CN103760494B - Method and system for estimating battery capacity online - Google Patents

Abstract

The invention belongs to the technical field of lithium batteries, and discloses an online battery capacity estimation method, comprising: S1. providing a capacity decay model and initializing capacity decay model parameters; S2. charging and discharging the battery to be tested Δn times; S3. calculating the current Estimated relative capacity attenuation of the battery to be tested; S4. Test to obtain the real relative capacity attenuation of the battery to be tested; S5. Calculate the estimated error; S6. If the estimated error is greater than the preset allowable error, use the estimated error to adjust the capacity attenuation model parameters Perform correction and update, and then jump to step S2; S7. If the estimated error is less than or equal to the preset allowable error, then determine the last corrected capacity fading model parameter as the final capacity fading model parameter, and obtain the accurate capacity fading model corresponding to the battery to be tested , and then estimate the relative capacity fading and battery capacity of the battery to be tested in the future. The invention also discloses an online battery capacity estimation system. The method and system of the invention are easy to operate and have high accuracy.

Description

Battery capacity online estimation method and system

technical field

The invention relates to the technical field of lithium batteries, in particular to a battery capacity online estimation method and system.

Background technique

Against the backdrop of increasingly serious problems such as environmental pollution, energy crisis, and greenhouse effect, the development of new energy technologies has become a top priority. Among them, battery technology, as one of the new energy technologies, has attracted more and more attention from everyone. Lithium-ion power batteries, in particular, are developing very rapidly and have been widely used in electric vehicles, energy storage stations and other fields. However, as the battery is charged and discharged cyclically, the battery will gradually age and its performance will gradually decline, especially its capacity will gradually decrease.

In actual situations, the reduction of battery capacity will affect the amount of energy stored inside the battery. For example, in electric vehicles, it will directly affect the driving range of electric vehicles. If there is no accurate estimation of battery capacity attenuation, accidents such as breaking down in the wild may occur due to misestimation of driving range. Moreover, if there is no reasonable understanding of capacity fading, it may lead to excessive use of the battery, which will lead to a sharp decline in battery life. Therefore, in the actual battery use process, the corresponding battery management system should have a reasonable estimation algorithm for the battery capacity.

The most accurate way to estimate the battery capacity is to accurately measure the capacity of the battery through the battery capacity test in the laboratory, generally speaking, it is a small rate constant current charge and discharge test. However, for the battery on the real car, its charging may be carried out on the charging pile, so its charging condition may be standard constant current charging or constant current and constant voltage charging, but its discharge is usually determined by the actual road conditions and driving conditions. It is definitely not possible to be a standard constant current discharge condition, and the battery may not be able to be discharged every time, so the actual power discharged by the battery each time is often not equal to its capacity, and for dynamic conditions It is very difficult to estimate the capacity of the next battery. If the calibration is performed regularly, accurate values can be obtained, but if the calibration is too frequent, the user experience will be poor, and if the calibration frequency is too low, the effect will be poor in other time periods except for a period of time immediately after the calibration.

The purpose of the present invention is to propose a battery capacity estimation method based on a model-based open-loop capacity estimation plus periodic calibration correction, so as to be able to reasonably estimate the battery capacity in an actual battery management system.

Contents of the invention

The present invention aims at at least solving the problems of complex operation and low accuracy in the prior art.

Therefore, an object of the present invention is to provide an online battery capacity estimation method with simple operation and high accuracy.

Another object of the present invention is to provide an online battery capacity estimation system with simple operation and high accuracy.

In order to achieve the above object, the battery capacity online estimation method according to an embodiment of one aspect of the present invention includes: S1. providing a capacity fading model and initializing the parameters of the capacity fading model; S2. charging and discharging the battery to be tested Δn times, wherein Δn is A preset positive integer; S3. According to the capacity fading model and capacity fading model parameters, obtain the estimated relative capacity fading of the current battery under test; S4. Test the actual capacity of the current battery under test to obtain the The actual relative capacity attenuation of the battery to be tested; S5. Subtracting the real relative capacity attenuation from the estimated relative capacity attenuation to obtain an estimated error; S6. If the estimated error is greater than the preset allowable error, use The estimated error corrects and updates the parameters of the capacity fading model, and then jumps to step S2; S7. If the estimated error is less than or equal to the preset allowable error, then determine the last corrected capacity fading model parameter as the final capacity fading model parameters to obtain an accurate capacity fading model corresponding to the battery under test, wherein the accurate capacity fading model is used to estimate the relative capacity fading amount and battery capacity of the battery under test in the future.

The battery capacity online estimation method according to the embodiment of the present invention has the advantages of simple operation and high accuracy.

In addition, the battery capacity online estimation method according to the embodiment of the present invention also has the following additional technical features:

In an example of the present invention, the capacity fading model of the battery to be tested is: in, is the estimated relative capacity fading of the battery under test after the nth charge-discharge cycle, is the estimated relative capacity decay of the battery under test after the n-1 charge-discharge cycle, T is the ambient temperature when the battery under test is carried out for the n-th charge-discharge cycle, and the first parameter represented by A, Indicates the second parameter, and z indicates the third parameter.

In an example of the present invention, the initializing the parameters of the capacity fading model includes: selecting the reference battery of the same type as the battery to be tested and whose final capacity fading model parameters are known, and initializing the A as the reference battery Corresponding to the first parameter in the capacity fading model, initialize the is the second parameter in the capacity fading model corresponding to the reference battery, and initializes z to be the third parameter in the capacity fading model corresponding to the reference battery.

In an example of the present invention, the calculation formula of the true relative capacity fading of the battery under test is Wherein, C is the detected real capacity of the battery under test at present, and _C0 is the initial capacity of the battery under test.

In an example of the present invention, the correcting and updating the parameters of the capacity fading model by using the estimation error specifically includes: updating the first parameter, and the formula for correcting the first parameter is: A _k =A _{k- 1} +k ₁ ×Δξ; to update the second parameter, the second parameter correction formula is: The third parameter is updated, and the third parameter correction formula is: z _k =z _k-1 +k ₃ ×Δξ, wherein, k in the subscript represents the corrected parameter, and k-1 in the subscript represents For parameters before correction, k ₁ represents a preset first correction coefficient, k ₂ represents a preset second correction coefficient, k ₃ represents a preset third correction coefficient, and Δξ represents the estimation error.

The battery capacity online estimation system according to another embodiment of the present invention includes: a charging and discharging cycle counting module, which is used to record the number of charging and discharging cycles of the battery to be tested, and restarts from zero every time the number of charging and discharging cycles reaches Δn times Start counting; the capacity fading model parameter storage unit is used to store the capacity fading model parameters; the estimated relative capacity fading calculation module, the estimated relative capacity fading calculation module is respectively connected with the charge-discharge cycle counting module and the capacity fading model The parameter storage unit is connected, and is used to bring the parameters of the capacity fading model into the capacity fading model to obtain the estimated relative capacity fading of the battery under test every time a charge-discharge cycle is completed; the real relative capacity fading test module, the The real relative capacity attenuation calculation module is connected to the charge-discharge cycle counting module, and is used to test the real capacity of the battery under test and calculate the capacity of the battery under test whenever the number of charge-discharge cycles is Δn times. Real relative capacity attenuation; a comparison and judgment module, the comparison and judgment module is respectively connected with the estimated relative capacity attenuation calculation module and the real relative capacity attenuation test module, and the comparison and judgment module is used to compare the real relative capacity attenuation Comparing the amount of capacity attenuation with the estimated relative capacity attenuation to obtain an estimation error, and judging the size relationship between the estimation error and the preset allowable error; the capacity attenuation model parameter correction module, the capacity attenuation model parameter correction module and the capacity attenuation model parameter correction module respectively The comparison and judgment module is connected to the capacity fading model parameter storage unit, and when the judgment result of the comparison and judgment module is that the estimation error is greater than a preset allowable error, the capacity fading model parameter correction module uses the estimation error Correcting and updating the capacity fading model parameters, and storing the updated capacity fading model parameters into the capacity fading model parameter storage unit; the final capacity fading model output module, the final capacity fading model output module and the said final capacity fading model output module respectively The comparison and judgment module is connected to the parameter storage unit of the capacity fading model, and when the judgment result of the comparison and judgment module is that the estimated error is less than or equal to the preset allowable error, the final capacity fading model output module converts the capacity fading model to The parameters of the capacity fading model stored in the parameter storage unit are used as the parameters of the final capacity fading model, and the final capacity fading model is obtained and output, wherein the final capacity fading model is used to carry out the relative capacity fading and battery capacity of the battery under test in the future. estimate.

The battery capacity online estimation system according to the embodiment of the present invention has the advantages of simple operation and high accuracy.

In addition, the battery capacity online estimation system according to the embodiment of the present invention also has the following additional technical features:

In an example of the present invention, the capacity fading model of the battery to be tested is: in, is the estimated relative capacity fading of the battery under test after the nth charge-discharge cycle, is the estimated relative capacity decay of the battery under test after the n-1 charge-discharge cycle, T is the ambient temperature when the battery under test is carried out for the n-th charge-discharge cycle, and the first parameter represented by A, Indicates the second parameter, and z indicates the third parameter.

In an example of the present invention, in the capacity fading model parameter storage unit, the initialization of the capacity fading model parameters includes: selecting the reference battery of the same type as the battery to be tested and whose final capacity fading model parameters are known, Initializing the A as the first parameter in the capacity fading model corresponding to the reference battery, initializing the is the second parameter in the capacity fading model corresponding to the reference battery, and initializes z to be the third parameter in the capacity fading model corresponding to the reference battery.

In an example of the present invention, in the real relative capacity fading test module, the formula for calculating the real relative capacity fading of the battery to be tested is: Wherein, C is the detected real capacity of the battery under test at present, and _C0 is the initial capacity of the battery under test.

In an example of the present invention, the capacity fading model parameter correction module specifically includes: a first parameter correction module, configured to update the first parameter, and the first parameter correction formula is: A _k =A _k-1 +k ₁ ×Δξ; the second parameter correction module is used to update the second parameter, and the second parameter correction formula is: The third parameter correction module is used to update the third parameter. The third parameter correction formula is: z _k =z _k-1 +k ₃ ×Δξ, where k in the subscript represents the corrected parameter, The k-1 in the subscript represents the parameter before correction, k ₁ represents the preset first correction coefficient, k ₂ represents the preset second correction coefficient, k ₃ represents the preset third correction coefficient, Δξ represents the Estimate error.

Additional aspects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and comprehensible from the description of the embodiments in conjunction with the following drawings, wherein:

Fig. 1 is a flowchart of an online battery capacity estimation method according to an embodiment of the present invention.

Fig. 2 is a structural block diagram of an online battery capacity estimation system according to an embodiment of the present invention.

Fig. 3 is a block diagram of the internal structure of the capacity fading model parameter correction module in the battery capacity online estimation system according to an embodiment of the present invention.

Fig. 4 is a schematic diagram of the variation of the parameters of the capacity fading model and the variation of the estimation error according to an embodiment of the present invention.

Fig. 5 is a comparison chart of an online estimation curve and an experimental test curve according to an embodiment of the present invention.

Fig. 6 is a principle diagram of the battery capacity online estimation of the present invention.

detailed description

Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

In the description of the present invention, it should be understood that the terms "first" and "second" are used for description purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Thus, a feature defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

In the present invention, unless otherwise clearly specified and limited, terms such as "installation", "connection", "connection" and "fixation" should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection , or integrated; it can be mechanically connected or electrically connected; it can be directly connected or indirectly connected through an intermediary, and it can be the internal communication of two components or the interaction relationship between two components. Those of ordinary skill in the art can understand the specific meanings of the above terms in the present invention according to specific situations.

Any process or method descriptions in flowcharts or otherwise described herein may be understood to represent modules, segments or portions of code comprising one or more executable instructions for implementing specific logical functions or steps of the process , and the scope of preferred embodiments of the invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order depending on the functions involved, which shall It is understood by those skilled in the art to which the embodiments of the present invention pertain.

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium, For use with instruction execution systems, devices, or devices (such as computer-based systems, systems including processors, or other systems that can fetch instructions from instruction execution systems, devices, or devices and execute instructions), or in conjunction with these instruction execution systems, devices or equipment for use.

It should be understood that various parts of the present invention can be realized by hardware, software, firmware or their combination. In the embodiments described above, various steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques known in the art: Discrete logic circuits, ASICs with suitable combinational logic gates, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. During execution, one or a combination of the steps of the method embodiments is included. The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, and the like.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing module, each unit may exist separately physically, or two or more units may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules. If the integrated modules are realized in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium.

As shown in Figure 1, the method for online estimation of battery capacity according to one embodiment of the present invention may include the following steps:

S1. Provide a capacity fading model and initialize parameters of the capacity fading model.

In an example of the present invention, the capacity fading model of the battery to be tested is: in, is the estimated relative capacity fading of the battery under test after the nth charge-discharge cycle, is the estimated relative capacity fading of the battery under test after the n-1 charge-discharge cycle, T is the ambient temperature when the battery under test performs the n-th charge-discharge cycle, the first parameter represented by A, Indicates the second parameter, and z indicates the third parameter.

In an example of the present invention, the process of initializing the parameters of the capacity fading model specifically includes: selecting a reference battery of the same type as the battery to be tested and whose final capacity fading model parameters are known, and initializing A as the value in the capacity fading model corresponding to the reference battery The first parameter, initialized To be the second parameter in the capacity fading model corresponding to the reference battery, initialize z to be the third parameter in the capacity fading model corresponding to the reference battery.

S2. Charge and discharge the battery to be tested Δn times, wherein Δn is a preset positive integer.

For example, Δn may take a value of 90 times, or any other number of times.

S3. According to the capacity fading model and the parameters of the capacity fading model, an estimated relative capacity fading amount of the current battery to be tested is obtained.

Specifically, the estimated relative capacity fading of the battery under test is calculated by using the capacity fading model and the parameters of the capacity fading model every charge and discharge cycle. If the accumulative charging and discharging cycles have been performed k*Δn times before, the current estimated relative capacity fading of the battery under test is also calculated through k*Δn times.

S4. Test the real capacity of the current battery under test to obtain the real relative capacity attenuation of the battery under test.

In an example of the present invention, the calculation formula of the true relative capacity decay of the battery to be tested is Wherein, C is the detected real capacity of the current battery under test, and C ₀ is the initial capacity of the battery under test. It should be noted that testing the actual capacity of the current battery to be tested usually needs to be performed in a laboratory, and the operation is cumbersome. Therefore, it is preferable not to test the real capacity of the battery under test after every charge and discharge cycle, but to test the real capacity of the current battery under test once after every charge and discharge cycle Δn times.

S5. Subtracting the actual relative capacity fading from the estimated relative capacity fading to obtain an estimation error.

the estimation error Among them, ξ is the detected real relative capacity fading of the battery under test, is the calculated estimated relative capacity fading of the battery under test.

S6. If the estimated error is greater than the preset allowable error, the estimated error is used to correct and update the parameters of the capacity fading model, and then skip to step S2.

In an example of the present invention, specifically, to update the first parameter, the first parameter correction formula is: A _k =A _k-1 +k ₁ ×Δξ; to update the second parameter, the second parameter correction formula for: The third parameter is updated, and the third parameter correction formula is: z _k =z _k-1 +k ₃ ×Δξ. Among them, k in the subscript represents the parameter after correction, k-1 in the subscript represents the parameter before correction, k ₁ represents the preset first correction coefficient, k ₂ represents the preset second correction coefficient, and k ₃ represents the preset third correction coefficient, and Δξ represents the estimation error. After the correction is completed, skip to step S2 to start a new round of iterative correction.

S7. If the estimated error is less than or equal to the preset allowable error, then determine the capacity fading model parameters after the last correction as the final capacity fading model parameters, and obtain the accurate capacity fading model corresponding to the battery to be tested, wherein the accurate capacity fading model is used for future treatment Estimate the relative capacity fading and battery capacity of the measured battery.

Specifically, after the first parameter, the second parameter and the third parameter are finally determined, an accurate capacity fading model can be obtained. According to the accurate capacity fading model, the relative capacity fading amount after each charge-discharge cycle can be obtained, and then according to the formula C=(1-ξ)C ₀ , the battery capacity can be estimated online.

The battery capacity online estimation method according to the above embodiments of the present invention has the advantages of simple operation and high accuracy.

As shown in Figure 2, the battery capacity online estimation system according to one embodiment of the present invention may include the following parts: charge and discharge cycle counting module 10, capacity decay model parameter storage unit 20, estimated relative capacity decay calculation module 30, real relative capacity Attenuation testing module 40 , comparison and judgment module 50 , capacity fading model parameter correction module 60 and final capacity fading model output module 70 .

The charging and discharging cycle counting module 10 is used to record the number of charging and discharging cycles of the battery under test, and restart counting from zero every time the number of charging and discharging cycles reaches Δn times.

The capacity fading model parameter storage unit 20 is used for storing capacity fading model parameters.

In an example of the present invention, in the capacity fading model parameter storage unit 20, the initially stored (i.e. initialized) capacity fading model parameters include: selecting a reference battery of the same type as the battery to be tested and whose final capacity fading model parameters are known , initialize A as the first parameter in the capacity fading model corresponding to the reference battery, and initialize To be the second parameter in the capacity fading model corresponding to the reference battery, initialize z to be the third parameter in the capacity fading model corresponding to the reference battery.

The estimated relative capacity fading calculation module 30 is connected to the charging and discharging cycle counting module 10 and the capacity fading model parameter storage unit 20 respectively. The estimated relative capacity fading calculation module 30 is used to bring the parameters of the capacity fading model into the capacity fading model to obtain the estimated relative capacity fading of the battery to be tested every time a charge-discharge cycle is completed.

In an example of the present invention, the capacity fading model of the battery to be tested is: in, is the estimated relative capacity fading of the battery under test after the nth charge-discharge cycle, is the estimated relative capacity fading of the battery under test after the n-1 charge-discharge cycle, T is the ambient temperature when the battery under test performs the n-th charge-discharge cycle, the first parameter represented by A, Indicates the second parameter, and z indicates the third parameter.

It should be noted that, every charge and discharge cycle of the battery under test, the estimated relative capacity fading calculation module 30 calculates the estimated relative capacity fading of the battery under test by using the capacity fading model and the parameters of the capacity fading model. If the accumulative charging and discharging cycles have been performed k*Δn times before, the current estimated relative capacity fading of the battery under test is also calculated through k*Δn times.

The real relative capacity decay calculation module 40 is connected to the charging and discharging cycle counting module 10 . The real relative capacity fading test module 40 is used to test the real capacity of the battery under test and calculate the real relative capacity fading of the battery under test every time the number of charge and discharge cycles is Δn.

In an example of the present invention, in the real relative capacity fading test module 40, the calculation formula of the real relative capacity fading of the battery to be tested is: Wherein, C is the detected real capacity of the current battery under test, and C ₀ is the initial capacity of the battery under test.

The comparison judgment module 50 is connected to the estimated relative capacity fading calculation module 30 and the real relative capacity fading testing module 40 respectively. The comparison and judgment module 50 is used to compare the actual relative capacity attenuation with the estimated relative capacity attenuation to obtain an estimation error, and judge the magnitude relationship between the estimation error and the preset allowable error.

Specifically, calculate the estimation error Among them, ξ is the detected real relative capacity fading of the battery under test, is the calculated estimated relative capacity fading of the battery under test. Then judge the size of the estimation error compared with the preset allowable error.

The capacity fading model parameter correction module 60 is connected to the comparison judgment module 50 and the capacity fading model parameter storage unit 20 respectively. When the judgment result of the comparison judgment module 50 is that the estimation error is greater than the preset allowable error, the capacity fading model parameter correction module 60 uses the estimation error to correct and update the capacity fading model parameters, and stores the updated capacity fading model parameters into the capacity fading Model parameter storage unit 20 .

In an example of the present invention, as shown in FIG. 3 , the capacity fading model parameter correction module 60 specifically includes: a first parameter correction module 610 , a second parameter correction module 620 and a third parameter correction module 630 . The first parameter correction module 610 is used to update the first parameter, and the first parameter correction formula is: A _k =A _k-1 +k ₁ ×Δξ. The second parameter correction module 620 is used to update the second parameter, and the second parameter correction formula is: The third parameter correction module 630 is used to update the third parameter, and the third parameter correction formula is: z _k =z _k-1 +k ₃ ×Δξ. In the above formula, k in the subscript represents the parameter after correction, k-1 in the subscript represents the parameter before correction, k ₁ represents the preset first correction coefficient, k ₂ represents the preset second correction coefficient, k ₃ represents a preset third correction coefficient, and Δξ represents an estimation error.

The final capacity fading model output module 70 is connected to the comparison judgment module 50 and the capacity fading model parameter storage unit 20 respectively. When the judgment result of the comparison judgment module 50 is that the estimation error is less than or equal to the preset allowable error, the final capacity fading model output module 70 uses the capacity fading model parameters stored in the capacity fading model parameter storage unit 20 as the final capacity fading model parameters to obtain the final Capacity decay model and output. The final capacity fading model is used to estimate the relative capacity fading and battery capacity of the battery to be tested in the future.

Specifically, after the first parameter, the second parameter and the third parameter are finally determined, an accurate capacity fading model can be obtained. According to the accurate capacity fading model, the relative capacity fading amount after each charge-discharge cycle can be obtained, and then according to the formula C=(1-ξ)C ₀ , the battery capacity can be estimated online.

The battery capacity online estimation system according to the embodiment of the present invention has the advantages of simple operation and high accuracy.

In order to enable those skilled in the art to better understand the effects of the present invention, the applicant cites a specific example as follows.

In a lithium-ion battery with lithium iron phosphate as the positive electrode and graphite as the negative electrode, first use a reference battery of the same type with known parameters to determine the initial battery capacity decay model parameters: A=0.15, Ea/R=1400, z= 0.5. The parameters of this capacity fading model are slightly different from the actual model parameters of the battery. Through the above-mentioned estimation method and system of the present invention, iterative corrections are made step by step. After a period of time, the parameters of the battery capacity attenuation model are gradually corrected. During the open-loop estimation, the estimation error of the capacity is basically less than 1%, and the model parameters are also gradually corrected. Converge to a fixed value, refer to Figure 4. After determining the final capacity fading model parameters, the battery is estimated online. It can be seen from FIG. 5 that the online estimation curve of the relative capacity of the lithium battery obtained by the method and system of the present invention has a high degree of coincidence with the experimental test curve, indicating that the accuracy of the online estimation result is high.

All in all, the idea of battery capacity online estimation in the present invention is that in order to reduce costs in the stage of battery grouping and management system, the battery capacity decay model parameters are not tested specifically, but the same type of battery capacity decay standard parameters are used; At this time, the capacity decay model is used to estimate the capacity of the battery in an open loop according to key parameters such as battery charge and discharge current, ambient temperature, and charge and discharge depth. The result of capacity estimation can be used in the management algorithm of the battery management system, so that the entire system Car energy management algorithms, etc.; every once in a while, calibrate the battery, measure its real capacity, compare the measurement results with the model estimation results, use the model estimation error feedback to correct the model parameters, and update the battery management with the measured capacity data The battery capacity value of the system. The logic block diagram of online estimation of the entire capacity is shown in Fig. 6 . Such an estimation algorithm has high estimation accuracy, low system cost, and can adapt to different batches of batteries from different manufacturers.

In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the described specific features, structures, materials or characteristics may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention, those skilled in the art can make the above-mentioned The embodiments are subject to changes, modifications, substitutions and variations.

Claims (6)

1. An online estimation method for battery capacity is characterized by comprising the following steps:

s1, providing a capacity attenuation model and initializing capacity attenuation model parameters, wherein the capacity attenuation model of the battery to be tested is as follows:wherein,the estimated relative capacity attenuation of the battery to be tested after the nth charge-discharge cycle,is the estimated relative capacity attenuation of the battery to be tested after the n-1 th charge-discharge cycle, T is the ambient temperature of the battery to be tested when the battery to be tested is subjected to the n-th charge-discharge cycle, A represents a first parameter,represents a second parameter, z represents a third parameter;

s2, circulating charge and discharge of the battery to be tested for delta n times, wherein delta n is a preset positive integer;

s3, obtaining the estimated relative capacity attenuation of the current battery to be tested according to the capacity attenuation model and the capacity attenuation model parameters;

s4, testing the current real capacity of the battery to be tested to obtain the real relative capacity attenuation of the battery to be tested, wherein the calculation formula of the real relative capacity attenuation of the battery to be tested isWherein C is the detected real capacity of the current battery to be detected, C₀The initial capacity of the battery to be tested is obtained;

s5, subtracting the real relative capacity attenuation amount from the estimated relative capacity attenuation amount to obtain an estimation error;

s6, if the estimation error is larger than a preset allowable error, correcting and updating the capacity attenuation model parameters by using the estimation error, and then jumping to the step S2;

and S7, if the estimation error is less than or equal to the preset allowable error, determining the capacity attenuation model parameter after the last correction as a final capacity attenuation model parameter to obtain an accurate capacity attenuation model corresponding to the battery to be measured, wherein the accurate capacity attenuation model is used for estimating the relative capacity attenuation and the battery capacity of the battery to be measured in the future.

2. The online estimation method of battery capacity according to claim 1, characterized in that the initializing capacity fading model parameters comprises: selecting a reference battery with the same type as the battery to be tested and known final capacity attenuation model parameters, initializing the A as a first parameter in a capacity attenuation model corresponding to the reference battery, and initializing the AInitializing z as a third parameter in the capacity fading model corresponding to the reference battery for a second parameter in the capacity fading model corresponding to the reference battery.

3. The method according to claim 1, wherein the modifying and updating the capacity fading model parameters by using the estimation error specifically comprises:

updating the first parameter, wherein a first parameter correction formula is as follows: a. the_k＝A_k-1+k₁×Δξ；

Updating the second parameter, wherein the second parameter modification formula is as follows:

updating the third parameter, wherein a third parameter correction formula is as follows: z is a radical of_k＝z_k-1+k₃×Δξ，

Wherein k in the subscript represents the parameter after correction, k-1 in the subscript represents the parameter before correction, k₁Representing a preset first correction factor, k₂Representing a preset second correction factor, k₃Represents a preset third correction coefficient, and Δ ξ represents the estimation error.

4. An online estimation system for battery capacity, comprising:

the charging and discharging cycle counting module is used for recording the charging and discharging cycle times of the battery to be tested, and counting from zero again when the charging and discharging cycle times reach delta n times;

a capacity fading model parameter storage unit is used for storing capacity fading model parameters, wherein a capacity fading model of a battery to be tested is as follows:wherein,the estimated relative capacity attenuation of the battery to be tested after the nth charge-discharge cycle,is the estimated relative capacity attenuation of the battery to be tested after the n-1 th charge-discharge cycle, T is the ambient temperature of the battery to be tested when the battery to be tested is subjected to the n-th charge-discharge cycle, A represents a first parameter,represents a second parameter, z represents a third parameter;

the estimated relative capacity attenuation calculation module is respectively connected with the charge-discharge cycle counting module and the capacity attenuation model parameter storage unit and is used for substituting the capacity attenuation model parameters into a capacity attenuation model to obtain the estimated relative capacity attenuation of the battery to be measured every time 1 charge-discharge cycle is completed;

the real relative capacity attenuation testing module is connected with the charging and discharging cycle counting module and used for testing the real capacity of the battery to be tested and calculating the real relative capacity attenuation of the battery to be tested when the charging and discharging cycle times are delta n times, wherein in the real relative capacity attenuation testing module, the calculation formula of the real relative capacity attenuation of the battery to be tested is thatWherein C is the detected real capacity of the current battery to be detected, C₀The initial capacity of the battery to be tested is obtained;

the comparison and judgment module is respectively connected with the estimation relative capacity attenuation calculation module and the real relative capacity attenuation test module, and is used for comparing the real relative capacity attenuation with the estimation relative capacity attenuation to obtain an estimation error and judging the size relationship between the estimation error and a preset allowable error;

the capacity attenuation model parameter correction module is respectively connected with the comparison judgment module and the capacity attenuation model parameter storage unit, and when the judgment result of the comparison judgment module is that the estimation error is larger than a preset allowable error, the capacity attenuation model parameter correction module corrects and updates the capacity attenuation model parameter by using the estimation error and stores the updated capacity attenuation model parameter into the capacity attenuation model parameter storage unit;

and the final capacity attenuation model output module is respectively connected with the comparison and judgment module and the capacity attenuation model parameter storage unit, and when the judgment result of the comparison and judgment module is that the estimation error is less than or equal to a preset allowable error, the final capacity attenuation model output module takes the capacity attenuation model parameters stored in the capacity attenuation model parameter storage unit as final capacity attenuation model parameters to obtain and output a final capacity attenuation model, wherein the final capacity attenuation model is used for estimating the relative capacity attenuation and the battery capacity of the battery to be detected in the future.

5. The battery capacity online estimation system according to claim 4, wherein in the capacity fading model parameter storage unit, the initialized capacity fading model parameters include: selecting the power to be testedInitializing a reference battery with the same type of battery and known final capacity attenuation model parameters, wherein A is a first parameter in a capacity attenuation model corresponding to the reference battery, and initializing the reference batteryInitializing z as a third parameter in the capacity fading model corresponding to the reference battery for a second parameter in the capacity fading model corresponding to the reference battery.

6. The system for online estimation of battery capacity according to claim 4, wherein the capacity fading model parameter modification module specifically comprises:

a first parameter modification module, configured to update the first parameter, where the first parameter modification formula is: a. the_k＝A_k-1+k₁×Δξ；

A second parameter modification module, configured to update the second parameter, where the second parameter modification formula is:

a third parameter modification module, configured to update the third parameter, where the third parameter modification formula is: z is a radical of_k＝z_k-1+k₃×Δξ，

Wherein k in the subscript represents the parameter after correction, k-1 in the subscript represents the parameter before correction, k₁Representing a preset first correction factor, k₂Representing a preset second correction factor, k₃Represents a preset third correction coefficient, and Δ ξ represents the estimation error.