Energy storage battery life prediction and operation and maintenance method, system and related equipment

Abstract

The invention discloses an energy storage battery life prediction and operation and maintenance method and system and related equipment, and relates to the technical field of batteries. Comprising the following steps that a data model matrix of each charging and discharging stage of a battery cell is constructed, and the data model matrix is constructed based on laboratory data and historical data of the battery cell; the charging and discharging stages and working parameters of the battery are obtained in real time, and a corresponding prediction model is called based on the charging and discharging stages of the battery; predicting the maximum available capacity and the capacity change trend of the battery based on the working parameters of charging and discharging of the battery and the corresponding prediction model; acquiring a real-time charging and discharging accumulated value based on the working parameters of charging and discharging of the battery, acquiring SOC based on the real-time charging and discharging accumulated value and the rated capacity of the battery, and acquiring SOH based on the maximum capacity predicted value of the battery and the rated capacity of the battery; and adjusting the operation and maintenance state of the battery based on the working parameters of charging and discharging of the battery, the SOC and the SOH. And through accurate prediction of the health state of the battery, an effective maintenance strategy is provided to delay the attenuation speed of the battery capacity.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, system and related equipment for predicting the life of an energy storage battery and for operating and maintaining the same. Background Art

[0002] With the development of new energy technologies, energy storage equipment such as household energy storage, industrial and commercial energy storage, and large-scale energy storage power stations have been widely used; in these applications, lithium-ion batteries, as the main energy storage unit, their safety and life have become the focus of investors' attention.

[0003] Although existing energy storage systems are able to ensure the safe operation of batteries, there are still shortcomings in maintaining battery life and extending their service life. Summary of the invention

[0004] To solve the above problems, the present application discloses a method, system and related equipment for predicting the life of energy storage batteries and operation and maintenance. Through real-time collection, transmission, storage, analysis and control of battery operation data, accurate prediction of the battery health status can be achieved, and effective maintenance strategies can be provided to slow down the rate of battery capacity decay.

[0005] The first technical solution adopted in this application is: to provide a method for predicting the life of an energy storage battery and operating and maintaining the battery, including:

[0006] Constructing a data model matrix for each stage of battery cell charging and discharging, wherein the data model matrix is ​​constructed based on laboratory data and battery cell historical data;

[0007] Acquire the battery charging and discharging stage and working parameters in real time, and call the corresponding prediction model based on the battery charging and discharging stage; predict the maximum available capacity and capacity change trend of the battery based on the battery charging and discharging working parameters and the corresponding prediction model;

[0008] Acquire a real-time charge and discharge cumulative value based on the battery charge and discharge working parameters, acquire the SOC based on the real-time charge and discharge cumulative value and the rated capacity of the battery, and acquire the SOH based on the maximum capacity prediction value of the battery and the rated capacity of the battery;

[0009] Adjust the operation and maintenance state of the battery based on the operating parameters of the battery charge and discharge, the SOC and the SOH

[0010] The construction of the data model matrix includes the following steps:

[0011] The battery charging and discharging stages are divided into an initial charging and discharging period, a plateau period, and a terminal period;

[0012] Obtain the operating parameters of the battery during charging and discharging, and obtain the SOC and SOH of the battery cell based on the operating parameters;

[0013] The data model matrix is ​​modified based on the SOC and SOH of the battery cell.

[0014] Wherein, the method further comprises:

[0015] Estimate the capacity of a single cell based on the ampere-hour integration method;

[0016] After charging or discharging is completed, the estimated data is compared with historical data to screen out risky cells.

[0017] The method monitors the SOH of the battery in real time; if the SOH of the battery is lower than a first preset threshold, the temperature and current values ​​are traced; based on the temperature and current values, the influencing factors of the SOH are obtained, and then the charging and discharging temperature and current reference values ​​are provided to slow down the battery capacity decay rate.

[0018] The method monitors the voltage of the battery cell in real time, and if the difference between the voltage of a single battery cell and the average voltage exceeds a pressure threshold, it is a faulty battery cell.

[0019] Wherein, if the faulty battery cell is in the initial stage of charge and discharge or the terminal stage of charge and discharge, the faulty battery cell is balanced, and the balancing steps are as follows:

[0020] Locate the battery pack where the faulty cell is located;

[0021] Reduce the charge and discharge power of the battery pack at the beginning or end of charge and discharge to extend the balancing time of the single battery;

[0022] Select a longer charging period or a longer discharging period to perform an operation on the battery.

[0023] If the faulty cell is in the charging and discharging plateau period, the faulty cell is balanced, and the balancing steps are as follows:

[0024] Obtain the charge and discharge current and temperature of the faulty battery cell;

[0025] If the difference between the temperature and the charge and discharge temperature reference value is too large, the charge and discharge power and / or the air conditioning temperature are adjusted;

[0026] If the temperature is normal, adjust the charge and discharge power.

[0027] The second technical solution adopted in this application is: to provide a storage battery life prediction and operation and maintenance system, including:

[0028] A data model matrix module, wherein the data model matrix module constructs a data model matrix based on laboratory data and battery cell historical data;

[0029] A battery parameter acquisition module is used to obtain the battery charging and discharging stages and operating parameters;

[0030] An operation and maintenance module, the operation and maintenance module is connected to the battery parameter acquisition module; the operation and maintenance module controls the working state of the battery based on the battery charging and discharging stages and working parameters;

[0031] A data analysis module is connected to the battery parameter acquisition module to analyze the acquired battery parameters; the data model matrix module is connected to the data analysis module, and the data model matrix module modifies the data model matrix based on the analysis result.

[0032] The third technical solution adopted in the present application is: an electronic device is provided, which includes: a memory and a processor coupled to each other, and the processor is used to execute program instructions stored in the memory to implement the energy storage battery life prediction and operation and maintenance method as described in any of the above items.

[0033] The fourth technical solution adopted in the present application is: providing a computer-readable storage medium, wherein the computer-readable storage medium stores program data, and the program data can be executed by a processor to implement the energy storage battery life prediction and operation and maintenance method as described in any of the above items.

[0034] Beneficial effects of the present application: Different from the prior art, the present application discloses a method, system and related equipment for predicting the life of an energy storage battery and for operation and maintenance, which relates to the field of battery technology; including the following steps: constructing a data model matrix for each stage of battery cell charging and discharging, the data model matrix is ​​constructed based on laboratory data and battery cell historical data; obtaining the stage and working parameters of battery charging and discharging in real time, and calling the corresponding prediction model based on the stage of battery charging and discharging; predicting the maximum available capacity and capacity change trend of the battery based on the working parameters of battery charging and discharging and the corresponding prediction model; obtaining the real-time charging and discharging cumulative value based on the working parameters of battery charging and discharging, obtaining the SOC based on the real-time charging and discharging cumulative value and the rated capacity of the battery, and obtaining the SOH based on the predicted value of the maximum capacity of the battery and the rated capacity of the battery; adjusting the operation and maintenance status of the battery based on the working parameters of battery charging and discharging, SOC and SOH. Through accurate prediction of the battery health status, an effective maintenance strategy is provided to slow down the rate of battery capacity decay. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] in:

[0037] Figure 1 A flow chart of an embodiment of a method for predicting the life of an energy storage battery and operating and maintaining the energy storage battery provided in this application;

[0038] Figure 2 A schematic diagram of the principle of an embodiment of a data model matrix provided in this application;

[0039] Figure 3 A schematic diagram of the structure of an embodiment of the energy storage battery life and operation and maintenance system provided in this application;

[0040] Figure 4 is a structural schematic diagram of an embodiment of a computer device provided by the present application;

[0041] Figure 5 It is a structural schematic diagram of an embodiment of a computer-readable storage medium provided by the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It will be appreciated that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some but not all structures related to the present application are shown in the drawings. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the art without making creative work are within the scope of protection of the present application.

[0043] The terms "first", "second", etc. in this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0044] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0045] See also Figure 1 As shown, Figure 1 The flowchart of an embodiment of the energy storage battery life prediction and operation and maintenance method provided in this application includes the following steps:

[0046] Step S11: construct a data model matrix for each stage of battery cell charging and discharging, the data model matrix is ​​constructed based on laboratory data and battery cell historical data; using laboratory test data and battery cell historical data, create a data model matrix containing key parameters of each stage; the matrix may include but is not limited to the following: voltage-capacity curves at different temperatures, charge and discharge efficiency at different rates, changes in internal resistance with cycle number, etc.; in one embodiment, the data model matrix established by the battery cell during the charging and discharging process is as follows:

[0047]

[0048] Among them, a is the instantaneous voltage, temperature and current capacity of the single battery cell; it should be clear that in other embodiments, the data model matrix can be in any other form without any limitation; the data model matrix combines laboratory data and actual operation data, and can more comprehensively describe the behavior of the battery cell, thereby improving the prediction accuracy of the maximum available capacity of the battery and the capacity change trend.

[0049] Step S12: Acquire the battery charging and discharging stages and working parameters in real time. The charging and discharging stages include the initial stage, the plateau stage, and the terminal stage. The working parameters include battery charging and discharging parameters such as voltage, current, and temperature. Call the corresponding prediction model based on the battery charging and discharging stage. That is, if the battery is in the charging and discharging plateau stage, call the data model matrix of the charging and discharging plateau stage for prediction.

[0050] Based on the battery charging and discharging operating parameters and the corresponding prediction model, the maximum available capacity of the battery and the capacity change trend are predicted; the real-time collected operating parameters (such as voltage, current, temperature, etc.) are passed as input to the loaded prediction model; the input parameters are processed using the prediction model to calculate the maximum available capacity of the battery (i.e. the available power under the current SOC) and its future capacity change trend; by obtaining the battery's operating parameters in real time and calling the appropriate prediction model, the battery's status at different charging and discharging stages can be more accurately understood, thereby providing a more reliable performance evaluation.

[0051] Step S13: Based on the working parameters of battery charge and discharge, the real-time charge and discharge cumulative value is obtained, the SOC (state of charge) is obtained based on the real-time charge and discharge cumulative value and the rated capacity of the battery, and the SOH (health state) is obtained based on the maximum capacity prediction value of the battery and the rated capacity of the battery; in this embodiment, the ampere-hour integration method is used to estimate the charge and discharge capacity of the battery. This method integrates the change of current over time to obtain the total amount of charge or discharge, and the formula is as follows:

[0052] △Q=∫I(t)dt

[0053] ΔQ is the charge and discharge power change, and I(t) is the current at time t. The remaining power of the battery is obtained by obtaining the initial power of the battery and ΔQ, and the SOC is obtained based on the predicted remaining power of the battery and the rated capacity of the battery.

[0054] Based on the pre-built data model matrix and combined with the current charging and discharging operating parameters, a machine learning algorithm is used to predict the maximum available capacity of the battery, and the SOH is obtained based on the maximum available capacity of the battery and the rated capacity of the battery.

[0055] By obtaining the cumulative charge and discharge values ​​in real time and accurately calculating the SOC, we can ensure that the battery is always in the best working condition and avoid overcharging or discharging, thereby extending the battery life. Through SOH monitoring, we can identify signs of battery aging early, take appropriate maintenance measures, slow down the capacity decay rate, and extend the battery life.

[0056] Step S14: Adjust the operation and maintenance status of the battery based on the working parameters of battery charge and discharge, SOC and SOH; for example, when the SOC is lower than the set lower limit (such as 20%), the system automatically starts the charging program to ensure that the battery will not be over-discharged; when the SOC is close to the upper limit (such as 80%), the system slows down or stops charging to avoid overcharging; during the charge and discharge process, according to the real-time SOC changes, dynamically adjust the charge and discharge power to keep the battery within a more ideal SOC range; dynamically adjust the charge and discharge power and SOC range to keep the battery in the best working state at all times, thereby improving the energy conversion efficiency and response speed of the energy storage system.

[0057] When SOH starts to decrease but is still within an acceptable range, the system recommends that users take preventive maintenance measures. When SOH drops to a critical value (such as below 50%), the system reminds users to consider replacing the battery to prevent a significant drop in performance from affecting system operation.

[0058] The real-time temperature data of the battery is obtained based on the temperature sensor, and the working mode of the air conditioning or cooling system is adjusted to ensure that the battery operates within an appropriate temperature range.

[0059] In summary, the energy storage battery life prediction and operation and maintenance method of this embodiment includes the following steps: constructing a data model matrix for each stage of battery cell charging and discharging, the data model matrix is ​​constructed based on laboratory data and battery cell historical data; obtaining the battery charging and discharging stage and working parameters in real time, and calling the corresponding prediction model based on the battery charging and discharging stage; predicting the maximum available capacity and capacity change trend of the battery based on the battery charging and discharging working parameters and the corresponding prediction model; obtaining the real-time charging and discharging cumulative value based on the battery charging and discharging working parameters, obtaining the SOC based on the real-time charging and discharging cumulative value and the rated capacity of the battery, and obtaining the SOH based on the maximum capacity prediction value of the battery and the rated capacity of the battery; adjusting the battery operation and maintenance status based on the battery charging and discharging working parameters, SOC and SOH. Through accurate prediction of the battery health status, an effective maintenance strategy is provided to slow down the rate of battery capacity decay.

[0060] In one embodiment, the construction of the data model matrix includes the following steps:

[0061] The battery charging and discharging stages are divided into the initial charging and discharging period, the plateau period and the terminal period; the initial charging and discharging period is usually accompanied by large current changes and voltage fluctuations; the voltage is relatively stable during the charging and discharging plateau period, and the current gradually decreases or remains constant, which is the main working stage of charging and discharging; near the end of charging and discharging, the voltage and current change again, and at the end of charging, the voltage may tend to be stable while the current drops significantly; the opposite is true at the end of discharging, and the voltage drops rapidly.

[0062] Obtain the operating parameters of the battery during charging and discharging, and obtain the SOC and SOH of the battery cell based on the operating parameters; the operating parameters include the voltage value V at both ends of the battery cell, the current I flowing into or out of the battery cell, the temperature T on the surface or inside of the battery cell, the internal resistance R of the battery cell, and the duration t of charging and discharging; the present application does not limit the selection of the operating parameters. In other embodiments, the operating parameters may also include the number of cycles, etc., without any limitation on this.

[0063] The data model matrix is ​​modified based on the SOC and SOH of the battery cell; the key parameters in the model are adjusted according to the actual measured SOC and SOH; abnormal data points are identified and processed to avoid their impact on the model. For example, when it is detected that the SOC or SOH of a battery cell deviates significantly from the normal range, it can be analyzed and processed separately.

[0064] like Figure 2 As shown, Figure 2 The schematic diagram of the principle of the data model matrix of the present application is described in detail below. Figure 2 The principle flow of the data model matrix in:

[0065] The data matrix model is composed of laboratory data and historical data; the actual operating monomer data includes stage temperature, stage current and stage charge and discharge. The laboratory data, historical data and actual operating monomer data are input into the neural network algorithm to complete the prediction; the error control correction algorithm based on the prediction results and calculation results changes the prediction factors of the neural network algorithm; the neural network algorithm can also obtain the parameters that cause deviations; the data matrix model outputs the prediction conclusion and correction method.

[0066] In one embodiment, the method further comprises:

[0067] The capacity of the single cell is estimated based on the ampere-hour integration method; in this embodiment, the calculation formula is as follows:

[0068]

[0069] Among them, i is the i-th estimation, and the time interval between two adjacent estimations is t; C0 is the rated capacity of the battery, I is the current, and η is the charge and discharge efficiency.

[0070] Assume that we have a lithium battery pack with a rated capacity of C0 = 100Ah and a charge / discharge efficiency of η = 0.95. During a certain charge / discharge process, we estimate the SOC every 1 hour (t = 1 hour). The initial SOC is 100% and the current I = 10A (discharge).

[0071] The first estimated SOC (1) is 99.05%, and the specific calculation process is omitted here.

[0072] After charging or discharging is completed, the estimated data is compared with the historical data to screen out risky cells; assuming that the estimated SOC after charging is 98%, while the historical average SOC is 99.5%, and the rated capacity C0 = 100Ah, the deviation is 1.5Ah; if the set deviation threshold is 1Ah, the cell is marked as a risky cell; in other embodiments, the deviation threshold may take other values, without any limitation.

[0073] Through precise ampere-hour integration method and efficiency factor correction, the state of charge (SOC) of the battery cell can be estimated more accurately, thereby improving the prediction accuracy of the battery capacity; after each charge and discharge cycle, the estimated capacity is compared with historical data to promptly detect capacity attenuation or other abnormal conditions of the battery cell, provide early warning of potential risks, and reduce the occurrence of unexpected failures.

[0074] The capacity of the battery cell can also be predicted, and the prediction formula is as follows:

[0075]

[0076] Among them, I is the instantaneous current, V is the instantaneous voltage, T is the current temperature, and SOHi is the current SOH state ratio. Through the neural network algorithm, the future SOH change trend is obtained; the future health risks of the battery cells are screened, compared with the risk batteries that have been located, the warning batteries are selected, and the battery cells are located.

[0077] In one embodiment, the energy storage battery life prediction and operation and maintenance method can also monitor the battery SOH in real time; if the battery SOH is lower than a first preset threshold, the temperature and current values ​​are traced; for example, the first preset threshold is 80%, if the battery SOH is lower than 80%, the temperature and current values ​​are traced; the value of the first preset threshold is not limited here.

[0078] Based on the temperature and current values, the influencing factors of SOH are obtained, and then the charge and discharge temperature and current reference values ​​are provided to slow down the battery capacity decay rate; key features such as the highest temperature, the lowest temperature, the average temperature, the maximum current, the minimum current, the average current, etc. are extracted from the traced temperature and current data; high temperature may lead to intensified chemical reactions inside the battery and accelerate aging; low temperature may affect the conductivity of the electrolyte and reduce the charge and discharge efficiency; current charge and discharge may cause more heat to be generated inside the battery, increase internal resistance, and shorten life. According to the analysis results, a reasonable charge and discharge temperature range is provided. For example, it is recommended to charge and discharge between 20℃ and 35℃ to avoid extreme temperature environments.

[0079] By real-time monitoring of SOH and timely adjustment of charging and discharging parameters, the aging process of the battery can be effectively delayed, its service life can be extended, and the replacement frequency and cost can be reduced; accurate SOH monitoring and temperature and current tracing mechanisms can promptly detect potential safety hazards such as overheating, overcharging, over-discharging, etc., and take appropriate protective measures to ensure the safe operation of the system.

[0080] In one embodiment, the energy storage battery life prediction and operation and maintenance method can also monitor the voltage of the battery cell in real time. If the difference between the voltage of a single battery cell and the average voltage exceeds the pressure threshold, it is a faulty battery cell. By real-time monitoring the battery cell voltage and setting a reasonable threshold, the voltage abnormality of the battery cell can be discovered in time, potential risks can be warned in advance, and the occurrence of unexpected failures can be reduced. By evenly charging the battery cells with large voltage differences, the consistency of the battery cells in the battery pack can be improved, the short board effect can be reduced, and the overall performance can be improved.

[0081] In one embodiment, if the faulty cell is in the initial stage of charge and discharge or the final stage of charge and discharge, the faulty cell is balanced, and the balancing steps are as follows:

[0082] Locate the battery pack where the faulty cell is located; obtain the voltage, current, temperature and other parameters of each cell in real time; determine which cells are faulty cells based on the pressure threshold, and record their location information to determine the battery pack number or position where the faulty cell is located.

[0083] At the beginning of charge and discharge or at the end of charge and discharge, reduce the charge and discharge power of the battery pack to extend the balancing time of the single cell; at the beginning of charge or discharge, appropriately reduce the charge and discharge power of the entire battery pack. For example, reduce the charging power from 1C to 0.5C, or reduce the discharging power from 1C to 0.3C; when charging is near the end (such as above 90% SOC) or discharging is near the end (such as below 10% SOC), also reduce the charge and discharge power to slow down the current change rate and ensure the stability of the balancing process. By reducing the charge and discharge power and extending the balancing time of the single cell, low-voltage cells have more time to charge and high-voltage cells have more time to discharge, thereby reducing the voltage difference and improving consistency.

[0084] Choose a longer charging period or a longer discharging period to operate the battery; choose a period of time during the day when the power load is low for charging, such as the nighttime valley electricity price period. This is not only conducive to grid load balancing, but also reduces electricity bills; choose a period of time during the day when the power demand is high for discharging, such as the daytime peak electricity price period. This can not only make full use of the energy of the energy storage system, but also achieve higher economic benefits.

[0085] In one embodiment, if the faulty cell is in the charging and discharging plateau period, the faulty cell is balanced, and the balancing steps are as follows:

[0086] Obtain the charge and discharge current and temperature of the faulty battery cell; monitor the charge and discharge current I and temperature T of the faulty battery cell in real time through the sensors installed on each battery cell; transmit the collected current and temperature data to the local control system or cloud platform through the communication interface (such as CAN bus, 485 interface, etc.).

[0087] If the difference between the temperature and the charge and discharge temperature reference value is too large, the charge and discharge power and / or the air conditioning temperature is adjusted; for example, the temperature reference value is 25°C, and the difference threshold is 5°C; the specific values ​​of the temperature reference value and the difference threshold are not limited here; if the temperature is too high, start the air conditioner or cooling system to lower the ambient temperature; if the temperature is too low, start the heating system to increase the ambient temperature to ensure that the battery cell operates within an appropriate temperature range.

[0088] If the temperature is normal, adjust the charge and discharge power; appropriately reduce the charge and discharge power, extend the balancing time of the single battery, so that the low-voltage battery has more time to charge and the high-voltage battery has more time to discharge, thereby reducing the voltage difference and improving consistency.

[0089] Through precise temperature control and power regulation, battery cells with large voltage differences can gradually converge to a consistent state, reducing the short board effect and improving the performance of the entire battery pack; through precise temperature control and power regulation, battery cells with large voltage differences can gradually converge to a consistent state, reducing the short board effect and improving the performance of the entire battery pack.

[0090] like Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an embodiment of the energy storage battery life and operation and maintenance system provided in this application, the system includes:

[0091] Data model matrix module: The data model matrix module builds a data model matrix based on laboratory data and battery cell historical data;

[0092] A battery parameter acquisition module is used to obtain the battery charging and discharging stages and operating parameters;

[0093] An operation and maintenance module, which is connected to the battery parameter acquisition module; the operation and maintenance module controls the working state of the battery based on the battery charging and discharging stages and working parameters;

[0094] A data analysis module is connected to the battery parameter acquisition module to analyze the acquired battery parameters; a data model matrix module is connected to the data analysis module, and the data model matrix module modifies the data model matrix based on the analysis results.

[0095] For the above embodiment, the present application provides a computer device, see Figure 4 , Figure 4 1 is a schematic diagram of the structure of an embodiment of a computer device of the present application. The computer device includes a memory and a processor, wherein the memory and the processor are coupled to each other, the memory stores program data, and the processor is used to execute the program data to implement the steps of any embodiment of the above-mentioned energy storage battery life and operation and maintenance method.

[0096] In this embodiment, the processor may also be referred to as a CPU (Central Processing Unit). The processor may be an integrated circuit chip having signal processing capabilities. The processor may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0097] The method of the above embodiment can be implemented in the form of a computer program, so the present application proposes a computer readable storage medium, see Figure 5 , Figure 5The structure diagram of an embodiment of a computer-readable storage medium of the present application is shown in FIG. The computer-readable storage medium stores program data that can be run by a processor, and the program data can be executed by the processor to implement the steps of any embodiment of the above-mentioned energy storage battery life prediction and operation and maintenance method.

[0098] The computer-readable storage medium in this embodiment may be a medium that can store program data, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or may be a server that stores the program data. The server may send the stored program data to other devices for execution, or may execute the stored program data by itself.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only illustrative, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0100] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0101] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0102] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for predicting the life of an energy storage battery and operating and maintaining the battery, characterized in that: include: Constructing a data model matrix for each stage of battery cell charging and discharging, wherein the data model matrix is ​​constructed based on laboratory data and battery cell historical data; Acquire the battery charging and discharging stage and working parameters in real time, and call the corresponding prediction model based on the battery charging and discharging stage; predict the maximum available capacity and capacity change trend of the battery based on the battery charging and discharging working parameters and the corresponding prediction model; Acquire a real-time charge and discharge cumulative value based on the battery charge and discharge working parameters, acquire the SOC based on the real-time charge and discharge cumulative value and the rated capacity of the battery, and acquire the SOH based on the maximum capacity prediction value of the battery and the rated capacity of the battery; The operation and maintenance state of the battery is adjusted based on the operating parameters of the battery charge and discharge, the SOC and the SOH.

2. The energy storage battery life prediction and operation and maintenance method according to claim 1 is characterized in that: The construction of the data model matrix includes the following steps: The battery charging and discharging stages are divided into an initial charging and discharging period, a plateau period, and a terminal period; Obtain the operating parameters of the battery during charging and discharging, and obtain the SOC and SOH of the battery cell based on the operating parameters; The data model matrix is ​​modified based on the SOC and SOH of the battery cell.

3. The energy storage battery life prediction and operation and maintenance method according to claim 2 is characterized in that: The method further comprises: Estimate the capacity of a single cell based on the ampere-hour integration method; After charging or discharging is completed, the estimated data is compared with historical data to screen out risky cells.

4. The energy storage battery life prediction and operation and maintenance method according to claim 3 is characterized in that: The method monitors the SOH of the battery in real time; if the SOH of the battery is lower than a first preset threshold, the temperature and current values ​​are traced back; based on the temperature and current values, the influencing factor of the SOH is obtained, and then the charging and discharging temperature and current reference values ​​are provided to slow down the battery capacity decay rate.

5. The energy storage battery life prediction and operation and maintenance method according to claim 4 is characterized in that: The method monitors the voltage of the battery cell in real time, and if the difference between the voltage of a single battery cell and the average voltage exceeds a pressure threshold, the battery cell is a faulty battery cell.

6. The energy storage battery life prediction and operation and maintenance method according to claim 5 is characterized in that: If the faulty battery cell is in the initial stage of charge and discharge or the final stage of charge and discharge, the faulty battery cell is balanced, and the balancing steps are as follows: Locate the battery pack where the faulty cell is located; Reduce the charge and discharge power of the battery pack at the beginning or end of charge and discharge to extend the balancing time of the single battery; Select a longer charging period or a longer discharging period to perform an operation on the battery.

7. The energy storage battery life prediction and operation and maintenance method according to claim 5 is characterized in that: If the faulty cell is in the charging and discharging plateau period, the faulty cell is balanced, and the balancing steps are as follows: Obtain the charge and discharge current and temperature of the faulty battery cell; If the difference between the temperature and the charge and discharge temperature reference value is too large, the charge and discharge power and / or the air conditioning temperature are adjusted; If the temperature is normal, adjust the charge and discharge power.

8. A storage battery life prediction and operation and maintenance system, characterized in that: The system includes: A data model matrix module, wherein the data model matrix module constructs a data model matrix based on laboratory data and battery cell historical data; A battery parameter acquisition module is used to obtain the battery charging and discharging stages and operating parameters; An operation and maintenance module, the operation and maintenance module is connected to the battery parameter acquisition module; the operation and maintenance module controls the working state of the battery based on the battery charging and discharging stages and working parameters; A data analysis module is connected to the battery parameter acquisition module to analyze the acquired battery parameters; the data model matrix module is connected to the data analysis module, and the data model matrix module modifies the data model matrix based on the analysis result.

9. An electronic device, characterized in that: The electronic device comprises: a memory and a processor coupled to each other, wherein the processor is used to execute program instructions stored in the memory to implement the energy storage battery life prediction and operation and maintenance method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program data, and the program data can be executed by a processor to implement the energy storage battery life prediction and operation and maintenance method as described in any one of claims 1-7.

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