阅读说明：本技术 电池信息处理系统、二次电池的容量推定方法、电池组及其制造方法 (Battery information processing system, method for estimating capacity of secondary battery, battery pack, and method for manufacturing battery pack ) 是由 泉纯太 三井正彦 八十岛珠仁 涩谷康太郎 于 2019-07-30 设计创作，主要内容包括：提供一种电池信息处理系统、二次电池的容量推定方法、电池组及其制造方法。已学习的神经网络模型是基于满充电容量处于基准范围内的多个模块的奈奎斯特图进行了学习的神经网络模型。处理系统(200)通过将从模块(M)的奈奎斯特图提取的至少一个特征量作为说明变数的判别分析,判别模块(M)属于满充电容量处于基准范围内的第1组群、和满充电容量处于基准范围外的第2组群中的哪一个。处理系统(200)在判别为模块(M)属于第1组群的情况下,使用已学习的神经网络模型推定模块(M)的满充电容量。(Provided are a battery information processing system, a capacity estimation method for a secondary battery, a battery pack, and a manufacturing method thereof. The learned neural network model is a neural network model that has been learned based on a nyquist diagram of a plurality of modules whose full charge capacities are within a reference range. A processing system (200) discriminates which of a group 1 in which the full charge capacity is within a reference range and a group 2 in which the full charge capacity is outside the reference range the module (M) belongs to by discriminant analysis using at least one feature quantity extracted from a Nyquist diagram of the module (M) as an explanatory variable. When the module (M) is determined to belong to the group 1, the processing system (200) estimates the full charge capacity of the module (M) using the learned neural network model.)

1. A battery information processing system is provided with:

a storage device that stores the learned neural network model; and

an estimation device that estimates a full charge capacity of a target secondary battery from a Nyquist plot representing a measurement result of alternating-current impedance of the target secondary battery using the learned neural network model,

the learned neural network model is a neural network model that has been learned based on nyquist plots of a plurality of secondary batteries whose full charge capacities are within a reference range,

the estimation device is used for estimating the position of the target,

discriminating which of a group 1 in which a full charge capacity is within the reference range and a group 2 in which the full charge capacity is outside the reference range the target secondary battery belongs to by discrimination analysis using at least one feature quantity extracted from a nyquist diagram of the target secondary battery as an explanatory variable,

when it is determined that the target secondary battery belongs to the group 1, the full charge capacity of the target secondary battery is estimated using the learned neural network model.

2. The battery information processing system according to claim 1,

the learned neural network model is a neural network model that has been learned based on nyquist plots of a plurality of secondary batteries whose full charge capacities exceed a reference capacity that is a lower limit value of the reference range,

the 1 st group is a group of secondary batteries whose full charge capacity exceeds the reference capacity,

the 2 nd group is a group of secondary batteries whose full charge capacity is lower than the reference capacity.

3. The battery information processing system according to claim 1 or 2,

the at least one characteristic amount includes an imaginary component of the alternating-current impedance at a predetermined frequency included in the straight line portion of the semicircular portion and the straight line portion of the nyquist diagram of the subject secondary battery, and an inclination of the straight line portion.

4. The battery information processing system according to any one of claims 1 to 3,

the learned neural network model includes an input layer provided with a numerical value of each pixel of an image in which a Nyquist diagram of the secondary battery is plotted in a region of a predetermined number of pixels,

the number of pixels in the region is greater than the sum of the number of real components and the number of imaginary components representing the measurement result of the ac impedance of the secondary battery.

5. The battery information processing system according to any one of claims 1 to 4,

the Nyquist diagram of the target secondary battery includes the measurement result of the alternating-current impedance when the frequency of the applied alternating-current signal is in the frequency range of 100mHz to 1 kHz.

6. A battery pack comprising a plurality of secondary batteries whose full charge capacity is estimated by the battery information processing system according to any one of claims 1 to 5.

7. A method for estimating a full charge capacity of a secondary battery, which estimates the full charge capacity of a target secondary battery, includes:

obtaining a measurement result of the alternating-current impedance of the target secondary battery; and

estimating a full charge capacity of the target secondary battery from a Nyquist plot representing a measurement result of alternating-current impedance of the target secondary battery using the learned neural network model,

the learned neural network model is a neural network model that has been learned using nyquist plots of a plurality of secondary batteries whose full charge capacities are within a reference range,

the full charge capacity estimation method includes:

a step of discriminating which of a group 1 in which a full charge capacity is within the reference range and a group 2 in which the full charge capacity is outside the reference range the target secondary battery belongs to by discrimination analysis using a feature quantity extracted from a nyquist diagram of the target secondary battery as an explanatory variable; and

and estimating a full charge capacity of the target secondary battery using the learned neural network model when it is determined that the target secondary battery belongs to the group 1.

8. A method of manufacturing a battery pack, comprising:

obtaining a measurement result of the alternating current impedance of the target secondary battery; and

estimating a full charge capacity of the target secondary battery from a Nyquist plot representing a measurement result of alternating-current impedance of the target secondary battery using the learned neural network model,

the learned neural network model is a neural network model that has been learned using nyquist plots of a plurality of secondary batteries whose full charge capacities are within a reference range,

the manufacturing method of the battery pack further includes:

a step of discriminating which of a group 1 in which a full charge capacity is within the reference range and a group 2 in which the full charge capacity is outside the reference range the target secondary battery belongs to by discrimination analysis using a feature quantity extracted from a nyquist diagram of the target secondary battery as an explanatory variable;

estimating a full charge capacity of the target secondary battery using the learned neural network model when it is determined that the target secondary battery belongs to the group 1; and

a step of manufacturing a battery pack using a plurality of the target secondary batteries of which full charge capacities are estimated by the estimating step.

Technical Field

The present disclosure relates to a battery information processing system, a capacity estimation method of a secondary battery, a battery pack, and a manufacturing method of the battery pack, and more particularly, to an information processing technique for estimating a full charge capacity of a secondary battery.

Background

In recent years, electric vehicles (hybrid vehicles, electric vehicles, and the like) equipped with a battery pack have been spreading. With the re-purchase of these electric vehicles, the battery packs mounted on the vehicles are collected. The number of collected battery packs is expected to increase rapidly in the future.

In general, a battery pack may deteriorate with the passage of time or repeated charge and discharge, but the degree of deterioration may vary for each collected battery pack. Therefore, it is required to evaluate the collected battery packs for characteristics (full charge capacity, etc.) reflecting the degree of progress of the deterioration, and reuse the battery packs based on the evaluation results.

As a method for evaluating the characteristics of a secondary battery, an alternating current impedance measurement method is known. For example, japanese patent laid-open publication No. 2003-317810 discloses the following method: the presence or absence of a micro short circuit in the secondary battery is determined based on the reaction resistance value of the secondary battery obtained by the ac impedance measurement method.

Disclosure of Invention

The characteristics particularly important for reflecting the degree of deterioration of the secondary battery include the full charge capacity of the secondary battery. This is because, for example, in a battery pack for a vehicle, the full charge capacity of the battery pack has a large influence on the travelable distance of an electric vehicle.

In general, a vehicle-mounted battery pack is configured by including a plurality of (e.g., several to ten) modules, each of which includes a plurality of (e.g., several ten) battery cells. In the estimation of the full charge capacity of the battery pack, the following procedure is considered. That is, a plurality of modules are taken out from the collected battery pack, and ac impedance is measured for each module. Then, the full charge capacity of each module is estimated based on the ac impedance measurement result of the module. In addition, based on the estimation result of the full charge capacity of the module, it is possible to determine whether or not the module is reusable, or a mode (use) of the reuse.

In the ac impedance measuring method, an ac signal having a frequency within a predetermined range is sequentially applied to a secondary battery, and a response signal of the secondary battery at that time is measured. The real component and the imaginary component of the impedance of the secondary battery are calculated from the applied alternating current signal (applied signal) and the measured response signal, and the calculation results are discretely plotted on a complex plane. This complex impedance plot is also referred to as a nyquist plot.

By analyzing the nyquist diagram, the full charge capacity of the secondary battery can be estimated. As will be described later in detail, various methods are conceivable as the nyquist diagram analysis method, and it is preferable to adopt a method capable of estimating the full charge capacity of the secondary battery as accurately as possible.

The present disclosure has been made to solve the above-described problems, and an object thereof is to improve the estimation accuracy of the full charge capacity of a secondary battery in a battery information system or a capacity estimation method of a secondary battery. Another object of the present disclosure is to provide a battery pack including a battery whose full charge capacity is estimated with high accuracy, and a method for manufacturing the battery pack.

(1) A battery information processing system according to an aspect of the present disclosure includes: a storage device that stores the learned neural network model; and an estimation device for estimating the full charge capacity of the secondary battery to be charged from a Nyquist diagram representing the measurement result of the AC impedance of the secondary battery, using the learned neural network model. The learned neural network model is a neural network model that has been learned based on nyquist plots of a plurality of secondary batteries whose full charge capacities are within a reference range. The estimation device determines which of group 1 in which the full charge capacity is within the reference range and group 2 in which the full charge capacity is outside the reference range the target secondary battery belongs to by discriminant analysis using at least one feature quantity extracted from a nyquist diagram of the target secondary battery as an explanatory variable. The estimation device estimates the full charge capacity of the target secondary battery using the learned neural network model when it is determined that the target secondary battery belongs to the group 1.

(2) The learned neural network model is a neural network model that has been learned based on nyquist plots of a plurality of secondary batteries whose full charge capacities exceed a reference capacity that is a lower limit value of a reference range. Group 1 is a group of secondary batteries whose full charge capacity exceeds a reference capacity. Group 2 is a group of secondary batteries whose full charge capacity is lower than the reference capacity.

(3) The at least one characteristic quantity includes an imaginary component of the alternating current impedance at a predetermined frequency included in a straight line portion of a semicircular portion and a straight line portion of a nyquist diagram of the target secondary battery, and an inclination of the straight line portion.

(4) The neural network model includes an input layer provided with a numerical value of each pixel of an image obtained by plotting a Nyquist diagram of the secondary battery in a region of a predetermined number of pixels. The number of pixels in the region is larger than the sum of the number of real components and the number of imaginary components representing the measurement result of the ac impedance of the secondary battery.

(5) The nyquist diagram of the target secondary battery includes the ac impedance measurement result when the frequency of the applied ac signal is in the frequency range of 100mHz to 1 kHz.

In the above-described configurations (1) to (5), the discrimination of the target secondary battery as to which of the group 1 and the group 2 the target secondary battery belongs is performed by the discrimination analysis of the target secondary battery. When the target secondary battery belongs to the group 1 as a result of the determination, the detailed full charge capacity of the target secondary battery is estimated using the learned neural network model. The learned neural network model is a neural network model that has been learned using only nyquist diagrams of secondary batteries belonging to the 1 st group, and the nyquist diagrams of secondary batteries belonging to the 2 nd group are not used for learning. Therefore, it can be said that the estimation of the full charge capacity of the secondary batteries belonging to the group 1 is most suitable as compared with the case where both the nyquist diagram of the secondary batteries belonging to the group 1 and the nyquist diagram of the secondary batteries belonging to the group 2 are used for learning. Therefore, according to the configurations (1) to (5), the full charge capacity of the secondary battery can be estimated with high accuracy.

(6) A battery pack according to another aspect of the present disclosure includes a plurality of secondary batteries whose full charge capacity is estimated by the battery information processing system.

According to the configuration of the above (6), it is possible to provide the battery pack including the secondary battery whose full charge capacity is estimated with high accuracy by the battery information system.

(7) A full charge capacity estimation method for a secondary battery according to another aspect of the present disclosure estimates a full charge capacity of a target secondary battery. The method for estimating the full charge capacity of a secondary battery includes steps 1 to 4. The 1 st step is a step of acquiring a nyquist diagram showing a measurement result of ac impedance of the target secondary battery. The 2 nd step is a step of estimating the full charge capacity of the target secondary battery from the ac impedance measurement result of the target secondary battery using the learned neural network model. The learned neural network model is a neural network model that has been learned based on nyquist plots of a plurality of secondary batteries whose full charge capacities are within a reference range. The 3 rd step is a step of discriminating which of the 1 st group having the full charge capacity within the reference range and the 2 nd group having the full charge capacity outside the reference range the target secondary battery belongs to by discrimination analysis using the feature quantity extracted from the nyquist diagram of the target secondary battery as an explanatory variable. The 4 th step is a step of estimating the full charge capacity of the target secondary battery using the learned neural network model when it is determined that the target secondary battery belongs to the 1 st group.

According to the method of the above (7), the accuracy of estimating the full charge capacity of the secondary battery can be improved in the same manner as the configuration of the above (1).

(8) A method for manufacturing a battery pack according to still another aspect of the present disclosure includes steps 1 to 5. The 1 st step is a step of obtaining a measurement result of the ac impedance of the target secondary battery. The 2 nd step is a step of estimating the full charge capacity of the target secondary battery from the ac impedance measurement result of the target secondary battery using the learned neural network model. The learned neural network model is a neural network model that has been learned based on nyquist plots of a plurality of secondary batteries whose full charge capacities are within a reference range. The 3 rd step is a step of discriminating which of the 1 st group having the full charge capacity within the reference range and the 2 nd group having the full charge capacity outside the reference range the target secondary battery belongs to by discrimination analysis using the feature quantity extracted from the nyquist diagram of the target secondary battery as an explanatory variable. The 4 th step is a step of estimating the full charge capacity of the target secondary battery using the learned neural network model when it is determined that the target secondary battery belongs to the 1 st group. The 5 th step is a step of manufacturing a battery pack using a plurality of target secondary batteries whose full charge capacity is estimated by the estimation step (the 4 th step).

According to the method of the above (8), it is possible to manufacture a battery pack including a secondary battery whose full charge capacity is estimated with high accuracy in the same manner as the configuration of the above (6).

The above and other objects, features, aspects and advantages of the present invention will become apparent from the following detailed description of the present invention, which is to be read in connection with the accompanying drawings.

Drawings

Fig. 1 is a diagram showing one mode of a material flow from collection to production and sale of a battery pack in the present embodiment.

Fig. 2 is a flowchart showing a process flow in the battery logistics model shown in fig. 1.

Fig. 3 is a diagram showing an example of the configuration of a battery management system applied to the battery logistics model shown in fig. 1.

Fig. 4 is a diagram showing the configuration of the battery information system.

Fig. 5 is a diagram showing an example of a nyquist diagram of ac impedance measurement results of modules.

Fig. 6 is a conceptual diagram for explaining learning of a neural network model in a comparative example.

Fig. 7 is a diagram for explaining a learning image.

Fig. 8 is a diagram for explaining an example of the capacity estimation result of the module in the comparative example.

Fig. 9 is a graph showing a relationship between a measurement result of the nyquist diagram and the full charge capacity.

Fig. 10 is a diagram for explaining discriminant analysis in the present embodiment.

Fig. 11 is a conceptual diagram for explaining learning of the neural network model in the present embodiment.

Fig. 12 is a flowchart showing a learning method of a neural network model in the present embodiment.

Fig. 13 is a flowchart showing a capacity estimation process of the module in the present embodiment.

Fig. 14 is a diagram for explaining an example of the capacity estimation result of the module in the present embodiment.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding portions are denoted by the same reference numerals, and description thereof will not be repeated.

In the present disclosure, a battery pack is configured by including a plurality of modules (or also referred to as blocks). The plurality of modules may be connected in series or may be connected in parallel with each other. Each of the plurality of modules includes a plurality of battery cells (cells) connected in series.

In the present disclosure, "manufacturing" of the battery pack means manufacturing the battery pack by replacing at least a part of a plurality of modules constituting the battery pack with another module (replacement module). Basically, the replacement module is a reusable module taken out from the recovered battery pack, but may be a new module.

Generally, the "reuse" of a battery pack is roughly divided into reuse (reuse), rebuild (rebuild), and recycle (recycle). In the case of reuse, the collected battery pack is shipped as a reuse product after necessary shipment inspection. In the case of reconfiguration, the recovered battery pack is temporarily disassembled into modules (or battery cells), for example. Then, of the disassembled modules, the modules that can be used after the performance recovery (or the modules that can be used directly) are combined to manufacture a new battery pack. The newly manufactured battery pack is shipped as a rebuilt product after shipment inspection. In contrast, in the recycling (resource recycling), since the recyclable materials are taken out from the respective battery cells, the collected battery packs are not used as other battery packs.

In the embodiment described below, the battery pack collected from the vehicle is temporarily disassembled into modules, and then performance inspection is performed on a module-by-module basis. The battery pack is manufactured from the modules determined to be reusable as a result of the performance check. Therefore, in the following, a reusable module means a reconfigurable module. However, depending on the configuration of the battery pack, the performance of the battery pack may be checked directly without disassembling the battery pack into modules. "reuse" in that case may include both reuse and reconstruction.

In the present embodiment, each battery cell is a nickel metal hydride battery. More specifically, the positive electrode is nickel (Ni (OH) hydroxide ₂) And a positive electrode to which an additive of cobalt oxide is added. The negative electrode is a hydrogen storage alloy (MnNi 5 series as a nickel series alloy). The electrolyte is potassium hydroxide (KOH). However, this is merely an example of a specific battery cell configuration, and the battery cell configuration to which the present disclosure is applicable is not limited thereto.