Health state determination method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a state-of-health determination method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining a plurality of parameter sets of a battery pack, and enabling the parameter sets to comprise parameter information of each battery in the battery pack, the parameter information comprises one of battery voltage, battery current and battery temperature, and the parameter information in the same parameter set is the same type of parameter information; determining an average value, a standard deviation, kurtosis and skewness corresponding to each type of parameters according to each parameter set; determining a threshold range of each type of parameters according to the average value, the standard deviation, the kurtosis and the skewness corresponding to each type of parameters; and determining the health state of each battery according to the parameter information of each battery and the threshold range of each type of parameter.

Description

Technical Field

[0001] This application relates to the field of electric power, and more particularly, to a method for determining the health status, a storage medium, and an electronic device. Background Art

[0002] With the development of the power system and the wide application of renewable energy, battery energy storage power stations, as an important part of modern energy systems, have the advantages of large energy storage capacity and fast response speed, and are widely used in fields such as power grid dispatching and renewable energy utilization. However, with the continuous increase in the scale of battery energy storage power stations, higher requirements are put forward for the safe operation and maintenance of the power stations.

[0003] First, as the scale of the battery energy storage power station expands, potential safety hazards also increase. There are certain safety risks in the battery units of large-scale battery energy storage systems, such as overheating, overcharging, and short circuits. At the same time, the operation of battery energy storage power stations has high requirements for the environment, and any loss of control may trigger serious accidents. Therefore, it is very necessary to timely and accurately identify faulty batteries, identify abnormal state batteries in time before a fault occurs, and perform necessary maintenance to prevent sudden faults and reduce losses. Second, large-scale battery energy storage systems usually consist of hundreds of thousands of batteries, and the integrated structure is complex.

[0004] Currently, in operation and maintenance, a set value alarm strategy is usually adopted. Certain threshold intervals are set for the main operating parameters of the battery, such as battery voltage, temperature, current, etc., and alarms are issued for batteries that exceed the threshold intervals. However, since the thresholds are set in advance before the power station is put into operation, they cannot flexibly adapt to the state changes during the use of the battery, resulting in a low accuracy rate for determining the health status of the battery.

[0005] In view of the problem of low accuracy rate for determining the health status of the battery in the prior art, no effective solution has been proposed yet.

[0006] Therefore, it is necessary to improve the related technology to overcome the defects in the related technology. Summary of the Invention

[0007] Embodiments of this application provide a method for determining the health status, a storage medium, and an electronic device, so as to at least solve the problem of low accuracy rate for determining the health status of the battery in the prior art.

[0008] According to an embodiment of the present application, a method for determining a health state is provided, including: obtaining a plurality of parameter sets of a battery pack, where the parameter set includes: parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is of the same type of parameter information; determining the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each parameter set; determining the threshold range of each type of parameter according to the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter; and determining the health state of each battery according to the parameter information of each battery and the threshold range of each type of parameter.

[0009] In an exemplary embodiment, determining the threshold range of each type of parameter according to the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter includes: determining the threshold lower limit L of each type of parameter through the following formula lower , including: L lower =μ-(w1 + w2·(k - 3)-w3·s)·σ, where μ is the average value, k is the kurtosis, s is the skewness, σ is the standard deviation, and w1, w2, w3 are weight coefficients; determining the threshold upper limit L of each type of parameter through the following formula upper , including: L upper =μ+(w1 + w2·(k - 3)+w3·s)·σ, where the threshold range includes: the threshold upper limit and the threshold lower limit.

[0010] In an exemplary embodiment, obtaining a plurality of parameter sets of a battery pack includes: obtaining the first parameter information of each battery in the battery pack at the charging cut-off moment, and obtaining the second parameter information of each battery in the battery pack at the discharging cut-off moment; determining the first parameter set of the battery pack according to the first parameter information, and determining the second parameter set of the battery pack according to the second parameter information, where the parameter set includes: the first parameter set and the second parameter set.

[0011] In an exemplary embodiment, determining the mean, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each set of parameters includes: determining a first mean, a first standard deviation, a first kurtosis, and a first skewness corresponding to each type of parameter under a charging condition according to the first set of parameters, and determining a second mean, a second standard deviation, a second kurtosis, and a second skewness corresponding to each type of parameter under a discharging condition according to the second set of parameters; determining a threshold range for each type of parameter according to the mean, standard deviation, kurtosis, and skewness corresponding to each type of parameter, including: determining a first threshold range for each type of parameter under a charging condition according to the first mean, the first standard deviation, the first kurtosis, and the first skewness, and determining a second threshold range for each type of parameter under a discharging condition according to the second mean, the second standard deviation, the second kurtosis, and the second skewness, where the threshold range includes: the first threshold range and the second threshold range.

[0012] In an exemplary embodiment, determining the health state of each battery according to the parameter information of each battery and the threshold range of each type of parameter includes: determining that the health state of the battery is a normal state when the parameter information of each type is within the threshold range of each type of parameter; and determining that the health state of the battery is an abnormal state when the parameter information of any type is not within the threshold range of the corresponding type of parameter.

[0013] In an exemplary embodiment, after determining the health state of each battery according to the parameter information of each battery and the threshold range of each type of parameter, the method further includes: determining the number of batteries with an abnormal health state; determining the abnormal battery ratio in the battery pack according to the number of batteries with an abnormal health state and the total number in the battery pack; and determining the health state of the battery pack according to the abnormal battery ratio.

[0014] In an exemplary embodiment, after determining the health state of the battery pack according to the abnormal battery ratio, the method further includes: determining the maintainability of the battery pack according to the number of batteries with an abnormal health state; and determining the maintenance strategy of the battery pack according to the health state of the battery pack and the maintainability.

[0015] According to another embodiment of the present application, a device for determining a health state is provided, including: an acquisition module, configured to acquire a plurality of parameter sets of a battery pack, where the parameter set includes: parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is of the same type; a first determination module, configured to determine the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each parameter set; a second determination module, configured to determine the threshold range of each type of parameter according to the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter; a third determination module, configured to determine the health state of each battery according to the parameter information of each battery and the threshold range of each type of parameter.

[0016] According to still another embodiment of the present application, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0017] According to still another embodiment of the present application, an electronic device is further provided, including a memory and a processor, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0018] According to still another embodiment of the present application, a computer program product is further provided, including a computer program, and the computer program realizes the steps in any one of the above method embodiments when executed by a processor.

[0019] Through the present application, a plurality of parameter sets of a battery pack are acquired, where the parameter set includes: parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is of the same type; the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter are determined according to each parameter set; the threshold range of each type of parameter is determined according to the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter; the health state of each battery is determined according to the parameter information of each battery and the threshold range of each type of parameter. That is, the embodiments of the present application can accurately judge the health condition of the battery by analyzing the voltage and / or current and / or temperature parameters of each battery in the battery pack and determining the threshold range of the health state. Therefore, the problem of low accuracy in determining the health state of the battery can be solved. Description of the Drawings

[0020] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0021] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 is the hardware structure block diagram of a server device for a method of determining a health status according to an embodiment of the present application;

[0023] Figure 2 is the flowchart (I) of a method of determining a health status according to an embodiment of the present application;

[0024] Figure 3 is the flowchart (II) of a method of determining a health status according to an embodiment of the present application;

[0025] Figure 4 is the schematic diagram (I) of the battery pack status diagnosis result according to an embodiment of the present application;

[0026] Figure 5 is the schematic diagram (II) of the battery pack status diagnosis result according to an embodiment of the present application;

[0027] Figure 6 is the structure block diagram of a device for determining a health status according to an embodiment of the present application. Detailed Embodiments

[0028] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above accompanying drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence.

[0030] The method embodiments provided in the embodiments of the present application can be executed on a server device or a similar computing device. Taking running on a server device as an example, Figure 1 is the hardware structure block diagram of a server device for a method of determining a health status according to an embodiment of the present application. As Figure 1 shown, the server device may include one or more ( Figure 1Only one processor 102 is shown (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a field programmable gate array FPGA), and a memory 104 for storing data. Among them, the above server device may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 The structure shown is only illustrative and does not limit the structure of the above server device. For example, the server device may further include more or fewer components than Figure 1 shown in, or have a different configuration from Figure 1 shown.

[0031] The memory 104 can be used to store computer programs. For example, software programs and modules of application software, such as the computer program corresponding to the method for determining the health status in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely provided with respect to the processor 102, and these remote memories can be connected to the server device through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0032] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by the communication provider of the server device. In one instance, the transmission device 106 includes a network adapter (abbreviated as NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] In this embodiment, a method for determining the health status is provided. Figure 2 is a flowchart (one) of the method for determining the health status according to the embodiments of the present application, as Figure 2 shown, and the process includes the following steps:

[0034] Step S202, obtain multiple parameter sets of the battery pack, where the parameter set includes: parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is of the same type of parameter information;

[0035] It should be noted that each set contains information on a certain type of parameter (such as battery voltage, battery current, battery temperature) of all the batteries in the battery pack. For example, parameter set 1 includes the battery voltage of each battery, parameter set 2 includes the battery current of each battery, and parameter set 3 includes the battery temperature of each battery.

[0036] Step S204: Determine the mean, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each parameter set.

[0037] For each type of parameter set collected, analyze it and calculate the mean, standard deviation, kurtosis, and skewness. These statistics respectively reflect the central tendency, dispersion degree, symmetry, and sharpness of the distribution of the parameters.

[0038] Step S206: Determine the threshold range of each type of parameter according to the mean, standard deviation, kurtosis, and skewness corresponding to each type of parameter.

[0039] Step S208: Determine the health status of each battery according to the parameter information of each battery and the threshold range of each type of parameter.

[0040] By comparing the parameter information of each battery with the threshold range of the corresponding type of parameter, determine the health status of each battery. If the parameters of the battery exceed the threshold range, it indicates that the battery may have health problems.

[0041] Through the above steps, multiple parameter sets of the battery pack are obtained, where the parameter set includes: the parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is the same type of parameter information; determine the mean, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each parameter set; determine the threshold range of each type of parameter according to the mean, standard deviation, kurtosis, and skewness corresponding to each type of parameter; determine the health status of each battery according to the parameter information of each battery and the threshold range of each type of parameter. That is, in the embodiment of the present application, by analyzing the voltage and / or current and / or temperature parameters of each battery in the battery pack, the threshold range of the health status is determined, and the health condition of the battery can be accurately judged. Therefore, the problem of low accuracy in determining the health status of the battery can be solved.

[0042] Optionally, the above step S206 can be implemented in the following manner: Determine the lower threshold L of each type of parameter through the following formula lower , including: L lower= μ - (w1 + w2·(k - 3) - w3·s)·σ, where μ is the mean value, k is the kurtosis, s is the skewness, σ is the skewness, and w1, w2, w3 are weight coefficients; the upper threshold L of the parameter of each type is determined by the following formula upper , including: L upper = μ + (w1 + w2·(k - 3) + w3·s)·σ, where the threshold range includes: the upper threshold and the lower threshold.

[0043] Optionally, the lower threshold L of the voltage parameter is determined by the following formula 电压lower , including: L 电压lower = μ 电压 - (w1 + w2·(k 电压 - 3) - w3·k 电压 )·σ 电压 , where μ 电压 is the voltage mean value, k 电压 is the voltage kurtosis, s 电压 is the voltage skewness, σ 电压 is the voltage skewness; the upper threshold L of the voltage parameter is determined by the following formula upper , including: L 电压upper = μ 电压 + (w1 + w2·(k 电压 - 3) + w3·k 电压 )·σ 电压 , where the threshold range includes: the upper threshold and the lower threshold.

[0044] The lower threshold L of the current parameter is determined by the following formula 电流lower , including: L 电流lower = μ 电流 - (w1 + w2·(k 电流 - 3) - w3·k 电流 )·σ 电流 , where μ 电流 is the current mean value, k 电流 is the current kurtosis, s 电流 is the current skewness, σ 电流 is the current skewness; the upper threshold L of the current parameter is determined by the following formula upper , including: L 电流upper = μ 电流 + (w1 + w2·(k 电流 - 3) + w3·k 电流 )·σ 电流 , where the threshold range includes: the upper threshold and the lower threshold.

[0045] The lower threshold L of the temperature parameter is determined by the following formula温度lower , including: L 温度lower = μ 温度 -(w1 + w2·(k 温度 - 3)- w3·k 温度 )·σ 温度 , where μ 温度 is the average temperature, k 温度 is the kurtosis of temperature, s 温度 is the skewness of temperature, σ 温度 is the skewness of temperature; the upper limit L of the threshold of the temperature parameter is determined by the following formula upper , including: L 温度upper = μ 温度 +(w1 + w2·(k 温度 - 3)+ w3·k 温度 )·σ 温度 , where the threshold range includes: the upper threshold and the lower threshold.

[0046] Optionally, obtain multiple parameter sets of the battery pack, including: obtaining first parameter information of each battery in the battery pack at the charging cut-off moment, and obtaining second parameter information of each battery in the battery pack at the discharging cut-off moment; determining the first parameter set of the battery pack according to the first parameter information, and determining the second parameter set of the battery pack according to the second parameter information, where the parameter set includes: the first parameter set and the second parameter set.

[0047] In the embodiment of the present application, at the end of the battery pack charging, the parameter information of each battery is recorded, and this information usually includes battery voltage, temperature, etc. Different types of data respectively constitute the first parameter set, which is used to analyze the health status of the battery in the fully charged state.

[0048] At the end of the battery pack discharging, the parameter information of each battery is recorded, which also includes battery voltage, temperature, etc. Different types of data respectively constitute the second parameter set, which is used to analyze the health status of the battery in the fully discharged state.

[0049] For example, assume a battery energy storage system composed of 1000 battery cells, and use a simple example to illustrate the construction process of the parameter set:

[0050] When the battery pack is charged to 100% State of Charge (SOC), the system automatically records the voltage and temperature of each battery. Suppose the voltage of the 100th battery is 3.7V and the temperature is 25°C; the voltage of the 200th battery is 3.6V and the temperature is 26°C; and so on until the data of all 1000 batteries are recorded. These data constitute the first parameter set corresponding to the voltage and the first parameter set corresponding to the temperature.

[0051] When the battery pack is discharged to a predetermined SOC (such as 20%), the system automatically records the voltage and temperature of each battery again. Suppose the voltage of the 100th battery after discharge is 3.1V and the temperature is 23°C; the voltage of the 200th battery after discharge is 3.0V and the temperature is 24°C; similarly, until the data of all 1000 batteries are recorded. These data constitute the second parameter set corresponding to the voltage and the second parameter set corresponding to the temperature.

[0052] By constructing and analyzing these parameter sets, the health status of the battery pack can be effectively monitored, abnormal batteries can be detected in a timely manner, and corresponding operation and maintenance measures can be taken, such as adjusting the charging strategy, replacing the battery, etc., to ensure the safe and efficient operation of the battery energy storage system.

[0053] Optionally, determine the mean, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each parameter set, including: determining the first mean, first standard deviation, first kurtosis, and first skewness corresponding to each type of parameter under the charging condition according to the first parameter set, and determining the second mean, second standard deviation, second kurtosis, and second skewness corresponding to each type of parameter under the discharging condition according to the second parameter set; determine the threshold range of each type of parameter according to the mean, standard deviation, kurtosis, and skewness corresponding to each type of parameter, including: determining the first threshold range of each type of parameter under the charging condition according to the first mean, the first standard deviation, the first kurtosis, and the first skewness, and determining the second threshold range of each type of parameter under the discharging condition according to the second mean, the second standard deviation, the second kurtosis, and the second skewness, where the threshold range includes: the first threshold range and the second threshold range.

[0054] Under the charging condition, all the collected battery voltage data or temperature data form the first parameter set, and calculate the mean, standard deviation, kurtosis, and skewness of this set, which are respectively marked as the first mean, first standard deviation, first kurtosis, and first skewness.

[0055] Similarly, under the discharging condition, based on the collected battery voltage data or temperature data, calculate the second mean, second standard deviation, second kurtosis, and second skewness.

[0056] Specifically, for the charging condition, the threshold range is:

[0057] L c,lower = μ c -(w1 + w2·(k c - 3)- w3·s c )·σ c ;

[0058] L c,upper = μ c +(w1 + w2·(k c - 3)+ w3·s c )·σ c ;

[0059] For the discharging condition, the detection threshold range is:

[0060] L d,lower = μ d -(w1 + w2·(k d - 3)- w3·s d )·σ d ;

[0061] L d,upper = μ d +(w1 + w2·(k d - 3)+ w3·s d )·σ d ;

[0062] Among them, w1, w2, and w3 are weighting coefficients. The subscripts c and d respectively represent the charging state and the discharging state, and the subscripts lower and upper respectively represent the threshold lower limit and the threshold upper limit. The values of w1, w2, and w3 need to be determined according to the actual data distribution characteristics to detect a reasonable number of abnormal batteries. For example, w1 ∈ [2, 10], w2 ∈ [0, 1], and w3 ∈ [0, 1] can be set.

[0063] Optionally, the above step S208 can be implemented in the following way: when each type of parameter information is within the threshold range of each type of parameter, determine that the health state of the battery is the normal state; when any type of parameter information is not within the corresponding type of parameter's threshold range, determine that the health state of the battery is the abnormal state.

[0064] By comparing the parameter information of each battery with the corresponding threshold range, the system can determine the health status of the battery in real time. If all types of parameter information are within their corresponding threshold ranges, the system considers the battery to be in a normal state. On the contrary, if at least one type of parameter information exceeds its threshold range, the system determines that the battery is in an abnormal state. The determination of this abnormal state can timely detect potential problems of the battery, such as overheating and over-discharge, so as to take measures in advance to avoid failures.

[0065] The embodiments of the present application can monitor and diagnose the battery in real time and comprehensively, avoiding misjudgment and missed judgment that may be caused by fixed thresholds, and improving the accuracy and timeliness of abnormal detection. In the fields of energy storage systems, electric vehicles, drones and other battery applications that require high reliability and safety, this health status determination technology based on real-time parameters will play an important role, helping to improve the overall performance and service life of the system.

[0066] Optionally, after determining the health status of each battery according to the parameter information of each battery and the threshold range of each type of parameter, it further includes: determining the number of batteries with the health status being abnormal; determining the abnormal battery ratio in the battery pack according to the number of abnormal batteries and the total number in the battery pack; determining the health status of the battery pack according to the abnormal battery ratio.

[0067] In the embodiments of the present application, the method for judging the health status of the entire battery pack based on the health status of a single battery is specifically:

[0068] After determining the health status of each battery, count the total number of batteries in the abnormal state, that is, quantify the number of unhealthy batteries in the battery pack. Next, use the number of abnormal batteries and the total number of batteries in the entire battery pack to calculate the ratio of abnormal batteries. This ratio is an important indicator for measuring the overall health status of the battery pack. The higher the abnormal battery ratio, the worse the health status of the battery pack. Finally, according to the calculated abnormal battery ratio, the health status of the entire battery pack can be further judged. For example, if the abnormal battery ratio is lower than a preset health threshold, the battery pack can be considered healthy as a whole; if the ratio exceeds this threshold, the battery pack is determined to be unhealthy and may need maintenance or battery replacement.

[0069] In an exemplary embodiment, after determining the health status of the battery pack according to the abnormal battery ratio, the method further includes: determining the maintainability of the battery pack according to the number of abnormal batteries; determining the maintenance strategy of the battery pack according to the health status of the battery pack and the maintainability.

[0070] Specifically, determining the maintainability of the battery pack according to the number of abnormal batteries includes:

[0071] When the number of batteries in the abnormal state is less than the preset number, it is determined that the maintainability of the battery pack is good;

[0072] When the number of batteries in the abnormal state is greater than or equal to the preset number, it is determined that the maintainability of the battery pack is poor.

[0073] Specifically, according to the health state of the battery pack and the maintainability, the maintenance strategy of the battery pack is determined, including:

[0074] (1) The battery pack is in a normal state and has good maintainability; the battery pack can be overhauled.

[0075] (2) The battery pack is in a normal state and has poor maintainability; there is no need to overhaul the battery pack.

[0076] (3) The battery pack is in an abnormal state and has good maintainability; it is recommended to overhaul the battery pack.

[0077] (5) The battery pack is in an abnormal state and has poor maintainability; it needs to be focused on, and separate operation and maintenance and overhaul strategies need to be formulated.

[0078] To better understand the process of the above method for determining the health state, the implementation method flow of the above determination of the health state is further described below in combination with optional embodiments, but it is not used to limit the technical solutions of the embodiments of the present application.

[0079] In this embodiment, a method for determining the health state is provided. Figure 3 It is a flowchart (2) of the method for determining the health state according to the embodiment of the present application, as Figure 3 shown, and the specific steps are as follows:

[0080] Step S301, collect a data set;

[0081] The collected parameters are key parameters that can characterize the battery state, such as battery voltage, temperature, etc. In the embodiment of the present application, the diagnosis of the abnormal state of the battery pack voltage is used for illustration. Collect all battery voltages at the charging cut-off moment of the battery pack, all battery voltages at the discharging cut-off moment of the battery pack, the current during the charge and discharge cycle of the battery pack, and the temperature. The data sampling time is not higher than 500 ms, the accuracy of the single-cell voltage value is not higher than 5 mV, and the accuracy of the battery pack current is not higher than 0.1%.

[0082] Step S302, calculate the abnormal threshold of the battery pack;

[0083] Taking the charging process as an example, at the end of charging, all battery voltages form a vector V = [v1, v2,... v n . Calculate the average value μ, standard deviation σ, kurtosis k and skewness s of the battery pack voltage:

[0084]

[0085] Among them, μ measures the magnitude of the average voltage; σ measures the degree of dispersion of the voltage values; s measures the skewness of the voltage distribution. When s > 0, the voltage distribution diagram is skewed to the right. When s < 0, the voltage distribution diagram is skewed to the left; k measures the sharpness of the voltage distribution. The standard kurtosis value of the normal distribution data is 3. The larger the kurtosis value, the higher and sharper the data distribution diagram; the smaller the kurtosis value, the shorter and fatter it is.

[0086] Based on the above statistics, a threshold is constructed. Among them,

[0087] For the charging condition, the detection threshold is:

[0088] L c,lower = μ c -(w1 + w2·(k c - 3)- w3·s c )·σ c ;

[0089] L c,upper = μ c +(w1 + w2·(k c - 3)+ w3·s c )·σ c ;

[0090] For the discharging condition, the detection threshold is:

[0091] L d,lower = μ d -(w1 + w2·(k d - 3)- w3·s d )·σ d ;

[0092] L d,upper = μ d +(w1 + w2·(k d - 3)+ w3·s d )·σ d ;

[0093] Among them, w1, w2, w3 are weighting coefficients. The subscripts c and d represent the charging state and the discharging state respectively, and the subscripts lower and upper represent the threshold lower limit and the threshold upper limit respectively. The values of w1, w2, w3 need to be determined according to the actual data distribution characteristics to detect a reasonable number of abnormal batteries. For example, w1 ∈ [2, 10], w2 ∈ [0, 1], w3 ∈ [0, 1] can be set.

[0094] Step S303, determine the health state of the battery pack;

[0095] Based on the calculated abnormal voltage threshold, abnormal battery screening is performed, and the state of health of the battery pack is calculated. In the charging state, it is judged based on the following criteria:

[0096]

[0097] In the discharging state, it is judged based on the following criteria:

[0098]

[0099] where, v i is the voltage of the i-th battery. At the same time, for the battery determined to be abnormal, the degree of abnormality of the battery is further determined. The farther the battery voltage deviates from the threshold it belongs to, the higher the degree of abnormality of the battery. Further, assuming that the number of batteries determined to be in an abnormal state within a complete charge-discharge cycle is n, then the proportion of abnormal batteries in the battery pack is: p = n / N;

[0100] where, N is the total number of batteries in the battery pack. The larger p is, the larger the proportion of abnormal batteries in the battery pack, and the worse the state of health of the battery pack; at the same time, the greater the degree of abnormality of the battery determined to be abnormal, the greater the overall degree of abnormality of the battery pack, and the worse the state of health of the battery pack.

[0101] Step S304, determine the maintainability of the battery pack;

[0102] According to steps S301 to S303, find all abnormal batteries in the charging and discharging states respectively, and calculate the maintenance units to which all abnormal batteries belong. If all abnormal batteries belong to one or a small number of maintenance units, the state of health of the entire battery pack can be improved by repairing a small number of maintenance units. At this time, the battery pack has good maintainability. If the abnormal batteries are distributed among the maintenance units, the state of health of the entire battery pack cannot be improved by repairing a small number of maintenance units. At this time, the battery pack has poor maintainability. That is, the data of all abnormal batteries belonging to the repairable units is denoted as af, then:

[0103]

[0104] where, af′ is the acceptable number of maintenance units, which is determined by aspects such as the acceptable maintenance cost and potential benefits of a specific power station. If af′ = 1, it means that the acceptable number of maintenance units for the battery pack is 1.

[0105] Step S305, formulate an operation and maintenance strategy according to the state of health and maintainability of the battery pack.

[0106] (1) The battery pack has a good state of health and good maintainability; the battery pack can be repaired.

[0107] (2) The battery pack has good health status but poor maintainability; no maintenance is required for the battery pack.

[0108] (3) The battery pack has poor health status but good maintainability; it is recommended to perform maintenance on the battery pack.

[0109] (5) The battery pack has poor health status and poor maintainability. Key attention is required, and separate operation and maintenance and repair strategies need to be formulated.

[0110] For the above-mentioned type (3) status, taking Figure 4 and Figure 5 as an example: Through the statistical analysis of the battery pack voltage, the abnormal voltage threshold is calculated and determined. The diagnosis result shows that there are 2 abnormal batteries (red dots) during the charging process of the battery pack and 2 abnormal batteries (red dots) during the discharging process of the battery pack. Then, within the entire charging-discharging cycle, there are a total of 4 abnormal batteries, and the voltages of the abnormal batteries deviate significantly from those of the normal batteries, indicating a large abnormality. Therefore, it is determined that the health status of this battery pack is poor; at the same time, the 4 diagnosed batteries belong to the same maintenance unit, and maintenance can be carried out by repairing or replacing the maintenance unit. Therefore, it is determined that the maintainability status of the battery pack is good. Therefore, the operation and maintenance suggestion is given: It is recommended to perform maintenance on this battery pack.

[0111] Through the description of the above implementation manners, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), including several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0112] In this embodiment, a device for determining the health status is also provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0113] Figure 6 is a structural block diagram of the device for determining the health status according to an embodiment of the present application. As Figure 6 shown, this device includes:

[0114] An acquisition module 62, configured to acquire multiple parameter sets of a battery pack, where each parameter set includes parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is of the same type of parameter information;

[0115] A first determination module 64, configured to determine the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each parameter set;

[0116] A second determination module 66, configured to determine the threshold range of each type of parameter according to the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter;

[0117] A third determination module 68, configured to determine the health status of each battery according to the parameter information of each battery and the threshold range of each type of parameter.

[0118] Through the above device, multiple parameter sets of a battery pack are acquired, where each parameter set includes parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is of the same type of parameter information; the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter are determined according to each parameter set; the threshold range of each type of parameter is determined according to the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter; the health status of each battery is determined according to the parameter information of each battery and the threshold range of each type of parameter. That is, in the embodiments of the present application, by analyzing the voltage and / or current and / or temperature parameters of each battery in the battery pack, the threshold range of the health status is determined, and the health condition of the battery can be accurately judged. Therefore, the problem of low accuracy in determining the health status of the battery can be solved.

[0119] In an exemplary embodiment, the second determination module 66 is configured to determine the threshold lower limit L of each type of parameter through the following formula lower , including: L lower = μ - (w1 + w2·(k - 3) - w3·s)·σ, where μ is the average value, k is the kurtosis, s is the skewness, and σ is the standard deviation; the threshold upper limit L of each type of parameter is determined through the following formula upper , including: L upper = μ + (w1 + w2·(k - 3) + w3·s)·σ, where the threshold range includes: the threshold upper limit and the threshold lower limit.

[0120] In an exemplary embodiment, an acquisition module 62 is configured to acquire first parameter information of each battery in the battery pack at a charging cut-off moment, and acquire second parameter information of each battery in the battery pack at a discharging cut-off moment; determine a first parameter set of the battery pack according to the first parameter information, and determine a second parameter set of the battery pack according to the second parameter information, where the parameter sets include: the first parameter set and the second parameter set.

[0121] In an exemplary embodiment, a first determination module 64 is configured to determine a first average value, a first standard deviation, a first kurtosis, and a first skewness corresponding to each type of parameter under a charging condition according to the first parameter set, and determine a second average value, a second standard deviation, a second kurtosis, and a second skewness corresponding to each type of parameter under a discharging condition according to the second parameter set.

[0122] In an exemplary embodiment, a second determination module 66 is configured to determine a first threshold range of each type of parameter under a charging condition according to the first average value, the first standard deviation, the first kurtosis, and the first skewness, and determine a second threshold range of each type of parameter under a discharging condition according to the second average value, the second standard deviation, the second kurtosis, and the second skewness, where the threshold ranges include: the first threshold range and the second threshold range.

[0123] In an exemplary embodiment, a third determination module 68 is configured to determine that the health state of the battery is a normal state when the parameter information of each type is within the threshold range of the parameter of each type; determine that the health state of the battery is an abnormal state when the parameter information of any type is not within the threshold range of the corresponding type of parameter.

[0124] In an exemplary embodiment, a third determination module 68 is configured to determine the number of batteries with an abnormal health state; determine the abnormal battery ratio in the battery pack according to the number of batteries with an abnormal state and the total number in the battery pack; determine the health state of the battery pack according to the abnormal battery ratio.

[0125] In an exemplary embodiment, a third determination module 68 is configured to determine the maintainability of the battery pack according to the number of batteries with an abnormal state; determine the maintenance strategy of the battery pack according to the health state of the battery pack and the maintainability.

[0126] It should be noted that the above-mentioned modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above-mentioned modules are all located in the same processor; or, the above-mentioned modules are respectively located in different processors in any combination form.

[0127] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. Wherein, the computer program is set to execute the steps in any one of the above method embodiments when running.

[0128] Optionally, in this embodiment, the above storage medium can be set to store program codes for executing the following steps:

[0129] S1. Obtain multiple parameter sets of the battery pack. Wherein, the parameter set includes: parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is of the same type of parameter information;

[0130] S2. Determine the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each parameter set.

[0131] S3. Determine the threshold range of each type of parameter according to the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter.

[0132] S4. Determine the health state of each battery according to the parameter information of each battery and the threshold range of each type of parameter.

[0133] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: various media such as USB flash drives, read-only memories (ROM for short), random access memories (RAM for short), mobile hard disks, magnetic disks, or optical discs that can store computer programs.

[0134] An embodiment of the present application also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is set to run the computer program to execute the steps in any one of the above method embodiments.

[0135] In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device. Wherein, the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0136] Optionally, in this embodiment, the above processor can be set to execute the following steps through a computer program:

[0137] S1. Obtain multiple parameter sets of the battery pack, where each parameter set includes parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is of the same type;

[0138] S2. Determine the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each parameter set;

[0139] S3. Determine the threshold range of each type of parameter according to the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter;

[0140] S4. Determine the health state of each battery according to the parameter information of each battery and the threshold range of each type of parameter.

[0141] An embodiment of the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0142] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium. The non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0143] An embodiment of the present application further provides a computer program. The computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in any one of the above method embodiments.

[0144] Optionally, in this embodiment, the above processor may be set to execute the following steps through a computer program:

[0145] S1. Obtain multiple parameter sets of the battery pack, where each parameter set includes parameter information of each battery in the battery pack, and the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is of the same type;

[0146] S2. Determine the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter according to each parameter set;

[0147] S3. Determine the threshold range of each type of parameter according to the average value, standard deviation, kurtosis, and skewness corresponding to each type of parameter;

[0148] S4. Determine the health status of each battery according to the parameter information of each battery and the threshold range of each type of parameter.

[0149] Specific examples in this embodiment may refer to the examples described in the above embodiments and exemplary embodiments, and will not be repeated here.

[0150] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to implement. In this way, the present application is not limited to any specific combination of hardware and software.

[0151] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a health status, characterized in that: include: Acquire multiple parameter sets of a battery pack, wherein the parameter set includes: parameter information of each battery in the battery pack, the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is parameter information of the same type; Determine the mean, standard deviation, kurtosis and skewness corresponding to each type of parameter according to each parameter set; Determine a threshold range of each type of parameter according to the mean value, standard deviation, kurtosis and skewness corresponding to each type of parameter; The health state of each battery is determined according to the parameter information of each battery and the threshold range of each type of parameter.

2. The method according to claim 1, characterized in that Determining a threshold range of each type of parameter according to the mean value, standard deviation, kurtosis and skewness corresponding to each type of parameter includes: The lower threshold L of each type of parameter is determined by the following formula: lower ,include: L lower =μ-(w1+w2·(k-3)-w3·s)·σ, where μ is the mean value, k is the kurtosis, s is the skewness, σ is the skewness, and w1, w2, w3 are weight coefficients; The upper threshold L of each type of parameter is determined by the following formula: upper ,include: L upper =μ+(w1+w2·(k-3)+w3·s)·σ, wherein the threshold range includes: the upper threshold limit and the lower threshold limit.

3. The method according to claim 1, characterized in that Get multiple parameter sets of the battery pack, including: Acquire first parameter information of each battery in the battery pack at a charging cut-off time, and acquire second parameter information of each battery in the battery pack at a discharging cut-off time; A first parameter set of the battery pack is determined according to the first parameter information, and a second parameter set of the battery pack is determined according to the second parameter information, wherein the parameter set includes: the first parameter set and the second parameter set.

4. The method according to claim 3, characterized in that Determine the mean, standard deviation, kurtosis and skewness of each type of parameter for each parameter set, including: Determine a first mean value, a first standard deviation, a first kurtosis, and a first skewness corresponding to each type of parameter under a charging condition according to the first parameter set, and determine a second mean value, a second standard deviation, a second kurtosis, and a second skewness corresponding to each type of parameter under a discharging condition according to the second parameter set; Determining a threshold range of each type of parameter according to the mean value, standard deviation, kurtosis and skewness corresponding to each type of parameter includes: A first threshold range of each type of parameter under charging conditions is determined based on the first mean value, the first standard deviation, the first kurtosis, and the first skewness, and a second threshold range of each type of parameter under discharging conditions is determined based on the second mean value, the second standard deviation, the second kurtosis, and the second skewness, wherein the threshold range includes: the first threshold range and the second threshold range.

5. The method according to claim 1, characterized in that: Determining the health status of each battery according to the parameter information of each battery and the threshold range of each type of parameter includes: When each type of parameter information is within a threshold range of each type of parameter, determining that the health state of the battery is a normal state; When any type of parameter information is not within the threshold range of the corresponding type of parameter, it is determined that the health state of the battery is an abnormal state.

6. The method according to claim 1, characterized in that After determining the health status of each battery according to the parameter information of each battery and the threshold range of each type of parameter, the method further includes: Determine the number of batteries whose health status is abnormal; Determine the proportion of abnormal batteries in the battery pack according to the number of batteries in the abnormal state and the total number of batteries in the battery pack; The health status of the battery pack is determined according to the abnormal battery ratio.

7. The method according to claim 6, characterized in that After determining the health status of the battery pack according to the abnormal battery ratio, the method further includes: determining the maintainability of the battery pack according to the number of batteries in the abnormal state; A maintenance strategy for the battery pack is determined according to the health status of the battery pack and the maintainability.

8. A device for determining health status, characterized in that: include: An acquisition module, used for acquiring multiple parameter sets of a battery pack, wherein the parameter set includes: parameter information of each battery in the battery pack, the parameter information includes one of the following: battery voltage, battery current, battery temperature, and the parameter information in the same parameter set is parameter information of the same type; A first determination module, used to determine the mean value, standard deviation, kurtosis and skewness corresponding to each type of parameter according to each parameter set; A second determination module is used to determine a threshold range of each type of parameter according to the mean value, standard deviation, kurtosis and skewness corresponding to each type of parameter; The third determination module is used to determine the health status of each battery according to the parameter information of each battery and the threshold range of each type of parameter.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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