Cost-effective yet still precise ascertainment of the degradation state of a rechargeable battery

A non-transitory machine-readable data carrier on which is stored a computer program for ascertaining an approximation and/or a prognosis for a true degradation state of a rechargeable battery of an least partly electrically driven vehicle, the computer program, when executed by one or more computers, causing the one or more computers to perform the following steps:

• providing a time sequence, discretized into predefined time steps, of values of a degradation state ascertained using measuring technology, for points in time in the past;
• providing a trained Hidden Markov Model (HMM), which indicates as a function of the true degradation state:
• at which probability which particular value of the degradation state is monitored during the ascertainment using the measuring technology, and
• at which probability the true degradation state is maintained for what length of time, and/or at which probability the true degradation state transitions to which worse degradation state in a next time step;
• ascertaining, from the provided time sequence and the HMM, a most probable characteristic of the true degradation state in the past that is in agreement with the provided time sequence, wherein:
• the approximation and/or prognosis is based on the most probable characteristic,
• during discharging of the battery, time sequences of a clamping voltage, a discharge current, and a charge state of the battery are acquired, and the values of the degradation state are ascertained from the time sequences of the clamping voltage using a physical model of the degradation state, and
• the physical model and the HMM are different from each other, wherein the most probable characteristic of the true degradation state in at least one past point in time that is in agreement with the provided time sequence is updated using a Viterbi algorithm, and wherein the approximation and/or prognosis is based on the most probable characteristic of the true degradation in the at least one past point in time that is in agreement with the provided time sequence.

A computer configured to ascertain an approximation and/or a prognosis for a true degradation state of a rechargeable battery, the computer configured to:

• provide a time sequence, discretized into predefined time steps, of values of a degradation state ascertained using measuring technology, for points in time in the past;
• provide a trained Hidden Markov Model (HMM), which indicates as a function of the true degradation state:
• at which probability which particular value of the degradation state is monitored during the ascertainment using the measuring technology, and
• at which probability the true degradation state is maintained for what length of time, and/or at which probability the true degradation state transitions to which worse degradation state in a next time step;
• ascertain, from the provided time sequence and the HMM, a most probable characteristic of the true degradation state in the past that is in agreement with the provided time sequence, wherein:
• a desired approximation and/or prognosis is based on the most probable characteristic,
• during discharging of the battery, time sequences of a clamping voltage, a discharge current, and a charge state of the battery are acquired, and the values of the degradation state are ascertained from the time sequences of the clamping voltage using a physical model of the degradation state, and
• the physical model and the HMM are different from each other, wherein the most probable characteristic of the true degradation state in at least one past point in time that is in agreement with the provided time sequence is updated using a Viterbi algorithm, and wherein the approximation and/or prognosis is based on the most probable characteristic of the true degradation in the at least one past point in time that is in agreement with the provided time sequence.