Online prediction method and apparatus for remaining life of energy storage battery

Abstract

A method for online predicting remaining life of an energy storage battery, and its device, electronic device, storage medium, computer program product, and computer program are disclosed. The method includes the steps of: for each of a plurality of identical sampled energy storage batteries, acquiring sampling data of the energy storage battery at different sampling depths of charge and discharge, and establishing a mapping relationship between the energy decay rate, accumulated discharge energy, and depth of charge and discharge of the energy storage battery; acquiring the depth of discharge and the accumulated discharge energy in each discharge process from the first discharge to the current time of the target energy storage battery, and combining the mapping relationship to acquire the single energy decay rate and average single energy decay rate of the target energy storage battery; acquiring a current energy decay rate of the target energy storage battery based on the single energy decay rate corresponding to the each discharge process; acquiring a preset maximum energy decay rate of the target energy storage battery, and combining the current energy decay rate and the average single energy decay rate to acquire the remaining life of the target energy storage battery.

Description

[Technical Field]

[0001] The present disclosure relates to the field of battery technology, and in particular to a method and apparatus for online prediction of remaining life of an energy storage battery, an electronic device, a storage medium, a computer program product, and a computer program.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority from Chinese Application No. 2023102013429, filed in China on March 6, 2023, the entire contents of which are incorporated herein by reference. [Background technology]

[0003] The remaining life of an energy storage battery refers to the number of cycles remaining in the battery when its capacity or energy has decayed to a predetermined value. This is affected by various factors, such as the battery's charge / discharge power, charge / discharge depth, and ambient temperature. Understanding the remaining life of an energy storage battery during its actual operation can provide a basis for optimizing its operating strategy, system maintenance, and economic evaluation. However, currently, neither energy storage battery management systems nor energy storage power plant monitoring systems lack a corresponding remaining life prediction function, and research on the remaining life of energy storage batteries remains largely at the laboratory level. Battery cycle tests at a fixed charge / discharge power and charge / discharge depth are conducted to establish a relationship between the battery's capacity (energy) decay rate and the number of cycles, thereby predicting the remaining life of an energy storage battery. However, the operating conditions of energy storage batteries vary greatly in different application scenarios, and even for the same energy storage battery, the charge / discharge depth is constantly changing. At the same time, due to the nonlinear relationship between the capacity (energy) decay of energy storage batteries and the depth of charge and discharge, the current remaining life prediction model, which is constructed in the laboratory using a constant depth of charge and discharge, cannot fully meet the needs of engineering applications. Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure aims to solve at least part of one of the technical problems in the related art. [Means for solving the problem]

[0005] An embodiment of a first aspect of the present disclosure provides an online method for predicting remaining life of an energy storage battery, the method comprising the steps of: obtaining, for each of a plurality of identically sampled energy storage batteries, sampling data of the corresponding energy storage batteries at different sampling charge / discharge depths; and establishing a mapping relationship among the energy decay rate, accumulated discharge energy, and charge / discharge depths corresponding to the energy storage batteries based on the sampling data of the energy storage batteries, wherein the mapping relationship is expressed by the following formula:

number

[0006] An embodiment of the second aspect of the present disclosure provides an online predictor of remaining life of an energy storage battery.

[0007] An embodiment of a third aspect of the present disclosure provides an electronic device.

[0008] An embodiment of the fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium.

[0009] An embodiment of the fifth aspect of the present disclosure provides a computer program product.

[0010] An embodiment of the sixth aspect of the present disclosure provides a computer program.

[0011] An embodiment of a first aspect of the present disclosure provides an online method for predicting remaining life of an energy storage battery, the method including the steps of: for each of a plurality of identically sampled energy storage batteries, obtaining sampling data of the corresponding energy storage battery at different sampling charge and discharge depths; and establishing a mapping relationship among the energy decay rate, accumulated discharge energy, and charge and discharge depths corresponding to the energy storage battery according to the sampling data of the energy storage battery, wherein the mapping relationship is expressed by the following formula:

number

[0012] According to an embodiment of the present disclosure, the step of establishing a mapping relationship among the energy decay rate, cumulative discharge energy, and charge / discharge depth corresponding to the energy storage battery based on the sampling data of the energy storage battery includes the step of fitting the sampling data of the energy storage battery to establish a mapping relationship among the energy decay rate, cumulative discharge energy, and charge / discharge depth corresponding to the energy storage battery, wherein each sampling energy storage battery corresponds to one sampling charge / discharge depth, and the sampling data of the energy storage battery includes the sampling charge / discharge depth corresponding to each sampling energy storage battery, the sampling energy decay rate at the corresponding sampling charge / discharge depth of each sampling energy storage battery, and the sampling cumulative discharge energy.

[0013] According to an embodiment of the present disclosure, the step of acquiring sampling data of the corresponding energy storage battery at different sampling depths of charge and discharge for each of the same sampled energy storage batteries includes the steps of: performing cycle charging and discharging at different sampling depths of charge and discharge for each of the plurality of sampled energy storage batteries at a specific power; performing energy location for any of the sampled energy storage batteries after a predetermined number of cycles has elapsed; and recording the estimated discharge energy at the corresponding sampling depth of charge and discharge for the sampled energy storage battery, where the estimated discharge energy is the total energy discharged from the sampled energy storage battery from a fully charged state to a fully discharged state at the time of location; acquiring the rated energy of the sampled energy storage battery; acquiring, for any of the sampled energy storage batteries, a sampling energy decay rate of the sampled energy storage battery at the corresponding sampling depth of charge and discharge based on the estimated discharge energy and the rated energy at the corresponding sampling depth of charge and discharge for the sampled energy storage battery; and generating sampling data of the energy storage battery based on the plurality of sampling depths of charge and discharge, the sampling energy decay rate of the sampled energy storage battery at each sampling depth of charge and discharge, and the sampled accumulated discharge energy.

[0014] According to one embodiment of the present disclosure, the step of obtaining a single discharge depth in each discharge process from the first discharge of the target energy storage battery to the present time includes the steps of obtaining, in each single discharge process during operation of the target energy storage battery, a discharge start time of the target energy storage battery and an initial charge state of the target energy storage battery at the discharge start time, obtaining a discharge end time of the target energy storage battery and a final charge state of the target energy storage battery at the discharge end time, and determining the difference between the initial charge state and the final charge state as a single discharge depth corresponding to the current discharge process of the target energy storage battery.

[0015] According to one embodiment of the present disclosure, a method for obtaining an average single-cycle energy decay rate includes the steps of obtaining the sum of all single-cycle energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time, obtaining the total number of discharges from the first discharge of the target energy storage battery to the current time, and calculating the quotient of the sum and the total number of discharges as the average single-cycle energy decay rate.

[0016] According to one embodiment of the present disclosure, a method for obtaining an average single energy decay rate includes the steps of: calculating an average single discharge depth in each discharge process from the first discharge to the current time for a target energy storage battery, thereby obtaining an average single discharge depth from the first discharge to the current time for the target energy storage battery; calculating an average single cumulative discharge energy in each discharge process from the first discharge to the current time for the target energy storage battery, thereby obtaining an average single cumulative discharge energy from the first discharge to the current time for the target energy storage battery; and obtaining an average single energy decay rate based on the average discharge depth, the average cumulative single discharge energy, and the mapping relationship.

[0017] According to one embodiment of the present disclosure, the step of obtaining a current energy decay rate corresponding to the current time of the target energy storage battery based on the single energy decay rate corresponding to each discharge process of the target energy storage battery includes the step of obtaining the sum of all single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time, and using the sum as the current energy decay rate.

[0018] An embodiment of a second aspect of the present disclosure provides an online prediction device for remaining life of an energy storage battery, including: for each of a plurality of identically sampled energy storage batteries, obtaining sampling data of the corresponding energy storage battery at different sampling charge and discharge depths; and, based on the sampling data of the energy storage battery, constructing a mapping relationship between the energy decay rate, accumulated discharge energy, and charge and discharge depth corresponding to the energy storage battery, wherein the mapping relationship is expressed by the following formula:

number

[0019] According to an embodiment of the present disclosure, the relationship building module is further used to fit the sampling data of the energy storage batteries to build a mapping relationship between the energy decay rate, the accumulated discharge energy, and the charge / discharge depth corresponding to the energy storage batteries, where each sampling energy storage battery corresponds to one sampling charge / discharge depth, and the sampling data of the energy storage batteries includes: the sampling charge / discharge depth corresponding to each sampling energy storage battery, the sampling energy decay rate at the corresponding sampling charge / discharge depth of each sampling energy storage battery, and the sampling accumulated discharge energy.

[0020] According to an embodiment of the present disclosure, the relationship establishment module further performs cycle charging and discharging at different sampling charge and discharge depths for each of the plurality of sampled energy storage batteries with a specific power; for any of the sampled energy storage batteries, after a predetermined number of cycles has elapsed, performs energy location for the sampled energy storage battery, records the location discharge energy at the corresponding sampling charge and discharge depth for the sampled energy storage battery, and obtains the rated energy of the sampled energy storage battery; for any of the sampled energy storage batteries, obtains a sampled energy decay rate of the sampled energy storage battery at the corresponding sampling charge and discharge depth based on the location discharge energy and the rated energy at the corresponding sampling charge and discharge depth for the sampled energy storage battery; and generates sampling data of the energy storage battery based on the plurality of sampling charge and discharge depths, the sampled energy decay rate of the sampled energy storage battery at each sampling charge and discharge depth, and the sampled accumulated discharge energy, wherein the location discharge energy is the total energy discharged from the sampled energy storage battery from a fully charged state to a fully discharged state during location.

[0021] According to an embodiment of the present disclosure, the first acquisition module is further used to acquire, in each single discharge process during operation of the target energy storage battery, the discharge start time of the target energy storage battery and the initial charge state of the target energy storage battery at the start of discharge, acquire the discharge end time of the target energy storage battery and the final charge state of the target energy storage battery at the end of discharge, and determine the difference between the initial charge state and the final charge state as the single discharge depth corresponding to the current discharge process of the target energy storage battery.

[0022] According to an embodiment of the present disclosure, the first acquisition module further acquires the sum of all single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time, acquires the total number of discharges from the first discharge of the target energy storage battery to the current time, and uses the quotient of the sum and the total number of discharges as the average single energy decay rate.

[0023] According to an embodiment of the present disclosure, the first acquisition module further calculates an average of the depth of discharge per cycle in the discharge process from the first discharge to the current time for the target energy storage battery to obtain an average depth of discharge from the first discharge to the current time for the target energy storage battery; calculates an average of the accumulated discharge energy per cycle in the discharge process from the first discharge to the current time for the target energy storage battery to obtain an average accumulated discharge energy per cycle from the first discharge to the current time for the target energy storage battery; and is used to obtain an average energy decay rate per cycle based on the average depth of discharge, the average accumulated discharge energy, and the mapping relationship.

[0024] According to an embodiment of the present disclosure, the second acquisition module further acquires the sum of all single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time, and uses the sum as the current energy decay rate.

[0025] An embodiment of a third aspect of the present disclosure provides an electronic device comprising at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to realize an online method for predicting remaining life of an energy storage battery as described in any of the embodiments of the first aspect of the present disclosure.

[0026] An embodiment of the fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium having stored thereon computer instructions, the computer instructions being used to implement the method for online prediction of remaining life of an energy storage battery described in any of the embodiments of the first aspect of the present disclosure.

[0027] An embodiment of a fifth aspect of the present disclosure provides a computer program product including a computer program, which, when executed by a processor, realizes the method for online prediction of remaining life of an energy storage battery according to any of the embodiments of the first aspect of the present disclosure.

[0028] An embodiment of a sixth aspect of the present disclosure provides a computer program comprising computer program code, which, when executed on a computer, causes the computer to perform the method for online prediction of remaining life of an energy storage battery according to any of the embodiments of the first aspect above. [Effects of the Invention]

[0029] The present disclosure provides at least the following beneficial effects: In the embodiment of the present disclosure, a charging / discharging test is performed on a sampled energy storage battery at different depths of discharge to establish a mapping relationship between the energy decay rate, accumulated discharge energy, and charging / discharging depth corresponding to the energy storage battery, and further, based on the depth of discharge and accumulated discharge energy per discharge in the actual operating process of the target energy storage battery, a current decay rate of the target energy storage battery is estimated, thereby making an accurate online prediction of the remaining lifespan. [Brief explanation of the drawings]

[0030] The above and / or additional aspects and advantages of the present disclosure will become apparent and will be readily understood from the following detailed description of the embodiments taken in conjunction with the accompanying drawings. [Figure 1] FIG. 1 is a schematic diagram of an embodiment of a method for online prediction of remaining life of an energy storage battery according to an embodiment of the present disclosure. [Figure 2] FIG. 1 is a schematic diagram of an embodiment of a method for online prediction of remaining life of an energy storage battery according to an embodiment of the present disclosure. [Figure 3] FIG. 1 is a schematic diagram of an online prediction device for remaining life of an energy storage battery according to an embodiment of the present disclosure. [Figure 4] FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0031]

[0023] The following detailed description of the embodiments of the present disclosure is provided below. Examples of the described embodiments are shown in the accompanying drawings, and the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the drawings are illustrative and are intended to help interpret the present disclosure, but should not be understood as limiting the present disclosure.

[0032] 1 is a schematic diagram of an embodiment of an online method for predicting remaining life of an energy storage battery according to an embodiment of the first aspect of the present disclosure. As shown in FIG. 1, the online method for predicting remaining life of an energy storage battery includes the following steps S101 to S104.

[0033] In S101, for each of a plurality of identically sampled energy storage batteries, sampling data of the corresponding energy storage battery at different sampling charge and discharge depths is obtained, and a mapping relationship is established between the energy decay rate, cumulative discharge energy, and charge and discharge depths corresponding to the energy storage battery according to the sampling data of the energy storage battery.

[0034] At a specific power, multiple sampling energy storage batteries are cycled at different sampling charge and discharge depths. After a predetermined number of cycles have elapsed for any of the sampling energy storage batteries, energy calibration is performed on the sampling energy storage battery, and the calibrated discharge energy at the corresponding sampling charge and discharge depth of the sampling energy storage battery is recorded. The calibrated discharge energy is the total energy discharged by the sampling energy storage battery from a fully charged state to a fully discharged state at the time of calibration. In some embodiments, multiple sampling energy storage batteries are cycled at a power of 0.5P at different sampling charge and discharge depths in a room temperature environment (25±5°C). In some embodiments, the charge and discharge depths are selected from 100%, 80%, 50%, and 20%. release The number of cycles may be set to 50.

[0035] Obtain the rated energy of the sampling energy storage battery, where the rated energy is the rated energy when the sampling energy storage battery is shipped from the factory.

[0036] For any sampled energy storage battery, obtain the sampled energy decay rate of the sampled energy storage battery at the corresponding sampled charge / discharge depth according to the target discharge energy and rated energy of the sampled energy storage battery at the corresponding sampled charge / discharge depth. It can be easily understood that if the sampled energy storage battery has multiple target discharge energies, multiple sampled energy decay rates can be obtained correspondingly.

[0037] Based on the multiple sampling charge and discharge depths, the sampling energy decay rate of the sampling energy storage battery at each sampling charge and discharge depth, and the sampling accumulated discharge energy, sampling data of the energy storage battery is generated, and based on the sampling data of the energy storage battery, a mapping relationship is established between the energy decay rate, the accumulated discharge energy, and the charge and discharge depth corresponding to the energy storage battery.

[0038] In S102, the single discharge depth and the single accumulated discharge energy in each discharge process from the first discharge to the current time of the target energy storage battery are obtained, and the single energy decay rate and the average single energy decay rate of the target energy storage battery are obtained based on the single accumulated discharge energy, the single discharge depth, and the mapping relationship.

[0039] The single discharge depth and the single accumulated discharge energy in each discharge process from the first discharge to the current time of the target energy storage battery are obtained, and the single accumulated discharge energy and the single discharge depth are substituted into the mapping relationship between the energy decay rate, the accumulated discharge energy, and the charge / discharge depth corresponding to the energy storage battery to obtain the single energy decay rate of the target energy storage battery.

[0040] The sum of all single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the present time is obtained, and the total number of discharges from the first discharge of the target energy storage battery to the present time is obtained. The quotient of this sum and the total number of discharges is the average single energy decay rate.

[0041] In S103, a current energy decay rate corresponding to the current time of the target energy storage battery is obtained according to a single energy decay rate corresponding to each discharge process of the target energy storage battery.

[0042] The sum of all single energy decay rates corresponding to each discharge process from the first discharge to the current time of the target energy storage battery is obtained, and the sum is taken as the current energy decay rate.

[0043] In S104, a preset maximum energy decay rate corresponding to the target energy storage battery is obtained, and the current energy decay rate and the average single-cycle energy decay rate are combined to obtain the remaining lifespan of the target energy storage battery, where the remaining lifespan refers to the remaining number of charge / discharge cycles of the target energy storage battery.

[0044] A preset maximum energy decay rate corresponding to the target energy storage battery is obtained, and the current energy decay rate and the average one-time energy decay rate are combined to obtain the remaining lifespan of the target energy storage battery, where the remaining lifespan refers to the number of remaining charge / discharge cycles of the target energy storage battery, wherein:

number

[0045] An embodiment of the present disclosure provides an online method for predicting the remaining life of an energy storage battery, including: for each of a plurality of identically sampled energy storage batteries, obtaining sampling data of the corresponding energy storage battery at different sampling charge and discharge depths; and establishing a mapping relationship between the energy decay rate, accumulated discharge energy, and charge and discharge depth corresponding to the energy storage battery based on the sampling data of the energy storage battery; obtaining the single-cycle discharge depth and accumulated single-cycle discharge energy in each discharge process from the first discharge of the target energy storage battery to a current time; and obtaining a single-cycle energy decay rate and an average single-cycle energy decay rate of the target energy storage battery based on the single-cycle energy decay rate corresponding to each discharge process of the target energy storage battery; and obtaining a predetermined maximum energy decay rate corresponding to the target energy storage battery, and combining the current energy decay rate and the average single-cycle energy decay rate to obtain the remaining life of the target energy storage battery, wherein the remaining life refers to the number of remaining charge and discharge cycles of the target energy storage battery. In the embodiments of the present disclosure, charging and discharging experiments are performed on a sampled energy storage battery at different depths of discharge, and a mapping relationship is established between the energy decay rate, cumulative discharge energy, and charging and discharging depth corresponding to the energy storage battery. Furthermore, based on the depth of discharge and the cumulative discharge energy per discharge in each cycle during the actual operating process of the target energy storage battery, the current decay rate of the target energy storage battery is estimated to make an accurate online prediction of the remaining lifespan.

[0046] 2 is a schematic diagram of an embodiment of an online prediction method for remaining life of an energy storage battery according to the first embodiment of the present disclosure. As shown in FIG. 2, the online prediction method for remaining life of an energy storage battery includes the following steps S201 to S209.

[0047] In S201, for each of a plurality of identically sampled energy storage batteries, sampling data of the corresponding energy storage battery at different sampling charge / discharge depths is obtained.

[0048] At a specific power, each of the plurality of sampled energy storage batteries is cycle-charged and discharged at a different sampling charge and discharge depth. After a predetermined number of cycles have elapsed for any of the sampled energy storage batteries, energy calibration is performed for the sampled energy storage battery, and the calibration discharge energy E at the corresponding sampling charge and discharge depth of the sampled energy storage battery is calculated. i The location discharge energy is the total energy discharged from the sampling energy storage battery from a fully charged state to a fully discharged state during location.

[0049] In some embodiments, a plurality of sampled energy storage batteries are cycled at different sampling charge and discharge depths at a power of 0.5 P in a room temperature environment (25±5°C). In some embodiments, the charge and discharge depths are selected from 100%, 80%, 50%, and 20%, respectively. release The number of cycles may be set to 50.

[0050] Specifically, when a cycle is performed on the sampling energy storage battery A at 100% charge / discharge depth, the sampling energy storage battery A is charged until the State of Charge (SOC) reaches 100% and then fully discharged. The sampling energy storage battery A is then charged again until the SOC reaches 100% and then fully discharged, and this process is repeated. After 50 cycles, energy estimation is performed on the sampling energy storage battery A at a power of 0.5P. That is, the total energy discharged from the sampling energy storage battery A from a fully charged state to a fully discharged state is calculated and defined as the first estimated discharge energy of the sampling energy storage battery A. Furthermore, the process of charging the sampling energy storage battery A until the SOC reaches 100% and then fully discharging it may be repeated 50 times. That is, after the 100th cycle of the sampling energy storage battery A is completed, the total energy discharged from the sampling energy storage battery A from a fully charged state to a fully discharged state is calculated and defined as the second estimated discharge energy of the sampling energy storage battery A. The same process continues.

[0051] Specifically, when the sampling energy storage battery B is cycled at 80% charge / discharge depth, the sampling energy storage battery B is charged to 90% SOC and then discharged to 10% SOC. The sampling energy storage battery B is then charged to 90% SOC and then discharged to 10% SOC, and this process is repeated. After 50 cycles, energy estimation is performed on the sampling energy storage battery B at a power of 0.5P. That is, the total energy discharged from the sampling energy storage battery B from a fully charged state to a fully discharged state is calculated and defined as the first estimated discharge energy of the sampling energy storage battery B. Furthermore, the process of charging the sampling energy storage battery B to 90% SOC and then discharging to 10% SOC may be repeated 50 times. That is, after the 100th cycle of the sampling energy storage battery B is completed, the total energy discharged from the sampling energy storage battery B from a fully charged state to a fully discharged state is calculated and defined as the second estimated discharge energy of the sampling energy storage battery B. The same process continues.

[0052] Specifically, when the sampling energy storage battery C is cycled at a 50% charge / discharge depth, the sampling energy storage battery C is charged to an SOC of 75% and then discharged to an SOC of 25%. The sampling energy storage battery C is then charged to an SOC of 75% and then discharged to an SOC of 25%, and this process is repeated. After 50 cycles, energy estimation is performed on the sampling energy storage battery C at a power of 0.5P. That is, the total energy discharged from the sampling energy storage battery C from a fully charged state to a fully discharged state is calculated and defined as the first estimated discharge energy of the sampling energy storage battery C. Furthermore, the process of charging the sampling energy storage battery C to an SOC of 75% and then discharging it to an SOC of 25% may be repeated 50 times. That is, after the 100th cycle of the sampling energy storage battery C, the total energy discharged from the sampling energy storage battery C from a fully charged state to a fully discharged state is calculated and defined as the second estimated discharge energy of the sampling energy storage battery C. The same process continues.

[0053] Specifically, when the sampling energy storage battery D is cycled at a 20% charge / discharge depth, the sampling energy storage battery D is charged to an SOC of 60% and then discharged to an SOC of 40%. The sampling energy storage battery D is then charged to an SOC of 60% and then discharged to an SOC of 40%, and this process is repeated. After 50 cycles, the sampling energy storage battery D is subjected to energy localization at a power of 0.5P. That is, the total energy discharged by the sampling energy storage battery D from a fully charged state to a fully discharged state is calculated and defined as the first localized discharge energy of the sampling energy storage battery D. Furthermore, the process of charging the sampling energy storage battery D to an SOC of 60% and then discharging it to an SOC of 40% may be repeated 50 times. That is, after the 100th cycle of the sampling energy storage battery D is completed, the total energy discharged by the sampling energy storage battery D from a fully charged state to a fully discharged state is calculated and defined as the second localized discharge energy of the sampling energy storage battery D. The same process continues.

[0054] Obtain the rated energy of the sampling energy storage battery, where the rated energy is the rated energy when the sampling energy storage battery is shipped from the factory.

[0055] For any sampled energy storage battery, the sampled energy decay rate of the sampled energy storage battery at the corresponding sampled charge / discharge depth is obtained based on the target discharge energy and rated energy of the sampled energy storage battery at the corresponding sampled charge / discharge depth. It can be easily understood that if the sampled energy storage battery has multiple target discharge energies, multiple sampled energy decay rates can be obtained correspondingly. The calculation formula for the energy loss rate is as follows:

number

[0056] Sampling data of the energy storage battery is generated based on a plurality of sampling charge and discharge depths, a sampling energy decay rate of the sampling energy storage battery at each sampling charge and discharge depth, and a sampling accumulated discharge energy.

[0057] In S202, the sampling data of the energy storage battery is fitted to establish a mapping relationship between the energy decay rate, the accumulated discharge energy, and the charge / discharge depth corresponding to the energy storage battery.

[0058] The sampling data of the energy storage battery is fitted to establish a mapping relationship between the energy decay rate, cumulative discharge energy, and charge / discharge depth of the energy storage battery. The mapping relationship is as follows:

number

[0059] In S203, the accumulated discharge energy of the target energy storage battery in each discharge process from the first discharge to the current time is obtained.

[0060] The cumulative discharge energy of each discharge process from the first discharge of the target energy storage battery to the present time is obtained, and E dis-iwhere i represents the ith time.

[0061] In S204, in each single discharge process during operation of the target energy storage battery, the discharge start time of the target energy storage battery and the initial charge state of the target energy storage battery at the start of discharge are obtained, and the discharge end time of the target energy storage battery and the final charge state of the target energy storage battery at the end of discharge are obtained.

[0062] In each single discharge process during operation of the target energy storage battery, the discharge start time of the target energy storage battery and the initial charge state of the target energy storage battery at the start of discharge are obtained, and the discharge end time of the target energy storage battery and the final charge state of the target energy storage battery at the end of discharge are obtained.

[0063] In some examples, it may be determined that the initial state of charge of the target energy storage battery is 60% at the start of discharge, and the final state of charge of the target energy storage battery is 15% at the end of discharge.

[0064] In S205, the difference between the initial state of charge and the final state of charge is determined as the single discharge depth corresponding to the current discharge process of the target energy storage battery.

[0065] The difference between the initial state of charge and the final state of charge is obtained, and the difference is taken as the single discharge depth corresponding to the current discharge process of the target energy storage battery.

[0066] In some embodiments, based on the initial state of charge of the target energy storage battery at the start of discharge being 60% and the final state of charge of the target energy storage battery at the end of discharge being 15%, the single discharge depth corresponding to the current discharge process of the target energy storage battery is 60%-15%=45%.

[0067] In S206, the single energy decay rate of the target energy storage battery is obtained based on the single cumulative discharge energy, the single discharge depth and the mapping relationship.

[0068] The single cumulative discharge energy and single discharge depth are substituted into the above mapping relationship to obtain the single energy decay rate of the target energy storage battery.

number

[0069] In S207, the average single energy decay rate of the target energy storage battery is obtained.

[0070] Obtain the average single energy decay rate of the target energy storage battery, E l-a and record it.

[0071] In some embodiments, the average single energy decay rate E l-a The method of obtaining is to calculate all the single energy decay rates E corresponding to each discharge process from the first discharge of the target energy storage battery to the present time. l-i The sum of

number

number

[0072] In some embodiments, the average single energy decay rate E l-aThe method of obtaining is to calculate the average depth of discharge x from the first discharge of the target energy storage battery to the current time by calculating the average depth of discharge in each discharge process from the first discharge of the target energy storage battery to the current time. a and calculating an average of the cumulative discharge energy of each discharge from the first discharge to the current time for the target energy storage battery, and obtaining an average cumulative discharge energy E dis-a and obtaining an average one-time energy decay rate based on the average depth of discharge, the average one-time cumulative discharge energy, and the mapping relationship. The formula for the average one-time energy decay rate is as follows:

number

[0073] In S208, the sum of all single energy decay rates corresponding to each discharge process from the first discharge to the current time of the target energy storage battery is obtained, and the sum is taken as the current energy decay rate.

[0074] The sum of all single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time is obtained, and the sum is taken as the current energy decay rate. l-c The formula for calculating the current energy decay rate is as follows:

number

[0075] In S209, a preset maximum energy decay rate corresponding to the target energy storage battery is obtained, and the current energy decay rate and the average single-cycle energy decay rate are combined to obtain the remaining lifespan of the target energy storage battery, where the remaining lifespan refers to the number of remaining charge / discharge cycles of the target energy storage battery.

[0076] The preset maximum energy decay rate E corresponding to the target energy storage battery end Obtain the current energy decay rate E l-c and the average one-time energy decay rate E l-a The remaining life of the target energy storage battery is obtained by combining the above. Here, the remaining life refers to the number of remaining charge / discharge cycles of the target energy storage battery. The calculation formula for the remaining life of the target energy storage battery is as follows:

number

[0077] In the embodiments of the present disclosure, charging and discharging experiments are performed on a sampled energy storage battery at different depths of discharge to establish a mapping relationship between the energy decay rate, cumulative discharge energy, and depth of charge and discharge corresponding to the energy storage battery; and further, based on the depth of discharge and the cumulative discharge energy per discharge in each cycle during the actual operating process of the target energy storage battery, the current decay rate of the target energy storage battery is estimated to make an accurate online prediction of the remaining lifespan.

[0078] 3 is a schematic diagram of an online prediction device for remaining life of an energy storage battery according to an embodiment of the second aspect of the present disclosure. As shown in FIG. 3, the online prediction device for remaining life of an energy storage battery 300 includes a relationship building module 301, a first acquisition module 302, a second acquisition module 303, and a third acquisition module 304.

[0079] The relationship construction module 301 is used to obtain, for each of a plurality of identically sampled energy storage batteries, sampling data of the corresponding energy storage battery at different sampling charge and discharge depths, and construct a mapping relationship between the energy decay rate, accumulated discharge energy, and charge and discharge depths corresponding to the energy storage battery based on the sampling data of the energy storage battery.

[0080] The first acquisition module 302 is used to acquire the single discharge depth and the single accumulated discharge energy in each discharge process from the first discharge to the present time of the target energy storage battery, and acquire the single energy decay rate of the target energy storage battery and the average single energy decay rate of the target energy storage battery based on the single accumulated discharge energy, the single discharge depth, and the mapping relationship.

[0081] The second obtaining module 303 is used to obtain a current energy decay rate corresponding to the current time of the target energy storage battery according to a single energy decay rate corresponding to each discharge process of the target energy storage battery.

[0082] The third obtaining module 304 obtains a preset maximum energy decay rate corresponding to the target energy storage battery, and combines the current energy decay rate and the average single-cycle energy decay rate to obtain the remaining lifespan of the target energy storage battery, where the remaining lifespan refers to the remaining number of charge / discharge cycles of the target energy storage battery.

[0083] The device performs charge and discharge experiments on a sampled energy storage battery at different discharge depths, establishes a mapping relationship between the energy decay rate, cumulative discharge energy, and charge and discharge depth corresponding to the energy storage battery, and further estimates the current decay rate of the target energy storage battery based on the single discharge depth and single cumulative discharge energy of each time during the actual operating process of the target energy storage battery, thereby making an accurate online prediction of the remaining lifespan.

[0084] According to an embodiment of the present disclosure, the relationship building module 301 is further used to fit the sampling data of the energy storage batteries to build a mapping relationship between the energy decay rate, the accumulated discharge energy, and the charge / discharge depth corresponding to the energy storage batteries, where each sampling energy storage battery corresponds to one sampling charge / discharge depth, and the sampling data of the energy storage batteries includes the sampling charge / discharge depth corresponding to each sampling energy storage battery, the sampling energy decay rate at the corresponding sampling charge / discharge depth of each sampling energy storage battery, and the sampling accumulated discharge energy.

[0085] According to an embodiment of the present disclosure, the relationship establishment module 301 further performs cycle charging and discharging at different sampling charge and discharge depths for each of the plurality of sampled energy storage batteries at a specific power, and for any of the sampled energy storage batteries, after a predetermined number of cycles have elapsed, performs energy location for the sampled energy storage battery and records the location discharge energy at the corresponding sampling charge and discharge depth of the sampled energy storage battery, where the location discharge energy is the total energy discharged by the sampled energy storage battery from a fully charged state to a fully discharged state during location; obtains the rated energy of the sampled energy storage battery; and for any of the sampled energy storage batteries, obtains the sampled energy decay rate of the sampled energy storage battery at the corresponding sampling charge and discharge depth based on the location discharge energy and rated energy at the corresponding sampling charge and discharge depth of the sampled energy storage battery; which is used to generate sampling data of the energy storage battery based on the plurality of sampling charge and discharge depths, the sampled energy decay rate of the sampled energy storage battery at each sampling charge and discharge depth, and the sampled accumulated discharge energy.

[0086] According to an embodiment of the present disclosure, the first acquisition module 302 is further used to acquire, in each single discharge process during operation of the target energy storage battery, the discharge start time of the target energy storage battery and the initial charge state of the target energy storage battery at the start of discharge, acquire the discharge end time of the target energy storage battery and the final charge state of the target energy storage battery at the end of discharge, and determine the difference between the initial charge state and the final charge state as the single discharge depth corresponding to the current discharge process of the target energy storage battery.

[0087] According to an embodiment of the present disclosure, the first obtaining module 302 further obtains the sum of all single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time, obtains the total number of discharges from the first discharge of the target energy storage battery to the current time, and uses the quotient of the sum and the total number of discharges as the average single energy decay rate.

[0088] According to an embodiment of the present disclosure, the first acquisition module 302 further calculates the average depth of discharge in each discharge process from the first discharge to the current time for the target energy storage battery to obtain the average depth of discharge from the first discharge to the current time for the target energy storage battery; calculates the average accumulated discharge energy in each discharge process from the first discharge to the current time for the target energy storage battery to obtain the average accumulated single discharge energy from the first discharge to the current time for the target energy storage battery; and is used to obtain the average single energy decay rate based on the average depth of discharge, the average accumulated single discharge energy, and the mapping relationship.

[0089] According to an embodiment of the present disclosure, the second obtaining module 303 further obtains the sum of all single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time, and uses the sum as the current energy decay rate.

[0090] To realize the above-described embodiments, an embodiment of the third aspect of the present disclosure further provides an electronic device 400. As shown in Fig. 4, the electronic device 400 includes a processor 401 and a memory 402 communicatively connected to the processor. The memory 402 stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor 401 to realize the online method for predicting remaining life of an energy storage battery according to any embodiment of the first aspect.

[0091] To realize the above embodiment, an embodiment of a fourth aspect of the present disclosure further provides a non-transitory computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to cause a computer to realize the method for online prediction of remaining life of an energy storage battery according to any embodiment of the first aspect above.

[0092] To realize the above embodiment, an embodiment of a fifth aspect of the present disclosure further provides a computer program product including a computer program, which, when executed by a processor, realizes the online prediction method for remaining life of an energy storage battery according to any embodiment of the first aspect above.

[0093] To realize the above embodiments, an embodiment of a sixth aspect of the present disclosure provides a computer program including computer program code, which, when executed on a computer, causes the computer to perform the method for online prediction of remaining life of an energy storage battery according to any embodiment of the first aspect.

[0094] It should be noted that the interpretation and explanation of the method and apparatus for online prediction of remaining life of an energy storage battery in the above-described embodiments are also applicable to the computer-readable storage medium, computer program product, and computer program in the embodiments of the present disclosure, and therefore will not be repeated here.

[0095] All embodiments of the present disclosure may be practiced individually or in combination with other embodiments and are all considered to be within the scope of the claims of the present disclosure.

[0096] Additionally, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or the number of technical features shown. Thus, a feature identified as "first" or "second" can explicitly or implicitly include one or more of that feature. In describing this disclosure, "plurality" means two or more unless expressly limited otherwise.

[0097] In the description herein, references to terms such as "one embodiment," "some embodiments," "exemplary," "particular examples," or "some examples" mean that the particular features, structures, materials, or characteristics described in combination with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the present specification, the use of the term "exemplary" does not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, unless mutually inconsistent, those skilled in the art may combine or combine different embodiments or examples and features of different embodiments or examples described herein.

[0098] Although examples of the present disclosure have been shown and described above, it should be understood that the above examples are illustrative and should not be construed as limiting the present disclosure, and those skilled in the art may make changes, modifications, substitutions, and variations to the above examples within the scope of the present disclosure.

Claims

1. 1. A method for online prediction of remaining life of an energy storage battery, comprising: For each of a plurality of identically sampled energy storage batteries, obtain sampling data of the corresponding energy storage battery at different sampling charge / discharge depths; and establish a mapping relationship between the energy decay rate, cumulative discharge energy, and charge / discharge depth corresponding to the energy storage battery according to the sampling data of the energy storage battery, wherein the mapping relationship is expressed by the following formula: [Equation 1] and a step represented by (where ELR is the energy decay rate, Add Ｅ is the cumulative discharge energy, x is the depth of discharge, the value of x ranges from 0 to 100%, and a, b, c, and d are fitting parameters. Obtaining a single discharge depth and a single accumulated discharge energy in each discharge process from the first discharge to the current time of the target energy storage battery, and obtaining a single energy decay rate of the target energy storage battery and an average single energy decay rate of the target energy storage battery according to the single accumulated discharge energy, the single discharge depth, and the mapping relationship; According to the single energy decay rate corresponding to each discharge process of the target energy storage battery, obtaining a current energy decay rate corresponding to the current time of the target energy storage battery; obtaining a preset maximum energy decay rate corresponding to the target energy storage battery, and combining the current energy decay rate and the average single-cycle energy decay rate to obtain a remaining lifespan of the target energy storage battery, wherein the remaining lifespan refers to the number of remaining charge / discharge cycles of the target energy storage battery.

2. Building a mapping relationship between an energy decay rate, a cumulative discharge energy, and a charge / discharge depth corresponding to the energy storage battery based on the sampling data of the energy storage battery, includes:

2. The method of claim 1, comprising: fitting sampling data of the energy storage batteries to establish a mapping relationship among energy decay rates, accumulated discharge energies, and charge / discharge depths corresponding to the energy storage batteries, wherein each of the sampled energy storage batteries corresponds to one sampled charge / discharge depth, and the sampling data of the energy storage batteries includes sampled charge / discharge depths corresponding to each of the sampled energy storage batteries, sampled energy decay rates at the corresponding sampled charge / discharge depths of each of the sampled energy storage batteries, and sampled accumulated discharge energies.

3. The step of acquiring sampling data of the corresponding energy storage battery at different sampling charge / discharge depths for each of the plurality of identically sampled energy storage batteries includes: a step of performing cycle charging and discharging at different sampling charge and discharge depths for each of the plurality of sampling energy storage batteries at a specific power, and performing energy localization for any of the sampling energy storage batteries after a predetermined number of cycles have elapsed, and recording the localized discharge energy of the sampling energy storage battery at the corresponding sampling charge and discharge depth, wherein the localized discharge energy is the total energy discharged by the sampling energy storage battery from a fully charged state to a fully discharged state during localization; obtaining a rated energy of the sampling energy storage battery; For any of the sampled energy storage batteries, obtaining a sampled energy decay rate of the sampled energy storage battery at a corresponding sampled charge / discharge depth based on the specified discharge energy and the rated energy at the corresponding sampled charge / discharge depth of the sampled energy storage battery; generating sampling data of the energy storage battery based on the plurality of sampling charge and discharge depths, a sampling energy decay rate of the sampling energy storage battery at each of the sampling charge and discharge depths, and a sampling accumulated discharge energy.

4. The step of obtaining a single discharge depth in each discharge process from the first discharge of the target energy storage battery to the present time includes: In each single discharge process during operation of the target energy storage battery, obtaining a discharge start time of the target energy storage battery and an initial charge state of the target energy storage battery at the start of the discharge, and obtaining a discharge end time of the target energy storage battery and a final charge state of the target energy storage battery at the end of the discharge; and determining a difference between the initial state of charge and the final state of charge as the single depth of discharge corresponding to a current discharge process of the target energy storage battery.

5. The method for obtaining the average single energy decay rate includes: Obtaining the sum of all the single energy decay rates corresponding to each discharge process from the first discharge to the current time of the target energy storage battery; Obtaining a total number of discharges from the first discharge to the current time of the target energy storage battery; The method according to any one of claims 1 to 4, further comprising the step of determining the average single-discharge energy decay rate as the quotient of said sum and said total number of discharges.

6. The method for obtaining the average single energy decay rate includes: Calculating an average depth of discharge of the target energy storage battery in each discharge process from the first discharge to the current time, thereby obtaining an average depth of discharge of the target energy storage battery from the first discharge to the current time; Calculating an average of the accumulated discharge energy of each discharge process from the first discharge to the current time for the target energy storage battery, and obtaining an average accumulated discharge energy of the target energy storage battery from the first discharge to the current time; and obtaining the average single-cycle energy decay rate based on the average depth of discharge, the average single-cycle cumulative discharge energy, and the mapping relationship.

7. According to the single energy decay rate corresponding to each discharge process of the target energy storage battery, obtaining a current energy decay rate corresponding to the current time of the target energy storage battery, The method according to any one of claims 1 to 6, comprising: obtaining a sum of all the single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time, and setting the sum as the current energy decay rate.

8. An online predictor of remaining life of an energy storage battery, comprising: A relationship construction module, for each of a plurality of identically sampled energy storage batteries, obtains sampling data of the corresponding energy storage battery at different sampling charge and discharge depths, and constructs a mapping relationship between the energy decay rate, cumulative discharge energy, and charge and discharge depth corresponding to the energy storage battery according to the sampling data of the energy storage battery, wherein the mapping relationship is expressed by the following formula: [Equation 2] and a relationship building module, denoted by (where ELR is the energy decay rate, Add Ｅ is the cumulative discharge energy, x is the depth of discharge, the value of x ranges from 0 to 100%, and a, b, c, and d are fitting parameters. A first acquisition module acquires a single discharge depth and a single accumulated discharge energy in each discharge process from the first discharge to the current time of the target energy storage battery, and acquires a single energy decay rate of the target energy storage battery and an average single energy decay rate of the target energy storage battery according to the single accumulated discharge energy, the single discharge depth, and the mapping relationship; A second acquisition module acquires a current energy decay rate corresponding to a current time of the target energy storage battery according to the single energy decay rate corresponding to each discharge process of the target energy storage battery; and a third acquisition module for acquiring a preset maximum energy decay rate corresponding to the target energy storage battery, and combining the current energy decay rate and the average single-cycle energy decay rate to acquire a remaining lifespan of the target energy storage battery, wherein the remaining lifespan refers to the number of remaining charge / discharge cycles of the target energy storage battery.

9. 9. The apparatus of claim 8, wherein the relationship building module is further used to fit the sampling data of the energy storage batteries to build a mapping relationship between the energy decay rate, the accumulated discharge energy, and the charge / discharge depth corresponding to the energy storage batteries, wherein each sampling energy storage battery corresponds to one sampling charge / discharge depth, and the sampling data of the energy storage batteries includes: the sampling charge / discharge depth corresponding to each sampling energy storage battery, the sampling energy decay rate at the corresponding sampling charge / discharge depth of each sampling energy storage battery, and the sampling accumulated discharge energy.

10. The relationship establishment module further performs cycle charging and discharging at different sampling charge and discharge depths for each of the plurality of sampling energy storage batteries at a specific power, and performs energy localization for any of the sampling energy storage batteries after a predetermined number of cycles have elapsed, and records the localized discharge energy of the sampling energy storage battery at the corresponding sampling charge and discharge depth; Obtain the rated energy of the sampling energy storage battery, For any sampled energy storage battery, obtain a sampled energy decay rate of the sampled energy storage battery at the corresponding sampled charge / discharge depth based on the standard discharge energy and rated energy of the sampled energy storage battery at the corresponding sampled charge / discharge depth; Generate sampling data of the energy storage battery according to a plurality of sampling charge and discharge depths, a sampling energy decay rate of the sampling energy storage battery at each sampling charge and discharge depth, and a sampling cumulative discharge energy; The device according to claim 8 or 9, wherein the location discharge energy is a total sum of energy discharged from a sampling energy storage battery from a fully charged state to a fully discharged state during location.

11. The first acquisition module further acquires, in each single discharge process during the operation of the target energy storage battery, a discharge start time of the target energy storage battery and an initial charge state of the target energy storage battery at the discharge start time, and acquires a discharge end time of the target energy storage battery and a final charge state of the target energy storage battery at the discharge end time; The device according to any one of claims 8 to 10, wherein the difference between the initial state of charge and the final state of charge is used as a single discharge depth corresponding to a current discharge process of the target energy storage battery.

12. The first acquisition module further acquires a sum of all single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time; Obtain the total number of discharges from the first discharge of the target energy storage battery to the present time, The device according to any one of claims 8 to 11, wherein the quotient of the sum and the total number of discharges is used as an average one-time energy decay rate.

13. The first acquisition module further calculates an average depth of discharge of the target energy storage battery in each discharge process from the first discharge to the current time, to obtain an average depth of discharge of the target energy storage battery from the first discharge to the current time; For the target energy storage battery, calculate the average of the single cumulative discharge energy in each discharge process from the first discharge to the current time, and obtain the average single cumulative discharge energy of the target energy storage battery from the first discharge to the current time; The device according to any one of claims 8 to 11, which is used to obtain an average single energy decay rate based on an average depth of discharge, an average single cumulative discharge energy, and a mapping relationship.

14. The device according to any one of claims 8 to 13, wherein the second acquisition module further acquires the sum of all single energy decay rates corresponding to each discharge process from the first discharge of the target energy storage battery to the current time, and the sum is used as a current energy decay rate.

15. at least one processor; a memory communicatively coupled to the at least one processor, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the method of any one of claims 1 to 7.

16. A non-transitory computer-readable storage medium having stored thereon computer instructions for causing a computer to carry out the method of any one of claims 1 to 7.

17. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1 to 7.

18. A computer program comprising computer program code which, when run on a computer, causes the computer to carry out a method according to any one of claims 1 to 7.

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