WO2024055548A1 - 动力电池梯度利用筛选方法、装置、设备及存储介质 - Google Patents
动力电池梯度利用筛选方法、装置、设备及存储介质 Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000012216 screening Methods 0.000 title claims abstract description 34
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- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000001514 detection method Methods 0.000 claims description 22
- 238000004590 computer program Methods 0.000 claims description 16
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Definitions
- This application relates to the technical field of battery detection, for example, to a power battery gradient utilization screening method, device, equipment and storage medium.
- the cascading utilization of batteries is to maximize the use of the product and extend its design cycle. It not only creates economic value for the society, but also reduces waste emissions for the society. It is a kind of circularity that should be vigorously promoted in modern society. , low-carbon production and lifestyle.
- This application provides a power battery gradient utilization screening method, device, equipment and storage medium, which can improve detection efficiency and detection accuracy when detecting a large number of battery cells.
- the first aspect of the embodiment of the present application provides a power battery gradient utilization screening method.
- the method includes: acquiring in real time the infrared image collected by the long-wave infrared instrument during the charging process of the battery pack, and the battery pack is a retired battery pack;
- Grid processing is performed on infrared images at multiple different times to obtain a gridded infrared image corresponding to the infrared image at each time, and determine the value of each grid in the gridded infrared image at each time.
- Root mean square temperature obtain the root mean square temperature of each grid at multiple different times;
- the second aspect of the embodiment of the present application provides a power battery gradient utilization screening device.
- Settings include:
- the acquisition module is set to acquire in real time the infrared image collected by the long-wave infrared instrument during the charging process of the battery pack, and the battery pack is a retired battery pack;
- the processing module is configured to perform grid processing on infrared images at multiple different times, obtain a gridded infrared image corresponding to the infrared image at each time, and determine each time in the gridded infrared image at each time.
- the root mean square temperature of each grid is obtained to obtain the root mean square temperature of each grid at multiple different times;
- the determination module is set to obtain the average temperature of the infrared image at each time, and determine each grid based on the root mean square temperature of each grid at multiple different times and the average temperature of the infrared image to which each grid belongs. Whether the battery cells corresponding to the grid are qualified, among which the qualified battery cells are used for gradient utilization.
- the third aspect of the embodiment of the present application provides an electronic device.
- the device includes a memory and a processor.
- the memory stores a computer program.
- the computer program is executed by the processor, the power battery gradient utilization screening method in the first aspect of the embodiment of the present application is implemented. .
- a fourth aspect of the embodiments of the present application provides a computer-readable storage medium.
- a computer program is stored on the computer-readable storage medium.
- the power battery gradient in the first aspect of the embodiments of the present application is realized. Utilize screening methods.
- Figure 1 is a flow chart of a power battery gradient utilization screening method provided by an embodiment of the present application
- Figure 2 is a schematic diagram of temperature detection of a battery pack provided by an embodiment of the present application.
- Figure 3 is a grid-processed thermal image provided by an embodiment of the present application.
- Figure 4 is a structural diagram of a power battery gradient utilization screening device provided by an embodiment of the present application.
- first and second are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as “first” and “second” may explicitly or implicitly include one or more of these features. In this disclosure In the description of the embodiments, unless otherwise specified, “plurality” means two or more.
- the cascading utilization of batteries is to maximize the use of the product and extend its design cycle. It not only creates economic value for the society, but also reduces waste emissions for the society. It is a kind of circularity that should be vigorously promoted in modern society. , low-carbon production and lifestyle.
- the power battery gradient utilization screening method obtains infrared images collected by the long-wave infrared instrument during the charging process of the battery pack in real time.
- the battery pack is a retired battery pack, and the battery pack is used for multiple different times.
- the infrared images are gridded to obtain gridded infrared images corresponding to each infrared image, and the root mean square temperature of each grid in the gridded infrared image at each time is determined to obtain each grid.
- Root mean square temperature at multiple different times by obtaining the average temperature of the infrared image at each moment, according to the root mean square temperature of each grid at multiple different times and the infrared image to which each grid belongs
- the average temperature of each grid is determined to determine whether the battery cells corresponding to each grid are qualified, and qualified battery cells are determined for gradient utilization.
- the power battery gradient utilization screening method provided by the embodiment of the present application grids the infrared image of the battery pack, and each grid corresponds to the battery cell, so that the detection can be improved when a large number of battery cells are detected. efficiency and detection accuracy.
- the execution subject of the power battery gradient utilization screening method provided by the embodiment of the present application can be an electronic device.
- the electronic device can be a computer device, a terminal device, a control chip or a micro control unit, etc.
- embodiments of the present application provide a power battery gradient utilization screening method. As shown in Figure 1, the method includes the following steps:
- Step 101 Obtain in real time the infrared image collected by the long-wave infrared instrument during the charging process of the battery pack.
- the battery pack is a retired battery pack. It can be understood that in the process of sequential utilization of batteries, it is necessary to conduct status detection on the battery cells in the recycled retired battery packs, and reassemble the battery cells that have passed the test into battery packs for reuse.
- the process of charging the battery pack can be as follows: before starting to charge, first detect the battery voltage. If the battery voltage is lower than the threshold voltage (about 2.5V), then use a small current of C/10 to trickle the battery. Charging makes the battery voltage rise slowly; when the battery voltage reaches the threshold voltage, it enters the constant state. Current charging, during this stage, the battery is quickly charged with a larger current (0.5C ⁇ 1C). The battery voltage rises quickly, and the battery capacity will reach about 85% of its rated value; when the battery voltage rises to the upper limit voltage ( 4.2V), the circuit switches to constant voltage charging mode, the battery voltage is basically maintained at 4.2V, the charging current gradually decreases, and the charging speed slows down. This stage is mainly to ensure that the battery is fully charged. When the charging current drops to 0.1C or 0.05 C, the battery is judged to be fully charged.
- a long-wave infrared instrument is used to monitor the temperature of the entire battery, as shown in Figure 2.
- Set the working frame rate of the long-wave infrared instrument to 2fps or you can set it according to the actual situation.
- Step 102 Perform gridding processing on multiple infrared images at different times to obtain a gridded infrared image corresponding to the infrared image at each time, and determine each of the gridded infrared images at each time.
- the root mean square temperature of the grid is obtained by obtaining the root mean square temperature of each grid at multiple different times.
- Step 103 Obtain the average temperature of the infrared image at each time, and determine the corresponding temperature of each grid based on the root mean square temperature of each grid at multiple different times and the average temperature of the infrared image to which each grid belongs. Whether the battery cells are qualified, among which, qualified battery cells are used for gradient utilization.
- the power battery gradient utilization screening method obtaineds infrared images collected by a long-wave infrared instrument during the charging process of the battery pack in real time.
- the battery pack is a retired battery pack, and the infrared images at multiple different times are networked.
- Grid processing is performed to obtain a gridded infrared image corresponding to the infrared image at each time, and the root mean square temperature of each grid of the gridded infrared image at each time is determined, and the multi-dimensional temperature of each grid is obtained.
- Root mean square temperature at different times obtain the average temperature of the infrared image at each time, based on the root mean square temperature of each grid at multiple different times and the average temperature of the infrared image to which each grid belongs , determine whether the battery cells corresponding to each grid are qualified, and determine the qualified battery cells for gradient utilization.
- the power battery gradient utilization screening method provided by the embodiment of the present application grids the infrared image of the battery pack, and each grid corresponds to the battery cell, so that the detection can be improved when a large number of battery cells are detected. efficiency and detection accuracy.
- perform grid processing on infrared images at multiple different times including:
- the distance between the long-wave infrared sensor and the battery pack, the focal length of the long-wave infrared sensor and the pixel size of the long-wave infrared sensor determine the grid size to be divided; according to the grid size, combine the infrared images at multiple different times The image is divided into multiple grid images.
- determine the root mean square temperature of each grid in the gridded infrared image at each time including: obtaining the root mean square temperature of the infrared image at each time before gridding and the edges of the grid. long; according to each The root mean square temperature of the infrared image at each time before gridding and the side length of the grid are used to determine the root mean square temperature of each grid in the gridded infrared image at each time.
- the root mean square temperature of each grid is the temperature of each grid.
- the root mean square calculation can be performed on the image acquired by the thermal imaging camera, as shown in the following formula.
- T(m,n,k) is the root mean square temperature of the grid
- N is the total number of pixels in the grid
- N [(a*f)/(b*c)]2
- t i is the temperature of each pixel before gridding
- (m, n) is the coordinate after gridding, corresponding to the single battery
- k is the image sequence related to the shooting time.
- determine whether the battery cell corresponding to each grid is qualified based on the root mean square temperature of each grid at multiple different times and the average temperature of the infrared image to which each grid belongs including: Based on the infrared image at a time, multiple candidate grids are determined from the grid based on the root mean square temperature of each grid and the average temperature of the infrared image to which each grid belongs; for each candidate grid , based on the root mean square temperature of each candidate grid at multiple different times, determine whether the battery cell corresponding to each candidate grid is qualified.
- multiple candidate grids are determined from each grid based on the root mean square temperature of each grid and the average temperature of the infrared image to which each grid belongs, including: for each grid , determine the temperature difference between the root mean square temperature of the grid and the average temperature of the infrared image to which the grid belongs, to obtain the temperature difference; determine the ratio of the temperature difference to the average temperature, obtain the temperature ratio; in response to the temperature ratio being less than the first Preset a threshold to determine the grid as a candidate grid.
- determining whether the battery cell corresponding to each candidate grid is qualified based on the detected expected value and the preset expected value includes: determining the difference between the detected expected value and the preset expected value to obtain the expected difference; determining the expected difference The ratio to the preset expected value, and in response to the ratio being less than the second preset threshold, it is determined that the battery cell corresponding to the candidate grid is qualified.
- the average temperature of the infrared image is recorded as W(k), where k is the image sequence related to the shooting time. like Then the battery cell corresponding to T(m,n,k) is unqualified and discarded directly. It should be noted that 30% is related to gradient utilization. If subsequent utilization requires lower battery performance, this indicator can be relaxed to, for example, 50%.
- the root mean square temperature at different times is used to obtain the temperature change curve of each candidate grid; the expected value of the second-order derivative of the temperature change curve is determined to obtain the detection expected value; based on the detection expected value and the preset expected value, each candidate grid is determined Whether the corresponding battery cell is qualified.
- the power battery gradient utilization screening method obtaineds infrared images collected by a long-wave infrared instrument during the charging process of the battery pack in real time.
- the battery pack is a retired battery pack, and the infrared images at multiple different times are networked.
- Grid processing is performed to obtain a gridded infrared image corresponding to the infrared image at each time, and the root mean square temperature of each grid in the gridded infrared image at each time is determined, and the value of each grid is obtained.
- Root mean square temperature at multiple different times obtain the average temperature of the infrared image at each time, based on the root mean square temperature of each grid at multiple different times and the average temperature of the infrared image to which each grid belongs temperature, determine whether the battery cells corresponding to each grid are qualified, and determine the qualified battery cells for gradient utilization.
- the power battery gradient utilization screening method provided by the embodiment of the present application grids the infrared image of the battery pack, and each grid corresponds to the battery cell, so that the detection can be improved when a large number of battery cells are detected. efficiency and detection accuracy.
- the power battery gradient utilization screening method provided by the embodiment of the present application can monitor the temperature of a large number of battery cells at one time through a thermal imager, and can efficiently and quickly screen the battery gradient utilization, and flexibly adjust the thermal imager data.
- the algorithm threshold can realize gradient utilization screening of power batteries in different scenarios.
- the embodiment of the present application also provides a power battery gradient utilization screening device, which includes:
- the acquisition module 11 is configured to acquire in real time the infrared image collected by the long-wave infrared instrument during the charging process of the battery pack, and the battery pack is a retired battery pack;
- the processing module 12 is configured to perform grid processing on infrared images at multiple different times, obtain a gridded infrared image corresponding to the infrared image at each time, and determine the number of elements in the gridded infrared image at each time.
- the root mean square temperature of each grid is obtained by obtaining the root mean square temperature of each grid at multiple different times;
- the determination module 13 is configured to obtain the average temperature of the infrared image at each time, and determine each grid based on the root mean square temperature of each grid at multiple different times and the average temperature of the infrared image to which each grid belongs. Whether the battery cells corresponding to the grid are qualified, where qualified battery cells are used for gradient utilization.
- the processing module 12 is configured to perform grid processing on infrared images at multiple different times in the following manner:
- the distance between the long-wave infrared detector and the battery pack, and the focal length of the long-wave infrared detector and the pixel size of the long-wave infrared instrument to determine the grid size to be divided;
- the processing module 12 is configured to determine the root mean square temperature of each grid in the gridded infrared image at each moment in the following manner:
- the root mean square temperature of each grid in the gridded infrared image at each time is determined.
- the determination module 13 is configured to calculate the root mean square temperature of the grid at each time at multiple different times and the average temperature of the infrared image to which each grid belongs in the following manner, Determine whether the battery cell corresponding to each grid is qualified:
- multiple candidate grids are determined from the grid based on the root mean square temperature of each grid and the average temperature of the infrared image to which each grid belongs;
- each candidate grid determine whether the battery cell corresponding to each candidate grid is qualified based on the root mean square temperature of each candidate grid at multiple different times.
- the determination module 13 is configured to determine whether the battery cell corresponding to each of the candidate grids is based on the root mean square temperature of each of the candidate grids at multiple different times in the following manner. qualified:
- the temperature change curve of each candidate grid is obtained based on the root mean square temperature of each candidate grid at multiple different times;
- the determination module 13 is configured to determine a plurality of parameters from the grid based on the root mean square temperature of each grid and the average temperature of the infrared image to which each grid belongs.
- For each grid determine the temperature difference between the root mean square temperature of the grid and the average temperature of the infrared image to which the grid belongs, and obtain the temperature difference;
- the grid is determined as the candidate grid.
- the determination module 13 is configured to determine whether the battery cell corresponding to each of the candidate grids is qualified according to the detected expected value and the preset expected value in the following manner:
- a ratio of the expected difference to the preset expected value is determined, and in response to the ratio being less than the second preset threshold, it is determined that the battery cell corresponding to the candidate grid is qualified.
- the power battery gradient utilization screening device provided in this embodiment can perform the above method embodiments. Its implementation principles and technical effects are similar, and will not be described in detail here.
- Each module in the above-mentioned power battery gradient utilization screening device can be implemented in whole or in part by software, hardware and combinations thereof.
- Each of the above modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.
- an electronic device including a memory and a processor.
- the memory stores a computer program.
- the computer program is executed by the processor, the steps of the power battery gradient utilization screening method according to the embodiment of the present application are implemented. .
- a computer-readable storage medium is also provided, on which a computer program is stored.
- the computer program is executed by a processor, the steps of the power battery gradient utilization screening method according to the embodiment of the present application are implemented.
- a computer program product is also provided.
- the computer program product includes computer instructions.
- the power battery gradient utilization screening device executes the above method embodiment.
- the method flow shown is the various steps performed by the screening method for power battery gradients.
- the computer program product includes one or more computer instructions.
- the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable device.
- Computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, e.g., computer instructions may be transmitted from a website, computer, server or data center via a wired (e.g.
- Coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless means to transmit to another website, computer, server or data center.
- Computer-readable storage media can be any available media that can be accessed by a computer or include one or more data storage devices such as servers and data centers that can be integrated with the media. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid state disks, SSD)) etc.
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Abstract
一种动力电池梯度利用筛选方法、装置、设备及存储介质，该方法包括：实时获取长波红外仪采集的电池组充电过程中的红外图像，电池组为退役的电池组（步骤101）；对多个不同时刻下的红外图像进行网格化处理，得到与每个时刻下的红外图像对应的网格化红外图像，并确定每个时刻下的网格化红外图像中每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度（步骤102）；获取每个时刻下的红外图像的平均温度，根据每个网格在多个不同时刻下的均方根温度以及每个网格所属的红外图像的平均温度，确定每个网格对应的电池单体是否合格，其中，合格的电池单体用于梯度利用（步骤103）。
Description
本申请要求在2022年09月16日提交中国专利局、申请号为202211126018.7的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
本申请涉及电池检测技术领域，例如涉及一种动力电池梯度利用筛选方法、装置、设备及存储介质。
电池的梯次利用的是使产品得到了最大限度地使用，其设计周期得到了延长，为社会创造了经济价值的同时，也为社会减少了垃圾排放，是现代社会应大力推行的一种循环性、低碳型生产生活方式。
在电池的梯次利用过程中，需要对回收的退役电池组中的电池单体进行状态检测，将通过检测的电池单体重新组装成电池组用于再次利用，但相关技术中的检测方法在进行大量的电池单体的检测时存在效率较低且准确性不高的问题。
发明内容
本申请提供一种动力电池梯度利用筛选方法、装置、设备及存储介质，能够在进行大量的电池单体的检测时，提高检测效率和检测的准确性。
本申请实施例第一方面，提供了一种动力电池梯度利用筛选方法，该方法包括：实时获取长波红外仪采集的电池组充电过程中的红外图像，电池组为退役的电池组；
对多个不同时刻下的红外图像进行网格化处理，得到与每个时刻下的红外图像对应的网格化红外图像，并确定每个时刻下的网格化红外图像中每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度；
获取每个时刻下的红外图像的平均温度，根据每个网格在多个不同时刻下的均方根温度以及每个网格所属的红外图像的平均温度，确定每个网格对应的电池单体是否合格，其中，合格的电池单体用于梯度利用。
本申请实施例第二方面，提供了在一种动力电池梯度利用筛选装置，该装
置包括：
获取模块，设置为实时获取长波红外仪采集的电池组充电过程中的红外图像，电池组为退役的电池组；
处理模块，设置为对多个不同时刻下的红外图像进行网格化处理，得到与每个时刻下的红外图像对应的网格化红外图像，并确定每个时刻下网格化红外图像中每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度；
确定模块，设置为获取每个时刻下的红外图像的平均温度，根据每个网格在多个不同时刻下的均方根温度以及每个网格所属的红外图像的平均温度，确定每个网格对应的电池单体是否合格，其中，合格的电池单体用于梯度利用。
本申请实施例第三方面提供一种电子设备，该设备包括存储器和处理器，存储器存储有计算机程序，计算机程序被处理器执行时实现本申请实施例第一方面中的动力电池梯度利用筛选方法。
本申请实施例第四方面，提供一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，计算机程序被处理器执行时实现本申请实施例第一方面中的动力电池梯度利用筛选方法。
图1为本申请实施例提供的一种动力电池梯度利用筛选方法的流程图；
图2为本申请实施例提供的一种电池组的温度检测示意图；
图3为本申请实施例提供的一种网格化处理后的热像图；
图4为本申请实施例提供的一种动力电池梯度利用筛选装置的结构图。
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
以下，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多个该特征。在本公开实
施例的描述中，除非另有说明，“多个”的含义是两个或两个以上。
另外，“基于”或“根据”的使用意味着开放和包容性，因为“基于”或“根据”一个或多个条件或值的过程、步骤、计算或其他动作在实践中可以基于额外条件或超出的值。
电池的梯次利用的是使产品得到了最大限度地使用，其设计周期得到了延长，为社会创造了经济价值的同时，也为社会减少了垃圾排放，是现代社会应大力推行的一种循环性、低碳型生产生活方式。
在电池的梯次利用过程中，需要对回收的退役电池组中的电池单体进行状态检测，将通过检测的电池单体重新组装成电池组用于再次利用，但相关技术中的检测方法在进行大量的电池单体的检测时存在效率较低且准确性不高的问题。
为了解决上述问题，本申请实施例提供的动力电池梯度利用筛选方法，通过实时获取长波红外仪采集的电池组充电过程中的红外图像，电池组为退役的电池组，并对多个不同时刻下的红外图像进行网格化处理，得到与每个红外图像对应的网格化红外图像，并确定每个时刻下网格化红外图像中每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度；通过获取每个时刻下的红外图像的平均温度，根据每个网格在多个不同时刻下的均方根温度以及每个网格所属的红外图像的平均温度，确定每个网格对应的电池单体是否合格，确定出合格的电池单体用于梯度利用。本申请实施例提供的动力电池梯度利用筛选方法，通过将电池组的红外图像进行网格化处理，每个网格与电池单体对应，这样在进行大量的电池单体的检测时能够提高检测效率和检测的准确性。
本申请实施例提供的动力电池梯度利用筛选方法的执行主体可以为电子设备，可选的，该电子设备可以为计算机设备，终端设备、控制芯片或微控制单元等。
基于上述执行主体，本申请实施例提供一种动力电池梯度利用筛选方法。如图1所示，该方法包括以下步骤：
步骤101、实时获取长波红外仪采集的电池组充电过程中的红外图像。
本实施例中，电池组为退役的电池组。可以理解的是，在电池的梯次利用过程中，需要对回收的退役电池组中的电池单体进行状态检测，将通过检测的电池单体重新组装成电池组用于再次利用。
本实施例中，对电池组进行充电的过程可以为：在开始充电之前，首先检测电池电压，若电池电压低于门限电压(2.5V左右)，则以C/10的小电流对电池进行涓充充电，使电池电压缓慢上升；当电池电压达到门限电压时，进入恒
流充电，在此阶段以较大的电流(0.5C～1C)强度对电池进行快速充电，电池电压上升较快，电池容量将达到其额定值的85％左右；在电池电压上升到上限电压(4.2V)后，电路切换到恒压充电模式，电池电压基本维持在4.2V，充电电流逐渐减小，充电速度变慢，这一阶段主要是保证电池充满，当充电电流降到0.1C或0.05C时，即判定电池充满。
充电过程中，采用长波红外仪对整个电池进行温度监测，如图2所示。设定长波红外仪工作帧频为2fps或者可以根据实际情况进行设置。
步骤102、对多个不同时刻下的红外图像进行网格化处理，得到与每个时刻下的红外图像对应的网格化红外图像，并确定每个时刻下的网格化红外图像中每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度。
步骤103、获取每个时刻下的红外图像的平均温度，根据每个网格在多个不同时刻下的均方根温度以及每个网格所属的红外图像的平均温度，确定每个网格对应的电池单体是否合格，其中，合格的电池单体用于梯度利用。
本申请实施例提供的动力电池梯度利用筛选方法，通过实时获取长波红外仪采集的电池组充电过程中的红外图像，电池组为退役的电池组，并对多个不同时刻下的红外图像进行网格化处理，得到与每个时刻下的红外图像对应的网格化红外图像，并确定每个时刻下的网格化红外图像每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度；获取每个时刻下的红外图像的平均温度，根据每个网格在多个不同时刻下的均方根温度以及每个网格所属的红外图像的平均温度，确定每个网格对应的电池单体是否合格，确定出合格的电池单体用于梯度利用。本申请实施例提供的动力电池梯度利用筛选方法，通过将电池组的红外图像进行网格化处理，每个网格与电池单体对应，这样在进行大量的电池单体的检测时能够提高检测效率和检测的准确性。
可选的，对多个不同时刻下的红外图像进行网格化处理，包括：
根据电池单体的尺寸、长波红外仪距离电池组的距离、长波红外仪的焦距和长波红外仪的像元尺寸，确定待划分的网格尺寸；根据网格尺寸将多个不同时刻下的红外图像划分为多个网格图像。
示例性的，假定电池单体尺寸为a，热像仪距离电池组的距离为b，热像仪焦距为f，热像仪像元尺寸为c，则每个网格对应的像素数量N＝[(a*f)/(b*c)]2。
因此对热像仪获取的二维图进行以d＝(a*f)/b为边长的网格划分，即每个网格对应一个电池单体，如图3所示。
可选的，确定每个时刻下的网格化红外图像中每个网格的均方根温度，包括：获取每个时刻下的红外图像网格化前的均方根温度和网格的边长；根据每
个时刻下的红外图像网格化前的均方根温度和网格的边长，确定每个时刻下的网格化红外图像中每个网格的均方根温度。
其中，每个网格的均方根温度即为每个网格的温度。示例性的，可以对热像仪获取到的图像进行均方根运算，如下式所示。
其中，T(m,n,k)为网格的均方根温度，N为网格内的总像元个数，N＝[(a*f)/(b*c)]2，ti为网格化前每一个像元的温度，(m，n)为网格化后的坐标，与单体电池相对应，k为图像序列与拍摄时间相关。
可选的，根据每个网格在多个不同时刻下的均方根温度以及每个网格所属的红外图像的平均温度，确定每个网格对应的电池单体是否合格，包括：针对每个时刻下的红外图像，根据每个网格的均方根温度和每个网格所属的红外图像的平均温度，从网格中确定出多个备选网格；针对每个备选网格，根据每个备选网格在多个不同时刻下的均方根温度，确定每个备选网格对应的电池单体是否合格。
本实施例中，根据每个网格的均方根温度和每个网格所属的红外图像的平均温度，从每个网格中确定出多个备选网格，包括：针对每个网格，确定网格的均方根温度与网格所属的红外图像的平均温度之间的温度差值，得到温度差；确定温度差与平均温度的比值，得到温度比；响应于温度比小于第一预设阈值，将网格确定为备选网格。
本实施例中，根据检测期望值和预设期望值，确定每个备选网格对应的电池单体是否合格，包括：确定检测期望值与预设期望值的差值，得到期望差值；确定期望差值与预设期望值的比值，响应于比值小于第二预设阈值，确定备选网格对应的电池单体合格。
示例性的，红外图像的平均温度记为W(k)，其中k为图像序列与拍摄时间相关。若则T(m,n,k)对应的电池单体不合格，直接舍去。需要说明的是30％与梯度利用有关，若后续利用对电池性能要求较低，该指标可放宽至例如50％。
可选的，根据每个备选网格在多个不同时刻下的均方根温度，确定每个备选网格对应的电池单体是否合格，包括：根据每个备选网格在多个不同时刻下的均方根温度得到每个备选网格的温度变化曲线；确定温度变化曲线的二阶导的期望值，得到检测期望值；根据检测期望值和预设期望值，确定每个备选网格对应的电池单体是否合格。
示例性的，可以绘制每一个网格温度随时间变化的曲线，对其求一阶导T'(m,n,k)，再求二阶导T”(m,n,k)，并计算二阶导的期望E(m,n)，若则判定(m，n)对应的单体电池不合格，直接舍去。需要说明的是，30％与梯度利用有关，若后续利用对电池性能要求较低，该指标可放宽至例如50％。
本申请实施例提供的动力电池梯度利用筛选方法，通过实时获取长波红外仪采集的电池组充电过程中的红外图像，电池组为退役的电池组，并对多个不同时刻下的红外图像进行网格化处理，得到与每个时刻下的红外图像对应的网格化红外图像，并确定每个时刻下的网格化红外图像中每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度；获取每个时刻下的红外图像的平均温度，根据每个网格在多个不同时刻下的均方根温度以及每个网格所属的红外图像的平均温度，确定每个网格对应的电池单体是否合格，确定出合格的电池单体用于梯度利用。本申请实施例提供的动力电池梯度利用筛选方法，通过将电池组的红外图像进行网格化处理，每个网格与电池单体对应，这样在进行大量的电池单体的检测时能够提高检测效率和检测的准确性。
本申请实施例提供的动力电池梯度利用筛选方法，通过热像仪可一次性实现对大量电池单体的温度监测，可高效、快速对电池梯度利用进行筛选，并通过灵活调整热像仪数据的算法阈值，可实现针对不同场景下的动力电池梯度利用筛选。
如图4所示，本申请实施例还提供了一种动力电池梯度利用筛选装置，该装置包括：
获取模块11，设置为实时获取长波红外仪采集的电池组充电过程中的红外图像，电池组为退役的电池组；
处理模块12，设置为对多个不同时刻下的红外图像进行网格化处理，得到与每个时刻下的红外图像对应的网格化红外图像，并确定每个时刻下网格化红外图像中每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度；
确定模块13，设置为获取每个时刻下的红外图像的平均温度，根据每个网格在多个不同时刻下的均方根温度以及每个网格所属的红外图像的平均温度，确定每个网格对应的电池单体是否合格，其中，合格的电池单体用于梯度利用。
在一个实施例中，处理模块12设置为通过如下方式对多个不同时刻下的红外图像进行网格化处理：
根据电池单体的尺寸、长波红外仪距离电池组的距离、长波红外仪的焦距
和长波红外仪的像元尺寸，确定待划分的网格尺寸；
根据网格尺寸将多个不同时刻下的红外图像划分为多个网格图像。
在一个实施例中，处理模块12设置为通过如下方式确定每个时刻下的所述网格化红外图像中每个网格的均方根温度：
获取每个时刻下的红外图像网格化前的均方根温度和网格的边长；
根据每个时刻下的红外图像网格化前的均方根温度和网格的边长，确定每个时刻下的网格化红外图像中每个网格的均方根温度。
在一个实施例中，确定模块13设置为通过如下方式根据每个时刻下的所述网格在多个不同时刻下的均方根温度以及每个所述网格所属的红外图像的平均温度，确定每个所述网格对应的电池单体是否合格：
针对每个时刻下的红外图像，根据每个网格的均方根温度和每个网格所属的红外图像的平均温度，从网格中确定出多个备选网格；
针对每个备选网格，根据每个备选网格在多个不同时刻下的均方根温度，确定每个备选网格对应的电池单体是否合格。
在一个实施例中，确定模块13设置为通过如下方式根据每个所述备选网格在多个不同时刻下的均方根温度，确定每个所述备选网格对应的电池单体是否合格：
根据每个备选网格在多个不同时刻下的均方根温度得到每个备选网格的温度变化曲线；
确定温度变化曲线的二阶导的期望值，得到检测期望值；
根据检测期望值和预设期望值，确定每个备选网格对应的电池单体是否合格。
在一个实施例中，确定模块13设置为通过如下方式根据每个所述网格的均方根温度和每个所述网格所属的红外图像的平均温度，从所述网格中确定出多个备选网格：
针对每个网格，确定网格的均方根温度与网格所属的红外图像的平均温度之间的温度差值，得到温度差；
确定温度差与平均温度的比值，得到温度比；
响应于温度比小于第一预设阈值，将网格确定为备选网格。
在一个实施例中，确定模块13设置为通过如下方式根据所述检测期望值和预设期望值，确定每个所述备选网格对应的电池单体是否合格：
确定检测期望值与预设期望值的差值，得到期望差值；
确定期望差值与预设期望值的比值，响应于比值小于第二预设阈值，确定备选网格对应的电池单体合格。
本实施例提供的动力电池梯度利用筛选装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再多加赘述。
关于动力电池梯度利用筛选装置可以参见上文中对于动力电池梯度利用筛选方法，在此不再赘述。上述动力电池梯度利用筛选装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于电子设备中的处理器中，也可以以软件形式存储于电子设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
本申请的另一实施例中，还提供一种电子设备，包括存储器和处理器，存储器存储有计算机程序，计算机程序被处理器执行时实现如本申请实施例的动力电池梯度利用筛选方法的步骤。
本申请另一实施例中，还提供一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现如本申请实施例的动力电池梯度利用筛选方法的步骤。
本申请另一实施例中，还提供一种计算机程序产品，该计算机程序产品包括计算机指令，当计算机指令在动力电池梯度利用筛选装置上运行时，使得动力电池梯度利用筛选装置执行上述方法实施例所示的方法流程中动力电池梯度利用筛选方法执行的各个步骤。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件程序实现时，可以全部或部分地以计算机程序产品的形式来实现。该计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行计算机执行指令时，全部或部分地产生按照本申请实施例的流程或功能。计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，计算机指令可以从一个网站站点、计算机、服务器或者数据中心通过有线(例如同轴电缆、光纤、数字用户线(digital subscriber line，DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可以用介质集成的服务器、数据中心等数据存储设备。可用介质可以是磁性介质(例如，软盘、硬盘、磁带)，光介质(例如，DVD)、或者半导体介质(例如固态硬盘(solid state disk，
SSD))等。
Claims (10)
- 一种动力电池梯度利用筛选方法，所述方法包括：实时获取长波红外仪采集的电池组充电过程中的红外图像，所述电池组为退役的电池组；对多个不同时刻下的红外图像进行网格化处理，得到与每个时刻下的所述红外图像对应的网格化红外图像，并确定每个时刻下的所述网格化红外图像中每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度；获取每个时刻下的所述红外图像的平均温度，根据每个所述网格在多个不同时刻下的均方根温度以及每个所述网格所属的红外图像的平均温度，确定每个所述网格对应的电池单体是否合格，其中，合格的电池单体用于梯度利用。
- 根据权利要求1所述的方法，其中，所述对多个不同时刻下的红外图像进行网格化处理，包括：根据所述电池单体的尺寸、所述长波红外仪距离所述电池组的距离、所述长波红外仪的焦距和所述长波红外仪的像元尺寸，确定待划分的网格尺寸；根据所述网格尺寸将多个不同时刻下的红外图像划分为多个网格。
- 根据权利要求1所述的方法，其中，所述确定每个时刻下的所述网格化红外图像中每个网格的均方根温度，包括：获取每个时刻下的所述红外图像网格化前的均方根温度和所述网格的边长；根据每个时刻下的所述红外图像网格化前的均方根温度和所述网格的边长，确定每个时刻下的所述网格化红外图像中每个网格的均方根温度。
- 根据权利要求1所述的方法，其中，所述根据每个所述网格在多个不同时刻下的均方根温度以及每个所述网格所属的红外图像的平均温度，确定每个所述网格对应的电池单体是否合格，包括：针对每个时刻下的所述红外图像，根据每个所述网格的均方根温度和每个所述网格所属的红外图像的平均温度，从所述网格中确定出多个备选网格；针对每个所述备选网格，根据每个所述备选网格在多个不同时刻下的均方根温度，确定每个所述备选网格对应的电池单体是否合格。
- 根据权利要求4所述的方法，其中，所述根据每个所述备选网格在多个不同时刻下的均方根温度，确定每个所述备选网格对应的电池单体是否合格，包括：根据每个所述备选网格在多个不同时刻下的均方根温度得到每个所述备选 网格的温度变化曲线；确定所述温度变化曲线的二阶导的期望值，得到检测期望值；根据所述检测期望值和预设期望值，确定每个所述备选网格对应的电池单体是否合格。
- 根据权利要求4所述的方法，其中，所述根据每个所述网格的均方根温度和每个所述网格所属的红外图像的平均温度，从所述网格中确定出多个备选网格，包括：针对每个网格，确定所述网格的均方根温度与所述网格所属的红外图像的平均温度之间的温度差值，得到温度差；确定所述温度差与所述平均温度的比值，得到温度比；响应于所述温度比小于第一预设阈值，将所述网格确定为备选网格。
- 根据权利要求5所述的方法，其中，所述根据所述检测期望值和预设期望值，确定每个所述备选网格对应的电池单体是否合格，包括：确定所述检测期望值与所述预设期望值的差值，得到期望差值；确定所述期望差值与所述预设期望值的比值，响应于所述比值小于第二预设阈值，确定所述备选网格对应的电池单体合格。
- 一种动力电池梯度利用筛选装置，所述装置包括：获取模块，设置为实时获取长波红外仪采集的电池组充电过程中的红外图像，所述电池组为退役的电池组；处理模块，设置为对多个不同时刻下的红外图像进行网格化处理，得到与每个时刻下的所述红外图像对应的网格化红外图像，并确定每个时刻下的所述网格化红外图像中每个网格的均方根温度，得到每个网格在多个不同时刻下的均方根温度；确定模块，设置为获取每个时刻下的所述红外图像的平均温度，根据每个所述网格在多个不同时刻下的均方根温度以及每个所述网格所属的红外图像的平均温度，确定每个所述网格对应的电池单体是否合格，其中，合格的电池单体用于梯度利用。
- 一种电子设备，包括存储器和处理器，所述存储器存储有计算机程序，所述计算机程序被所述处理器执行时实现权利要求1至7任一项所述的动力电池梯度利用筛选方法。
- 一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机 程序，所述计算机程序被处理器执行时实现权利要求1至7任一项所述的动力电池梯度利用筛选方法。
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