Battery SOP estimation method and system based on self-learning

Abstract

The present invention provides a battery SOP estimation method and system based on self-learning, the method includes the following steps: when it is detected that the battery meets the first cut-off condition, start this self-learning; set this charging key factor α and this discharge key factor β; calculate this SOP charging limit and / or this SOP discharge limit respectively according to this charging key factor α and this discharge key factor β and charge and / or discharge the battery; when it is detected that the battery meets the first cut-off condition again, record this discharge capacity Cc, and if this discharge capacity Cc is greater than backup discharge capacity Cl, then record this charging key factor α and this discharge key factor β at the same time. The SOP estimation method and system of the present invention can make the battery always charge and discharge with preferably SOP charge and discharge limit during multiple operations, reduce the computational workload while ensuring SOP estimation accuracy, and save the economic cost and time cost of SOP algorithm research and development.

Description

Technical Field

[0001] The present invention mainly relates to the field of battery management systems, and in particular to a battery SOP estimation method and system based on self-learning. Background Art

[0002] Lithium-ion batteries are widely used in the fields of new energy electric vehicles and energy storage systems. Accurate estimation and monitoring of the status of lithium batteries is an important research topic in the battery management system (BMS). The battery power state SOP (State of Power) is used to characterize the peak charge and discharge power of the battery in its current state. It is an important performance parameter related to the starting, acceleration, and climbing of electric vehicles, as well as the grid frequency regulation and peak-shaving and valley-filling of power storage systems. SOP is closely related to factors such as the battery state of charge, aging state, battery internal resistance characteristics, and temperature, and changes nonlinearly and dynamically. These factors make it difficult to accurately estimate SOP. At present, SOP estimation methods mainly include estimation methods based on experimental data interpolation, methods based on equivalent models, and methods based on data-driven methods.

[0003] First, the experimental data interpolation method is an offline power estimation method. The estimation method is relatively simple and mainly includes the Hybrid Pulse Power Characteristic (HPPC) method, the Japanese Electric Vehicle Power Test Method (JEVS method), and the SOP true value test method given in the technical conditions for battery management systems for electric vehicles in my country. The HPPC test method assumes that the pulse current is constant, and the JEVS test method does not consider the influence of temperature factors. Both have certain limitations, thus affecting the accuracy of SOP estimation. The method for testing the true SOP value given in the technical conditions for battery management systems for electric vehicles in my country can accurately reflect the SOP of the battery, but at the same temperature and the same state of charge (SOC), at least five constant power pulse tests are required. Therefore, in order to obtain complete SOP data for lithium batteries at different temperatures, different SOC states, and different peak durations such as 10s / 30s / 60s, the experimental testing workload required is very large.

[0004] SOP estimation methods based on equivalent models can reflect the dynamic characteristics of lithium batteries. They typically include first-order RC models, extended first-order RC models, and second-order RC models. These methods are often combined with Kalman filtering, extended Kalman filtering, and double Kalman filtering. The accuracy requirements for the selected model are relatively high, and a balance must be struck between the complexity of the selected model, the accuracy of the model, and the robustness of the SOP estimation results. Data-driven SOP estimation methods, on the other hand, require a large amount of data support and place high demands on computational complexity.

[0005] In summary, in the battery management system, there are still problems that need to be further studied and solved for the estimation of battery power status. It is very meaningful to propose a method for determining the battery power status with a small amount of experimental testing and a simple estimation method. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a battery SOP estimation method and system based on self-learning, which can enable the battery to always charge and discharge at a relatively optimal SOP charge and discharge limit during multiple operations, while ensuring the SOP estimation accuracy and reducing the computational workload, saving the economic and time costs of SOP algorithm development.

[0007] To solve the above technical problems, the present invention provides a battery SOP estimation method based on self-learning, which is suitable for estimating the SOP discharge limit and SOP charge limit of the battery during multiple operation processes of the battery, wherein the operation process includes a charging process and / or a discharging process, and the method comprises the following steps:

[0008] During the current operation of the battery, when it is detected that the battery meets the first cut-off condition, the current self-learning is started;

[0009] Setting a key factor α for this charge and a key factor β for this discharge, wherein the values ​​of the key factor α for this charge and the key factor β for this discharge are both constants greater than 0 and less than 1;

[0010] Calculate the current SOP charge limit and / or the current SOP discharge limit according to the current charging key factor α and the current discharging key factor β respectively;

[0011] Charging and / or discharging the battery according to the current SOP charging limit and / or the current SOP discharging limit;

[0012] When it is detected that the battery meets the first cut-off condition again, the current discharge capacity Cc is recorded, and if the current discharge capacity Cc is greater than the backup discharge capacity Cl, the current charge key factor α and the current discharge key factor β are also recorded; and

[0013] The current self-learning is stopped until the first cut-off condition is detected again during the next battery operation and the next self-learning is started.

[0014] In one embodiment of the present invention, the first cut-off condition includes the battery SOC=0 and reaching a discharge cut-off condition, or the battery SOC=100% and reaching a charge cut-off condition.

[0015] In one embodiment of the present invention, when the first cut-off condition is that the battery SOC=0 and the discharge cut-off condition is reached, the method specifically includes:

[0016] During the battery operation, when it is detected that the battery meets the battery SOC=0 and reaches the discharge cut-off condition, the self-learning is started;

[0017] Setting the current charging key factor α and the current discharging key factor β, wherein the values ​​of the current charging key factor α and the current discharging key factor β are both constants greater than 0 and less than 1;

[0018] Calculate the current SOP charge limit and the current SOP discharge limit according to the current charging key factor α and the current discharging key factor β respectively;

[0019] Charging the battery according to the current SOP charging limit;

[0020] During the charging process of the battery, when it is detected that the second cut-off condition is met, the battery is discharged according to the current discharge key factor β until the battery meets the first cut-off condition again, wherein the second cut-off condition is that the battery SOC=100% and the charging cut-off condition is met.

[0021] In one embodiment of the present invention, the method further includes, when it is detected that the battery does not satisfy the battery SOC=0 and reaches the discharge cut-off condition, calculating the current SOP discharge limit value based on the previous discharge key factor β recorded during the previous operation of the battery, and discharging the battery according to the current SOP discharge limit value until the battery satisfies the battery SOC=0 and reaches the discharge cut-off condition, and then starting this self-learning.

[0022] In one embodiment of the present invention, the method further includes determining not to start self-learning this time when it is detected that the battery does not meet the battery SOC=0 and reaches the discharge cut-off condition, and the battery is being charged at the same time, until it is detected again that the battery meets the battery SOC=0 and reaches the discharge cut-off condition.

[0023] In an embodiment of the present invention, for the three processes of the previous self-learning before the current self-learning, the current self-learning, and the next self-learning, the method specifically includes:

[0024] If the current charging key factor α and the current discharging key factor β are recorded in the current self-learning, then in the process of the next self-learning, constant values different from the values of the current charging key factor α and the current discharging key factor β are assigned to the next charging key factor α and the next discharging key factor β within the range greater than 0 and less than 1, and for the process of the next self-learning, the backup discharge capacity Cl is the discharge capacity in the process of the current self-learning;

[0025] If the current charging key factor α and the current discharging key factor β are not recorded in the current self-learning, and the previous charging key factor α and the previous discharging key factor β are recorded in the previous self-learning, then in the process of the next self-learning, constant values different from the values of the previous charging key factor α and the previous discharging key factor β are assigned to the next charging key factor α and the next discharging key factor β within the range greater than 0 and less than 1, and for the next self-learning, the backup discharge capacity Cl is the discharge capacity in the process of the previous self-learning.

[0026] In an embodiment of the present invention, the step of calculating the current SOP charging limit includes calculating the current SOP charging limit SOP according to the SOC of the battery, the rated power P of the battery T and the current charging factor α according to the following rules 充电 :

[0027] If SOC <= α, then SOP 充电 = P T *(SOC / α);

[0028] If α < SOC < 1 - α, then SOP 充电 = P T ; and

[0029] If SOC >= 1 - α, then SOP 充电 = P T *(1 - SOC / α).

[0030] In an embodiment of the present invention, the step of calculating the current SOP discharge limit includes calculating the current SOP discharge limit SOP according to the battery SOC, the rated power P of the battery T and the current discharge factor β according to the following rules 放电 :

[0031] If SOC <= β, then SOP放电 = P T *(SOC / β);

[0032] If β < SOC < 1 - β, then SOP 放电 = P T ; and

[0033] If SOC >= 1 - β, then SOP 放电 = P T *(1 - SOC / β).

[0034] To solve the above technical problems, another aspect of the present invention also proposes a battery SOP estimation system based on self - learning, including: a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement the above battery SOP estimation method based on self - learning.

[0035] A computer - readable medium storing computer program code, the computer program code implementing the above battery SOP estimation method based on self - learning when executed by a processor.

[0036] Compared with the prior art, the present invention has the following advantages: The present invention mainly aims at the problems of the current SOP estimation method, such as a large amount of preliminary test work, a high complexity of the equivalent model, and a large amount of solving and calculation. Combining with the actual application situation of lithium - ion batteries, it does not completely use the charging cut - off voltage and the discharging cut - off voltage as the voltage limit conditions for SOP estimation in traditional SOP tests, but dynamically adjusts the SOP estimation result during the operation of the battery. The self - learning - based SOP intelligent tracking self - learning algorithm of the present invention can efficiently determine the power state of lithium - ion batteries. The present invention reduces the test workload in the process of obtaining battery parameters in the off - grid state. In addition, the present invention combines with the actual project application of lithium - ion batteries, which is beneficial to improving the battery safety and service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are provided to provide a further understanding of the present application. They are incorporated and constitute a part of the present application. The drawings illustrate embodiments of the present application and, together with this specification, serve to explain the principles of the present invention. In the drawings:

[0038] Figure 1 is a flowchart showing a battery SOP estimation method based on self - learning according to an embodiment of the present invention;

[0039] Figure 2 is a flowchart showing a battery SOP estimation method based on self - learning according to another embodiment of the present invention; and

[0040] Figure 3This is a system block diagram of a battery SOP estimation system based on self-learning according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] To more clearly illustrate the technical solutions of the embodiments of this application, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0042] As used in this application and the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0043] Unless otherwise specifically stated, the relative arrangement of the parts and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to actual proportional relationships. The techniques, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered as part of the authorization specification. In all examples shown and discussed here, any specific values ​​should be interpreted as being merely exemplary and not as limitations. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following figures, and therefore, once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0044] In the description of this application, it should be understood that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "horizontal, vertical, vertical, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the scope of protection of this application; the directional words "inside and outside" refer to the inside and outside relative to the outline of each component itself.

[0045] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used herein to describe the spatial positional relationship of a device or feature to other devices or features as shown in the figures. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figures. For example, if the device in the drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0046] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is solely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. Furthermore, while the terms used in this application are selected from commonly known and commonly used terms, some terms mentioned in this specification may have been selected by the applicant at his or her discretion, and their detailed meanings are explained in the relevant sections of this description. Furthermore, this application should be understood not only by the actual terms used, but also by the meaning implied by each term.

[0047] An embodiment of the present invention refers to Figure 1 A battery SOP estimation method 10 based on self-learning (hereinafter referred to as "estimation method 10") is proposed, which is suitable for estimating the SOP discharge limit and SOP charge limit of the battery during multiple battery operations. Estimation method 10 can make the battery always charge and discharge at a better SOP charge and discharge limit during multiple operations, while ensuring the accuracy of SOP estimation and reducing the computational workload, saving the economic and time costs of SOP algorithm development. Figure 1 The estimation method 10 is further described in detail.

[0048] like Figure 1 As shown, the estimation method 10 includes the following steps.

[0049] Step 11 is to start this self-learning when it is detected that the battery meets the first cut-off condition during this operation of the battery. It is understandable that the battery often needs to work based on the system, such as the battery management system BMS in the car, or the battery energy storage system, etc. The present invention is not limited to the test scenario of a single battery, but places the battery in any specific application scenario and continuously optimizes the SOP estimate of the battery during its life cycle. For ease of explanation, the initial startup step of step 11 is to power on the system where the battery is located.

[0050] Step 12 is to set the current charging key factor α and the current discharging key factor β. The values ​​of the current charging key factor α and the current discharging key factor β are both constants greater than 0 and less than 1. The values ​​and significance of the two factors will be further explained below.

[0051] Step 13 is to calculate the current SOP charge limit and / or the current SOP discharge limit based on the current charge key factor α and the current discharge key factor β. The specific calculation method will be further described below with reference to the specific embodiments.

[0052] Step 14 is to charge and / or discharge the battery according to this SOP charge limit and / or this SOP discharge limit. As mentioned above, estimation method 10 is suitable for estimating the SOP discharge limit and SOP charge limit of the battery during multiple operations of the battery, wherein the operation process includes the charging process or the discharging process of the battery. Whether the battery is specifically charged or discharged in step 14 depends on whether the battery initial state when the self-learning is started is charging or discharging. Generally speaking, according to the characteristics of the battery core, the self-learning process in different embodiments of the present invention all needs to complete charging first and then discharge, so in different embodiments of the present invention, step 14 all ends with the discharge process. But it is not excluded that in some specific embodiments, the order of charging and discharging can change, such as being the process of recharging and discharging after first discharging is completed, and these embodiments can be reasonably adjusted for the step of specific charging and discharging according to the conception to be described in detail below of the present invention. Specific details about charging and discharging will be further described below according to other accompanying drawings.

[0053] Step 15 is to record the current discharge capacity Cc when it is detected that the battery meets the first cut-off condition again. If the current discharge capacity Cc is greater than the backup discharge capacity Cl, the current charging key factor α and the current discharge key factor β are recorded at the same time. Step 16 stops the current self-learning. Until the first cut-off condition is detected again during the next battery operation and the next self-learning is started. Figure 1 In the example, step 16 is directly connected to the initial step of system power-on, which means that Figure 1The estimation method 10 shown is a repetitive cycle during the long-term operation of the system in which the battery is located, so that the SOP charge limit and the SOP discharge limit can be continuously optimized during the battery life, thereby optimizing the battery performance as a whole and maintaining a better power state.

[0054] exist Figure 1 In the estimation method 10 shown, step 15 is the core step for achieving self-learning. Step 15 means the process of performing self-learning each time. As described above, for most embodiments of the present invention, the charging process will be completed before discharging during the self-learning process, so in step 15, the current discharge capacity Cc will be compared with the previous discharge capacity Cl. If the result obtained is Cc>Cl, it means that the values ​​of the charging key factor α and the current discharge key factor β set in this self-learning process are better, and it is determined that it can make the battery have a better SOP charging limit and SOP discharge limit than the previous time. In this case, the charging key factor α and the discharge key factor β set for this self-learning are recorded as a reference for the next self-learning. Exemplarily, in some embodiments of the present invention, the charging key factor α and the discharge key factor β in each self-learning process can be set in the following manner.

[0055] Taking the three processes of the previous self-learning, the current self-learning, and the next self-learning as an example, the specific method of setting the charging key factor α and the discharging key factor β is as follows:

[0056] If the current charging key factor α and the current discharging key factor β are recorded in the current self-learning process, then in the next self-learning process, the next charging key factor α and the next discharging key factor β are assigned constant values ​​different from the values ​​of the current charging key factor α and the current discharging key factor β in the range of greater than 0 and less than 1. In such an embodiment, for the next self-learning process, the backup discharge capacity Cl is the discharge capacity during the current self-learning process;

[0057] If the current charging key factor α and the current discharging key factor β are not recorded in the current self-learning, and the previous charging key factor α and the previous discharging key factor β are recorded in the previous self-learning, then in the next self-learning process, the next charging key factor α and the next discharging key factor β are assigned constant values ​​different from the values ​​of the previous charging key factor α and the previous discharging key factor β within the range of greater than 0 and less than 1. In such an embodiment, for the next self-learning, the backup discharge capacity Cl is the discharge capacity during the previous self-learning process.

[0058] Generally speaking, each time the charging key factor α and the discharging key factor β are assigned a value, a random number determination method is generally adopted. However, by referring to the previously recorded key factor α and the discharging key factor β, a more effective dynamic adjustment of the charging key factor α and the discharging key factor β can be performed during the battery life. Each time the charging key factor α and the discharging key factor β are attempted to be set, the values ​​of the charging key factor α and the discharging key factor β that have been determined to be better are excluded, in the hope of finding a better value for the charging key factor α and the discharging key factor β again through this self-learning. Further, it can be seen from the above enumeration that the so-called backup discharge capacity is the discharge capacity recorded during the self-learning process of the charging key factor α and the discharging key factor β. The above enumeration is only an example of how to determine the backup discharge capacity and set the specific values ​​of the charging key factor α and the discharging key factor β during three adjacent self-learning processes. In this way, the SOP charging limit and the SOP discharging limit are continuously optimized, so that the battery charging and discharging process has better power performance as a whole.

[0059] Further preferably, in different embodiments of the present invention, Figure 1 The first cut-off condition in step 11 shown includes the battery SOC = 0 and reaches the discharge cut-off condition, or the battery SOC = 100% and reaches the charge cut-off condition. This means that the trigger condition for starting this self-learning is the battery charging completion or the battery discharging completion two nodes, thereby ensuring that each self-learning process is carried out during the battery full charge and discharge process, so that Figure 1 In step 15 shown, the comparison criteria for two adjacent discharge capacities are unified, thereby optimizing the effect and stability of the self-learning.

[0060] The following is based on Figure 2 Taking the first cut-off condition that the battery SOC=0 and the discharge cut-off condition are reached as an example, a battery SOP estimation method 20 based on self-learning (hereinafter referred to as "estimation method 20") according to an embodiment of the present invention is described. The estimation method 20 can be understood as follows: Figure 1A specific implementation of the estimation method 10 shown. Specifically, the estimation method 20 includes: starting this self-learning when it is detected that the battery meets the battery SOC=0 and reaches the discharge cut-off condition; setting the current charging key factor α and the current discharge key factor β, wherein the values ​​of the current charging key factor α and the current discharge key factor β are both constants greater than 0 and less than 1; calculating the current SOP charging limit and the current SOP discharge limit according to the current charging key factor α and the current discharge key factor β respectively; charging the battery according to the current SOP charging limit; during the battery charging process, when it is detected that the second cut-off condition is met, the battery is discharged according to the current discharge key factor β until the battery meets the first cut-off condition again, wherein the second cut-off condition is that the battery SOC=100% and reaches the charging cut-off condition. After the above steps are specifically expanded into a flow chart, the order or specific process direction may be slightly different from the text description. The following is based on Figure 2 Each step shown in the following is further described in detail.

[0061] according to Figure 2 After the system is powered on, step 201 determines whether the battery SOC = 0 and the discharge cutoff condition has been met. If the conditions in step 201 are met, the process proceeds to step 202 to determine whether self-learning has been initiated. Step 202 is a fail-safe measure to prevent malfunctions in the overall process of estimation method 20. Since the final step of estimation method 20 is "until the battery again meets the first cutoff condition," step 202 is included to effectively distinguish whether the first cutoff condition has been met first or again. If the result of step 202 is negative, self-learning is initiated and the process proceeds to step 203.

[0062] Specifically, step 203 is to set the key factor α of this charge and the key factor β of this discharge. As mentioned above, the values ​​of the key factor α of this charge and the key factor β of this discharge are both constants greater than 0 and less than 1. Preferably, as mentioned above, Figure 1 It is noted that the values ​​of the current charging key factor α and the current discharging key factor β can refer to the results of the previous self-learning. If the previous charging key factor α and the previous discharging key factor β were recorded in the previous self-learning, when setting the two this time, the values ​​of the previous charging key factor α and the previous discharging key factor β that have been determined to be better can be avoided, so as to further optimize the values ​​of the charging key factor α and the discharging key factor β during this self-learning process, and further optimize the SOP charging limit and SOP discharge limit calculated this time.

[0063] Furthermore, step 204 is to calculate the current SOP charging limit based on the key factor α of this charging. Step 205 is to charge the battery according to the current SOP charging limit. Step 206 is the second cut-off condition mentioned above, that is, the battery SOC = 100% and the charging cut-off condition is met. If the second cut-off condition is met, step 207 is continued to be executed, at which time the current SOP discharge limit is calculated according to the key factor β of this discharge. Correspondingly, during the charging process, it is usually judged in real time whether the battery cell has reached full charge, that is, SOC = 100% and the charging cut-off condition is met. Therefore, when the SOC does not reach 100% or the charging cut-off condition is not met, that is, the battery cell voltage does not reach the charging limit, the process will return to step S204 and continue until the battery cell reaches SOC = 100% and the charging cut-off condition is met through charging. Step S208 is continued to discharge the battery according to the current SOP discharge limit until the battery again meets the first cut-off condition in step S201. At this time, step S202 is continued to be executed. Since the process satisfies step S201 again, the judgment result of step S202 should be yes at this time, then step S209 is continued to be executed to record the current discharge capacity Cc. In addition, the current discharge capacity Cc is compared with the backup discharge capacity Cl. If Cc>=Cl, the current charging key factor α and the current discharge key factor β are recorded in the system at the same time, so as to provide a reference for the subsequent self-learning. It can be understood that the functional module for storing the data of the current discharge capacity Cc and the current charging key factor α and the current discharge key factor β can be selected according to the system in which the battery is running. For example, when the battery is running in an electric vehicle, the above data can be written to the storage unit in the battery management system BMS of the vehicle. The present invention does not limit this.

[0064] On the other hand, Figure 2 It can be seen that if the judgment result of step S201 is no after the system is powered on, it means that the battery does not meet the first condition of battery SOC=0 and reaches the discharge cut-off condition at the moment the system is powered on. At this time, in order to make the battery run at a more preferred power, step S207 can be directly executed. At this time, the SOP discharge limit value is calculated based on the previous discharge key factor β recorded during the previous operation of the battery, and the battery is discharged according to the SOP discharge limit value until the battery meets the battery SOC=0 and reaches the discharge cut-off condition, and then the self-learning is started.

[0065] Further preferably, in some embodiments of the present invention, Figure 2 Based on the estimation method 20 shown in FIG, some situations where self-learning is not started are further expanded. For example, referring to Figure 2, when it is detected according to step S201 that the battery does not meet the condition of battery SOC = 0 and reaches the discharge cut-off condition, and the battery is charging simultaneously, it is determined that self-learning is not started this time, and the process returns to the starting position until it is detected again that the battery meets the first cut-off condition of battery SOC = 0 and reaches the discharge cut-off condition in step S021. In this way, the situation where the battery is not fully discharged but continues to charge can be more comprehensively excluded. Similarly, if the determination result in step S206 is negative, but it is detected simultaneously that the battery is discharging, this means that the battery is discharging without being fully charged. At this time, it is also determined that the self-learning is invalid, and the process returns to the starting position again until it is detected again that the battery meets the first cut-off condition of battery SOC = 0 and reaches the discharge cut-off condition in step S021, thereby excluding the self-learning when the battery is discharging without being fully charged but continuing to discharge, so as to ensure that the charging key factor α and the discharge key factor β set in each self-learning process are not interfered by multiple factors and improve the optimization effect of SOP estimation.

[0066] In different embodiments of the present invention including Figure 1 and Figure 2 , the steps of calculating the current SOP charging limit value and the current SOP discharge limit value are involved. The following takes Figure 2 as an example for a detailed description. According to Figure 2 , the step of calculating the current SOP charging limit value according to the current charging factor α in step S204 is specifically to calculate the current SOP charging limit value SOP according to the SOC of the battery, the rated power P of the battery T and the current charging factor α according to the following rules 充电 :

[0067] If SOC <= α, then SOP 充电 = P T * (SOC / α);

[0068] If β < SOC < 1 - α, then SOP 充电 = P T ; and

[0069] If SOC >= 1 - α, then SOP 充电 = P T * (1 - SOC / α).

[0070] Similarly, the step of calculating the current SOP discharge limit value in step S207 is specifically to calculate the current SOP discharge limit value SOP according to the battery SOC, the rated power P of the battery T and the current discharge factor β according to the following rules 放电 :

[0071] If SOC <= β, then SOP 放电 = PT *(SOC / β);

[0072] If β < SOC < 1 - β, then SOP 放电 = P T ; and

[0073] If SOC >= 1 - β, then SOP 放电 = P T *(1 - SOC / β).

[0074] Through such a calculation method, the charging and discharging power limit of the battery can be reduced when the battery is approaching full charge or discharge completion, so that the power performance of the battery can be continuously improved during the battery life.

[0075] Finally, it should be noted that in the above application of the present invention Figure 1 and Figure 2 a flowchart is used to illustrate the operations performed by the system according to an embodiment of the present application. It should be understood that the operations before or below may not necessarily be executed precisely in order. On the contrary, various steps may be executed in reverse order or simultaneously. Also, other operations may be added to these processes, or one or several steps may be removed from these processes.

[0076] An embodiment of the present invention also proposes a system 30 for estimating the battery SOP based on self - learning as shown in Figure 3 . According to Figure 3 , the system 30 for estimating the battery SOP based on self - learning may include an internal communication bus 31, a processor (Processor) 32, a read - only memory (ROM) 33, a random access memory (RAM) 34, and a communication port 35. When applied to a personal computer, the system 30 for estimating the battery SOP based on self - learning may further include a hard disk 36.

[0077] The internal communication bus 31 can enable data communication between components of the system 30 for estimating the battery SOP based on self - learning. The processor 32 can make judgments and issue prompts. In some embodiments, the processor 32 may be composed of one or more processors. The communication port 35 can enable data communication between the system 30 for estimating the battery SOP based on self - learning and the external. In some embodiments, the system 30 for estimating the battery SOP based on self - learning can send and receive information and data from the network through the communication port 35.

[0078] The self-learning-based battery SOP estimation method system 30 may also include various forms of program storage units and data storage units, such as a hard disk 36, a read-only memory (ROM) 33, and a random access memory (RAM) 34, capable of storing various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 32. The processor executes these instructions to implement the main part of the method. The results of the processor processing are transmitted to the user device through the communication port and displayed on the user interface.

[0079] In addition, another aspect of the present invention further provides a computer-readable medium storing computer program code, which implements the above-mentioned battery SOP estimation method based on self-learning when executed by a processor.

[0080] The basic concepts have been described above. It will be apparent to those skilled in the art that the above disclosures are merely illustrative and do not constitute limitations on this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to this application. Such modifications, improvements, and amendments are suggested in this application and remain within the spirit and scope of the exemplary embodiments of this application.

[0081] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0082] Some aspects of the present application can be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software can be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". The processor can be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors or combinations thereof. In addition, various aspects of the present application may be expressed as computer products located in one or more computer-readable media, which include computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, tapes...), optical disks (e.g., compact disks CDs, digital versatile disks DVDs...), smart cards, and flash memory devices (e.g., cards, sticks, key drives...).

[0083] A computer-readable medium may include a propagated data signal embodying computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination thereof. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transmit the program for use. The program code on the computer-readable medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above.

[0084] Similarly, it should be noted that, in order to simplify the presentation of this application and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this application sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not mean that the subject matter of this application requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single embodiment disclosed above.

[0085] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0086] Although the present application has been described with reference to the current specific embodiments, ordinary technicians in this technical field should recognize that the above embodiments are only used to illustrate the present application, and various equivalent changes or substitutions can be made without departing from the spirit of the present application. Therefore, as long as the changes and modifications to the above embodiments are within the scope of the essential spirit of the present application, they will fall within the scope of the claims of the present application.

Claims

1. A battery SOP estimation method based on self-learning, suitable for estimating the SOP discharge limit and SOP charge limit of the battery during multiple operation of the battery, wherein: The operation process includes a charging process and / or a discharging process, and is characterized in that the method includes the following steps: During the current operation of the battery, when it is detected that the battery meets the first cut-off condition, the current self-learning is started; The current charging key factor α and the current discharging key factor β are set by random numbers, wherein the values ​​of the current charging key factor α and the current discharging key factor β are both constants greater than 0 and less than 1; Calculate the current SOP charge limit and / or the current SOP discharge limit according to the current charging key factor α and the current discharging key factor β respectively; Charging and / or discharging the battery according to the current SOP charging limit and / or the current SOP discharging limit; When it is detected that the battery meets the first cut-off condition again, the current discharge capacity Cc is recorded, and if the current discharge capacity Cc is greater than the backup discharge capacity Cl, where the backup discharge capacity Cl is the discharge capacity during the previous self-learning process, the current charging key factor α and the current discharging key factor β are also recorded; and Stop the current self-learning until the first cut-off condition is detected again during the next battery operation and the next self-learning is started. The step of calculating the SOP charging limit value includes: T And the key factor α of this charging is calculated according to the following rules to calculate the SOP charging limit SOP 充电 : If SOC <= α, then SOP 充电 =P T *(SOC / α); If α < SOC < 1 - α, then SOP 充电 = P T ; and If SOC>=1-α, then SOP 充电 =P T *(1-SOC / α).

2. The method according to claim 1, wherein The first cut-off condition includes the battery SOC=0 and reaching a discharge cut-off condition, or the battery SOC=100% and reaching a charge cut-off condition.

3. The method according to claim 2, wherein When the first cut-off condition is that the battery SOC=0 and the discharge cut-off condition is reached, the method specifically includes: During the battery operation, when it is detected that the battery meets the battery SOC=0 and reaches the discharge cut-off condition, the self-learning is started; Setting the current charging key factor α and the current discharging key factor β, wherein the values ​​of the current charging key factor α and the current discharging key factor β are both constants greater than 0 and less than 1; Calculate the current SOP charge limit and the current SOP discharge limit according to the current charging key factor α and the current discharging key factor β respectively; Charging the battery according to the current SOP charging limit; During the charging process of the battery, when it is detected that a second cut-off condition is met, the battery is discharged according to the current discharge key factor β until the battery meets the first cut-off condition again, wherein the second cut-off condition is that the battery SOC=100% and the charging cut-off condition is met.

4. The method according to claim 3, wherein The method further includes, when it is detected that the battery does not meet the battery SOC=0 and reaches the discharge cut-off condition, calculating the current SOP discharge limit value according to the previous discharge key factor β recorded during the previous operation of the battery, and discharging the battery according to the current SOP discharge limit value until the battery meets the battery SOC=0 and reaches the discharge cut-off condition, and then starting the current self-learning.

5. The method according to claim 4, wherein The method further includes determining not to start self-learning this time when it is detected that the battery does not meet the battery SOC=0 and reaches the discharge cut-off condition, and the battery is being charged at the same time, until it is detected again that the battery meets the battery SOC=0 and reaches the discharge cut-off condition.

6. The method according to claim 1, wherein For the three processes of the previous self-learning before the current self-learning, the current self-learning, and the next self-learning, the method specifically includes: If the current charging key factor α and the current discharging key factor β are recorded in the current self-learning, then during the next self-learning process, the next charging key factor α and the next discharging key factor β are assigned constant values ​​different from the values ​​of the current charging key factor α and the current discharging key factor β in the range of greater than 0 and less than 1, and for the next self-learning process, the backup discharge capacity Cl is the discharge capacity during the current self-learning process; If the current charging key factor α and the current discharging key factor β are not recorded in the current self-learning, and the previous charging key factor α and the previous discharging key factor β are recorded in the previous self-learning, then in the process of the next self-learning, the next charging key factor α and the next discharging key factor β are assigned constant values ​​different from the values ​​of the previous charging key factor α and the previous discharging key factor β within the range of greater than 0 and less than 1, and for the next self-learning, the backup discharge capacity Cl is the discharge capacity in the previous self-learning process.

7. The method according to claim 1, wherein The step of calculating the SOP discharge limit value comprises the following steps: T And the key factor β of this discharge is calculated according to the following rules to calculate the SOP discharge limit SOP 放电 : If SOC <= β, then SOP 放电 =P T *(SOC / β); If β < SOC < 1 - β, then SOP 放电 = P T ; and If SOC>=1-β, then SOP 放电 =P T *(1-SOC / β).

8. A battery SOP estimation system based on self-learning, comprising: a memory for storing instructions executable by the processor; and a processor, configured to execute the instructions to implement the method according to any one of claims 1 to 7.

9. A computer-readable medium storing computer program code, wherein the computer program code implements the method according to any one of claims 1 to 7 when executed by a processor.

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