Battery evaluation method, device and electronic equipment

Abstract

Embodiments of the present application provide a battery evaluation method and device and electronic equipment. The method comprises: obtaining a first parameter value of a first battery at a current time; determining a working state of the first battery according to the first parameter value; in a case where the working state of the first battery is a discharge state, determining a predicted parameter value of the first battery according to the first parameter value; and performing health evaluation on the first battery by using the predicted parameter value to obtain an evaluation result.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a battery evaluation method, apparatus, and electronic device. Background Technology

[0002] In a data center power system, batteries serve as an emergency backup power source. Battery maintenance and hazard management are crucial for improving the stability of the battery system and the robustness of the data center.

[0003] However, batteries in the grid are large in size and are usually inspected and maintained manually, which is not only labor-intensive but also inefficient. Summary of the Invention

[0004] This invention provides a battery evaluation method, apparatus, and electronic device to solve the problem of low efficiency caused by manual inspection and maintenance of batteries.

[0005] In a first aspect, embodiments of the present invention provide a battery evaluation method, the method comprising: Get the first parameter value of the first battery at the current time; The operating status of the first battery is determined based on the first parameter value; When the first battery is in a discharging state, the predicted parameter value of the first battery is determined based on the first parameter value. The health of the first battery is assessed using the predicted parameter values ​​to obtain the assessment results.

[0006] Secondly, embodiments of the present invention also provide a battery evaluation device, the device comprising: The first acquisition module is used to acquire the first parameter value of the first battery at the current time; The first determining module is used to determine the operating state of the first battery based on the first parameter value. The second determining module is used to determine the predicted parameter value of the first battery based on the first parameter value when the first battery is in a discharging state. The first evaluation module is used to perform a health assessment on the first battery using the predicted parameter values, and obtain the evaluation result.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a transceiver, and a processor: The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer programs in the memory and execute the battery evaluation method as described above.

[0008] Fourthly, embodiments of the present invention also provide a processor-readable storage medium storing a computer program for causing the processor to execute the battery evaluation method described above.

[0009] The above embodiments of the present invention obtain a first parameter value of the first battery at the current time, determine the operating state of the first battery based on the first parameter value, and if the first battery is in a discharging state, determine a predicted parameter value of the first battery based on the first parameter value. A health assessment of the first battery is then performed using the predicted parameter value to obtain an assessment result. This solution solves the problem of manual inspection and maintenance of batteries, automates the health assessment of batteries, and improves the efficiency and accuracy of battery health assessment. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of the steps of the battery evaluation method provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of parameter value curves during the battery evaluation process provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of the battery evaluation device provided in an embodiment of the present invention; Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0012] In this embodiment of the invention, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0013] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0015] Specifically, this invention provides a battery evaluation method that is applied to an artificial intelligence (AI) evaluation model, such as... Figure 1 As shown, the specific steps may include the following: Step 101: Obtain the first parameter value of the first battery at the current time.

[0016] Before the AI ​​evaluation model obtains the first parameter value, the environmental monitoring system collects the parameter values ​​of the first battery in real time. If the first battery is in a discharging state, the environmental monitoring system issues an alarm. These parameter values ​​include, but are not limited to: voltage, current, memory, and temperature.

[0017] If the environmental monitoring system malfunctions or is offline, the AI ​​model sends a notification to the user, which can be sent via email. If the environmental monitoring system is functioning normally, the AI ​​evaluation model retrieves the first parameter value of the first battery at the current time from the environmental monitoring system every first interval (e.g., 2 minutes).

[0018] The AI ​​evaluation model, after acquiring the first parameter value, can generate a log related to that parameter value and determine whether the log is normal based on its content. If an anomaly is detected, the AI ​​model sends an error message to the user, which can be sent via email. Furthermore, after acquiring the first parameter value multiple times, the AI ​​evaluation model can generate a first curve graph showing the first parameter value and its corresponding discharge time.

[0019] Step 102: Determine the operating status of the first battery based on the first parameter value.

[0020] Specifically, the AI ​​evaluation model determines whether the first battery is currently in a float charging state or a discharging state based on the first parameter value.

[0021] Among them, the float charge state is a state in which the battery is continuously and for a long time charged at a constant voltage in order to balance the capacity loss caused by the battery's self-discharge.

[0022] Step 103: When the first battery is in a discharging state, determine the predicted parameter value of the first battery based on the first parameter value.

[0023] Specifically, if the AI ​​evaluation model determines that the first battery is in a discharging state, the AI ​​evaluation model obtains the corresponding predicted parameter value based on the first parameter value.

[0024] After determining the different predicted parameter values ​​for different discharge times, a second curve can be generated relating the discharge time and the predicted parameter values.

[0025] Step 104: Perform a health assessment on the first battery using the predicted parameter values ​​to obtain the assessment results.

[0026] Specifically, the AI ​​assessment model performs a health assessment on the first battery by predicting parameter values, and obtains the assessment results. The health status of the battery can be determined through the assessment results, so that the inspection personnel can take appropriate measures for the first battery.

[0027] In the above embodiments of the present invention, by obtaining a first parameter value of the first battery at the current time, the operating state of the first battery is determined based on the first parameter value. If the first battery is in a discharging state, a predicted parameter value is determined based on the first parameter value. A health assessment of the first battery is then performed using the predicted parameter value to obtain an assessment result. This solution solves the problem of manual inspection and maintenance of batteries, automates the health assessment of batteries, and improves the efficiency and accuracy of battery health assessment.

[0028] In an optional specific embodiment, step 103, which determines the predicted parameter value of the first battery based on the first parameter value, may specifically include: Obtain the first discharge time corresponding to the first parameter value and the usage time of the first battery; Based on the first correlation between discharge time and parameter value, obtain the second parameter value corresponding to the first discharge time; Based on the second parameter value and the usage time, the predicted parameter value of the first battery is determined.

[0029] Specifically, if the AI ​​evaluation model determines that the first battery is in a discharging state, the first discharge time corresponding to the first parameter value can be obtained through the first curve, and the usage time (e.g., years of use) of the first battery can also be obtained. Based on the first correlation between discharge time and parameter values ​​in the database, the second parameter value corresponding to the first discharge time is obtained. The AI ​​evaluation model calculates the predicted parameter value of the first battery based on the usage time and the second parameter value; that is, the AI ​​evaluation model adjusts the second parameter value using the usage time, and the adjusted second parameter value is the predicted parameter value of the first battery.

[0030] It should be noted that the AI ​​evaluation model is obtained through iterative training based on the Sarima model (Seasonal Autoregressive Integrated Moving Average), a seasonally differentiated autoregressive moving average model and one of the methods for time series forecasting analysis. During training, the parameter values ​​in the training data need to be converted into time series, and a unit root test is performed on the time series to determine whether it is a stationary sequence, i.e., whether the time series is continuous. If it is continuous, it indicates that the time series is stationary and can be used as training data. Furthermore, during training, the first discharge voltage curve is recorded as V1, and compared with the theoretical voltage curve V... t A fitting process is performed, and a relatively smooth curve is obtained by eliminating differences, which is taken as V1. Furthermore, the discharge voltage curve V for each subsequent discharge is... n Both are similar to the previous discharge voltage curve V n-1 Smoothing is performed to obtain the predicted voltage curve. This process is iterated to optimize the Sarima model and obtain the AI ​​evaluation model.

[0031] The AI ​​evaluation model expression is: The model is divided into two parts: a seasonal model and a non-seasonal model. p, d, and q are the parameters of the non-seasonal model. p represents the autoregressive part of the non-seasonal model, incorporating the second curve of the battery pack into the calculation. d represents the differencing order, which is the integration part of the non-seasonal model and represents the difference required to stabilize the time series. For example, if the predicted parameter values ​​of the battery pack in this discharge are not significantly different from those in the previous discharge, then the predicted parameter values ​​for several discharge times in this discharge process may be close to the predicted parameter values ​​of the previous discharge. d=1 indicates that the predicted parameter values ​​of this discharge are differencing those of the previous discharge. q represents the moving average order. P represents the order of the seasonal autoregression (generally less than or equal to 3), similar to p. D represents the number of seasonal differencing operations (generally taken as 1, indicating only one differencing operation). Q represents the order of the seasonal moving average (generally less than or equal to 3), similar to q. M represents the period length, i.e., the battery discharge time. Using the above, the predicted parameter values ​​can be calculated by differencing the second curve and the first curve.

[0032] In an optional specific embodiment, when the first battery is discharging for the first time, the first correlation represents the correlation between the discharge time index and the parameter value index; When the first battery is not undergoing its first discharge, the first correlation characterizes the relationship between the discharge time and parameter values ​​obtained during the previous discharge of the first battery.

[0033] Specifically, if the first battery is discharging for the first time, and there is no historical discharge data for the first battery in the database, then the index value (i.e., theoretical value) of the first battery will be used as the historical discharge data. That is, the first correlation is the correlation between the discharge time index and the parameter value index.

[0034] If the first battery is not being discharged for the first time, the database includes the historical discharge data of the first battery. The discharge time and parameter values ​​obtained during the previous discharge of the first battery (i.e., the predicted parameter values ​​obtained during the previous discharge) are used as historical discharge data. That is, the first association is the association between the discharge time and parameter values ​​obtained during the previous discharge of the first battery.

[0035] It is understandable that when retrieving parameter data from the database, the retrieved parameter data is cleaned to obtain the first association after cleaning.

[0036] As an optional specific embodiment, step 104 performs a health assessment on the first battery using the predicted parameter values ​​to obtain the assessment result, which may specifically include: Obtain the second correlation between discharge time, parameter values, and remaining battery capacity; Based on the second correlation, the discharge time corresponding to the predicted parameter value, and the predicted parameter value, the remaining capacity of the first battery is obtained; A health assessment of the first battery is performed based on its remaining capacity, and the assessment results are obtained.

[0037] Specifically, after determining the predicted parameter values ​​of the first battery, the AI ​​evaluation model obtains a second correlation between the discharge time, parameter values, and remaining battery capacity. Through this second correlation, the remaining capacity of the first battery under the predicted parameter values ​​and corresponding discharge times can be determined. This remaining capacity serves as the evaluation result of the remaining capacity of the first battery. The health status of the first battery can be determined through the evaluation result, so that inspection personnel can take appropriate action on the first battery.

[0038] The second correlation is updated in real time as the first battery discharges, so as to obtain a more accurate evaluation result through the second correlation and improve the evaluation accuracy of the battery.

[0039] For example: if the remaining capacity of the first battery is less than 60%, the assessment result of the first battery will be marked as "lagging", prompting the inspection and maintenance personnel to rectify or replace the battery; if the remaining capacity of the first battery is between 60% and 80%, the assessment result of the first battery will be marked as "moderate", prompting the inspection and maintenance personnel to strengthen the maintenance of the first battery; if the remaining capacity of the first battery is higher than 80%, the assessment result of the first battery will be marked as "healthy".

[0040] As an optional specific embodiment, after step 102 determines the operating state of the first battery based on the first parameter value, the method may further include: When the first battery is in float charging mode, the second parameter values ​​of N second batteries at the current time are obtained. The N second batteries are the N batteries in the battery pack containing the first battery, excluding the first battery, and N is an integer greater than 0. The first battery is assessed for health using the first parameter value and N second parameter values, and the assessment results are obtained.

[0041] Specifically, if the first battery is in a float charging state, the AI ​​evaluation model obtains the second parameter values ​​of the other batteries (i.e., N second batteries) in the battery pack where the first battery is located at the current time. Based on the first parameter value of the first battery at the current time and the second parameter value of each second battery at the current time, the model performs a health assessment on the first battery and obtains the assessment result.

[0042] As an optional specific embodiment, the step of performing a health assessment on the first battery using the first parameter value and N second parameter values ​​to obtain the assessment result may specifically include: Determine the median of the first parameter value and the N second parameter values; Calculate the first difference between the first parameter value and the median of the parameters; If at least one of the following conditions is met: the first parameter value is greater than the median of the parameters and the first difference is greater than the first threshold, the evaluation result of the first battery is determined to be abnormal.

[0043] Specifically, after obtaining the first parameter value and N second parameter values, the median of the parameters among the first parameter value and the N second parameter values ​​is calculated. The first parameter value is then compared with the median. If the first parameter value is greater than the median, the evaluation result of the first battery is determined to be abnormal. If the first parameter value is less than or equal to the median, the first difference obtained by subtracting the first parameter value from the median is calculated. This first difference is then compared with a first threshold (which can be set as needed, preferably 0.05). If the first difference is greater than the first threshold, the evaluation result of the first battery is determined to be abnormal.

[0044] The relationship between the first parameter value and the median parameter of the battery pack is illustrated in Table 1 below, with the first threshold being 0.05: Table 1

[0045] When the number of batteries n in the battery pack is odd, the median parameter is V. mid =V （n+1） / 2, when n is even, the median of the parameters is V. mid ={ V （n / 2） + V （n / 2+1）} / 2.

[0046] As another optional specific embodiment, the step of performing a health assessment on the first battery using the first parameter value and N second parameter values ​​to obtain the assessment result may specifically include: Calculate the second difference between the first parameter value and the parameter value of the first battery; If at least one of the following conditions is met: the first parameter value is greater than the parameter value index, or the second difference is greater than the second threshold, the evaluation result of the first battery is determined to be abnormal.

[0047] Specifically, after obtaining the first parameter value and N second parameter values, the parameter index (i.e., the theoretical parameter value) of the first battery is acquired. The first parameter value is then compared with the parameter index. If the first parameter value is greater than the parameter index, the evaluation result of the first battery is determined to be abnormal. If the first parameter value is less than or equal to the parameter index, the second difference obtained by subtracting the first parameter value from the parameter index is calculated. This second difference is then compared with a second threshold (which can be set as needed, preferably 0.05). If the second difference is greater than the second threshold, the evaluation result of the first battery is determined to be abnormal. If the second difference is less than or equal to the second threshold, the evaluation result of the first battery is determined to be normal.

[0048] The relationship between the first parameter value and the parameter value index is illustrated in Table 2 below: Table 2

[0049] It should be noted that if the first parameter value is less than or equal to the parameter median, and the first difference is less than or equal to the first threshold, and the first parameter value is less than or equal to the parameter value index, and the second difference is less than or equal to the second threshold, then the evaluation result of the first battery is determined to be normal.

[0050] The evaluation process of the first battery in the above-mentioned discharge state is described below through a specific embodiment: Taking voltage as an example, a discharge test was conducted on 24 batteries in the ZL09 / 48X01 battery pack of the 48V DC system in a communication building in City A. Simultaneously, an AI evaluation model captured the discharge dynamics. The discharge ended at 11:09, lasting 112 minutes. Since different discharge rates correspond to different discharge characteristic curves, the AI ​​evaluation model obtained a rectifier load current of 164A and identified a discharge rate of 10hr, thus determining the corresponding discharge characteristic curve. This curve records the first correlation between discharge time and parameter values. For example... Figure 2 As shown, when the discharge time reached 62 minutes, the parameter value of the first battery (i.e., the theoretical value) was 2.09V, the first parameter value (i.e., the actual value) was 2.06V, and the predicted parameter value (i.e., the predicted value) was 2.08V according to the first correlation. The evaluation result was that the remaining capacity of the first battery was 42.28%, and the health of the first battery was evaluated as "lagging".

[0051] It should be noted that the above method can not only evaluate individual batteries, but also perform health assessments on battery packs. That is, by multiplying the remaining capacity of individual batteries by the number of individual batteries, the remaining capacity of the battery pack can be evaluated. Alternatively, the remaining capacity of the battery pack can be evaluated through other methods.

[0052] In summary, the above embodiments of the present invention obtain the first parameter value of the first battery at the current time. If the first battery is in a discharging state, a predicted parameter value is calculated based on the first parameter value. A health assessment of the first battery is then performed using the predicted parameter value to obtain the assessment result. This automated health assessment of the first battery does not require prolonged discharge, solving the problems of manual inspection and maintenance, and improving the efficiency and accuracy of battery health assessment. Furthermore, the anomaly alert function simplifies and enhances battery maintenance, providing stronger data transmission capabilities.

[0053] The battery evaluation method provided by the embodiments of the present invention has been described above. The battery evaluation device provided by the embodiments of the present invention will be described below with reference to the accompanying drawings.

[0054] like Figure 3 As shown, this embodiment of the invention also provides a battery evaluation device 300, the device comprising: The first acquisition module 301 is used to acquire the first parameter value of the first battery at the current time; The first determining module 302 is used to determine the working state of the first battery based on the first parameter value; The second determining module 303 is used to determine the predicted parameter value of the first battery based on the first parameter value when the first battery is in a discharging state. The first evaluation module 304 is used to perform a health evaluation on the first battery using the predicted parameter values ​​and obtain the evaluation result.

[0055] In the above embodiments of the present invention, by obtaining a first parameter value of the first battery at the current time, the operating state of the first battery is determined based on the first parameter value. If the first battery is in a discharging state, a predicted parameter value is determined based on the first parameter value. A health assessment of the first battery is then performed using the predicted parameter value to obtain an assessment result. This solution solves the problem of manual inspection and maintenance of batteries, automates the health assessment of batteries, and improves the efficiency and accuracy of battery health assessment.

[0056] Optionally, the second determining module 303 is specifically used for: Obtain the first discharge time corresponding to the first parameter value and the usage time of the first battery; Based on the first correlation between discharge time and parameter value, obtain the second parameter value corresponding to the first discharge time; Based on the second parameter value and the usage time, the predicted parameter value of the first battery is determined.

[0057] Optionally, when the first battery is discharging for the first time, the first correlation represents the correlation between the discharge time index and the parameter value index. When the first battery is not undergoing its first discharge, the first correlation characterizes the relationship between the discharge time and parameter values ​​obtained during the previous discharge of the first battery.

[0058] Optionally, the first evaluation module 304 is specifically used for: Obtain the second correlation between discharge time, parameter values, and remaining battery capacity; Based on the second correlation, the discharge time corresponding to the predicted parameter value, and the predicted parameter value, the remaining capacity of the first battery is obtained; A health assessment of the first battery is performed based on its remaining capacity, and the assessment results are obtained.

[0059] Optionally, the device further includes: The second acquisition module is used to acquire the second parameter values ​​of N second batteries at the current time when the first battery is in the floating charge state. The N second batteries are the N batteries in the battery pack where the first battery is located, excluding the first battery, and N is an integer greater than 0. The second evaluation module is used to perform a health evaluation on the first battery using the first parameter value and N second parameter values, and obtain the evaluation result.

[0060] Optionally, the second evaluation module is specifically used for: Determine the median of the first parameter value and the N second parameter values; Calculate the first difference between the first parameter value and the median of the parameters; If at least one of the following conditions is met: the first parameter value is greater than the median of the parameters and the first difference is greater than the first threshold, the evaluation result of the first battery is determined to be abnormal.

[0061] Optionally, the second evaluation module is specifically used for: Obtain the preset parameter values ​​of the first battery; Calculate the second difference between the first parameter value and the preset parameter value; If at least one of the following conditions is met: the first parameter value is greater than the preset parameter value and the second difference is greater than the second threshold, the evaluation result of the first battery is determined to be abnormal.

[0062] In summary, the above embodiments of the present invention obtain the first parameter value of the first battery at the current time. If the first battery is in a discharging state, a predicted parameter value is calculated based on the first parameter value. A health assessment of the first battery is then performed using the predicted parameter value to obtain the assessment result. This automated health assessment of the first battery does not require prolonged discharge, solving the problems of manual inspection and maintenance, and improving the efficiency and accuracy of battery health assessment. Furthermore, the anomaly alert function simplifies and enhances battery maintenance, providing stronger data transmission capabilities.

[0063] It should be noted that the battery evaluation device provided in this embodiment of the invention can implement all the method steps implemented in the above-mentioned battery evaluation method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0064] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0065] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] like Figure 4 As shown, embodiments of the present invention also provide an electronic device, including a memory 420, a transceiver 410, and a processor 400: Memory 420 is used to store computer programs; Transceiver 410 is used to send and receive data under the control of the processor; The processor 400 is configured to read a computer program from memory and execute the steps of the battery evaluation method as described in any of the above embodiments.

[0067] Among them, Figure 4 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 400) and memory (memory 420). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 410 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor 400 is responsible for managing the bus architecture and general processing, and the memory 420 can store data used by the processor 400 during operation.

[0068] The processor 400 can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0069] The processor executes any of the battery evaluation methods provided in the embodiments of this application according to the obtained executable instructions by calling a computer program stored in memory. The processor and memory can also be physically separated.

[0070] It should be noted that the electronic device provided in this embodiment of the invention can implement all the method steps implemented in the above-described battery evaluation method embodiment and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0071] Embodiments of the present invention also provide a processor-readable storage medium storing a computer program for causing the processor to execute the above-described battery evaluation method.

[0072] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0073] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0074] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These processors can execute instructions that can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for evaluating a storage battery, characterized in that, The method includes: Get the first parameter value of the first battery at the current time; The operating status of the first battery is determined based on the first parameter value; When the first battery is in a discharging state, the predicted parameter value of the first battery is determined based on the first parameter value. The first battery is then subjected to a health assessment using the predicted parameter values ​​to obtain the assessment results. The step of determining the predicted parameter value of the first battery based on the first parameter value includes: Obtain the first discharge time corresponding to the first parameter value and the usage time of the first battery; Based on the first correlation between discharge time and parameter value, obtain the second parameter value corresponding to the first discharge time; Based on the second parameter value and the usage time, the predicted parameter value of the first battery is determined; The step of performing a health assessment on the first battery using the predicted parameter values ​​to obtain the assessment results includes: Obtain the second correlation between discharge time, parameter values, and remaining battery capacity; Based on the second correlation, the discharge time corresponding to the predicted parameter value, and the predicted parameter value, the remaining capacity of the first battery is obtained; The health of the first battery is assessed based on its remaining capacity, and the assessment results are obtained.

2. The method according to claim 1, characterized in that, When the first battery is discharging for the first time, the first correlation represents the correlation between the discharge time index and the parameter value index. When the first battery is not undergoing its first discharge, the first correlation characterizes the relationship between the discharge time and parameter values ​​obtained during the previous discharge of the first battery.

3. The method according to claim 1, characterized in that, After determining the operating state of the first battery based on the first parameter value, the method further includes: When the first battery is in float charging mode, the second parameter values ​​of N second batteries at the current time are obtained. The N second batteries are the N batteries in the battery pack containing the first battery, excluding the first battery, and N is an integer greater than 0. The first battery is assessed for health using the first parameter value and N second parameter values, and the assessment results are obtained.

4. The method according to claim 3, characterized in that, The health assessment of the first battery is performed using the first parameter value and N second parameter values ​​to obtain the assessment result, including: Determine the median of the first parameter value and the N second parameter values; Calculate the first difference between the first parameter value and the median of the parameters; If at least one of the following conditions is met: the first parameter value is greater than the median of the parameters and the first difference is greater than the first threshold, the evaluation result of the first battery is determined to be abnormal.

5. The method according to claim 3, characterized in that, The health assessment of the first battery is performed using the first parameter value and N second parameter values ​​to obtain the assessment result, including: Calculate the second difference between the first parameter value and the parameter value of the first battery; If at least one of the following conditions is met: the first parameter value is greater than the parameter value index, or the second difference is greater than the second threshold, the evaluation result of the first battery is determined to be abnormal.

6. A battery evaluation device, characterized in that, The device includes: The first acquisition module is used to acquire the first parameter value of the first battery at the current time; The first determining module is used to determine the operating state of the first battery based on the first parameter value. The second determining module is used to determine the predicted parameter value of the first battery based on the first parameter value when the first battery is in a discharging state. The first evaluation module is used to perform a health assessment on the first battery using the predicted parameter values ​​and obtain the evaluation result. The second determining module is specifically used for: Obtain the first discharge time corresponding to the first parameter value and the usage time of the first battery; Based on the first correlation between discharge time and parameter value, obtain the second parameter value corresponding to the first discharge time; Based on the second parameter value and the usage time, the predicted parameter value of the first battery is determined; The first evaluation module is specifically used for: Obtain the second correlation between discharge time, parameter values, and remaining battery capacity; Based on the second correlation, the discharge time corresponding to the predicted parameter value, and the predicted parameter value, the remaining capacity of the first battery is obtained; The health of the first battery is assessed based on its remaining capacity, and the assessment results are obtained.

7. An electronic device, characterized in that, Includes memory, transceiver, and processor: Memory, used to store computer programs; Transceiver, used to send and receive data under the control of the processor; A processor for reading a computer program from the memory and executing the battery evaluation method as described in any one of claims 1 to 5.

8. A processor-readable storage medium, characterized in that, The processor-readable storage medium stores a computer program for causing the processor to perform the battery evaluation method as described in any one of claims 1 to 5.

Images