Power battery sampling abnormality identification device, method and battery swap station

Abstract

The application provides a power battery sampling anomaly identification device, a power battery sampling anomaly identification method and a battery swap station. The device comprises the following modules connected in communication: a data acquisition module configured to acquire sampling data of the power battery within a preset time window; a probability calculation module configured to acquire fluctuation values of the sampling data at adjacent time points and calculate a probability that a fluctuation value greater than a preset fluctuation value threshold appears within the preset time window; and a judgment module configured to judge whether the power battery sampling is abnormal based on the probability. If the probability is greater than a preset probability threshold or the probability is less than the probability threshold and a change rate of the probability with respect to time is greater than a preset change rate threshold, the judgment module determines that the power battery sampling is abnormal. Through longitudinal comparison of the power battery sampling data with respect to time, the influence of external factors can be reduced, and thus the anomaly identification accuracy is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to a device and a method for identifying abnormal sampling of a power battery and a battery swap station capable of performing such identification. BACKGROUND

[0002] For a power battery, such as a lithium ion battery, of a new energy vehicle, relevant parameters of the power battery need to be collected in real time during use of the power battery, including relevant parameters at a power battery level or relevant parameters at a power battery component level, so as to timely grasp the working state of the power battery and control the power battery based on the working state. Collection of the relevant parameters of the power battery is realized by a BMS (Battery Management System) system integrated.

[0003] With an increase in the service life of the power battery, the failure probability of the sampling function of the power battery increases accordingly. If the sampling function of the power battery fails, information is missing, which may cause false positives of the power battery and may cause safety problems such as overcharging and overdischarging of the power battery and may even cause an explosion in a serious case. SUMMARY

[0004] According to different aspects, the present application aims to provide a device and a method for identifying abnormal sampling of a power battery and a battery swap station provided with such a device.

[0005] In addition, the present application also aims to solve or alleviate other technical problems existing in the prior art.

[0006] The present application solves the above problems by providing a device for identifying abnormal sampling of a power battery, which specifically comprises the following modules in communication connection:

[0007] A data acquisition module configured to acquire sampling data of the power battery within a preset time window;

[0008] A probability calculation module configured to acquire a fluctuation value of the sampling data at an adjacent time and calculate a probability of the fluctuation value greater than a preset fluctuation value threshold appearing within the preset time window;

[0009] A judgment module configured to judge whether the sampling of the power battery is abnormal based on the probability, and if the probability is greater than a preset probability threshold or the probability is less than the probability threshold and a change rate of the probability with respect to time is greater than a preset change rate threshold, the judgment module determines that the sampling of the power battery is abnormal.

[0010] The power battery sampling abnormality identification device according to an aspect of the present application further comprises a probability threshold value determination module configured to determine the probability threshold value by means of a statistical method and transmit the probability threshold value to the determination module.

[0011] The power battery sampling abnormality identification device according to an aspect of the present application, the probability threshold value determination module is configured to call historical sampling data of the power battery and determine the probability threshold value based on the historical sampling data, or the probability threshold value determination module is configured to call reference sampling data of a reference power battery of the same type and determine the probability threshold value based on the reference sampling data.

[0012] The power battery sampling abnormality identification device according to an aspect of the present application, the sampling data acquired by the data acquisition module carries overall information about the power battery as a whole or carries cell information about the cells of the power battery.

[0013] The power battery sampling abnormality identification device according to an aspect of the present application further comprises a time window determination module configured to determine a preset time window depending on the working state of the power battery and transmit the determined preset time window to the data acquisition module in a signal manner.

[0014] The power battery sampling abnormality identification device according to an aspect of the present application, the time window determination module is configured to set the preset time window in the power battery charging and discharging state to be smaller than the preset time window in the power battery resting state.

[0015] The power battery sampling abnormality identification device according to an aspect of the present application, the sampling data carries voltage information, temperature information or current information.

[0016] The power battery sampling abnormality identification device according to an aspect of the present application further comprises a pre-warning module communicatively connected with the determination module and capable of sending a pre-warning signal in response to the determination of the power battery sampling abnormality.

[0017] According to another aspect of the present application, a power battery sampling abnormality identification method executable by the device is provided, comprising the following steps:

[0018] Acquiring sampling data of the power battery within a preset time window;

[0019] Within the preset time window, acquiring fluctuation values of the sampling data at adjacent time points, and calculating a probability that a fluctuation value greater than the fluctuation value threshold value occurs within the preset time window;

[0020] determining that the power battery sampling is abnormal in response to the probability being greater than the probability threshold, or in response to the probability being less than the probability threshold and a rate of change of the probability with respect to time being greater than the change rate threshold.

[0021] According to another aspect of the present application, a method for identifying power battery sampling abnormality is provided, wherein the probability threshold is obtained based on the historical sampling data or the reference sampling data by means of a statistical method

[0022] According to another aspect of the present application, a method for identifying power battery sampling abnormality is provided, wherein the preset time window is set depending on the working state of the power battery, and the preset time window in the power battery charging and discharging state is smaller than the preset time window in the power battery resting state.

[0023] According to still another aspect of the present application, a battery swap station for performing charging and swapping operation on a power battery is provided, wherein the power battery sampling abnormality identification device of the type described above is provided in the battery swap station or in a cloud server associated with the battery swap station.

[0024] According to still another aspect of the present application, the battery swap station comprises a storage unit for storing relevant information about the serviced power battery, and the power battery sampling abnormality identification device can call the relevant information as the reference sampling data

[0025] By means of the longitudinal comparison with respect to time based on the power battery sampling data, the power battery sampling abnormality identification device according to the present application can reduce the influence of external factors and thereby improve the accuracy of abnormality identification. BRIEF DESCRIPTION OF DRAWINGS

[0026] The above and other features of the present application will become apparent from specific embodiments thereof which will be described with reference to the attached drawings, wherein:

[0027] Figure 1 a schematic block diagram of a power battery sampling abnormality identification device according to the present application is shown;

[0028] Figure 2 a flow chart of a method for identifying power battery sampling abnormality according to the present application is shown. DETAILED DESCRIPTION

[0029] It is readily understood that the technical solution according to the present application can be implemented in various ways and structures which can be substituted for each other without changing the spirit of the present application, and the skilled in the art can easily make such substitutions. Therefore, the following detailed description and the drawings are merely exemplary and should not be considered as the whole or as limiting the technical solution of the present application.

[0030] The orientation terms mentioned or possibly mentioned in the present specification, such as up, down, left, right, front, back, front side, back side, top, bottom, etc., are defined with respect to the configuration shown in the drawings, and are relative concepts, so they can change accordingly according to different positions, different use states, etc. Therefore, these or other orientation terms should not be interpreted as restrictive terms. In addition, the terms "first", "second", "third", etc. or similar expressions are only used for description and differentiation purposes, and cannot be understood as indicating or implying the relative importance of the corresponding components.

[0031] Reference Figure 1 which shows an embodiment of the power battery sampling identification device 100 according to the present application in a block diagram, which mainly includes a data acquisition module 110, a probability calculation module 120, and a judgment module 130. The data acquisition module 110 can be directly connected with the BMS system of the power battery for acquiring the sampling data for a preset time window; or the data acquisition module 110 is connected with the vehicle bus for acquiring the sampling data collected by the BMS system stored therein. The probability calculation module 120 is configured to receive and process the acquired sampling data to know the fluctuation of the sampling data, specifically, it is configured to calculate the proportion of the sampling data whose fluctuation degree does not conform to the regulation. From a mathematical point of view, the probability calculation module 120 is configured to acquire the fluctuation value of the sampling data at adjacent time and calculate the probability that the case where the fluctuation value is greater than the preset fluctuation value threshold occurs within the preset time window. Then, the judgment module 130 judges whether the power battery meets the preset condition according to the received probability and thereby judges whether there is a sampling anomaly, specifically, if the received probability exceeds the preset probability threshold, it is determined that the power battery has a sampling anomaly; or the change rate of the received probability with respect to time exceeds the preset change rate threshold, it is determined that the power battery has a sampling anomaly. Here, the change rate of the probability essentially reflects the failure or aging problem of the power battery sampling hardware, because in a healthy state, the change rate of the probability within the preset time window is basically stable or not too large.

[0032] Correspondingly, the power battery sampling anomaly identification method according to the present application performed by such a device mainly includes the following steps:

[0033] S1: acquiring the sampling data of the power battery within a preset time window by means of the above-mentioned data acquisition module 110;

[0034] S2: within the preset time window, acquiring the fluctuation value of the sampling data at adjacent time by means of the above-mentioned probability calculation module 120, and calculating the probability that the fluctuation value greater than the fluctuation value threshold occurs within the preset time window;

[0035] S3: In the judging module 130, the probability is compared with a probability threshold value and the rate of change of the probability is compared with a rate of change threshold value, if the probability is greater than the probability threshold value or the probability is less than the probability threshold value and the rate of change of the probability with respect to time is greater than the rate of change threshold value, step S4 is executed: determining that the power battery sampling is abnormal.

[0036] First of all, it should be pointed out that the present application is based on the data from the power battery sampling component (generally, the power battery BMS system) to determine whether the sampling component has an abnormality in sampling function, and the present application aims to identify the functional failure of the power battery sampling hardware, mainly involving the abnormality of inaccurate sampling, and does not involve the complete failure of the sampling component so as to be unable to continue to work, because this situation can be directly identified based on the judgment basis of whether there is sampling data.

[0037] Here, the sampling data involves any type of sampling data carrying power battery state information, which can exemplarily involve sampling data carrying overall information about the power battery as a whole, or sampling data carrying information about any level of components (such as modules, each cell or a group of cells) of the power battery, especially cell information about a certain cell. The sampling data can optionally carry voltage information, temperature information or current information, in other words, the sampling data can be directly collected voltage data, current data or temperature data of the object to be detected (such as the power battery as a whole or a certain cell thereof). In addition, it should be pointed out that the above defined "fluctuation value of the sampling data at adjacent time points" involves the same type of data of the same object to be detected and should be understood as such, taking the voltage data of a certain cell as an example, that is, within the preset time window, the difference or the absolute value of the difference between the voltage value of the cell at the t+1 time point and the voltage value at the t time point.

[0038] The fluctuation value applied herein only relates to one object to be detected or the same object to be detected, for example, the same numbered "n" battery cell, and the identification process involves longitudinal comparison of the sampling data with respect to time. Exemplarily, the fluctuation value only relates to the voltage difference of a certain battery cell between different time points, without involving the transverse comparison between this battery cell and other battery cells. It is conceivable that if other battery cells have faults, for example, the resistance changes, the voltage value directly measured by the BMS system will change accordingly, in which case, if the acquired voltage data is compared with the voltage data of the other battery cell, the fault of the other battery cell will indirectly reflect on the identification accuracy of the specific battery cell and reduce the identification accuracy. Unlike this, according to the present application, only under the premise of longitudinal comparison, within the preset time window, the voltage fluctuation degree of the battery cell essentially reflects the defect of the sampling device hardware (that is, reflects the sampling anomaly of the power battery BMS system itself), which is basically not affected by the state change of the battery cell itself.

[0039] In addition, when the sampling data relates to the battery cell information of a certain battery cell, the sampling anomaly identification process can be traversed for each battery cell, and thus the specific position of the sampling hardware where the sampling anomaly occurs can be obtained.

[0040] Further, instead of identifying the power battery sampling anomaly according to the transverse comparison at a certain time point, according to the present application, the use of the sampling data within the preset time window excludes the contingency due to the too short time and realizes the statistical observation of the sampling data of the same object to be detected, thereby further improving the accuracy of the sampling anomaly identification. In practice, the time span of the preset time window can be optionally designed according to the working state of the power battery, which involves the charging state, the discharging state or the static state, wherein in the static state, the power battery does not or basically does not perform the discharging or charging operation. For a vehicle equipped with a power battery, in the static state, the starter or other components are mainly powered by other additional power sources with lower battery capacity. Here, the working state of the power battery can also be extended to the working state of the vehicle accordingly.

[0041] Optionally, the preset time window for the charging state or discharging state of the power battery is set relatively small compared to the resting state of the power battery, mainly for the consideration that, for example, the cell voltage of the power battery changes rapidly in the charging / discharging state and better recognition accuracy can be achieved based on the sampling data within a short time range. In contrast, in the resting state, the cell voltage or other parameters change relatively slowly, so that the preset time window associated with the resting state can be designed to be relatively large. For example, the preset time window is set to 1 minute for the power battery in the charging state, and the preset time window is set to 5 minutes for the power battery in the resting state. By setting the preset time window depending on the working state of the power battery, a balance between data processing load and sampling anomaly recognition accuracy can be achieved. Accordingly, the power battery sampling anomaly recognition device according to the present application also has a time window determination module 150 configured to set the preset time window in the manner described above and transmit it to the data acquisition module 110 in the form of a signal.

[0042] Of course, the preset time window can also be designed to be the same for any working state of the power battery and can be set to an average value according to experience. The determined preset time window can be directly input into the data acquisition module or stored in the time window determination module 150.

[0043] In a possible embodiment, the sampling data of the power battery in a certain determined working state can be acquired by means of the data acquisition module 110, in which case the preset time window is set to a fixed time span, and the probability threshold also has a fixed value and is stored in the judgment module 130. Specifically, before step S1, the sampling data of the power battery in a certain working state within a certain time span is acquired by pre-screening and cleaning the sampling data, and subsequent identification of sampling anomalies is based on this sampling data.

[0044] In another possible embodiment, the sampling data within any time period can be acquired in real time and the preset time window and the probability threshold are determined based on the working state of the power battery at that time, wherein the preset time window can be set in the manner described above, and the probability threshold relates to a statistical probability threshold. Accordingly, the power battery sampling anomaly recognition device 100 has a probability threshold determination module 140 in communication with the judgment module 130, so as to transmit the probability threshold determined by means of statistical methods to the judgment module 130.

[0045] It should be noted that the probability threshold determined by statistical method can be understood as follows: when the sampling data carries the battery information of a certain battery, the probability threshold is obtained by averaging the probability of the fluctuation degree of the historical sampling data of the battery under the same working condition, or by averaging the probability defined by the above method of a plurality of (especially a large number of) power battery cells of the same type. In other words, the probability threshold involves the past historical sampling data of the object to be detected, or involves the reference sampling data of the same type as the object to be detected. When the sampling data obtained in step S1 involves the overall information of the power battery, it can be calculated based on the historical sampling data of the power battery or based on the reference data of a plurality of (especially a large number of) reference power batteries of the same type, and obtained in particular in the form of average or weighted average. It should be understood that the sampling data and the historical sampling data or reference sampling data matched therewith involve the same type of data.

[0046] Optionally, the acquisition and processing of the historical sampling data and the reference sampling data for determining the probability threshold can be performed on any device that can meet the operation conditions. When the power battery sampling anomaly identification device 100 is integrated on the vehicle, the probability threshold determination module 140 retrieves the historical sampling data or the reference sampling data from the cloud under the condition that the calculation load is allowed, and then processes it at the vehicle end. Of course, this data processing process can also be processed in the cloud close to the data source and subsequently transmitted and stored in the probability threshold determination module 140.

[0047] In another optional embodiment, the power battery sampling abnormality identification device 100 can be arranged in the battery swap station according to the application, and the battery swap station can obtain the corresponding historical sampling data from the cloud or the vehicle side. When the reference sampling data relates to the information of the battery cell, the battery swap station, especially the probability threshold determination module 140, can call the reference sampling data from the cloud, especially the cloud belonging to the battery swap station. If the reference sampling relates to the overall information of the power battery, the battery swap station, especially the probability threshold determination module 140, can not only obtain the reference sampling data from the cloud, especially the cloud belonging to the battery swap station, but also directly call the relevant reference sampling data from the control system of the battery swap station. Such reference sampling data can relate to the historical order information of the battery swap station, that is, the power battery that has undergone charging or battery swap operation in the battery swap station (i.e. the reference power battery). Generally, when the battery swap station charges or swaps the power battery, it will store the relevant data of the power battery in the storage unit belonging to the battery swap station, which is arranged in the control system of the battery swap station or in the cloud of the battery swap station. The application of the relevant data ensures the diversity of the reference sampling data for the probability threshold determination process and improves the credibility of the probability threshold, thereby further improving the identification accuracy of the power battery sampling abnormality.

[0048] Optionally, the power battery abnormality identification device 100 further comprises a warning module 160, which is in communication connection with the judgment module 130 and can optionally send a warning signal according to the received judgment result. The warning signal can be directly displayed to the driver or transmitted to the vehicle controller in the form of a signal, or can also be provided to the battery swap station control system in the form of a signal so that the battery swap station maintenance personnel can timely repair. Specifically, for the battery swap station, if the power battery sampling abnormality can be known in time or in advance, the overcharging caused by information loss or information error can be avoided, and the operation safety can be improved. After receiving the warning signal, the maintenance personnel can take measures to ensure the safety of the power battery and the vehicle, such as limiting battery charging, limiting power battery service, or manual intervention or maintenance, etc. Here, the real-time automatic interaction between the battery swap station and the power battery can be fully utilized to timely warn and dispose the power battery, and the efficiency of battery operation and maintenance is improved.

[0049] Correspondingly, the power battery sampling abnormality identification method according to the application also comprises step S5: if the judgment module determines that the power battery has sampling abnormality, a warning signal is sent, which is displayed to the driver or the vehicle controller or the battery swap station control system, especially the maintenance personnel of the battery swap station, which will be described later.

[0050] Finally, the application also provides a battery swap station for performing charging and swapping operation on power batteries, in which the power battery sampling and identifying device described above is arranged or the battery swap station can perform the sampling and abnormality identifying method for vehicle power batteries described above. As to the battery swap station according to the application, the description of the power battery sampling and abnormality identifying device and the power battery sampling and abnormality identifying method according to the application can be referred to accordingly, and no further description is given here.

[0051] In summary, based on the longitudinal comparison of the power battery sampling data with respect to time, the influence of external factors can be reduced and thus the abnormality identifying accuracy can be improved. In an embodiment of the application, by setting the preset time window depending on the working state of the power battery, the balance between the data processing load and the sampling abnormality identifying accuracy can be achieved. In another embodiment of the application, the sampling data can involve different types of sampling data at any level of the power battery, thus the applicability of the power battery sampling and abnormality identifying device and method according to the application can be improved. In another embodiment of the application, the power battery sampling and abnormality identifying device is arranged in the battery swap station, the maintenance personnel of the battery swap station can be timely informed by the early warning module, and thus the safety problem caused by information loss or information error can be avoided. In another embodiment of the application, by utilizing the real-time interaction between the battery swap station and the power battery end, the battery can be timely warned and disposed, thus the efficiency and safety of the battery operation and maintenance can be improved. In another embodiment of the application, the historical sampling data or the reference sampling data is applied when determining the probability threshold, i.e. the present stage data is combined with the historical data, thus the sampling abnormality can be identified at a relatively early stage.

[0052] It should be understood that all the above preferred embodiments are exemplary but not limiting, and various modifications or variations of the above described specific embodiments made by those skilled in the art under the concept of the application shall be within the legal protection scope of the application.

Claims

1. A power battery sampling anomaly identification device, characterized in that, The following modules are included for communication connectivity: The data acquisition module is configured to acquire sampled data from the power battery within a preset time window. The probability calculation module is configured to acquire the fluctuation values ​​of sampled data at adjacent time points and calculate the probability of fluctuation values ​​exceeding a preset fluctuation value threshold occurring within the preset time window. The judgment module is configured to determine whether the power battery sampling is abnormal based on the probability. If the probability is greater than a preset probability threshold, or if the probability is less than the probability threshold and the rate of change of the probability with respect to time is greater than a preset rate of change threshold, then the judgment module determines that the power battery sampling is abnormal. The power battery sampling anomaly identification device further includes a probability threshold determination module, which is configured to determine the probability threshold using statistical methods and transmit the probability threshold to the judgment module. The probability threshold determination module is configured to call up historical sampling data of the power battery and determine the probability threshold based on the historical sampling data, or the probability threshold determination module is configured to call up reference sampling data of a reference power battery of the same type and determine the probability threshold based on the reference sampling data.

2. The power battery sampling anomaly identification device according to claim 1, characterized in that, The sampling data acquired by the data acquisition module carries overall information about the power battery as a whole or cell information about the cells of the power battery.

3. The power battery sampling anomaly identification device according to claim 1, characterized in that, It also includes a time window determination module, which is configured to determine a preset time window based on the operating state of the power battery and transmit the determined preset time window to the data acquisition module in the form of a signal.

4. The power battery sampling anomaly identification device according to claim 3, characterized in that, The time window determination module is configured to set a preset time window for the power battery in the charging and discharging state to be smaller than the preset time window for the power battery in the idle state.

5. The power battery sampling anomaly identification device according to any one of claims 1 to 4, characterized in that, The sampled data carries voltage, temperature, or current information.

6. The power battery sampling anomaly identification device according to any one of claims 1 to 4, characterized in that, It also includes an early warning module, which is communicatively connected to the judgment module and issues an early warning signal accordingly when the power battery sampling is determined to be abnormal.

7. A method for identifying abnormal sampling in a power battery, which can be executed by a power battery sampling abnormality identification device according to any one of claims 1 to 6, characterized in that, Includes the following steps: Acquire sampling data of the power battery within a preset time window; Within the preset time window, the fluctuation values ​​of the sampled data at adjacent times are obtained, and the probability of fluctuation values ​​greater than the fluctuation value threshold occurring within the preset time window is calculated. If the probability is greater than the probability threshold, or if the probability is less than the probability threshold and the rate of change of the probability with respect to time is greater than the rate of change threshold, then the power battery sampling is determined to be abnormal.

8. The method for identifying abnormal sampling of power batteries according to claim 7, characterized in that, The probability threshold is obtained using statistical methods based on the historical sampling data or the reference sampling data.

9. The method for identifying abnormal sampling of power batteries according to claim 7 or 8, characterized in that, The preset time window is set according to the working state of the power battery, and the preset time window in the charging and discharging state of the power battery is smaller than the preset time window in the resting state of the power battery.

10. A battery swapping station, characterized in that, It is used for charging and swapping power batteries, and the power battery sampling anomaly identification device according to any one of claims 1 to 6 is provided in the power battery swapping station or in the cloud associated with the power battery swapping station.

11. The battery swapping station according to claim 10, characterized in that, The battery swapping station includes a storage unit for storing relevant information about the servicing power batteries, and the power battery sampling anomaly identification device can call up the relevant information as the reference sampling data.

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