Battery fast charging control strategy generation method, fast charging method, device, and storage medium

Abstract

The application provides a battery fast charging control strategy generation method, first, the battery state information at the current charging period is acquired; then, based on the battery state information, a target fast charging control strategy is determined from a preset fast charging control strategy library; wherein, the fast charging control strategy library is configured with multiple optimal fast charging control strategies corresponding to different battery state conditions; finally, the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library are executed weighted fusion to generate a final fast charging control strategy. Through the above method, the fast charging control strategy of the same vehicle can be adaptively adjusted for different users based on the fast charging habits of the users, so that each user has the best fast charging experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fast charging strategy control, in particular to a battery fast charging control strategy generation method, a fast charging method, equipment and a storage medium. BACKGROUND

[0002] The battery fast charging control strategy refers to a strategy for optimizing the control of charging power, voltage and current and other parameters during the charging process of an electric vehicle, so as to maximize the charging speed of the battery. This strategy aims to shorten the charging time as much as possible under the premise of ensuring the safety and life of the battery, and to improve the charging efficiency. Through reasonable control strategy, the charging time can be effectively reduced, the user experience can be improved, and the popularization and development of new energy vehicles can also be promoted.

[0003] In the related art, the battery fast charging strategy lacks flexibility and cannot be adjusted according to the usage habits and differences of individual users. SUMMARY

[0004] To overcome the deficiencies in the prior art, the present application provides a battery fast charging control strategy generation method, a fast charging method, equipment and a storage medium, to solve the technical problem that the battery fast charging strategy in the prior art lacks flexibility and cannot be adjusted according to the usage habits and differences of individual users.

[0005] Technical solution: To solve the above technical problems, the technical solution adopted by the present application is:

[0006] In a first aspect, the present application provides a battery fast charging control strategy generation method, which comprises:

[0007] obtaining battery state information at the current charging period;

[0008] determining a target fast charging control strategy from a pre-set fast charging control strategy library based on the battery state information; wherein the fast charging control strategy library is configured with a plurality of optimal fast charging control strategies corresponding to different battery state conditions;

[0009] performing weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy; wherein the target fast charging control strategy is given a higher weight when performing weighted fusion, and the final control fast charging control strategy is used to indicate the charging rate at the next charging period.

[0010] In a second aspect, the present application provides a battery fast charging method, which comprises:

[0011] receiving an instruction to perform fast charging on the battery;

[0012] based on the instruction of performing fast charging on the battery to call a final control fast charging control strategy, and performing fast charging on the battery based on the final control fast charging control strategy; wherein the final control fast charging control strategy is obtained by the method in the first aspect.

[0013] In a third aspect, the present application provides a battery fast charging control strategy generation device, comprising:

[0014] an acquisition module configured to acquire battery state information at a current charging period;

[0015] a determination module configured to determine a target fast charging control strategy from a preset fast charging control strategy library based on the battery state information; wherein the fast charging control strategy library is configured with a plurality of optimal fast charging control strategies corresponding to different battery state conditions;

[0016] a generation module configured to perform weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy; wherein the target fast charging control strategy is given a higher weight when performing weighted fusion, and the final control fast charging control strategy is used to indicate a charging rate at a next charging period.

[0017] In a fourth aspect, the present application provides a device, comprising,

[0018] a memory;

[0019] a processor;

[0020] and

[0021] a computer program;

[0022] wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method of the first aspect.

[0023] In a fifth aspect, the present application provides a storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method of the first aspect.

[0024] Beneficial effects: the embodiment of the application provides a battery fast charging control strategy generation method, which comprises the following steps: firstly, acquiring battery state information at a current charging period; then, determining a target fast charging control strategy from a preset fast charging control strategy library based on the battery state information; wherein, the fast charging control strategy library is configured with a plurality of optimal fast charging control strategies corresponding to different battery state conditions; finally, performing weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy; wherein, when the weighted fusion is performed, the target fast charging control strategy is given a higher weight, and the final fast charging control strategy is used to indicate a charging rate at a next charging period. Through the above method, the fast charging control strategy of the same vehicle can be adaptively adjusted for different users based on the fast charging habits of the users, so that each user has the best fast charging experience, and the situation that the fast charging time is longer if the user is used to other fast charging modes can be avoided. BRIEF DESCRIPTION OF DRAWINGS

[0025] The accompanying drawings, which form a part of the specification, are included to provide a further understanding of the application and are incorporated herein in conjunction with the description of the application. The embodiments of the present application, and their

[0026] Figure 1 and Figure 2 A schematic diagram of a fast charging map provided for a vehicle enterprise;

[0027] Figure 3 A flowchart of a battery fast charging control strategy generation method provided in the embodiment;

[0028] Figure 4 A flowchart of a step of determining a target fast charging control strategy from a preset fast charging control strategy library provided in the embodiment;

[0029] Figure 5 A Q value table matrix schematic diagram provided in the embodiment;

[0030] Figure 6 A block diagram of a battery fast charging control strategy generation device provided in the embodiment;

[0031] Figure 7 A block diagram of an electronic device provided in the embodiment. DETAILED DESCRIPTION

[0032] The application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0033] The following detailed description is merely exemplary in nature and is intended to provide further detail on the application. All the technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this application belongs. The terminology used by the present application is for describing particular embodiments only and is not intended to be limiting of the example embodiments according to the present application.

[0034] In the related art, most of the battery fast charging control strategies are provided by vehicle manufacturers with fixed fast charging maps. The fast charging map refers to a map or strategy optimized for temperature, SOC (State of Charge), charging rate and other parameters during the battery fast charging process. The fast charging map is configured in the battery management system (BMS) to control the charging process of the battery. The fast charging map is often optimized only for a single fixed fast charging interval and does not take into account the use differences of individual users. For example, the fast charging map provided by the vehicle manufacturer is specifically as shown in Figure 1 and Figure 2 The fast charging map shown in Figure 1 and Figure 2 is only optimized for the interval of SOC 30%-80% at room temperature 25℃, but if the user is used to fast charging at 20℃, 20%-80%, the fast charging map provided by the vehicle manufacturer is not the optimal solution. Since the fastest interval of fast charging is located at 30%-80%, it may cause the charging rate before 30% to be slow, and in turn, the charging of SOC 20%-80% to be slow, even slower than the competitor, and the fast charging time will be longer than the theoretical time, affecting the user experience.

[0035] It can be understood that different users have different use habits and use environments. For example, some users consider charging only when the SOC is below 5%, while some users worry about insufficient power and start charging when the SOC is around 50%. Correspondingly, some users are located in areas with high temperature all year round due to geographical location and other factors, while some users are located in areas with low temperature all year round.

[0036] Therefore, in the prior art, the fast charging strategies configured in the battery management system (BMS) are too rigid and lack flexibility. This rigid fast charging strategy cannot be adjusted according to individual user habits and the environment, resulting in low charging efficiency in certain situations and affecting user experience. To address this technical problem, the embodiments of this application provide a method for generating a battery fast charging control strategy. This method first obtains battery state information at the current charging cycle; then, based on the battery state information, it determines a target fast charging control strategy from a preset fast charging control strategy library; wherein, the fast charging control strategy library is configured with multiple optimal fast charging control strategies corresponding to different battery state conditions; finally, it performs a weighted fusion of the target fast charging control strategy with at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy; wherein, during the weighted fusion, the target fast charging control strategy is given a higher weight, and the final fast charging control strategy is used to indicate the charging rate in the next charging cycle. By using the above methods, we can deepen the understanding of users' commonly used fast charging control strategies based on their fast charging habits. This allows the fast charging control strategy for the same vehicle to be adaptively adjusted for different users, ensuring that each user has the best fast charging experience. This avoids the situation where traditional fast charging is only optimized for specific conditions, and if users are accustomed to other fast charging methods, the fast charging time will be longer.

[0037] Example 1

[0038] like Figure 3 As shown, this embodiment provides a method for generating a battery fast charging control strategy. This method can be configured in the vehicle's battery management system (BMS), and the method may include:

[0039] Step S110: Obtain battery status information for the current charging cycle.

[0040] In this embodiment, the acquisition action can be that the Battery Management System (BMS) obtains battery state information for the current charging cycle through its internal sensors and monitoring devices. These sensors can monitor parameters such as battery charge (SOC) and temperature. The battery state information can include data such as initial SOC, final SOC, and initial and final battery temperatures.

[0041] Step S120: Based on the battery state information, determine the target fast charging control strategy from the preset fast charging control strategy library; wherein, the fast charging control strategy library is configured with multiple optimal fast charging control strategies corresponding to different battery states.

[0042] In the embodiment, the above-mentioned fast charging control strategy library is pre-configured, in which a plurality of fast charging control strategies are configured, and different fast charging control strategies can adapt to different battery state conditions. Specifically, in some embodiments, one fast charging control strategy can correspond to one battery state information in the above-mentioned battery state information, for example, can only correspond to the initial battery temperature, can only correspond to the initial SOC, or can only correspond to the cut-off SOC. In the embodiment, one fast charging control strategy can correspond to a plurality of battery state information, for example, one fast charging control strategy corresponds to the initial battery temperature, the initial SOC and the cut-off SOC at the same time. The fast charging control strategies in the fast charging control strategy library are pre-configured. Specifically, the fast charging control strategies in the fast charging control strategy library can be a pre-generated fast charging control strategy mapping table, the corresponding fast charging control strategy can be directly found according to the battery state and the charging demand; or can be a model pre-trained by machine learning technology or deep information technology, the result output by the model can be directly called to perform charging control according to the real-time battery state and the charging demand when the fast charging is specifically performed.

[0043] In the embodiment, the fast charging control strategy in the fast charging control strategy library can adopt the form of a fast charging map, which can adopt the form of a table and can be structured data in the format of CSV or Excel. The fast charging map can include the fast charging rate value under different SOCs and different battery temperatures in one charging period. The fast charging rate value refers to the multiple of the battery charging rate relative to the standard charging rate during fast charging. The higher the fast charging rate value is, the faster the battery charging rate is and the shorter the charging time is. The above-mentioned fast charging map is pre-configured, for example, can be obtained by simulation calculation based on a calibrated simulation model or a battery pack bench. Specifically, a mathematical model of the battery needs to be established first, including the electrochemical characteristics, thermal characteristics, internal resistance and other parameters of the battery. These parameters can be calibrated by experimental testing, literature research or model fitting, so as to ensure the accuracy and reliability of the model. Then, according to the battery model and the charging demand, the fast charging control strategy is designed, including the charging rate, the cut-off SOC and other parameters. These parameters can be optimized or set according to the characteristics of the battery and the charging demand. Then, the calibrated battery model and the designed fast charging control strategy are used to perform simulation calculation in the simulation software. By simulating the charging process of the battery under different SOCs and temperatures, the fast charging rate value, i.e. the multiple of the fast charging rate relative to the standard charging rate, can be obtained. Finally, the fast charging rate value obtained by simulation calculation is arranged in the form of a table, including the fast charging rate value under different SOCs and temperatures. These data can be saved as structured data in the format of CSV or Excel to construct the fast charging map.

[0044] As described above, each fast charging control strategy in the fast charging control strategy library corresponds to a battery state condition, and thus, after the battery state information of the current charging period is obtained in step S110, the target fast charging control strategy can be determined from the fast charging control strategy library according to the battery state information. Specifically, the closest battery state condition in the fast charging control strategy library can be determined based on the battery state information in step S110, and the fast charging control strategy corresponding to the closest battery state condition is determined as the target fast charging control strategy. For example, the closest battery state condition can be found by using distance measurement or similarity calculation.

[0045] In step S130, the target fast charging control strategy is weightedly fused with at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy, wherein the target fast charging control strategy is given a higher weight when the weighted fusion is performed, and the final fast charging control strategy is used to indicate the charging rate in the next charging period.

[0046] After the target fast charging control strategy is determined, the target fast charging control strategy can be weightedly fused with one or more fast charging control strategies in the fast charging control strategy library. In this embodiment, the target fast charging control strategy can be weightedly fused with all fast charging control strategies in the fast charging control strategy library except the target fast charging control strategy, and the target fast charging control strategy is given a higher weight when the weighted fusion is performed, so as to obtain the final fast charging control strategy.

[0047] In some embodiments, in the above-described method for generating a battery fast charging control strategy, the battery state information includes a starting battery temperature, a starting SOC or a cutoff SOC, the fast charging control strategy is a fast charging map, and the step of determining a target fast charging control strategy from a preset fast charging control strategy library can include steps of: Figure 4 As shown in FIG. 12, the step of determining a target fast charging control strategy from a preset fast charging control strategy library can include steps of: in step S121, determining the closest battery state condition in the fast charging control strategy library based on at least one of the starting battery temperature, the starting SOC and the cutoff SOC; and in step S122, determining the fast charging map corresponding to the closest battery state condition as the target fast charging control strategy. In this embodiment, at least one of the starting battery temperature, the starting SOC and the cutoff SOC is introduced, which can more comprehensively describe the state of the battery and help to more accurately select a fast charging control strategy suitable for the current battery state. By introducing more battery state information, the closest battery state condition in the fast charging control strategy library can be more accurately determined, thereby improving the matching degree of the target fast charging control strategy and the current battery state and further improving the charging efficiency and the battery life.

[0048] In some embodiments, in the above-mentioned battery fast charging control strategy generation method, the fast charging control strategy library is a Q-value table matrix, and the plurality of optimal fast charging control strategies corresponding to different battery state conditions are stored in the form of a Q-value table. In this embodiment, the Q-value table matrix is introduced as the fast charging control strategy library, and the plurality of optimal fast charging control strategies corresponding to different battery state conditions are stored in the form of a Q-value table. This can more effectively manage and store a large number of fast charging control strategies, and can achieve more efficient fast charging control strategy selection. The Q-value table matrix can effectively manage and store a plurality of optimal fast charging control strategies corresponding to different battery state conditions, and store these strategies in a structured form in the matrix, facilitating quick retrieval and selection. By using the Q-value table matrix to store the optimal fast charging control strategies, a more efficient fast charging control strategy selection process can be achieved. According to the current battery state information, the corresponding optimal control strategy can be directly found in the Q-value table, reducing the calculation time and complexity of strategy selection.

[0049] In some embodiments, in the above-mentioned battery fast charging control strategy generation method, the step of performing weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy includes: performing weighted fusion of the target fast charging control strategy and all fast charging control strategies in the fast charging control strategy library; wherein the weighted fusion is performed by the following formula:

[0050] Q-value final = (w1+1) / (n+1)*Q1+(w2) / (n+1)*Q2+…+(wn) / (n+1)*Qn

[0051] In the formula, Q-value final represents the final control fast charging control strategy, Q1 represents the target fast charging control strategy, Q2 to Qn represent the Q-value table corresponding to other fast charging control strategies in the Q-value table matrix except the target fast charging control strategy, w1 to wn are preset weights for weighting each fast charging control strategy, and n is the number of fast charging control strategies.

[0052] In this embodiment, by introducing the method of weighted fusion, the target fast charging control strategy is weightedly fused with all the fast charging control strategies in the fast charging control strategy library to generate the final fast charging control strategy. This method can comprehensively consider the advantages and disadvantages of multiple fast charging control strategies to obtain a more comprehensive and optimized final control strategy. The method in this embodiment can comprehensively consider all the fast charging control strategies in the fast charging control strategy library by weighted fusion, and weightedly fuse them with the target fast charging control strategy to obtain the final control strategy. In this way, the advantages of different strategies can be fully utilized to obtain a more comprehensive and optimized control strategy. Secondly, by the method of weighted fusion, the final control strategy can be adjusted according to the weights of different fast charging control strategies, thereby improving the adaptability and flexibility of the control strategy. The weights can be adjusted according to specific conditions to make the final control strategy more in line with actual needs.

[0053] In some embodiments, in the above-mentioned battery fast charging control strategy generation method, the plurality of Q-value tables in the Q-value table matrix are obtained by pre-simulation calculation through a calibrated simulation model or a battery pack bench. In this embodiment, by pre-simulation calculation through a simulation model or a battery pack bench, the Q-value table matrix can more accurately simulate and calculate the optimal fast charging control strategy under different battery state conditions. This can improve the accuracy of the control strategy and make the final control strategy more in line with actual conditions. Specifically, the plurality of Q-value tables in the Q-value table matrix can be calculated by the following algorithm, specifically including:

[0054] 1. Algorithm initialization: divide the density of SOC and battery temperature and initialize the fast charging map;

[0055] More specifically, for example, SOC every 10%, (0%, 10%, …, 90%, 100%); the number of SOC after division is a; battery temperature every 5°C, (-30°C, -25°C, …, 55°C, 60°C); the number of temperature after division is b. The lithium precipitation map can be used as the initial fast charging map.

[0056] 2. Calculate the fast charging time under the condition that the cut-off SOC is 80%+k, the cycle number is n=0, the calculation step of SOC is i=0, and the calculation step of temperature is j=0.

[0057] 3. Determine whether the cycle number n>a*b? If greater, then k=k+10% and return to 2 to start a new calculation process of cut-off SOC, if less, then enter 4.

[0058] 4. Determine whether the calculation step of SOC i>a? If greater, then return to 3, if less, then enter 5.

[0059] 5, judge the calculation step length of temperature j > b? If greater, it means the end of temperature interval traversal, then i = i + 1, and return to 4 to execute the judgment of i, if less than then enter 6.

[0060] 6, calculate the shortest fast charging time when the starting SOC is i and the starting temperature is j, which can use neural network, genetic algorithm, linear programming algorithm, etc. to calculate the optimal fast charging map. In order to obtain the global optimum, linear programming algorithm can be selected.

[0061] 7, record the obtained current ratio under different SOC intervals into the Q value table (in fact, it is the optimal fast charging map in 6).

[0062] 8, let j = j + 1 and n = n + 1, and return to 5 to perform loop calculation.

[0063] 9, when the entire cutoff SOC is traversed, the complete Q value table matrix can be obtained, see Figure 5 .

[0064] When the complete Q value table matrix is obtained, it can be input into the battery management system (BMS) for user's fast charging self-learning optimization strategy.

[0065] In some embodiments, in the battery fast charging control strategy generation method described above, the step of performing weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy can include: judging whether the battery state information exceeds a preset frequency threshold; in the case where the battery state information exceeds the preset frequency threshold, performing weighted fusion of the target fast charging control strategy and all fast charging control strategies in the fast charging control strategy library; wherein, when performing weighted fusion, a preset weight update algorithm is called to update the weight of the target fast charging control strategy, so that the weight of the target fast charging control strategy can rise in an exponential form. In the above embodiments, the preset frequency threshold refers to a specific frequency value set when monitoring the battery state information, which is used to judge whether the battery state information exceeds the frequency threshold. Specifically, when the frequency of a certain battery state information (such as the initial battery temperature and the initial SOC state) exceeds the preset frequency threshold within a certain period of time, the corresponding control strategy is triggered. This preset frequency threshold can be set according to the specific application scenario and user demand, for example, if the user frequently fast charges in the condition of an initial temperature of 30 degrees and an SOC of 20%, then the fast charging frequency threshold of the initial temperature of 30 degrees and the initial SOC of 20% can be set to more than 3 times per week. When this condition occurs more than 3 times, the corresponding fast charging control strategy is triggered. By setting a suitable preset frequency threshold, the charging preferences and habits of the user can be more accurately identified, so as to optimize the fast charging control strategy and improve the charging efficiency and user experience. The weight update algorithm described above can be designed in the form of exponential growth, that is, the weight increases according to an exponential function each time the condition is triggered. For example, an exponential function such as an exponential growth function or a Sigmoid function can be used to adjust the growth speed of the weight, so that the weight can rise faster. In the above embodiments, by judging whether the battery state information exceeds the preset frequency threshold, the system can monitor the charging behavior of the user in real time, and dynamically adjust the fast charging control strategy according to the charging preferences and habits of the user. This can better meet the individual needs of the user and improve the charging efficiency and user experience; by calling the preset weight update algorithm, the weight of the target fast charging control strategy can rise in an exponential form, which can speed up the adjustment speed of the weight and adapt to the charging preferences and habits of the user faster. This can improve the intelligent degree of the system and optimize the selection and execution of the charging strategy.

[0066] In some embodiments, the initial fast charging map of the battery cell can be obtained from the supplier. The fast charging map needs to meet the condition of reaching the battery safety, the fast charging rate needs to be the maximum under different SOC and different battery temperature. The theoretical optimal fast charging time of fast charging to different cut-off SOC under different initial SOC and different initial battery temperature can be calculated and evaluated according to the offline battery model. The optimal fast charging rate evaluated under different initial battery temperature, initial SOC and cut-off SOC can be listed as an array. Different initial SOC, initial battery temperature and cut-off SOC are set as the input columns of the fast charging Q value table, and the fast charging rate under the corresponding state is set as the output column; the cut-off SOC can be considered in the range commonly used by users, which can be set to 80%, 90% and 100%. More cut-off SOC can also be set. For different cut-off SOC, the optimal fast charging map of different initial SOC and battery temperature needs to be calculated. The optimal fast charging rate of different initial SOC, initial battery temperature and cut-off SOC can be updated into the respective fast charging map. The self-learning equation can be set for the fast charging strategy, and the default fast charging map is the weighted average of all fast charging maps. Each fast charging map is assigned a certain weight, and the sum of the weights is 1. Each time the user fast charges, the optimal fast charging map updated last time is used for fast charging. After the fast charging is completed, the closest fast charging map used for the fast charging is assigned a higher weight, and the weights of other fast charging maps are proportionally reduced. The latest fast charging map can be used for fast charging next time the user fast charges. The iteration of the weight can be optimized to a certain extent, so that the increase of the weight is in the form of exponential rise. When the user is used to fast charging in a certain state or some states, the weight of the corresponding fast charging map will quickly rise, so that the fast charging rate can be improved each time the user fast charges.

[0067] Embodiment 2

[0068] In a third aspect, based on the embodiment 1, the embodiment provides a battery fast charging method, which can include:

[0069] Step S210, receiving an instruction of performing fast charging on the battery.

[0070] Step S220, calling a final control fast charging control strategy based on the instruction of performing fast charging on the battery, and performing fast charging on the battery based on the final control fast charging control strategy; wherein the final control fast charging control strategy is obtained by any method in the embodiment 1.

[0071] Embodiment 3

[0072] In a third aspect, as shown in the embodiment 1, based on the embodiment 1, the embodiment provides a battery fast charging control strategy generation device, which can include: Figure 6

[0073] ​acquire a battery state information at a current charging period;

[0074] determine a target fast charging control strategy from a preset fast charging control strategy library based on the battery state information, wherein the fast charging control strategy library is configured with a plurality of optimal fast charging control strategies corresponding to different battery state conditions;

[0075] generate a final fast charging control strategy by performing weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library, wherein the target fast charging control strategy is given a higher weight in the weighted fusion, and the final fast charging control strategy is used to indicate a charging rate at a next charging period.

[0076] Embodiment 4

[0077] In a fourth aspect, based on the embodiment 1, the present embodiment provides a device, comprising, Figure 7

[0078] a memory;

[0079] a processor;

[0080] and

[0081] a computer program;

[0082] wherein the computer program is stored in the memory and configured to be executed by the processor to implement the battery fast charging control strategy generation method in the embodiment 1.

[0083] Embodiment 5

[0084] In a fifth aspect, based on the embodiment 1, the present embodiment provides a storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the battery fast charging control strategy generation method in the embodiment 1.

[0085] Embodiment 6

[0086] In a sixth aspect, based on the embodiment 1, the present embodiment further provides a computer program product containing instructions, which are executed by a computer to cause the computer to perform the battery fast charging control strategy generation method in the embodiment 1.

[0087] It is known from common general knowledge that the present application can be implemented by other embodiments which do not depart from the spirit or essential characteristics thereof. Therefore, the above disclosed embodiments are merely illustrative in all aspects and are not the only ones. All changes within the scope of the present application or within the scope equivalent to the present application are intended to be embraced by the present application.

[0088] ​Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0089] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0090] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0092] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing the technical solutions of the present application, but not for limiting it. Although the present application is described in detail with reference to the above embodiments, those skilled in the field should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for generating a battery fast charging control strategy, characterized in that, The method comprises: acquiring battery state information at a current charging period; determining a target fast charging control strategy from a preset fast charging control strategy library based on the battery state information; wherein the fast charging control strategy library is configured with a plurality of optimal fast charging control strategies corresponding to different battery state conditions; performing weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy; wherein the target fast charging control strategy is given a higher weight in performing the weighted fusion, and the final fast charging control strategy is used to indicate a charging rate at a next charging period; The step of performing weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy comprises: determining whether the battery state information exceeds a preset frequency threshold; in a case where the battery state information exceeds the preset frequency threshold, performing weighted fusion of the target fast charging control strategy and all fast charging control strategies in the fast charging control strategy library; wherein a preset weight updating algorithm is called to update the weight of the target fast charging control strategy in performing the weighted fusion, so that the weight of the target fast charging control strategy can rise in an exponential form.

2. The method of claim 1, wherein, The battery state information comprises a starting battery temperature, a starting SOC or a cut-off SOC, the fast charging control strategy is a fast charging map, and the step of determining a target fast charging control strategy from a preset fast charging control strategy library comprises: determining a closest battery state condition in the fast charging control strategy library based on at least one of the starting battery temperature, the starting SOC and the cut-off SOC; determining a fast charging map corresponding to the closest battery state condition as the target fast charging control strategy.

3. The method of claim 2, wherein, The fast charging control strategy library is a Q-value table matrix, and the plurality of optimal fast charging control strategies corresponding to different battery state conditions are stored in the form of Q-value tables.

4. The method of claim 3, wherein, The step of performing weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy comprises: performing weighted fusion of the target fast charging control strategy and all fast charging control strategies in the fast charging control strategy library; wherein the weighted fusion is performed by the following formula: Q-value final = (w1+1) / (n+1)*Q1+(w2) / (n+1)*Q2+…+ (wn) / (n+1)*Qn In the formula, Q-value final represents the final fast charging control strategy, Q1 represents the target fast charging control strategy, Q2 to Qn represent Q-value tables corresponding to other fast charging control strategies in the Q-value table matrix except the target fast charging control strategy, w1 to wn are preset weights for performing weighting on each fast charging control strategy, and n is the number of fast charging control strategies.

5. The method of claim 3, wherein, The plurality of Q-value tables in the Q-value table matrix are obtained through simulation calculation by a calibrated simulation model or a battery pack bench in advance.

6. A battery fast charging method, characterized in that, The method comprises: receiving an instruction to perform fast charging on the battery; based on the instruction to perform fast charging on the battery, invoking a final fast charging control strategy, and performing fast charging on the battery based on the final fast charging control strategy; wherein the final fast charging control strategy is obtained by the method of any one of claims 1-5. 7.A battery fast charging control strategy generation apparatus, characterized by, comprising: an obtaining module configured to obtain battery state information at a current charging period; a determining module configured to determine a target fast charging control strategy from a preset fast charging control strategy library based on the battery state information; wherein the fast charging control strategy library is configured with a plurality of optimal fast charging control strategies corresponding to different battery state conditions; a generating module configured to perform weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy; wherein in the weighted fusion, the target fast charging control strategy is given a higher weight, and the final fast charging control strategy is used to indicate a charging rate at a next charging period; the step of performing weighted fusion of the target fast charging control strategy and at least one fast charging control strategy in the fast charging control strategy library to generate a final fast charging control strategy, comprising: determining whether the battery state information exceeds a preset frequency threshold; in a case where the battery state information exceeds the preset frequency threshold, performing weighted fusion of the target fast charging control strategy and all fast charging control strategies in the fast charging control strategy library; wherein in the weighted fusion, a preset weight updating algorithm is invoked to update the weight of the target fast charging control strategy, so that the weight of the target fast charging control strategy can rise in an exponential form.

8. An apparatus, comprising: comprising: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1 to 6.

9. A storage medium, characterized by a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1 to 6.

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