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 2019-07-24 设计创作，主要内容包括：本公开涉及电池信息处理系统、二次电池的容量推定方法、电池组以及该电池组的制造方法。电池信息处理系统(200)对用于推定模块(M)的满充电容量的信息进行处理。电池信息处理系统(200)具备：存储装置(220),其存储已学习的神经网络模型；和解析装置(230),其使用已学习的神经网络模型,根据模块(M)的交流阻抗测定结果推定二次电池的满充电容量。已学习的神经网络模型包括输入层(x),所述输入层(x)被提供推定用图像的各像素的数值的输入层(x),所述推定用图像是在预先确定的像素数的区域描绘了表示模块(M)的交流阻抗测定结果的奈奎斯特图而得到的图像。(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. A battery information processing system (200) processes information for estimating the full charge capacity of a module (M). A battery information processing system (200) is provided with: a storage device (220) that stores the learned neural network model; and an analysis device (230) that estimates the full charge capacity of the secondary battery from the alternating current impedance measurement result of the module (M) using the learned neural network model. The learned neural network model includes an input layer (x) to which a numerical value of each pixel of an estimation image is supplied, the estimation image being an image in which a Nyquist diagram representing the measurement result of the alternating current impedance of the module (M) is drawn in a region of a predetermined number of pixels.)

1. A battery information processing system for processing information for estimating a full charge capacity of a secondary battery, comprising:

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

an estimating device that estimates a full charge capacity of the secondary battery from an alternating current impedance measurement result of the secondary battery using the learned neural network model,

the learned neural network model includes an input layer to which a numerical value of each pixel of an image in which a Nyquist diagram representing a result of measurement of alternating current impedance of the secondary battery is plotted in a region of a predetermined number of pixels is provided.

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

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.

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

the ac impedance measurement result of the secondary battery includes an ac impedance measurement result in a case where the frequency of the applied ac signal is in a frequency range of 100mHz or more and 1kHz or less.

4. 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 3.

5. A capacity estimation method for a secondary battery includes:

obtaining a measurement result of the ac impedance of the secondary battery; and

estimating a full charge capacity of the secondary battery from an alternating current impedance measurement result of the secondary battery using the learned neural network model,

the learned neural network model includes an input layer to which a numerical value of each pixel of an image in which a Nyquist diagram representing a result of measurement of alternating current impedance of the secondary battery is plotted in a region of a predetermined number of pixels is provided.

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

obtaining a measurement result of the ac impedance of the secondary battery;

estimating a full charge capacity of the secondary battery from an ac impedance measurement result of the secondary battery using a learned neural network model; and

a step of manufacturing a battery pack using a plurality of the secondary batteries of which full charge capacity is estimated by the estimating step,

the learned neural network model includes an input layer to which a numerical value of each pixel of an image in which a Nyquist diagram representing a result of measurement of alternating current impedance of the secondary battery is plotted in a region of a predetermined number of pixels is provided.

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 processes information for estimating a full charge capacity of a secondary battery. The battery information processing system includes: a storage device that stores the learned neural network model; and an estimation device that estimates the full charge capacity of the secondary battery from the ac impedance measurement result of the secondary battery using the learned neural network model. The learned neural network model includes an input layer to which a numerical value of each pixel of an image in which a nyquist diagram representing a result of measuring the ac impedance of the secondary battery is plotted in a region of a predetermined number of pixels is provided.

(2) 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.

(3) The ac impedance measurement result of the secondary battery includes the ac impedance measurement result in the case where the frequency of the applied ac signal is in the frequency range of 100mHz to 1 kHz.

According to the configurations (1) to (3) described above, an image (numerical data of each pixel of the image) in which a nyquist diagram is drawn in a region of a predetermined number of pixels is supplied to the input layer as input information. The amount of input information provided to the input layer is significantly larger than in the case where the numerical data of the ac impedance measurement result of the module is used as it is (comparative example 2 described later). This means that: when learning a neural network in advance, input of information that can represent a difference in full charge capacity between a certain secondary battery and another secondary battery is increased, and highly accurate learning of the neural network can be achieved (details will be described later). Therefore, the accuracy of estimating the full charge capacity of the secondary battery can be improved.

(4) A battery pack according to an 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 (4), it is possible to provide a battery pack including a secondary battery whose full charge capacity is estimated with high accuracy by a battery information system.

(5) A capacity estimation method for a secondary battery according to another aspect of the present disclosure includes: obtaining a measurement result of the ac impedance of the secondary battery; and estimating a full charge capacity of the secondary battery from the ac impedance measurement result of the secondary battery using the learned neural network model. The learned neural network model includes an input layer to which a numerical value of each pixel of an image in which a nyquist diagram representing a result of measuring the ac impedance of the secondary battery is plotted in a region of a predetermined number of pixels is provided.

According to the method of the above (5), similarly to the configuration of the above (1), the estimation accuracy of the full charge capacity of the secondary battery can be improved.

(6) A method of manufacturing a battery pack according to still another aspect of the present disclosure includes: obtaining a measurement result of the ac impedance of the secondary battery; estimating a full charge capacity of the secondary battery from an ac impedance measurement result of the secondary battery using the learned neural network model; and a step of manufacturing a battery pack using a plurality of secondary batteries of which full charge capacity is estimated by the estimating step. The learned neural network model includes an input layer to which a numerical value of each pixel of an image in which a nyquist diagram representing a result of measuring the ac impedance of the secondary battery is plotted in a region of a predetermined number of pixels is provided.

According to the method of the above (6), similarly to the configuration of the above (4), it is possible to manufacture a battery pack including a secondary battery whose full charge capacity is estimated with high accuracy.

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 flowchart showing a capacity estimation process of the module in comparative example 1.

Fig. 6 is a diagram showing an example of a nyquist diagram of the ac impedance measurement result of the module.

Fig. 7 is a diagram showing an equivalent circuit model of the module in comparative example 1.

Fig. 8 is a diagram showing an impedance curve obtained by fitting processing of the ac impedance measurement results of the modules.

Fig. 9 is a diagram for explaining an example of the capacity estimation accuracy of the module in comparative example 1.

Fig. 10 is a conceptual diagram for explaining neural network model learning in comparative example 2.

Fig. 11 is a diagram for explaining an example of the capacity estimation accuracy of the module in comparative example 2.

Fig. 12 is a conceptual diagram for explaining machine learning of the neural network model in the present embodiment.

Fig. 13 is a flowchart showing learning of the neural network model in the present embodiment.

Fig. 14 is a flowchart showing a capacity estimation process of the module in the present embodiment.

Fig. 15 is a diagram for explaining an example of the capacity estimation accuracy 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), recyclable materials are taken out from the respective battery cells, and therefore, 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.