A battery state estimation method comprising: periodically measuring voltage and current of a battery in use to generate a voltage value and a current value; using an adaptive filter to generate a G parameter value and an H parameter value in real time from the voltage value and the current value, said parameters indicating the present state of the battery, wherein the adaptive filter is a filter using a recursive least squares method; and using the G parameter value and the H parameter value to estimate the state of the battery in real time, wherein the G parameter is a parameter that represents sensitivity of the voltage to changes in the current of the battery, the H parameter is a parameter that represents an effective potential determined by local equilibrium potential distribution and resistance distribution inside the battery, initializing a state vector composed of the G parameter and the H parameter, and a covariance matrix, wherein the generating of the G parameter value and the H parameter value in real time comprises: calculating a present voltage estimate of the battery based on the present current value and an immediately preceding value of the state vector and updating a gain matrix and the covariance matrix on a basis of the present current value and the immediately preceding value of the covariance matrix; calculating a voltage error between the present voltage value and the present voltage estimate; and updating the state vector based on the immediately preceding value of the state vector, a present value of the gain matrix, and the voltage error so as to generate a present value of the G parameter and a present value of the H parameter. periodically measuring voltage and current of a battery in use to generate a voltage value and a current value; using an adaptive filter to generate a G parameter value and an H parameter value in real time from the voltage value and the current value, said parameters indicating the present state of the battery, wherein the adaptive filter is a filter using a recursive least squares method; and using the G parameter value and the H parameter value to estimate the state of the battery in real time, wherein the G parameter is a parameter that represents sensitivity of the voltage to changes in the current of the battery, the H parameter is a parameter that represents an effective potential determined by local equilibrium potential distribution and resistance distribution inside the battery, initializing a state vector composed of the G parameter and the H parameter, and a covariance matrix, wherein the generating of the G parameter value and the H parameter value in real time comprises: calculating a present voltage estimate of the battery based on the present current value and an immediately preceding value of the state vector and updating a gain matrix and the covariance matrix on a basis of the present current value and the immediately preceding value of the covariance matrix; calculating a voltage error between the present voltage value and the present voltage estimate; and updating the state vector based on the immediately preceding value of the state vector, a present value of the gain matrix, and the voltage error so as to generate a present value of the G parameter and a present value of the H parameter. calculating a present voltage estimate of the battery based on the present current value and an immediately preceding value of the state vector and updating a gain matrix and the covariance matrix on a basis of the present current value and the immediately preceding value of the covariance matrix; calculating a voltage error between the present voltage value and the present voltage estimate; and updating the state vector based on the immediately preceding value of the state vector, a present value of the gain matrix, and the voltage error so as to generate a present value of the G parameter and a present value of the H parameter. <EOS>