Method for determining an approximation and/or prediction for the true degradation state of a rechargeable battery, having the following steps: • a time series, discretized into predefined time steps, of values of the degradation state, which are determined using metrology for points in time in the past, is provided, ∘ wherein, during the discharging of the battery, time series of the terminal voltage, the discharge current and the state of charge of the battery are recorded, and wherein the observed values of the degradation state are determined from these time series on the basis of a physical model of the degradation state, ∘ wherein a time series of at least one temperature in the battery is additionally recorded during discharging, and wherein this temperature is taken into account in the physical model; • a trained hidden Markov model, HMM, is provided and, on the basis of the true degradation state, indicates with which probability which value of the degradation state is observed during the metrological determination, and ∘ with which probability this true degradation state is maintained for how long and/or with which probability this true degradation state changes to which worse degradation state in the next time step; • the observed time series and the HMM are used to determine the most probable progression (2*) of the true degradation state in the past that is in accordance with the observed time series; • the most probable progression (2*) is used to evaluate the approximation and/or prediction sought. • a time series, discretized into predefined time steps, of values of the degradation state, which are determined using metrology for points in time in the past, is provided, ∘ wherein, during the discharging of the battery, time series of the terminal voltage, the discharge current and the state of charge of the battery are recorded, and wherein the observed values of the degradation state are determined from these time series on the basis of a physical model of the degradation state, ∘ wherein a time series of at least one temperature in the battery is additionally recorded during discharging, and wherein this temperature is taken into account in the physical model; ∘ wherein, during the discharging of the battery, time series of the terminal voltage, the discharge current and the state of charge of the battery are recorded, and wherein the observed values of the degradation state are determined from these time series on the basis of a physical model of the degradation state, ∘ wherein a time series of at least one temperature in the battery is additionally recorded during discharging, and wherein this temperature is taken into account in the physical model; • a trained hidden Markov model, HMM, is provided and, on the basis of the true degradation state, indicates with which probability which value of the degradation state is observed during the metrological determination, and ∘ with which probability this true degradation state is maintained for how long and/or with which probability this true degradation state changes to which worse degradation state in the next time step; with which probability which value of the degradation state is observed during the metrological determination, and ∘ with which probability this true degradation state is maintained for how long and/or with which probability this true degradation state changes to which worse degradation state in the next time step; • the observed time series and the HMM are used to determine the most probable progression (2*) of the true degradation state in the past that is in accordance with the observed time series; • the most probable progression (2*) is used to evaluate the approximation and/or prediction sought.