A management control method and system for a battery swap battery pack

Abstract

Embodiments of the present disclosure provide a management control method and system for battery swap battery pack. The method comprises: in response to a selection mode of a user, obtaining a plurality of target battery swap cabinets and selectively obtaining target data sets and / or current battery pack data; based on the plurality of target battery swap cabinets, selectively updating an existing module database of each target battery swap cabinet; substituting the selection mode of the user, the selectively obtained target data sets and / or the current battery pack data, and the existing module database of the plurality of target battery swap cabinets into a management task generation model to obtain management task instructions corresponding to the target data sets; and issuing the management task instructions to the battery swap cabinets corresponding to the battery swap cabinet numbers, so as to adjust the battery pack of the user or replace the battery pack by the user. The adjusted or replaced battery pack can meet the requirements of the target data sets or be consistent with the current battery pack, while the adjustment efficiency or replacement efficiency of the battery pack of the user is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of management and control of portable battery packs, and in particular to a management and control method and system for a battery-swap battery pack. Background Art

[0002] With the rapid development of new energy technology, battery swaps are widely used in the field of mobile energy. Due to different power demands of users, the types of battery packs required also vary.

[0003] However, the battery modules in each battery swap cabinet are not the same, which can easily lead to differences in the output parameters of the battery packs when users use battery packs from different battery swap cabinets due to different battery module parameters, resulting in unstable output power, which in turn affects the user's usage experience.

[0004] Accordingly, the art needs a new management and control solution for battery swapping battery packs to solve the above problems. Summary of the Invention

[0005] The present disclosure provides a management and control method and system for battery swapping battery packs, which solves the technical problem in the prior art that when users use battery packs of different battery swapping cabinets, the output parameters of the battery packs differ due to different battery module parameters, resulting in unstable output power, which in turn affects the user's usage experience.

[0006] According to a first aspect of the present disclosure, a management and control method for a battery swapping battery pack is provided. The method is applied to a management and control system of a battery swapping battery pack, and the method comprises the following steps:

[0007] In response to a user's selection mode, obtaining at least one target battery swap cabinet and selectively obtaining a target data group and / or current battery pack data, wherein the target data group includes target input data, target output data, target storage data, and target structure data;

[0008] Based on the at least one target battery swap cabinet, selectively updating the existing module database of each target battery swap cabinet, wherein the existing module database includes at least a plurality of adapter elements existing in the target battery swap cabinet, data information of each adapter element, a plurality of battery modules, and data information of each battery module;

[0009] Substitute the user's selection mode, the selectively acquired target data group and / or the current battery pack data, and the existing module database of at least one target battery swap cabinet into the management task generation model to obtain a management task instruction corresponding to the target data group, wherein the management task instruction at least includes the user's selection mode, the battery swap cabinet number that executes the management task instruction, and the execution combination data corresponding to the battery swap cabinet;

[0010] The management task instruction is sent to the battery swap cabinet corresponding to the battery swap cabinet number to facilitate adjustment of the user's battery pack or replacement of the battery pack by the user.

[0011] According to the above aspects and any possible implementation, an implementation is further provided, wherein, in response to a user's selection mode, obtaining at least one target battery swap cabinet and selectively obtaining a target data group and / or current battery pack data includes:

[0012] If the user's selection mode is replacement mode, obtaining at least one target battery swap cabinet and obtaining a target data group or current battery pack data;

[0013] If the user's selection mode is adjustment mode, obtaining at least one target battery swap cabinet and obtaining a target data group and current battery pack data;

[0014] The current battery pack data at least includes battery structure data and battery storage data of the battery pack currently used by the user.

[0015] According to the above aspects and any possible implementation, an implementation is further provided, wherein before obtaining at least one target battery swap cabinet and selectively obtaining a target data group and / or current battery pack data in response to a user's selection mode, the method further includes:

[0016] Get the idle time of the battery swap cabinet;

[0017] If the idle time of the battery swap cabinet exceeds a preset time threshold, a parameter range generation model is substituted into the existing module database of the battery swap cabinet to obtain the battery adjustment parameter range and battery replacement parameter range of the battery swap cabinet;

[0018] Based on the battery adjustment parameter range and the battery replacement parameter range of the battery swap cabinet, the battery adjustment real-time parameter range and the battery replacement real-time parameter range of the battery swap cabinet are updated.

[0019] According to the above aspects and any possible implementation, further provided is an implementation, wherein selectively updating the existing module database of each target battery swap cabinet based on the at least one target battery swap cabinet includes:

[0020] Based on the at least one target battery swap cabinet and the user's selection mode, selectively obtain the battery adjustment real-time parameter range or the battery replacement real-time parameter range of each target battery swap cabinet;

[0021] If the user's selection mode is replacement mode, the real-time parameter range of battery replacement of the target battery swap cabinet is compared with the target data group, and according to the comparison result, the existing module database of the target battery swap cabinet is selectively updated;

[0022] If the user's selection mode is adjustment mode, the real-time parameter range of the battery adjustment of the target battery swap cabinet will be comprehensively compared with the current battery pack data and the target data group, and based on the comprehensive comparison results, the existing module database of the target battery swap cabinet will be selectively updated.

[0023] According to the above aspects and any possible implementation, a further implementation is provided, in which before substituting the user's selection mode, the selectively acquired target data group and / or the current battery pack data, and the existing module database of at least one target battery swap cabinet into the management task generation model, the method further trains the management task generation model through the following steps:

[0024] Acquire multiple groups of target data groups obtained historically, multiple groups of current battery pack data, and multiple groups of existing module databases obtained historically from multiple battery swap cabinets, and generate multiple groups of training sample sets with different selection modes, at least one target battery swap cabinet corresponding to each group of training sample sets, and an existing module database corresponding to each target battery swap cabinet corresponding to each group of training sample sets, wherein the training sample set includes at least one selection mode, a group of battery pack data corresponding to the selection mode, and / or a group of target data groups;

[0025] Based on the selection mode in each set of training samples, the target structure data in the target data group and / or the battery structure data in the current battery pack data, and the existing module database of each target battery swap cabinet corresponding to each set of training samples, multiple sets of battery structure parameters corresponding to each target battery swap cabinet corresponding to each set of training samples are obtained;

[0026] Based on the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group and / or the battery storage data in the current battery group data, the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets are screened to obtain multiple groups of reference combination parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets after screening, wherein the reference combination parameters include at least battery structure parameters and battery connection parameters and battery combination data corresponding to the battery structure parameters;

[0027] Based on the multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets after screening and the selection mode of each group of training sample sets, simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets are obtained, wherein the simulation results at least include the completion rates of different simulation types;

[0028] Based on the simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets, a group of reference combination parameters is selected as the execution combination data in the management task instructions corresponding to the training sample set, and the battery swap cabinet number of the target battery swap cabinet corresponding to the reference combination parameter is used as the battery swap cabinet number for executing the management task instructions in the management task instructions corresponding to the training sample set, and the selection mode corresponding to the training sample set is used as the selection mode in the management task instructions corresponding to the training sample set, so as to obtain the management task instructions corresponding to each group of training sample sets.

[0029] The above aspects and any possible implementation methods further provide an implementation method, wherein the acquisition of multiple sets of target data groups obtained historically, multiple sets of current battery pack data, and multiple sets of existing module databases obtained historically for multiple battery swap cabinets generates multiple sets of training sample sets with different selection modes, at least one target battery swap cabinet corresponding to each set of training sample sets, and an existing module database corresponding to each target battery swap cabinet corresponding to each set of training sample sets, including:

[0030] Acquire multiple sets of historical battery data and multiple sets of historical target data;

[0031] Randomly selecting multiple sets of battery pack data and multiple sets of target data groups of the same number, and randomly matching the selected multiple sets of battery pack data, selection patterns and multiple sets of target data groups to form multiple sets of training sample sets with different selection patterns;

[0032] Randomly select multiple battery swap cabinets as multiple target battery swap cabinets corresponding to each training sample set;

[0033] Based on the selection mode corresponding to each group of training sample sets, the target data group in each group of training sample sets, and the multiple groups of existing module databases obtained historically for each target battery swap cabinet, an existing module database corresponding to the target battery swap cabinet is selectively selected as the existing module database corresponding to the target battery swap cabinet in the training sample set to obtain at least one target battery swap cabinet corresponding to each group of training sample sets and an existing module database corresponding to each target battery swap cabinet corresponding to each group of training sample sets.

[0034] The above aspects and any possible implementation methods further provide an implementation method, wherein based on the target data group in each training sample set and the multiple groups of existing module databases historically obtained for each target power swap cabinet, selectively selecting an existing module database corresponding to the target power swap cabinet as the existing module database corresponding to the target power swap cabinet in the training sample set includes:

[0035] If the selection mode corresponding to the training sample set is the replacement mode, and the target data group in the training sample set exists in the battery replacement real-time parameter range corresponding to an existing module database corresponding to a target battery swap cabinet corresponding to the training sample set, then the existing module database is selected as the existing module database corresponding to the target battery swap cabinet in the training sample set;

[0036] If the selection mode corresponding to the training sample set is the adjustment mode, and there is a difference data group corresponding to the training sample set in the battery adjustment real-time parameter range corresponding to an existing module database corresponding to a target battery swap cabinet corresponding to the training sample set, then the existing module database is selected as the existing module database corresponding to the target battery swap cabinet in the training sample set.

[0037] The above aspects and any possible implementation methods further provide an implementation method, which is based on the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group and / or the battery storage data in the current battery pack data, and the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets are screened, and the multiple groups of reference combination parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets after screening include:

[0038] Based on the multiple groups of battery structure parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group in each group of training sample sets and / or the battery storage data in the current battery group data, the multiple groups of battery connection parameters and battery combination data corresponding to each target battery swap cabinet corresponding to each group of battery structure parameters are obtained to obtain the multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets;

[0039] Simulate multiple sets of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of training sample sets to obtain simulation data corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet;

[0040] If the simulation data corresponding to a set of reference combination parameters of the target battery swap cabinet corresponding to the training sample set does not match any data of the target data group, it is determined that the reference combination parameters do not match the training sample set, and the reference combination parameters are eliminated from the multiple sets of reference combination parameters corresponding to the training sample set;

[0041] Otherwise, it is determined that the reference combination parameters of the target battery swap cabinet match the training sample set, and the reference combination parameters are used as one of the reference combination parameters of the target battery swap cabinet corresponding to the filtered training sample set.

[0042] The above aspects and any possible implementation methods further provide an implementation method, wherein the simulation is performed based on multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets after screening and the selection mode of each group of training sample sets, and the simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets include:

[0043] The simulation is performed based on multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets after screening, and the simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets include:

[0044] Simulate multiple sets of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of filtered training sample sets to obtain simulation data corresponding to each set of reference combination parameters;

[0045] Comparing the simulation data corresponding to each set of reference combination parameters with the target data set in the training sample set corresponding to each set of reference combination parameters to obtain comparison data of different simulation types corresponding to each set of reference combination parameters, wherein the different simulation types include at least structure type, input type, output type, and storage type;

[0046] Based on the comparison data of different simulation types corresponding to each set of reference combination parameters, the completion rates of different simulation types corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of training sample sets are obtained, so as to obtain the simulation results corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of training sample sets.

[0047] According to a second aspect of the present disclosure, a management and control system for battery swapping is provided. The device includes a communication unit, a management task generation model, and a control device. The system interacts with the user's battery pack and each battery swap cabinet through the communication unit. The management task generation model is used to generate management task instructions corresponding to the target data group based on the user's selection mode, the selectively acquired target data group and / or the current battery pack data, and the existing module database of at least one target battery swap cabinet. The control device includes a memory and a processor. The memory stores a computer program. When the processor executes the program, it implements the method described above.

[0048] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the program.

[0049] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect and / or the second aspect of the present disclosure is implemented.

[0050] The above one or more technical solutions disclosed herein have at least one or more of the following beneficial effects:

[0051] By responding to the user's selection mode, obtaining the target battery swap cabinet and selectively obtaining the target data group and / or the current battery pack data, and selectively updating the existing module database of the target battery swap cabinet according to the target battery swap cabinet, it is achieved that according to the user's selection mode and the obtained data, the target battery swap cabinet is selectively determined and the existing modules of the target battery swap cabinet are obtained, thereby improving the matching degree of the battery swap cabinet to the user's needs, and then the obtained data is substituted into the management task generation model to obtain the management task instructions corresponding to the target data group, thereby realizing the automatic generation of the management task instructions corresponding to the target data group, and thereby realizing the adjustment plan or The system automatically generates a replacement plan, and then sends the management task instruction to the corresponding battery swap cabinet to facilitate adjustment of the user's battery pack or replacement of the battery pack by the user, thereby adjusting the current battery pack or replacing the battery pack required by the user according to the target data group, ensuring that the adjusted or replaced battery pack meets the requirements of the target data group or is consistent with the current battery pack, while improving the adjustment efficiency or replacement efficiency of the user's battery pack, and avoiding the technical problem in the prior art that when users use battery packs of different battery swap cabinets, the output parameters of the battery pack are different due to different battery module parameters, resulting in unstable output power, which in turn affects the user's usage effect.

[0052] In implementing the technical solution disclosed herein, the system timely acquires the status of each battery swap cabinet by timely updating the battery adjustment real-time parameter range and battery replacement real-time parameter range of the battery swap cabinet during the idle time of the battery swap cabinet. Then, based on the battery adjustment real-time parameter range or the battery replacement real-time parameter range, the existing module database of the target battery swap cabinet is selectively updated, thereby realizing preliminary screening of the target battery swap cabinet selected by the user, avoiding the user from selecting the wrong battery swap cabinet or the user arriving at the battery swap cabinet and finding that the battery swap cabinet cannot meet the user's needs, saving the user's search time, and thus realizing intelligent management of the system.

[0053] In the technical solution of the present disclosure, during model training, multiple groups of target data groups obtained historically, multiple groups of current battery pack data and multiple groups of existing module databases obtained selectively are obtained to generate multiple groups of training sample sets with different selection modes and existing module databases corresponding to each target battery swap cabinet corresponding to each group of training sample sets, thereby realizing the random generation of multiple groups of training sample sets, thereby improving the versatility of the training of the management task generation model, and then according to the multiple groups of training sample sets and the existing module databases corresponding to each target battery swap cabinet corresponding to each group of training sample sets, multiple groups of battery structure parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets are obtained, and according to the multiple groups of battery structure parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets, the selection mode of each group of training sample sets, the battery storage in the target data group and / or current battery pack data, The data is stored, and the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets are screened to obtain the multiple groups of reference combination parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets after screening, thereby realizing the generation and screening of the multiple groups of reference combination parameters corresponding to the target battery swap cabinets corresponding to the training sample sets, and improving the matching degree between the training sample sets and the multiple groups of reference combination parameters corresponding to them. Then, the multiple groups of reference combination parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets are simulated, and according to the simulation results, a group of reference combination parameters are selected as the execution combination data in the management task instructions of the training sample sets, thereby realizing the accurate screening of the reference combination parameters corresponding to the training sample sets, and improving the matching degree between the training sample sets and the execution combination data in the management task instructions, thereby realizing the intelligent screening of the execution combination data.

[0054] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings provided are for illustrative purposes only and are not intended to limit the scope of the present disclosure. The same or similar components have the same or similar reference numbers regardless of the drawing in which they are depicted, and

[0056] Figure 1 A main step flowchart diagram of a management control method of a battery swap pack in which embodiments of the present disclosure can be implemented is shown.

[0057] Figure 2 A main step flowchart diagram of a management task generation model training method in which embodiments of the present disclosure can be implemented is shown.

[0058] Figure 3 A main structure block diagram of a management control system of a battery swap pack in which embodiments of the present disclosure can be implemented is shown.

[0059] List of reference signs:

[0060] 300: management control system of a battery swap pack; 301: control device; 3011: processor; 3012: memory; 3013: program code; 302: management task generation model; 3021: weight data reference library; 303: communication unit. DETAILED DESCRIPTION

[0061] In order to make the objects, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some but not all of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present disclosure.

[0062] In addition, the term "and / or" herein merely describes an association relationship of associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0063] Reference is made to the accompanying Figure 1 , Figure 1 A main step flowchart diagram of a management control method of a battery swap pack in which embodiments of the present disclosure can be implemented is shown. As shown in Figure 1 The management control method of a battery swap pack in the embodiments of the present disclosure is applied to a management control system of a battery swap pack, and the method includes the following steps S101-S104.

[0064] Step S101: In response to a user's selection mode, obtaining at least one target battery swap cabinet and selectively obtaining a target data group and / or current battery pack data, wherein the target data group includes target input data, target output data, target storage data, and target structure data;

[0065] In some embodiments, in response to the user's selection mode, obtaining at least one target battery swap cabinet and selectively obtaining a target data set and / or current battery pack data includes:

[0066] If the user's selection mode is replacement mode, obtaining at least one target battery swap cabinet and obtaining a target data group or current battery pack data;

[0067] If the user's selection mode is adjustment mode, obtaining at least one target battery swap cabinet and obtaining a target data group and current battery pack data;

[0068] The current battery pack data at least includes battery structure data and battery storage data of the battery pack currently used by the user.

[0069] In some embodiments, before obtaining at least one target battery swap cabinet and selectively obtaining a target data set and / or current battery pack data in response to a user's selection mode, the method further includes:

[0070] Get the idle time of the battery swap cabinet;

[0071] If the idle time of the battery swap cabinet exceeds a preset time threshold, a parameter range generation model is substituted into the existing module database of the battery swap cabinet to obtain the battery adjustment parameter range and battery replacement parameter range of the battery swap cabinet;

[0072] Based on the battery adjustment parameter range and the battery replacement parameter range of the battery swap cabinet, the battery adjustment real-time parameter range and the battery replacement real-time parameter range of the battery swap cabinet are updated.

[0073] In some embodiments, obtaining the idle time of the battery swap cabinet includes:

[0074] Get the update time and current time of the existing module database;

[0075] Based on the update time of the existing module database of the power swap cabinet and the current time, the idle time of the power swap cabinet is obtained.

[0076] In some embodiments, the method further comprises:

[0077] If the idle time of the battery swap cabinet is lower than the preset time threshold, the real-time parameter range of the battery adjustment and the real-time parameter range of the battery replacement of the battery swap cabinet will not be updated.

[0078] In some embodiments, the preset time threshold may be 24 hours or 20 hours. The selection of the preset time threshold here is only an example. In actual testing, those skilled in the art may make a selection according to actual needs, which will not be elaborated here.

[0079] In some embodiments, the idle time of the battery swap cabinet is obtained by the following formula:

[0080] The idle time of the battery swap cabinet = current time - update time of the existing module database of the battery swap cabinet.

[0081] In some embodiments, before obtaining at least one target battery swap cabinet and selectively obtaining a target data set and / or current battery pack data in response to a user's selection mode, the method further includes:

[0082] Get the idle time of the battery swap cabinet;

[0083] If the idle time of the battery swap cabinet exceeds a preset time threshold, a parameter range generation model is substituted into the existing module database of the battery swap cabinet to obtain the battery adjustment parameter range and battery replacement parameter range of the battery swap cabinet;

[0084] Based on the battery adjustment parameter range and the battery replacement parameter range of the battery swap cabinet, the battery adjustment real-time parameter range and the battery replacement real-time parameter range of the battery swap cabinet are updated.

[0085] In the above embodiment, by timely updating the battery adjustment real-time parameter range and battery replacement real-time parameter range of the battery swap cabinet during the idle time of the battery swap cabinet, the system can timely obtain the status of each battery swap cabinet, and then selectively update the existing module database of the target battery swap cabinet based on the battery adjustment real-time parameter range or the battery replacement real-time parameter range, thereby realizing preliminary screening of the target battery swap cabinet selected by the user, avoiding the user from selecting the wrong battery swap cabinet or the user arriving at the battery swap cabinet only to find that the battery swap cabinet cannot meet the user's needs, saving the user's search time, and thus realizing intelligent management of the system.

[0086] Step S102: Based on the at least one target battery swap cabinet, selectively update the existing module database of each target battery swap cabinet, wherein the existing module database at least includes multiple adapter components existing in the target battery swap cabinet, data information of each adapter component, multiple battery modules, and data information of each battery module;

[0087] In some embodiments, selectively updating the existing module database of each target battery swap cabinet based on the at least one target battery swap cabinet includes:

[0088] Based on the at least one target battery swap cabinet and the user's selection mode, selectively obtain the battery adjustment real-time parameter range or the battery replacement real-time parameter range of each target battery swap cabinet;

[0089] If the user's selection mode is replacement mode, the real-time parameter range of battery replacement of the target battery swap cabinet is compared with the target data group, and according to the comparison result, the existing module database of the target battery swap cabinet is selectively updated;

[0090] If the user's selection mode is adjustment mode, the real-time parameter range of the battery adjustment of the target battery swap cabinet will be comprehensively compared with the current battery pack data and the target data group, and based on the comprehensive comparison results, the existing module database of the target battery swap cabinet will be selectively updated.

[0091] In some embodiments, selectively updating the existing module database of the target battery swap cabinet according to the comparison results includes:

[0092] If the target data group exists in the real-time parameter range of battery replacement of the target battery swap cabinet, the existing module database of the target battery swap cabinet is updated; otherwise, the existing module database of the target battery swap cabinet is not updated, and the target battery swap cabinet is excluded from the at least one target battery swap cabinet.

[0093] In some embodiments, the data type of the target input data in the target data group is input type, the data type of the target output data is output type, the data type of the target storage data is storage type, and the data type of the target structure data is structure type.

[0094] In some embodiments, selectively updating the existing module database of the target battery swap cabinet based on the comprehensive comparison results includes:

[0095] Based on the current battery pack data and the target data group, obtaining a balance data group corresponding to the user, wherein the balance data group includes at least data of different data types, and the data types correspond one-to-one to the data types of the target data group;

[0096] If the difference data group corresponding to the user exists in the real-time parameter range of the battery adjustment of the target battery swap cabinet, the existing module database of the target battery swap cabinet is updated; otherwise, the existing module database of the target battery swap cabinet is not updated, and the target battery swap cabinet is excluded from the at least one target battery swap cabinet.

[0097] In some embodiments, data of different data types in the balance data group corresponding to the user are obtained by the following formula:

[0098] Data of one data type in the balance data set corresponding to the user = data of the data type in the target data set - data of the data type in the current battery pack data.

[0099] In some embodiments, updating the existing module database of the target battery swap cabinet includes:

[0100] Determining a target battery swap cabinet based on the target data group;

[0101] Obtain the data currently existing in the target battery swap cabinet, and update the existing module database of the target battery swap cabinet based on the data currently existing in the target battery swap cabinet, wherein the data currently existing in the target battery swap cabinet at least includes the component number of each adapter component currently existing in the target battery swap cabinet and the battery number of each battery module.

[0102] In some embodiments, the target battery swap cabinet stores a preset battery module data comparison library and a preset adapter component data comparison library. The acquiring of the data existing in the target battery swap cabinet and updating the existing module database of the target battery swap cabinet based on the data existing in the target battery swap cabinet include:

[0103] Obtain the battery number of each existing battery module and the component number of each adapter component of the target battery swap cabinet;

[0104] Based on the numbers of the battery modules and a preset battery module data comparison library, obtaining data information of each battery module;

[0105] Based on the component number of each adapter component and a preset adapter component data comparison library, obtaining data information of each adapter component;

[0106] Based on the data information of each adapter element and the data information of each battery module, the existing module database of the target battery swap cabinet is updated.

[0107] In some embodiments, the preset battery module data comparison library includes at least the numbers of several battery modules and the data information corresponding to the number of each battery module; the preset adapter component data comparison library includes at least the component numbers of several adapter components and the data information corresponding to the component number of each adapter component.

[0108] Step S103: Substitute the user's selection mode, the selectively acquired target data group and / or the current battery pack data, and the existing module database of at least one target battery swap cabinet into the management task generation model to obtain a management task instruction corresponding to the target data group, wherein the management task instruction at least includes the user's selection mode, the battery swap cabinet number that executes the management task instruction, and the execution combination data corresponding to the battery swap cabinet;

[0109] like Figure 2 As shown, in some embodiments, before substituting the user's selection mode, the selectively acquired target data group and / or the current battery pack data, and the existing module database of at least one target battery swap cabinet into the management task generation model, the method further trains the management task generation model through the following steps:

[0110] Acquire multiple groups of target data groups obtained historically, multiple groups of current battery pack data, and multiple groups of existing module databases obtained historically from multiple battery swap cabinets, and generate multiple groups of training sample sets with different selection modes, at least one target battery swap cabinet corresponding to each group of training sample sets, and an existing module database corresponding to each target battery swap cabinet corresponding to each group of training sample sets, wherein the training sample set includes at least one selection mode, a group of battery pack data corresponding to the selection mode, and / or a group of target data groups;

[0111] Based on the selection mode in each set of training samples, the target structure data in the target data group and / or the battery structure data in the current battery pack data, and the existing module database of each target battery swap cabinet corresponding to each set of training samples, multiple sets of battery structure parameters corresponding to each target battery swap cabinet corresponding to each set of training samples are obtained;

[0112] Based on the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group and / or the battery storage data in the current battery group data, the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets are screened to obtain multiple groups of reference combination parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets after screening, wherein the reference combination parameters include at least battery structure parameters and battery connection parameters and battery combination data corresponding to the battery structure parameters;

[0113] Based on the multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets after screening and the selection mode of each group of training sample sets, simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets are obtained, wherein the simulation results at least include the completion rates of different simulation types;

[0114] Based on the simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets, a group of reference combination parameters is selected as the execution combination data in the management task instructions corresponding to the training sample set, and the battery swap cabinet number of the target battery swap cabinet corresponding to the reference combination parameter is used as the battery swap cabinet number for executing the management task instructions in the management task instructions corresponding to the training sample set, and the selection mode corresponding to the training sample set is used as the selection mode in the management task instructions corresponding to the training sample set, so as to obtain the management task instructions corresponding to each group of training sample sets.

[0115] In some embodiments, the acquisition of multiple sets of target data groups obtained historically, multiple sets of current battery pack data, and multiple sets of existing module databases obtained historically by multiple battery swap cabinets, generates multiple sets of training sample sets with different selection modes, at least one target battery swap cabinet corresponding to each set of training sample sets, and the existing module database corresponding to each target battery swap cabinet corresponding to each set of training sample sets, including:

[0116] Acquire multiple sets of historical battery data and multiple sets of historical target data;

[0117] Randomly selecting multiple sets of battery pack data and multiple sets of target data groups of the same number, and randomly matching the selected multiple sets of battery pack data, selection patterns and multiple sets of target data groups to form multiple sets of training sample sets with different selection patterns;

[0118] Randomly select multiple battery swap cabinets as multiple target battery swap cabinets corresponding to each training sample set;

[0119] Based on the selection mode corresponding to each group of training sample sets, the target data group in each group of training sample sets, and the multiple groups of existing module databases obtained historically for each target battery swap cabinet, an existing module database corresponding to the target battery swap cabinet is selectively selected as the existing module database corresponding to the target battery swap cabinet in the training sample set to obtain at least one target battery swap cabinet corresponding to each group of training sample sets and an existing module database corresponding to each target battery swap cabinet corresponding to each group of training sample sets.

[0120] In some embodiments, based on the target data group in each training sample set and the multiple groups of existing module databases historically obtained for each target battery swap cabinet, selectively selecting an existing module database corresponding to the target battery swap cabinet as the existing module database corresponding to the target battery swap cabinet in the training sample set includes:

[0121] If the selection mode corresponding to the training sample set is the replacement mode, if the target data group in the training sample set exists in the battery replacement real-time parameter range corresponding to the existing module database corresponding to the target battery swap station corresponding to the training sample set, the existing module database is selected as the existing module database corresponding to the target battery swap station in the training sample set;

[0122] If the selection mode corresponding to the training sample set is the adjustment mode, if the difference data group corresponding to the training sample set exists in the battery adjustment real-time parameter range corresponding to the existing module database corresponding to the target battery swap station corresponding to the training sample set, the existing module database is selected as the existing module database corresponding to the target battery swap station in the training sample set.

[0123] In some embodiments, if the selection mode corresponding to the training sample set is the replacement mode, if the target data group in the training sample set does not exist in the battery replacement real-time parameter range corresponding to the existing module database corresponding to the target battery swap station corresponding to the training sample set, the existing module database is not selected;

[0124] If the selection mode corresponding to the training sample set is the replacement mode, if the target data group in the training sample set does not exist in the battery replacement real-time parameter range corresponding to the existing module database corresponding to the target battery swap station corresponding to the training sample set, the existing module database is not selected.

[0125] In some embodiments, based on the battery storage data in each group of battery structure parameters corresponding to each target battery swap station corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group and / or the current battery group data, the battery structure parameters corresponding to each target battery swap station corresponding to each group of training sample sets are screened to obtain the reference combination parameters corresponding to each target battery swap station corresponding to each group of training sample sets after screening.

[0126] Based on the battery storage data in each group of battery structure parameters corresponding to each target battery swap station corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group and / or the current battery group data, the battery connection parameters corresponding to each target battery swap station corresponding to each group of battery structure parameters and the battery combination data are obtained to obtain the reference combination parameters corresponding to each target battery swap station corresponding to each group of training sample sets;

[0127] Each group of reference combination parameters corresponding to each target battery swap station corresponding to each group of training sample sets is simulated to obtain simulation data corresponding to each group of reference combination parameters corresponding to each target battery swap station;

[0128] If the simulation data corresponding to a set of reference combination parameters of the target battery swap cabinet corresponding to the training sample set does not match any data of the target data group, it is determined that the reference combination parameters do not match the training sample set, and the reference combination parameters are eliminated from the multiple sets of reference combination parameters corresponding to the training sample set;

[0129] Otherwise, it is determined that the reference combination parameters of the target battery swap cabinet match the training sample set, and the reference combination parameters are used as one of the reference combination parameters of the target battery swap cabinet corresponding to the filtered training sample set.

[0130] In some embodiments, the simulation data can be simulated through a preset simulation model or a simulation method in the prior art. The choice of the method for obtaining the simulation data here is only an exemplary explanation. In actual testing, those skilled in the art can make a choice according to actual needs. As long as it is possible to obtain the simulation data corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet according to each set of training sample sets, it will be sufficient to obtain the simulation data corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet. No further details will be given here.

[0131] In some embodiments, the method of obtaining multiple groups of battery connection parameters and battery combination data corresponding to each target battery swap cabinet corresponding to each group of battery structure parameters can be achieved through a trained deep learning network model. The selection of obtaining multiple groups of battery connection parameters and battery combination data corresponding to each target battery swap cabinet corresponding to each group of battery structure parameters here is only an exemplary explanation. In actual testing, those skilled in the art can make a choice according to actual needs. As long as it is possible to obtain multiple groups of battery connection parameters and battery combination data corresponding to each target battery swap cabinet corresponding to each group of battery structure parameters based on the multiple groups of battery structure parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group in each group of training sample sets and / or the battery storage data in the current battery group data, it will not be repeated here.

[0132] In some embodiments, the trained deep learning network model is obtained by randomly matching multiple groups of battery pack data obtained historically, multiple groups of target data groups obtained historically, and multiple existing module databases obtained historically for each target battery swap cabinet, and training according to the matched data groups, so that the trained deep learning network model can obtain multiple groups of battery connection parameters and battery combination data corresponding to each target battery swap cabinet corresponding to each group of battery structure parameters based on the multiple groups of battery structure parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data groups in each group of training sample sets and / or the battery storage data in the current battery pack data. No further details are given here.

[0133] In some embodiments, the simulation data includes at least simulation structure data, simulation input data, simulation output data, and simulation storage data.

[0134] In some embodiments, the simulation is performed based on multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets after screening and the selection mode of each group of training sample sets, and the simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets include:

[0135] The simulation is performed based on multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets after screening, and the simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets include:

[0136] Simulate multiple sets of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of filtered training sample sets to obtain simulation data corresponding to each set of reference combination parameters;

[0137] Comparing the simulation data corresponding to each set of reference combination parameters with the target data set in the training sample set corresponding to each set of reference combination parameters to obtain comparison data of different simulation types corresponding to each set of reference combination parameters, wherein the different simulation types include at least structure type, input type, output type, and storage type;

[0138] Based on the comparison data of different simulation types corresponding to each set of reference combination parameters, the completion rates of different simulation types corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of training sample sets are obtained, so as to obtain the simulation results corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of training sample sets.

[0139] In some embodiments, the simulation data can be simulated by a preset simulation model, or can be a simulation method in the prior art. The selection of the simulation data acquisition method is only exemplary, and in actual testing, a person skilled in the art can select according to actual needs, as long as the simulation data corresponding to each group of reference combination parameters of each target battery swap cabinet corresponding to each filtered training sample set can be obtained, and details are not repeated here.

[0140] In some embodiments, the simulation data at least includes simulation structure data, simulation input data, simulation output data and simulation storage data; and the comparison data of different simulation types corresponding to each group of reference combination parameters is obtained by the following formula:

[0141] The comparison data of one type corresponding to the reference combination parameters = the target data of the type in the target data group in the training sample set corresponding to the reference combination parameters - the simulation data of the type corresponding to the reference combination parameters.

[0142] In some embodiments, the completion rate of different simulation types corresponding to each group of reference combination parameters corresponding to each training sample set is obtained by the following formula:

[0143] The completion rate of one type corresponding to the reference combination parameters = (1 - the comparison data of the type corresponding to the reference combination parameters / the target data of the type in the target data group in the training sample set corresponding to the reference combination parameters) * 100%.

[0144] In some embodiments, the simulation type corresponds to the simulation type and the data type one by one.

[0145] In some embodiments, based on the simulation results of each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each training sample set, a group of reference combination parameters is selected as the execution combination data in the corresponding management task instruction of the training sample set, including:

[0146] Based on the simulation results of multiple groups of reference combination parameters corresponding to the same group of training sample sets, one or more groups of reference combination parameters corresponding to the same group of training sample sets are selectively removed to obtain multiple groups of reference combination parameters after removal corresponding to each training sample set;

[0147] Based on the simulation results of the multiple groups of reference combination parameters after removal corresponding to each training sample set, a completion score of each group of reference combination parameters is obtained.

[0148] Based on the completion scores of the eliminated multiple reference combination parameters corresponding to each training sample set, a set of reference combination parameters is selected as the execution combination data in the management task instruction corresponding to the training sample set.

[0149] In some embodiments, based on the simulation results corresponding to the multiple sets of reference combination parameters corresponding to the same set of training sample sets, selectively eliminating one or more sets of reference combination parameters corresponding to the same set of training sample sets to obtain the multiple sets of reference combination parameters corresponding to each set of training sample sets after elimination includes:

[0150] If the completion rate of any simulation type corresponding to the reference combination parameters is lower than the preset completion rate threshold of the simulation type, the reference combination parameters are eliminated from the multiple groups of reference combination parameters corresponding to the training sample set corresponding to the reference combination parameters to obtain the eliminated multiple groups of reference combination parameters corresponding to each group of training sample sets.

[0151] In some embodiments, the management task generation model stores a preset weight data comparison library, which stores a plurality of weight coefficients, a plurality of weight values ​​corresponding to each weight coefficient, and a completion rate corresponding to each weight value. In addition, the plurality of weight coefficients correspond one-to-one to different data types, and the completion score of each set of reference combination parameters is obtained by the following formula:

[0152] The completion score of the reference combination parameter=the sum of (the weight value corresponding to the completion rate of each data type of the reference combination parameter*the weight coefficient of the data type).

[0153] In the above embodiment, during model training, multiple groups of reference combination parameters corresponding to each target battery swap cabinet after screening are simulated to obtain simulation data corresponding to each group of reference combination parameters, and then the simulation data is compared with the target data group of the corresponding training sample set to obtain comparison data of different simulation types corresponding to each group of reference combination parameters, and then the simulation results of each group of reference combination parameters are obtained, thereby achieving the acquisition of simulation results for different reference combination parameters, and then based on each simulation result, the multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each training sample set are further screened, thereby achieving re-screening of the reference combination parameters corresponding to the training sample set, further improving the matching degree of the reference combination parameters corresponding to the training sample set, and obtaining the completion scores of the multiple groups of reference combination parameters after screening, and based on the completion scores, selecting a group of reference combination parameters with the highest completion score as the execution combination data in the management task instructions corresponding to the training sample set, thereby achieving automatic generation of execution combination data and ensuring accurate matching of execution combination data with the training sample set.

[0154] In some embodiments, the step of substituting the user's selection mode, the selectively acquired target data group and / or the current battery pack data, and the existing module database of at least one target battery swap cabinet into the management task generation model to obtain a management task instruction corresponding to the target data group includes:

[0155] Based on the user's selection mode, the target structure data in the target data group and / or the battery structure data in the current battery pack data, and the existing module database of at least one target battery swap cabinet, multiple groups of battery structure parameters corresponding to each target battery swap cabinet are obtained;

[0156] Based on the multiple groups of battery structure parameters corresponding to each target data group, the user's selection mode, the target data group and / or the battery storage data in the current battery group data, the multiple groups of battery structure parameters are screened to obtain multiple groups of reference combination parameters corresponding to each screened target battery swap cabinet, wherein the reference combination parameters at least include battery structure parameters and battery connection parameters and battery combination data corresponding to the battery structure parameters;

[0157] Based on the multiple groups of reference combination parameters corresponding to each screened target battery swap cabinet and the user's selection mode, simulation is performed to obtain simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet, wherein the simulation results at least include completion rates of different simulation types;

[0158] Based on the simulation results corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet, a set of reference combination parameters is selected as the execution combination data in the management task instruction corresponding to the target data group, and the battery swap cabinet number of the target battery swap cabinet corresponding to the reference combination parameter is used as the battery swap cabinet number for executing the management task instruction in the management task instruction corresponding to the target data group, and the user's selection mode is used as the selection mode in the management task instruction corresponding to the target data group, so as to obtain the management task instruction corresponding to the target data group.

[0159] Step S104: Send the management task instruction to the battery swap cabinet corresponding to the battery swap cabinet number, so as to adjust the user's battery pack or replace the battery pack.

[0160] In some embodiments, the execution combination data includes at least battery structure parameters, battery connection parameters and battery combination data; the battery combination data includes at least the number of adapter elements, the element number of the adapter elements, the number of battery modules and the battery number of the battery module; after the battery swap cabinet corresponding to the battery swap cabinet number receives the management task instruction, it performs the following operations:

[0161] Based on the battery combination data in the execution combination data in the management task instruction, select the battery modules and / or adapter components that match the battery combination data existing in the target battery swap cabinet;

[0162] Based on the user selection mode in the management task instruction, the selected battery module and / or adapter element, and the target structural parameters and battery connection parameters in the execution combination data, the user's battery pack is adjusted or replaced.

[0163] In some embodiments, the battery structure parameters include the position of each battery module and / or adapter element; the battery connection parameters include at least the required connection method of the battery module or adapter element, wherein the connection method includes at least parallel or series connection.

[0164] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and models involved are not necessarily required by the present disclosure.

[0165] The above is an introduction to the method embodiment. The following is a further explanation of the solution disclosed in the present disclosure through an apparatus embodiment.

[0166] Furthermore, the present disclosure also provides a management and control system for a battery-swap battery pack.

[0167] See attached Figure 3 , Figure 3 The main structural block diagram of the management and control system of the battery pack that can implement the embodiment of the present disclosure is shown. Figure 3As shown, the management and control system 300 of the battery swapping battery pack in the embodiment of the present disclosure includes a communication unit 303, a management task generation model 302 and a control device. The system interacts with the user's battery pack and each battery swapping cabinet through the communication unit 303. The management task generation model 302 is used to generate management task instructions corresponding to the target data group according to the user's selection mode, the selectively acquired target data group and / or the current battery pack data and the existing module database of at least one target battery swapping cabinet. The control device 301 includes a processor 3011 and a memory 3012. The memory 3012 can be configured to store a program code 3013 for executing the management and control method of the battery swapping battery pack of the above-mentioned method embodiment. The processor 3011 can be configured to execute the program code 3013 in the memory 3012. The program code 3013 includes but is not limited to the program code 3013 for executing the management and control method of the battery swapping battery pack of the above-mentioned method embodiment. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present disclosure. The control device 301 can be a control device formed by various electronic devices.

[0168] In some embodiments, the management task generation model 302 stores a preset weight data comparison library 3021, and the preset weight data comparison library 3021 stores multiple weight coefficients, multiple weight values ​​corresponding to each weight coefficient, and the completion rate corresponding to each weight value, and the multiple weight coefficients correspond one-to-one to different data types.

[0169] In some embodiments, the battery exchange cabinet stores a preset battery module data comparison library, a preset adapter component data comparison library and an existing module database, wherein the preset battery module data comparison library includes at least the numbers of several battery modules and the data information corresponding to the number of each battery module; the preset adapter component data comparison library includes at least the component numbers of several adapter components and the data information corresponding to the component number of each adapter component.

[0170] In one embodiment, the description of the specific implementation functions can refer to steps S101 to S104.

[0171] The management and control system 300 of the battery pack is used to execute Figure 1 The embodiment of the management and control method for a battery swapping battery pack shown in the figure has similar technical principles, technical problems solved and technical effects produced. Technicians in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the management and control system 300 of the battery swapping battery pack can refer to the contents described in the embodiment of the management and control method for the battery swapping battery pack, and will not be repeated here.

[0172] It should be understood that the various forms of flow shown above can be used to reorder, add, or delete steps. For example, the steps described in the present disclosure can be performed in parallel, in series, or in a different order, as long as the desired results of the technology disclosed in the present disclosure can be achieved, which is not limited herein.

[0173] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A management and control method for a battery replacement battery pack, characterized in that: The method is applied to a management and control system of a battery replacement battery pack, and the method comprises the following steps: In response to a user's selection mode, obtaining at least one target battery swap cabinet and selectively obtaining a target data group and / or current battery pack data, wherein the target data group includes target input data, target output data, target storage data, and target structure data; Based on the at least one target battery swap cabinet, selectively updating the existing module database of each target battery swap cabinet, wherein the existing module database includes at least a plurality of adapter elements existing in the target battery swap cabinet, data information of each adapter element, a plurality of battery modules, and data information of each battery module; Substitute the user's selection mode, the selectively acquired target data group and / or the current battery pack data, and the existing module database of at least one target battery swap cabinet into the management task generation model to obtain a management task instruction corresponding to the target data group, wherein the management task instruction at least includes the user's selection mode, the battery swap cabinet number that executes the management task instruction, and the execution combination data corresponding to the battery swap cabinet; The management task instruction is sent to the battery swap cabinet corresponding to the battery swap cabinet number to facilitate adjustment of the user's battery pack or replacement of the battery pack by the user.

2. The method according to claim 1, characterized in that The acquiring of at least one target battery swap cabinet and selectively acquiring a target data set and / or current battery pack data in response to a user's selection mode includes: If the user's selection mode is replacement mode, obtaining at least one target battery swap cabinet and obtaining a target data group or current battery pack data; If the user's selection mode is adjustment mode, obtaining at least one target battery swap cabinet and obtaining a target data group and current battery pack data; The current battery pack data at least includes battery structure data and battery storage data of the battery pack currently used by the user.

3. The method according to claim 1, characterized in that Before obtaining at least one target battery swap cabinet and selectively obtaining a target data set and / or current battery pack data in response to a user's selection mode, the method further includes: Get the idle time of the battery swap cabinet; If the idle time of the battery swap cabinet exceeds a preset time threshold, a parameter range generation model is substituted into the existing module database of the battery swap cabinet to obtain the battery adjustment parameter range and battery replacement parameter range of the battery swap cabinet; Based on the battery adjustment parameter range and the battery replacement parameter range of the battery swap cabinet, the battery adjustment real-time parameter range and the battery replacement real-time parameter range of the battery swap cabinet are updated.

4. The method according to claim 3, characterized in that The selectively updating the existing module database of each target battery swap cabinet based on the at least one target battery swap cabinet includes: Based on the at least one target battery swap cabinet and the user's selection mode, selectively obtain the battery adjustment real-time parameter range or the battery replacement real-time parameter range of each target battery swap cabinet; If the user's selection mode is replacement mode, the real-time parameter range of battery replacement of the target battery swap cabinet is compared with the target data group, and according to the comparison result, the existing module database of the target battery swap cabinet is selectively updated; If the user's selection mode is adjustment mode, the real-time parameter range of the battery adjustment of the target battery swap cabinet will be comprehensively compared with the current battery pack data and the target data group, and based on the comprehensive comparison results, the existing module database of the target battery swap cabinet will be selectively updated.

5. The method according to claim 3, characterized in that Before substituting the user's selection mode, the selectively acquired target data set and / or the current battery pack data, and the existing module database of at least one target battery swap cabinet into the management task generation model, the method further trains the management task generation model through the following steps: Acquire multiple groups of target data groups obtained historically, multiple groups of current battery pack data, and multiple groups of existing module databases obtained historically from multiple battery swap cabinets, and generate multiple groups of training sample sets with different selection modes, at least one target battery swap cabinet corresponding to each group of training sample sets, and an existing module database corresponding to each target battery swap cabinet corresponding to each group of training sample sets, wherein the training sample set includes at least one selection mode, a group of battery pack data corresponding to the selection mode, and / or a group of target data groups; Based on the selection mode in each set of training samples, the target structure data in the target data group and / or the battery structure data in the current battery pack data, and the existing module database of each target battery swap cabinet corresponding to each set of training samples, multiple sets of battery structure parameters corresponding to each target battery swap cabinet corresponding to each set of training samples are obtained; Based on the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group and / or the battery storage data in the current battery group data, the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets are screened to obtain multiple groups of reference combination parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets after screening, wherein the reference combination parameters include at least battery structure parameters and battery connection parameters and battery combination data corresponding to the battery structure parameters; Based on the multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets after screening and the selection mode of each group of training sample sets, simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets are obtained, wherein the simulation results at least include the completion rates of different simulation types; Based on the simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets, a group of reference combination parameters is selected as the execution combination data in the management task instructions corresponding to the training sample set, and the battery swap cabinet number of the target battery swap cabinet corresponding to the reference combination parameter is used as the battery swap cabinet number for executing the management task instructions in the management task instructions corresponding to the training sample set, and the selection mode corresponding to the training sample set is used as the selection mode in the management task instructions corresponding to the training sample set, so as to obtain the management task instructions corresponding to each group of training sample sets.

6. The method according to claim 5, characterized in that The method of obtaining multiple sets of target data groups obtained historically, multiple sets of current battery pack data, and multiple sets of existing module databases obtained historically by multiple battery swap cabinets, generating multiple sets of training sample sets with different selection modes, at least one target battery swap cabinet corresponding to each training sample set, and existing module databases corresponding to each target battery swap cabinet corresponding to each training sample set include: Acquire multiple sets of historical battery data and multiple sets of historical target data; Randomly selecting multiple sets of battery pack data and multiple sets of target data groups of the same number, and randomly matching the selected multiple sets of battery pack data, selection patterns and multiple sets of target data groups to form multiple sets of training sample sets with different selection patterns; Randomly select multiple battery swap cabinets as multiple target battery swap cabinets corresponding to each training sample set; Based on the selection mode corresponding to each group of training sample sets, the target data group in each group of training sample sets, and the multiple groups of existing module databases obtained historically for each target battery swap cabinet, an existing module database corresponding to the target battery swap cabinet is selectively selected as the existing module database corresponding to the target battery swap cabinet in the training sample set to obtain at least one target battery swap cabinet corresponding to each group of training sample sets and an existing module database corresponding to each target battery swap cabinet corresponding to each group of training sample sets.

7. The method according to claim 6, characterized in that The method of selectively selecting an existing module database corresponding to the target power swap cabinet as the existing module database corresponding to the target power swap cabinet in the training sample set based on the target data group in each training sample set and multiple groups of existing module databases obtained historically for each target power swap cabinet includes: If the selection mode corresponding to the training sample set is the replacement mode, and the target data group in the training sample set exists in the battery replacement real-time parameter range corresponding to an existing module database corresponding to a target battery swap cabinet corresponding to the training sample set, then the existing module database is selected as the existing module database corresponding to the target battery swap cabinet in the training sample set; If the selection mode corresponding to the training sample set is the adjustment mode, and there is a difference data group corresponding to the training sample set in the battery adjustment real-time parameter range corresponding to an existing module database corresponding to a target battery swap cabinet corresponding to the training sample set, then the existing module database is selected as the existing module database corresponding to the target battery swap cabinet in the training sample set.

8. The method according to claim 7, characterized in that Based on the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group and / or the battery storage data in the current battery group data, the multiple groups of battery structure parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets are screened, and the multiple groups of reference combination parameters corresponding to the target battery swap cabinets corresponding to each group of training sample sets after screening include: Based on the multiple groups of battery structure parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets, the selection mode in each group of training sample sets, the target data group in each group of training sample sets and / or the battery storage data in the current battery group data, the multiple groups of battery connection parameters and battery combination data corresponding to each target battery swap cabinet corresponding to each group of battery structure parameters are obtained to obtain the multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets; Simulate multiple sets of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of training sample sets to obtain simulation data corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet; If the simulation data corresponding to a set of reference combination parameters of the target battery swap cabinet corresponding to the training sample set does not match any data of the target data group, it is determined that the reference combination parameters do not match the training sample set, and the reference combination parameters are eliminated from the multiple sets of reference combination parameters corresponding to the training sample set; Otherwise, it is determined that the reference combination parameters of the target battery swap cabinet match the training sample set, and the reference combination parameters are used as one of the reference combination parameters of the target battery swap cabinet corresponding to the filtered training sample set.

9. The method according to claim 8, characterized in that The simulation is performed based on multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets after screening and the selection mode of each group of training sample sets, and the simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets include: The simulation is performed based on multiple groups of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets after screening, and the simulation results corresponding to each group of reference combination parameters corresponding to each target battery swap cabinet corresponding to each group of training sample sets include: Simulate multiple sets of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of filtered training sample sets to obtain simulation data corresponding to each set of reference combination parameters; Comparing the simulation data corresponding to each set of reference combination parameters with the target data set in the training sample set corresponding to each set of reference combination parameters to obtain comparison data of different simulation types corresponding to each set of reference combination parameters, wherein the different simulation types include at least structure type, input type, output type, and storage type; Based on the comparison data of different simulation types corresponding to each set of reference combination parameters, the completion rates of different simulation types corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of training sample sets are obtained, so as to obtain the simulation results corresponding to each set of reference combination parameters corresponding to each target battery swap cabinet corresponding to each set of training sample sets.

10. A management and control system for a battery replacement battery pack, characterized in that: The system includes a communication unit, a management task generation model and a control device. The system exchanges information with the user's battery pack and each battery swap cabinet respectively through the communication unit. The management task generation model is used to generate management task instructions corresponding to the target data group according to the user's selection mode, the selectively acquired target data group and / or the current battery pack data and the existing module database of at least one target battery swap cabinet. The control device includes a processor and a memory. The memory is suitable for storing multiple program codes. The program code is suitable for being loaded and run by the processor to execute the management and control method of the battery swap battery pack described in any one of claims 1 to 9.

Images