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DE-102025000732-B3 - Method for detecting an alternative use of a vehicle battery

DE102025000732B3DE 102025000732 B3DE102025000732 B3DE 102025000732B3DE-102025000732-B3

Abstract

The invention relates to a method for detecting the alternative usability of a battery (B) of a vehicle (F), wherein at least one battery characteristic (BK) is recorded and monitored by the vehicle (F), wherein the occurrence of at least one trigger condition (T) for replacing the battery (B) is checked, wherein a decision algorithm (EA) comprising artificial intelligence, in particular a neural network, determines, based on the at least one battery characteristic (BK), the trigger condition (T) and at least one dynamic threshold (DSW) from an external source, whether the battery (B) is qualified for a second-life application and, in this case, generates a purchase offer (KA) for the battery (B) and submits it to a customer (K), and proposes a possible allocation of the battery (B) for a specific second-life application.

Inventors

  • Kris Meissmer
  • Laura Siegel
  • Fabian Oliver Flohr
  • Lukas Dennert

Assignees

  • Mercedes-Benz Group AG

Dates

Publication Date
20260513
Application Date
20250227

Claims (10)

  1. Method for detecting an alternative use of a battery (B) of a vehicle (F), characterized in that at least one battery characteristic (BK) is recorded and monitored by the vehicle (F), wherein the occurrence of at least one trigger condition (T) for replacing the battery (B) is checked, wherein a decision algorithm (EA) comprising artificial intelligence, in particular a neural network, determines, based on the at least one battery characteristic (BK), the trigger condition (T) and at least one dynamic threshold (DSW) from an external source, whether the battery (B) is qualified for a second-life application and, in this case, generates a purchase offer (KA) for the battery (B) and submits it to a customer (K), and proposes a possible allocation of the battery (B) for a specific second-life application.
  2. Procedure according to Claim 1 , characterized in that a battery characteristic value (BK) is recorded and monitored, which includes a health status, a residual capacity, a number of switching cycles of at least one contactor of the battery (B), an internal resistance, an age and/or a technical characteristic of the battery (B), in particular a capacity and/or information on the cell chemistry of the battery (B).
  3. Procedure according to Claim 1 or 2 , characterized in that the trigger condition (T) is checked to determine whether at least one threshold value relating to the battery (B) is reached, exceeded or fallen below, for example a minimum age, a residual capacity and/or a minimum number of switching cycles of a contactor of the battery (B), the existence of irreparable damage or a customer request for replacement.
  4. Method according to one of the preceding claims, characterized in that a market demand, market availability and/or a market price for the battery (B) and/or for at least one component of the battery (B) and/or for at least one resource required for the manufacture of the battery (B) and/or a type of possible and/or demanded second-life application for the battery (B) is taken into account as a dynamic threshold (DSW).
  5. Procedure according to Claim 4 , characterized in that for some or all types of second-life applications, certain requirements for the battery characteristics (BK), in particular the state of health, are taken into account by the decision algorithm (EA).
  6. Method according to one of the preceding claims, characterized in that a temporal progression of the dynamic thresholds (DSW) over a minimum period of time is taken into account by the decision algorithm (EA).
  7. A method according to one of the preceding claims, characterized in that the decision algorithm (EA) classifies the battery (B) as qualified for a second-life application if: - the battery characteristics (BK) have reached predetermined thresholds and the market demand and/or the market price for the battery (B) exceeds a certain threshold, and/or - if at least one trigger condition (T) for replacing the battery (B) by means of battery characteristics (BK) is met, and/or - if there is a customer request to replace the battery (B).
  8. Method according to one of the preceding claims, characterized in that the decision algorithm (EA) is located in a backend which communicates wirelessly or via a wired connection with the vehicle (F).
  9. Method according to one of the preceding claims, characterized in that the decisions of the decision algorithm (EA) are monitored by a battery manager (BM) which trains the artificial intelligence by confirming or rejecting the decision.
  10. Method according to one of the preceding claims, characterized in that the type of second-life application is the use of the battery (B) as a buffer storage device (PS) or as a traction battery for another vehicle model. (FM) or the recycling (RC) of the battery (B) is taken into account.

Description

The invention relates to methods for detecting an alternative use of a vehicle battery according to the preamble of claim 1. The battery of a vehicle, such as an electric vehicle, that is older and/or has high mileage may have reduced performance. This could render the battery unsuitable for use as a vehicle battery. However, the battery could potentially be used in various second-life applications. For example, several batteries that have reached the end of their service life could be connected to form a larger energy storage system. Currently, however, it is not possible to automatically detect whether a battery is suitable for a second-life application. DE 20 2021 105 689 U1 describes an intelligent system for improving the condition of electric vehicle batteries using machine learning and artificial intelligence, the intelligent system comprising: an electric vehicle battery pack, consisting of: several cell modules arranged in series and parallel; a battery monitoring device typically used to monitor the voltage of each battery cell in the system, the temperature of various points in the battery packs, and other vehicle conditions; a battery management system, the battery management system comprising: Monitoring components are located near the battery cells themselves; one or more power conversion stages are selected according to the vehicle's needs; and an intelligent control unit is placed at strategic points in the architecture to manage various aspects of the vehicle's energy subsystem, with data being reported to a battery cell management controller and, depending on the system's complexity, to higher-level processing elements. the intelligent control unit is used to control the efficient charging and discharging of the battery, as this avoids thermal outliers or other conditions that would reduce either the capacity or the lifespan of the battery, the control unit using a machine learning algorithm that is used to manage and improve the condition of electric vehicle batteries. Furthermore, the DE 10 2020 201 697 A B3 procedure for categorizing a battery with regard to its suitability for further handling, such a battery, a battery recycling system, and an associated motor vehicle. In the DE 10 2016 220 860 A1 A method, a device, and a system for evaluating a traction battery are described. EP 4 123 321 A1 describes a high-precision coulometry measurement for used batteries in order to obtain an estimate of their residual value, in particular their suitability for a second-life application in a stationary energy storage system. The invention is based on the objective of providing a novel method for detecting an alternative use of a vehicle battery. The problem is solved according to the invention by a method for detecting an alternative use of a vehicle battery with the features of claim 1. Advantageous embodiments of the invention are the subject of the dependent claims. A method for detecting the alternative use of a vehicle battery, for example, a traction battery of an electric vehicle, is proposed. According to the invention, at least one battery characteristic is detected and monitored by the vehicle, in particular by at least one control unit of the vehicle, and the occurrence of at least one trigger condition for battery replacement is checked. A decision algorithm, which includes artificial intelligence, in particular a neural network, uses the at least one battery characteristic, the trigger condition, and at least one dynamic threshold from an external source to determine whether the battery is qualified for a second-life application. If so, a purchase offer for the battery is generated and submitted to a customer, and a possible allocation of the battery for a specific second-life application is proposed. In one embodiment, a battery characteristic value is recorded and monitored, including a state of health, a remaining capacity, a number of switching cycles of at least one contactor of the battery, an internal resistance, an age and/or a technical property of the battery, in particular a capacity and/or information on the cell chemistry of the battery. In one embodiment, the trigger condition is checked to determine whether at least one threshold relating to the battery has been reached, exceeded or fallen below, for example a minimum age, a remaining capacity and/or a minimum number of switching cycles of a contactor of the battery, the presence of irreparable damage or a customer request for replacement. In one embodiment, a market demand, market availability and/or market price for the battery and/or for at least one component of the battery and/or for at least one resource required to manufacture the battery and/or a type of possible and/or demanded second-life application for the battery is taken into account as a dynamic threshold. In one embodiment, the decision algorithm takes into account certain requirements for battery characteristics, in particular the state of health, fo