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EP-4741208-A1 - BATTERY DEGRADATION LEVEL PREDICTION SYSTEM

EP4741208A1EP 4741208 A1EP4741208 A1EP 4741208A1EP-4741208-A1

Abstract

A battery degradation level prediction system includes: a database configured to accumulate a degradation level of a traction secondary battery mounted on each of a plurality of vehicles; and a dataset selector configured to change, in accordance with the accumulation status of the database, a dataset to be used to train a model that predicts a future degradation level of the secondary battery of each of the vehicles.

Inventors

  • BUNAZAWA, HIDEAKI
  • INOUE, MASAAKI
  • Fukushima, Shintaro
  • OKAMOTO, MASANORI

Assignees

  • TOYOTA JIDOSHA KABUSHIKI KAISHA

Dates

Publication Date
20260513
Application Date
20250825

Claims (4)

  1. A battery degradation level prediction system comprising: a database configured to accumulate a degradation level of a secondary battery for traction mounted on each of a plurality of vehicles; and a dataset selector configured to change, in accordance with an accumulation status of the database, a dataset to be used to train a model that predicts a future degradation level of the secondary battery of each of the vehicles.
  2. The battery degradation level prediction system according to claim 1, wherein the dataset selector is configured to select, in accordance with the accumulation status of the database, a dataset including a plurality of degradation levels of the secondary battery of one of the vehicles accumulated in the database, as the dataset to be used to train the model that predicts the future degradation level of the secondary battery of the one vehicle.
  3. The battery degradation level prediction system according to claim 1, wherein the dataset selector is configured to select, in accordance with the accumulation status of the database, a dataset including a plurality of degradation levels of the secondary battery of one of the vehicles and the secondary battery of a vehicle of the same model as the one vehicle accumulated in the database, as the dataset to be used to train the model that predicts the future degradation level of the secondary battery of the one vehicle.
  4. The battery degradation level prediction system according to claim 1, wherein the dataset selector is configured to select, in accordance with the accumulation status of the database, a dataset including a plurality of degradation levels of the secondary battery of one of the vehicles and the secondary battery of the same model as the secondary battery of the one vehicle accumulated in the database, as the dataset to be used to train the model that predicts the future degradation level of the secondary battery of the one vehicle.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to the technical field of battery degradation level prediction systems that predict the degradation level of a secondary battery. 2. Description of Related Art As an example of this type of system, a system has been proposed that trains a predictive model for estimating an indicator related to the degradation state of a battery based on training data and estimates an indicator related to the degradation state of a battery based on the trained predictive model (see Japanese Unexamined Patent Application Publication No. 2023-51009 (JP 2023-51009 A)). SUMMARY OF THE INVENTION In the technique described in JP 2023-51009 A, an indicator related to the degradation state of a battery is estimated based on the predictive model trained by machine learning. However, the estimation accuracy may decrease depending on the training data used for machine learning. The present invention has been made in view of the above issue, and an object of the present invention is to provide a battery degradation level prediction system that can improve estimation accuracy. A battery degradation level prediction system according to an aspect of the present invention includes: a database configured to accumulate a degradation level of a traction secondary battery mounted on each of a plurality of vehicles; and a dataset selector configured to change, in accordance with the accumulation status of the database, a dataset to be used to train a model that predicts a future degradation level of the secondary battery of each of the vehicles. BRIEF DESCRIPTION OF THE DRAWINGS Features, advantages, and technical and industrial significance of exemplary embodiments of the invention will be described below with reference to the accompanying drawings, in which like signs denote like elements, and wherein: FIG. 1 is a conceptual diagram showing the configuration of a battery degradation level prediction system according to an embodiment;FIG. 2 is a flowchart showing the operation of the battery degradation level prediction system according to the embodiment;FIG. 3 is a flowchart showing a method for determining data to be used for learning; andFIG. 4 is a flowchart showing a method for constructing a model. DETAILED DESCRIPTION OF EMBODIMENTS An embodiment of a battery degradation level prediction system will be described with reference to FIGS. 1 to 4. In FIG. 1, a battery degradation level prediction system 10 includes a database 11, a dataset selection unit 12, and a model training unit 13. For example, the battery degradation level prediction system 10 may be implemented by a server. The battery degradation level prediction system 10 may be implemented by a single server or may be implemented by a plurality of servers. The server may be a cloud server. The battery degradation level prediction system 10 is configured to communicate bidirectionally with a plurality of vehicles V1, V2, ... , Vn via the Internet. The vehicles V1, V2, ... , Vn are equipped with traction secondary batteries B1, B2, ... , Bn, respectively. The vehicles V1, V2, ... , Vn may include at least one of a battery electric vehicle, a plug-in hybrid electric vehicle, a hybrid electric vehicle, and a fuel cell electric vehicle. The secondary batteries B1, B2, ... , Bn may include at least one of a lithium-ion battery, a nickel-metal hydride battery, and an all-solid-state battery. Each of the vehicles V1, V2, ... , Vn periodically transmits data including the degradation level (state of health (SOH)) of its secondary battery to the battery degradation level prediction system 10. The data may include specification information, timestamps, characteristics related to the vehicle and the battery, and information on the degradation level. For example, the specification information includes a vehicle identification number (VIN), a battery number, a battery model, a vehicle model, a destination, and a design change version. The information on the degradation level may include the degradation level, the number of days elapsed since line-off (L/O) (that is, the date of completion of production), a distance traveled, a cumulative parking time, a state-of-charge (SOC) history and battery temperature history during ignition-on, and an SOC history and battery temperature history during ignition-off. "During ignition-on" may include at least one of the following: while the vehicle is traveling, during an electric power transfer mode, and during charging. "During ignition-off" may include either or both of the following: while the vehicle is parked and during transport. A plurality of pieces of data transmitted from the vehicles V1, V2, ... , Vn is stored in the database 11. As a result, the degradation levels of the secondary batteries B1, B2, ... , Bn are accumulated in the database 11. The battery degradation level prediction system 10 uses the data including the degradation levels of the secondary batteri