Lithium-ion batteries (LIBs) are widely used in electric vehicles due to its high energy density and low pollution. As the key monitoring parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) and state of health (SOH) can promote the utilization rate of battery, which is of great significance to ensure the safe use of LIBs. In this paper, a novel dual Kalman filter method is proposed to achieve simultaneous SOC and SOH estimation. This paper improves the estimation accuracy of SOC and SOH from the following four aspects. Firstly, the widely used equivalent circuit model is established as the battery model in this paper, and the forgetting factor recursive least squares (FFRLS) method is applied to identify the model parameters. Secondly, two kinds of single-variable battery states are established to analyze the influence of OCV-SOC curve and battery capacity on SOC estimation. Based on this, an error model is proposed combined with Kalman filter to achieve better estimation results of SOC and SOH. Besides, to promote the accuracy of SOC estimation, based on the error innovation sequence (EIS) and residual innovation sequence (RIS), the improved dual adaptive extended Kalman filter (IDAEKF) algorithm based on dynamic window is proposed. Finally, the superiority of the proposed model is verified under different cycles. Experimental results show that the estimation error of SOC and SOH is controlled within 1%. K E Y W O R D S dynamic window, improved dual-adaptive extended Kalman filter (IDAEKF), lithium-ion battery (LIB), state of charge (SOC), state of health (SOH), error model
Lithium‐ion batteries (LIBs) are widely used in electric vehicles because of their high energy density and less pollution. As an important parameter of the battery management system, accurate estimation of the state of charge (SOC) of the battery can ensure the energy distribution and safe use of the battery. This paper obtains better estimation accuracy from four aspects. First, the battery model is established via Thevenin equivalent circuit model, and the parameters are identified by the forgetting factor recursive least squares. Second, the influence of dual extended Kalman filter on SOC estimation is analysed, a novel algorithm‐based improved dual Kalman filter is proposed. Besides, to reduce the influence of the system noise on the estimation results, an adaptive intelligent algorithm is applied to promote the accuracy of SOC estimation. Finally, compared with the estimated SOC results of the traditional algorithm, the experimental results show the effectiveness of the algorithm.
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