JP-2026074466-A - Secondary battery degradation prediction system and secondary battery degradation prediction method

Abstract

【assignment】 This invention provides a secondary battery degradation prediction system and method that can predict degradation with high accuracy even when the time series data of secondary batteries is short or the usage period is short. [Solution] The secondary battery degradation prediction system 2 is a secondary battery degradation prediction system 2 that predicts the degradation of a secondary battery 13 to be diagnosed based on the usage history information of other secondary batteries, and comprises a learning model construction unit 222 that constructs a learning model 223 using characteristic data of other secondary batteries and features that are finally determined during the learning process as input variables, a feature acquisition unit 224 that acquires features that are finally determined during the learning process of the secondary battery 13 to be diagnosed from the usage history information of the secondary battery 13 to be diagnosed, and a degradation prediction unit 225 that inputs the usage history information of the secondary battery to be diagnosed and the features that are finally determined during the learning process into the learning model 223 and estimates the degradation of the secondary battery to be diagnosed. [Selection Diagram] Figure 1

Inventors

• 一藁 真未
• 小西 宏明
• 堀越 伸也
• 前田　潤一
• 馬場 健治
• 米元 雅浩

Assignees

• 株式会社日立ハイテク

Dates

Publication Date

20260507

Application Date

20241021

Claims (15)

1. A secondary battery degradation prediction system that predicts the degradation of a secondary battery to be diagnosed based on the usage history information of other secondary batteries, A model building unit constructs a learning model using characteristic data of other secondary batteries and features that are finally determined during the learning process as input variables. A feature acquisition unit that obtains feature quantities that are ultimately determined during the learning process of the secondary battery to be diagnosed from the usage history information of the secondary battery to be diagnosed, A secondary battery degradation prediction system comprising: a degradation prediction unit that inputs usage history information of a secondary battery to be diagnosed and a feature quantity finalized in the learning process into the learning model, and estimates the degradation of the secondary battery to be diagnosed.
2. A secondary battery degradation prediction system according to claim 1, The feature that is ultimately determined during the aforementioned learning process is a parametric bias value. The secondary battery degradation prediction system is characterized in that the aforementioned parametric bias value is a learnable input variable that represents the characteristics of each secondary battery common to all time points.
3. A secondary battery degradation prediction system according to claim 2, The secondary battery degradation prediction system is characterized in that the parametric bias value is updated at each time t to minimize the evaluation function.
4. A secondary battery degradation prediction system according to claim 3, The secondary battery degradation prediction system is characterized in that the characteristic data is time-series data showing the characteristics of the secondary battery for each charge and discharge, including discharge current or charge voltage, and is an input variable input to the learning model.
5. A secondary battery degradation prediction system according to claim 3, The aforementioned degradation prediction unit is characterized by estimating the degradation of a secondary battery to be diagnosed using the learning model, the feature quantities that are finally determined during the learning process of the secondary battery to be diagnosed from the usage history information of the secondary battery to be diagnosed, and the usage history information of the secondary battery to be diagnosed.
6. A secondary battery degradation prediction system according to claim 3, The secondary battery degradation prediction system is characterized in that the degradation prediction unit estimates the similarity between batteries based on the feature quantities of each secondary battery other than the secondary battery to be diagnosed and the feature quantities of the secondary battery to be diagnosed, and estimates degradation based on the feature quantities of the secondary battery that has a high similarity to the secondary battery to be diagnosed.
7. A secondary battery degradation prediction system according to claim 6, The aforementioned degradation prediction unit is characterized by determining whether it can predict the secondary battery to be diagnosed based on the presence or absence of other secondary batteries that have similar characteristics to the secondary battery to be diagnosed.
8. A secondary battery degradation prediction system according to claim 3, A secondary battery degradation prediction system characterized by having an output device that outputs the result of predicting the degradation of a secondary battery to be diagnosed.
9. A secondary battery degradation prediction system according to claim 3, A secondary battery degradation prediction system characterized by using modified usage conditions as input variables and predicting degradation when the load pattern is changed during the operation of the secondary battery.
10. A secondary battery degradation prediction method that predicts the degradation of a secondary battery to be diagnosed based on the usage history information of other secondary batteries, The model building unit constructs a learning model using characteristic data from other secondary batteries and features that are ultimately determined during the learning process as input variables. The feature acquisition unit performs the step of acquiring the features that are ultimately determined during the learning process of the secondary battery to be diagnosed from the usage history information of the secondary battery to be diagnosed, A secondary battery degradation prediction method characterized by comprising the steps of: a degradation prediction unit inputting usage history information of a secondary battery to be diagnosed and a feature quantity finalized in the learning process into the learning model, and estimating the degradation of the secondary battery to be diagnosed.
11. A method for predicting secondary battery degradation according to claim 10, The feature that is ultimately determined during the aforementioned learning process is a parametric bias value. A secondary battery degradation prediction method characterized in that the aforementioned parametric bias value is a learnable input variable that represents the characteristics of each secondary battery common to all time points.
12. A method for predicting secondary battery degradation according to claim 11, A method for predicting secondary battery degradation, characterized in that the parametric bias value is updated at each time t to minimize the evaluation function.
13. A method for predicting secondary battery degradation according to claim 12, The method for predicting secondary battery degradation is characterized in that the characteristic data is time-series data showing the characteristics of the secondary battery for each charge and discharge cycle, including discharge current or charge voltage, and is an input variable input to the learning model.
14. A method for predicting secondary battery degradation according to claim 12, The degradation prediction unit is characterized by estimating the degradation of a secondary battery to be diagnosed using the learning model, the feature quantities that are finally determined during the learning process of the secondary battery to be diagnosed from the usage history information of the secondary battery to be diagnosed, and the usage history information of the secondary battery to be diagnosed.
15. A method for predicting secondary battery degradation according to claim 12, A secondary battery degradation prediction method characterized by using modified usage conditions as input variables and predicting degradation when the load pattern is changed during the operation of the secondary battery.

Description

This invention relates to a secondary battery degradation prediction system and a secondary battery degradation prediction method. One method for predicting the degradation of secondary batteries is described in Patent Document 1. Patent Document 1 describes acquiring a plurality of first log data indicating the state of a secondary battery during charging or discharging, obtained from a device equipped with a rechargeable battery (secondary battery), and a plurality of first degradation degrees indicating the degree of degradation of the secondary battery calculated by a degradation degree estimation method using each first log data. Based on the content of the first charge/discharge, which is the charge or discharge corresponding to each first log data, a confidence score indicating the likelihood of the first degradation degree corresponding to each first log data is calculated, and a first trained model is generated by machine learning the relationship between the plurality of first degradation degrees and the plurality of first log data. A second trained model is generated by machine learning the relationship between the first degradation degrees among the plurality of first degradation degrees whose confidence score is above a predetermined value and the first log data corresponding to that first degradation degree. The documentation discloses that the estimation accuracy of the secondary battery degradation level is evaluated for each of the first and second trained models described above, and that the trained model evaluated as having the best estimation accuracy among the first and second trained models is output. International Publication No. 2023/238636 This is a configuration diagram showing an overview of the entire system according to Embodiment 1 of the present invention.This is a diagram illustrating an example of a learning model.This is a flowchart showing the processing flow for degradation prediction.This flowchart shows the processing flow for building a learning model using characteristic data of secondary batteries other than the secondary battery you want to diagnose.This flowchart shows the processing flow for acquiring feature quantities of a secondary battery to be diagnosed, using the characteristic data of the secondary battery to be diagnosed.This flowchart shows the processing flow for predicting the degradation of a secondary battery using its characteristic data.This figure shows an example of a screen displaying the predicted results on a vehicle equipped with a secondary battery and an output device.This is an example of a schematic graph showing the results of obtaining the final determined feature quantities during the learning process of secondary batteries other than the secondary battery being diagnosed.This is a schematic diagram showing the route operation status used for creating a route plan according to Embodiment 2 of the present invention.This flowchart shows the processing flow for predicting bus deterioration when the route is changed midway through the journey. In this specification, "features that are finally determined during the learning process" refers to the PB (Parametric Bias) value, and is described as "features that are finally determined during the learning process (PB value)." Furthermore, in this specification, "secondary battery" may refer to, for example, a lithium-ion battery, a lead-acid battery, a sodium battery, a fluoride battery, a magnesium battery, a redox flow battery, and the like. The following describes embodiments of the present invention with reference to the drawings.