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. <EOS>