharge rate, temperature variation and aging cycle life, the status model was improved. The observation model was also compensated for battery relaxation and polarization effect. Thus, the battery modeling accuracy was enhanced, and state of charge (SOC) estimation error caused by battery model was reduced under different conditions. Then, on the basis of the parameters identification for battery model, an improved adaptive sigma Kalman filter algorithm was proposed to construct the SOC estimation model. According to state variables distribution of the nonlinear model, the sigma sample sequence was built. Each model residual error covariance of output was used to update the covariance of the noise in real time. The optimal estimate sigma sampling sequence and the weights of noise were also reassigned by real-time updates with low computational complexity. The experiments were carried out to estimate the properties by charge, continuous-current discharge, backlash-current discharge and varying current discharge mode. The results verify the rapid convergence and more accurate mathematical description. It is shown that the accuracy of SOC estimation is improved using proposed model and algorithm.
Liu Yi,Tan Guojun,He Xiaoqun. Optimized Battery Model Based Adaptive Sigma Kalman Filter for State of Charge Estimation[J]. Transactions of China Electrotechnical Society, 2017, 32(2): 108-118.
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2017, Vol. 32 
