Method for predicting a residual service life of vehicle batteries of a fleet of electric vehicles
A computer-implemented method is introduced for predicting a residual service life of vehicle batteries of a fleet of electric vehicles. In the method, parameters of the vehicle batteries are measured during the operation of the electric vehicles and transmitted to a server; a conditional probability is determined that the residual service life of a specific vehicle battery undershoots a predefined limit value at a point in time lying in the past; and the residual service life of vehicle batteries of the fleet is predicted as a function of the conditional probability.
1 . A computer-implemented method for predicting residual service lives of vehicle batteries of a fleet of electric vehicles, comprising the following steps:
measuring parameters of the vehicle batteries during an operation of the electric vehicles, and transmitting the measured parameters to a server;
determining a conditional probability that a residual service life of a specific one of the vehicle batteries undershoots a predefined limit value at a point in time lying in the past; and
predicting the residual service lives of the vehicle batteries of the fleet as a function of the conditional probability, wherein the vehicle batteries are labeled by a binary classifier, which has a first value for a first subset of the vehicle batteries whose residual service life is greater than a threshold value, and which has a second value that differs from the first value, for a second subset of the vehicle batteries whose residual service life is less than a threshold value, wherein a probability that the binary classifier has the second value at a point in time T that is later than a specific point in time t is described by a survival function which is estimated by a Kaplan-Meier estimator, and wherein the probability that the binary classifier assumes the second value corresponds to the probability of an event that is calculated by a cumulative death distribution function from survival analysis.
2 . The method as recited in claim 1 , wherein the parameters measured during the operation of the electric vehicles for each vehicle battery are combined to a feature vector characterizing the specific one of the vehicle batteries.
3 . The method as recited in claim 2 , wherein the conditional probability is determined as a quotient whose denominator is a function of a probability that the specific vehicle battery has a specific feature vector at the point in time lying in the past.
4 . The method as recited in claim 3 , wherein the denominator is:
estimated by an empirical distribution based on an event frequency, or
determined based on a parametric distribution, or
determined based on a normal distribution, or
determined based on a uniform distribution.
5 . The method as recited in claim 3 , wherein the quotient has a numerator, which is a function of a joint probability that the specific vehicle battery having the specific feature vector has a residual service life that undershoots the predefined limit value at the point in time lying in the past.
6 . The method as recited in claim 5 , wherein the joint probability is modeled by a Bayesian network, that is, by a directed cyclic graph B=(ν, ε), where ν is the set of vertices that represents the variables, and ε forms a set of edges that encode the dependencies between variables.
7 . The method as recited in claim 6 , wherein the Bayesian network has a vertex without parents.
8 . The method as recited in claim 6 , wherein the structure of the Bayesian network is determined using a criterion of a minimum description length.
9 . A device configured to predict a residual service life of vehicle batteries of a fleet of electric vehicles, the device comprising:
a sensor system configured to measure parameters of the vehicle batteries occurring during operation of the electric vehicles; and
a transmitter configured to transmit the measured parameters to a server, the server being configured to:
determine a conditional probability that a residual service life of a specific one of the vehicle batteries undershoots a predefined limit value at a point in time lying in the past; and
predict the residual service life of the vehicle batteries of the fleet as a function of the conditional probability, wherein the vehicle batteries are labeled by a binary classifier, which has a first value for a first subset of the vehicle batteries whose residual service life is greater than a threshold value, and which has a second value that differs from the first value, for a second subset of the vehicle batteries whose residual service life is less than a threshold value, wherein a probability that the binary classifier has the second value at a point in time T that is later than a specific point in time t is described by a survival function which is estimated by a Kaplan-Meier estimator, and wherein the probability that the binary classifier assumes the second value corresponds to the probability of an event that is calculated by a cumulative death distribution function from survival analysis.
10 . A non-transitory computer-readable medium on which is stored a computer program including instructions for predicting a residual service life of vehicle batteries of a fleet of electric vehicles, the instructions, when executed by a computer, causing the computer to perform the following steps:
measuring parameters of the vehicle batteries during an operation of the electric vehicles, and transmitting the measured parameters to a server;
determining a conditional probability that a residual service life of a specific one of the vehicle batteries undershoots a predefined limit value at a point in time lying in the past; and
predicting the residual service life of the vehicle batteries of the fleet as a function of the conditional probability, wherein the vehicle batteries are labeled by a binary classifier, which has a first value for a first subset of the vehicle batteries whose residual service life is greater than a threshold value, and which has a second value that differs from the first value, for a second subset of the vehicle batteries whose residual service life is less than a threshold value, wherein a probability that the binary classifier has the second value at a point in time T that is later than a specific point in time t is described by a survival function which is estimated by a Kaplan-Meier estimator, and wherein the probability that the binary classifier assumes the second value corresponds to the probability of an event that is calculated by a cumulative death distribution function from survival analysis.