A control method of a battery state estimation system, the control method comprising: performing initialization by inputting an initial current and voltage into a battery and measuring a current, I b and a voltage, U, input to the battery; calculating at least one of parameters of an equivalent circuit of the battery including a first internal resistor (R i), a second resistor (R ct), and a capacitor (C dl) in the battery by inputting a hysteresis coefficient and a diffusion coefficient into an open circuit voltage, OCV, equation and using a battery state estimation method comprising: estimating the at least one of parameters of the equivalent circuit of the battery according to an Auto Regressive with External Input, ARX, model, wherein the ARX defines a correlation among an output sequence y(t), an input sequence u(t), and a shift operator q; and estimating a battery state by applying the at least one of the parameters of the battery to a dual extended Kalman filter, wherein the dual extended Kalman filter comprises a state filter for calculating an estimate of a state of charge, SOC, of the battery and a weight filter for calculating an estimate of a state of health, SOH, of the battery, characterized in that the ARX model comprises a recursive least square algorithm for tracking time variant parameters to calculate coefficient factors of the ARX model, wherein an update gain of the recursive square algorithm is expressed as L k = P k − 1 φ k λ k + φ T k P k − 1 φ k where L(k) is an update gain, P(k-1) is a covariance matrix, λ(k), 0<λ(k)<1 is a temporary coefficient which is used to assign more weight to recent data than the previous data and φ(k) is an input vector obtained from input data values including a time index k-1, the battery currents Ib(k) and I b (k-1) at time steps k and k-1, and a dynamic effect voltage U RR c(k-1) for the battery parameters, whereby U RRC =U-OCV. performing initialization by inputting an initial current and voltage into a battery and measuring a current, I b and a voltage, U, input to the battery; calculating at least one of parameters of an equivalent circuit of the battery including a first internal resistor (R i), a second resistor (R ct), and a capacitor (C dl) in the battery by inputting a hysteresis coefficient and a diffusion coefficient into an open circuit voltage, OCV, equation and using a battery state estimation method comprising: estimating the at least one of parameters of the equivalent circuit of the battery according to an Auto Regressive with External Input, ARX, model, wherein the ARX defines a correlation among an output sequence y(t), an input sequence u(t), and a shift operator q; and estimating a battery state by applying the at least one of the parameters of the battery to a dual extended Kalman filter, wherein the dual extended Kalman filter comprises a state filter for calculating an estimate of a state of charge, SOC, of the battery and a weight filter for calculating an estimate of a state of health, SOH, of the battery, characterized in that the ARX model comprises a recursive least square algorithm for tracking time variant parameters to calculate coefficient factors of the ARX model, wherein an update gain of the recursive square algorithm is expressed as L k = P k − 1 φ k λ k + φ T k P k − 1 φ k where L(k) is an update gain, P(k-1) is a covariance matrix, λ(k), 0<λ(k)<1 is a temporary coefficient which is used to assign more weight to recent data than the previous data and φ(k) is an input vector obtained from input data values including a time index k-1, the battery currents Ib(k) and I b (k-1) at time steps k and k-1, and a dynamic effect voltage U RR c(k-1) for the battery parameters, whereby U RRC =U-OCV. estimating the at least one of parameters of the equivalent circuit of the battery according to an Auto Regressive with External Input, ARX, model, wherein the ARX defines a correlation among an output sequence y(t), an input sequence u(t), and a shift operator q; and estimating a battery state by applying the at least one of the parameters of the battery to a dual extended Kalman filter, wherein the dual extended Kalman filter comprises a state filter for calculating an estimate of a state of charge, SOC, of the battery and a weight filter for calculating an estimate of a state of health, SOH, of the battery, characterized in that the ARX model comprises a recursive least square algorithm for tracking time variant parameters to calculate coefficient factors of the ARX model, wherein an update gain of the recursive square algorithm is expressed as L k = P k − 1 φ k λ k + φ T k P k − 1 φ k where L(k) is an update gain, P(k-1) is a covariance matrix, λ(k), 0<λ(k)<1 is a temporary coefficient which is used to assign more weight to recent data than the previous data and φ(k) is an input vector obtained from input data values including a time index k-1, the battery currents Ib(k) and I b (k-1) at time steps k and k-1, and a dynamic effect voltage U RR c(k-1) for the battery parameters, whereby U RRC =U-OCV.