A method for managing energy consumption during low-temperature charging preheating and a management system for preheating energy consumption.

Abstract

The embodiments of this disclosure provide a low-temperature charging preheating energy consumption management method and a preheating energy consumption management system. The method includes: responding to a user's charging demand by acquiring the ambient temperature at the location of the preheating energy consumption management system; acquiring the operating status of each charging compartment of the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment; inputting the user's charging demand, ambient temperature, operating status of each charging compartment, and execution data of multiple heating modules around each charging compartment into the preheating energy consumption management model to obtain the location of the charging compartment corresponding to each charging battery and the operating parameters of the heating modules around each charging compartment; feeding back the location of the charging compartment corresponding to each charging battery to the user and performing a preheating operation based on the operating parameters of the heating modules around each charging compartment, thereby achieving reasonable utilization of energy consumption and enabling targeted preheating of batteries according to demand, so that the batteries are charged at a suitable temperature.

Description

Technical Field

[0001] This disclosure relates to the field of low-temperature charging preheating energy consumption management technology, and in particular to a low-temperature charging preheating energy consumption management method and a preheating energy consumption management system. Background Technology

[0002] With the popularization and application of new energy resources, rechargeable batteries have become an important means of storing and converting electrical energy. The convenience of batteries has brought great convenience to our lives. However, when batteries are charged in cold environments, there is a problem that the charging efficiency is low at low temperatures, resulting in the charging and discharging capacity of the battery being far lower than the rated capacity.

[0003] Currently, existing low-temperature charging solutions generally address the above issues by preheating the charging compartment. However, the preheating methods used generally employ a uniform heating temperature and heating time, which cannot guarantee that the preheating temperature of the charging compartment is suitable for the rechargeable battery. In some cases, it may even cause the battery to be charged at excessively high temperatures, which could damage the battery.

[0004] Accordingly, there is a need in this field for a new low-temperature charging preheating energy consumption management solution to address the above-mentioned problems. Summary of the Invention

[0005] This disclosure provides a low-temperature charging preheating energy consumption management method and a preheating energy consumption management system, which solves the technical problem that the existing technology generally uses a uniform heating temperature and heating time, which cannot guarantee that the preheating temperature of the charging compartment is suitable for the charging battery, and may even cause the battery to be charged at an excessively high temperature, which will damage the battery.

[0006] According to a first aspect of this disclosure, a method for managing low-temperature charging preheating energy consumption is provided. This method is applied to a preheating energy consumption management system, the system comprising at least several charging compartments, each charging compartment being surrounded by multiple heating modules. The method includes the following steps:

[0007] In response to the user's charging needs, the ambient temperature at the location of the preheating energy consumption management system is obtained, wherein the charging needs include at least the expected start charging time, the number of charging batteries, and the battery parameters of each battery.

[0008] The operating status of each charging compartment in the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment are obtained. The operating status includes at least one of the following: idle state, preheating state, charging state, and other states.

[0009] The user's charging needs, ambient temperature, operating status of each charging compartment, and execution data of multiple heating modules around each charging compartment are input into the preheating energy consumption management model to obtain the location of the charging compartment corresponding to each charging battery and the operating parameters of the heating modules around each charging compartment. The operating parameters include at least the start heating time, heating duration, and heating power.

[0010] The location of the charging compartment corresponding to each rechargeable battery is fed back to the user, and a preheating operation is performed based on the operating parameters of the heating modules around each charging compartment.

[0011] In addition to the aspects and any possible implementations described above, a further implementation is provided in which, before inputting the user's charging needs, ambient temperature, operating status of each charging compartment, and execution data of multiple heating modules around each charging compartment into the preheating energy consumption management model, the method further trains the preheating energy consumption management model through the following steps:

[0012] Multiple training sample sets are obtained under different ambient temperatures. The training sample sets include at least training samples with different charging parameters. The charging parameters include at least the expected start charging time, the number of training samples, the amount of charge to be generated for each training sample, the material of each training sample, and the current temperature of each training sample.

[0013] Based on the material of each training sample, the current temperature of each training sample, the amount of charge to be charged of each training sample, and the preset historical charging database, the appropriate charging temperature and the expected charging temperature rise corresponding to the material of each training sample are obtained.

[0014] Based on the ambient temperature, the current temperature of each training sample, the expected charging rise temperature of each training sample, and the suitable charging temperature of each training sample, the preheating temperature of each training sample is determined.

[0015] Based on the obtained operating status of each charging compartment in the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment, the location of each charging compartment in the idle state is determined and the internal temperature of each charging compartment in the idle state is selectively obtained and / or predicted. The execution data includes at least the heating state and the execution power and remaining time corresponding to the heating state, or the unheated state.

[0016] Based on the location of each charging compartment in an idle state, the internal temperature of each charging compartment and / or the predicted internal temperature, the preheating temperature of each training sample and the expected start charging time, multiple distribution schemes are obtained for each training sample set. The distribution scheme includes at least the location of the charging compartment corresponding to each training sample in the training sample set and the operating parameters of the heating module around the charging compartment.

[0017] Based on the multiple distribution schemes corresponding to each training sample set, the preheating energy consumption of each distribution scheme is obtained.

[0018] The distribution scheme with the lowest preheating energy consumption is selected as the location of the charging compartment corresponding to each training sample in the training sample set and the operating parameters of the heating module around each charging compartment. The operating parameters include at least the start heating time, heating duration and heating power.

[0019] In addition to the aspects and any possible implementations described above, a further implementation is provided in which a heating chamber is provided between adjacent charging chambers, and the heating module is installed inside the heating chamber. The step of determining the location of each charging chamber in an idle state and selectively acquiring and / or predicting the internal temperature of each charging chamber in an idle state, based on the obtained operating status of each charging chamber from the preheating energy consumption management system and the execution data of multiple heating modules around each charging chamber, includes:

[0020] Based on the obtained operating status of each charging compartment in the preheating energy consumption management system, the location of each charging compartment in the idle state of the preheating energy consumption management system is determined.

[0021] Based on the location of each charging compartment in the idle state, the execution data of multiple heating modules around each charging compartment in the idle state is selected, wherein the execution data includes at least the heating state and the execution power and remaining time corresponding to the heating state, or the unheated state.

[0022] If multiple heating modules around the charging compartment that is in an idle state are not in a heated state, then the internal temperature of the charging compartment is obtained;

[0023] If at least one of the multiple heating modules around the charging compartment that is in an idle state is in a heating state, then the execution power and remaining heating time of all heating modules around the charging compartment that are in a heating state are obtained, and the temperature is predicted based on the current temperature inside the charging compartment, the execution power of all heating modules around the charging compartment that are in a heating state, and the remaining heating time, to obtain the predicted temperature inside the charging compartment.

[0024] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein obtaining the suitable charging temperature and expected charging rise temperature corresponding to the material of each training sample based on the material of each training sample, the current temperature of each training sample, the amount of charge to be generated by each training sample, and a preset historical charging database includes:

[0025] Based on the material of each training sample, the amount of electricity to be charged for each training sample, and the preset historical charging database, the estimated charging time for each training sample is obtained.

[0026] Based on the current temperature of each training sample, the expected charging time of each training sample, and the preset historical charging database, the expected charging temperature rise of each training sample is determined.

[0027] Based on the material of each training sample and the preset historical charging database, the appropriate charging temperature corresponding to the training sample of each material is obtained.

[0028] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further verifies the training results of the trained preheating energy management model through the following steps:

[0029] Multiple sets of verification samples are obtained under different ambient temperatures. The verification sample sets include at least verification samples with different charging parameters. The charging parameters include at least the expected start time of charging, the number of verification samples, the amount of power to be charged for each verification sample, the material of each verification sample, and the current temperature of each verification sample.

[0030] The multiple sets of verification sample sets and the corresponding ambient temperatures of each set of verification sample sets are input into the trained preheating energy consumption management model to obtain the location of the charging compartment, the preheating temperature, and the operating parameters of the heating modules around each charging compartment for each verification sample in each set of verification sample sets.

[0031] Multiple sets of verification sample sets, the ambient temperature corresponding to each set of verification sample sets, the location of the charging compartment corresponding to each verification sample in each set of verification sample sets, and the operating parameters of the heating modules around each charging compartment are simulated to obtain the preheating simulation data corresponding to the charging compartment of each verification sample in each set of verification sample sets.

[0032] Based on the preheating simulation data of the charging compartment corresponding to each verification sample in each set of verification samples, the internal temperature of the charging compartment corresponding to each verification sample in each set of verification samples is obtained when the expected charging start time is reached.

[0033] Based on the charging compartment temperature and preheating temperature corresponding to each verification sample in each verification sample set, the preheating completion rate and energy consumption redundancy rate of each verification sample set are obtained:

[0034] If the preheating completion rate reaches a preset preheating completion rate threshold, and the energy redundancy rate is lower than a preset energy redundancy rate threshold, then the preheating energy consumption management model is determined to be trained successfully.

[0035] If the preheating completion rate does not reach the preset preheating completion rate threshold, or if the energy redundancy rate is higher than the preset energy redundancy rate threshold, then the preheating energy consumption management model is determined to have failed to train, and the preheating energy consumption management model is retrained.

[0036] In addition to the aspects and any possible implementations described above, a further implementation is provided in which, after feeding back the position of the charging compartment corresponding to each rechargeable battery to the user and performing a preheating operation based on the operating parameters of the heating modules around each charging compartment, the method further includes:

[0037] In response to the preheating operation performed by the heating modules around each charging compartment, the preheating temperature corresponding to each charging compartment and the internal temperature data of the charging compartment when the heating modules around each charging compartment stop running are obtained.

[0038] If the heating modules around each charging compartment stop operating and the temperature inside the charging compartment is lower than the preheating temperature corresponding to the charging compartment, and no battery is placed in the charging compartment, then the heating modules around the charging compartment will continue to perform heating and the temperature inside the charging compartment will be monitored in real time.

[0039] If the temperature inside the charging compartment reaches the preheating temperature of the charging compartment, the heating modules around the charging compartment will be controlled to stop heating.

[0040] In addition to the aspects and any possible implementations described above, a further implementation is provided in which, after the heating module around the charging compartment continues to perform heating, the method further includes:

[0041] The heating data collected from the heating modules around each charging compartment to continue heating includes at least heating power and heating time.

[0042] Based on the heating data of the heating modules around each charging compartment continuing to perform heating, the redundant energy consumption for continued heating of each charging compartment is obtained;

[0043] Based on the redundant energy consumption of continued heating in each charging compartment, the average redundant energy consumption corresponding to the user's charging needs is obtained.

[0044] If the average redundant energy consumption is higher than the preset redundant energy consumption threshold, the trained preheating energy consumption management model will be retrained.

[0045] In addition to the aspects and any possible implementations described above, a further implementation is provided in which, after the temperature inside the charging compartment reaches the preheating temperature of the charging compartment, the method further includes:

[0046] Acquire the starting temperature data of the battery in each charging compartment when it begins charging, the preheating temperature of each charging compartment, and the ending temperature data when the battery finishes charging.

[0047] Based on the preheating temperature of each charging compartment, the start temperature data when the battery starts charging, and the end temperature data when the battery finishes charging, the preheating completion rate and heat preservation completion rate of each charging compartment are obtained.

[0048] Based on the preheating completion rate and heat preservation completion rate of each charging compartment, the average preheating completion rate and average heat preservation completion rate corresponding to the user's charging needs are obtained.

[0049] Based on the average preheating completion rate or the average insulation completion rate, the trained preheating energy consumption management model is selectively retrained.

[0050] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the method also constructs a preset historical charging database through the following steps:

[0051] Battery parameters of each rechargeable battery were collected under different ambient temperatures, wherein the battery parameters include at least the material of the rechargeable battery;

[0052] The charging data of each rechargeable battery is collected under different ambient temperatures. The charging data includes at least the current battery level, the battery temperature corresponding to each battery level during the charging process, and the charging time.

[0053] Based on the battery parameters and charging data of each rechargeable battery under different ambient temperatures, a preset historical charging database is constructed.

[0054] According to a second aspect of this disclosure, a preheating energy consumption management system is provided. The system includes at least a plurality of charging chambers, a preheating energy consumption management model, and a control device. A heating chamber is provided between adjacent charging chambers, and a heating module is installed in the heating chamber. The control device includes a memory and a processor. The memory stores a computer program, and the processor executes the program to implement the method described above.

[0055] The above-disclosed technical solutions have at least one or more of the following beneficial effects:

[0056] By inputting the acquired charging demand, ambient temperature, charging compartment operating status, and heating module execution data into the preheating energy consumption management model, the model obtains the location of the charging compartment corresponding to each rechargeable battery and the operating parameters of the heating modules around that charging compartment. The charging compartment location is then fed back to the user, and preheating operations are performed accordingly based on these operating parameters. This achieves the determination of the lowest energy consumption distribution scheme, corresponding to the charging compartment and its corresponding heating module operating parameters for each battery, based on a comprehensive consideration of battery condition, ambient temperature, charging compartment operating status, and heating module execution data. This enables rational energy utilization, avoids resource waste, and allows for targeted preheating of batteries based on demand, ensuring that batteries are charged at suitable temperatures, guaranteeing charging safety, and improving charging efficiency. This avoids the technical problem of existing technologies that commonly use uniform heating temperatures and times, which cannot guarantee that the preheating temperature of the charging compartment is suitable for the rechargeable battery, and may even lead to charging at excessively high temperatures, causing battery damage.

[0057] In implementing the technical solution of this disclosure, by using the training model, based on the material of the training sample, the amount of electricity to be charged, and a preset historical charging database, the suitable charging temperature and the expected charging rise temperature corresponding to each training sample are obtained. Then, based on the ambient temperature, the suitable charging temperature, and the expected charging rise temperature, the preheating temperature of each training sample is determined. Then, based on the operating status of each charging compartment and the execution data of the heating modules around each charging compartment, the location of the charging compartment in the idle state is determined, and the internal temperature of the charging compartment and / or the predicted internal temperature of the charging compartment are selectively obtained. Then, combined with the expected start charging time of each training sample, multiple distribution schemes corresponding to each training sample set are obtained. Then, the preheating energy consumption of each distribution scheme is obtained, and the distribution scheme with the lowest preheating energy consumption is selected as the location of the charging compartment corresponding to each training sample in the training sample set and the operating parameters of the heating modules around each charging compartment. This ensures that the battery is charged at the corresponding suitable charging temperature and the preheating energy consumption is minimized, ensuring that the preheating energy consumption management system has the lowest preheating energy consumption, improving the charging efficiency of the battery, and thus improving the utilization rate of the heat heated by each heating module of the preheating energy consumption management system.

[0058] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0059] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0060] Figure 1 A flowchart illustrating the main steps of a low-temperature charging preheating energy consumption management method according to an embodiment of the present disclosure is shown.

[0061] Figure 2 A block diagram of a preheating energy consumption management system according to an embodiment of the present disclosure is shown;

[0062] Figure 3 A flowchart illustrating the main steps of training a preheating energy management model for a low-temperature charging preheating energy management method according to an embodiment of the present disclosure is shown.

[0063] List of reference numerals in the attached diagram:

[0064] 200: Preheating energy consumption management system; 201: Control device; 2011: Processor; 2012: Memory; 2013: Program code; 202: Preheating energy consumption management model; 203: Charging chamber; 204: Heating chamber; 2041: Heating module. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0066] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0067] The directional terms used in this article, such as "front", "front side", "front part", "rear", "rear side" and "rear part", are all based on the front-back direction of the charging components installed in the charging compartment.

[0068] Figure 1 A flowchart illustrating the main steps of a low-temperature charging preheating energy consumption management method according to an embodiment of the present disclosure is shown.

[0069] like Figure 1 As shown, the low-temperature charging preheating energy consumption management method in this embodiment is applied to a preheating energy consumption management system. The system includes at least several charging chambers, and multiple heating modules are arranged around each charging chamber. The method mainly includes the following steps S101-S104:

[0070] Step S101: In response to the user's charging demand, obtain the ambient temperature at the location of the preheating energy consumption management system, wherein the charging demand includes at least the expected charging start time, the number of charging batteries, and the battery parameters of each battery.

[0071] In some embodiments, obtaining the ambient temperature at the location of the preheating energy management system in response to a user's charging demand includes:

[0072] In response to users' charging needs, the location of the preheating energy consumption management system is obtained;

[0073] Based on the location of the preheating energy consumption management system, the ambient temperature corresponding to the location of the preheating energy consumption management system is obtained.

[0074] In some embodiments, the battery parameters of the battery include at least the battery material. Specifically, if the battery's charge-to-charge capacity and current battery temperature are collected, the battery parameters of the battery also include the charge-to-charge capacity and current battery temperature. The method for collecting the battery's charge-to-charge capacity and current battery temperature here adopts the existing method. The selection of the collection method here is only an example. In actual testing, those skilled in the art can select according to actual needs, which will not be elaborated here.

[0075] In some embodiments, the method further includes:

[0076] Determine whether the battery parameters of each battery contain the amount of charge to be charged and the current battery temperature:

[0077] If it is determined that the battery exists, then obtain the amount of charge to be generated by each battery and the current battery temperature.

[0078] If it is determined that it does not exist, then based on the user corresponding to the charging demand and the preset historical charging habits of the user, the estimated amount of charge to be generated for each battery is obtained, and based on the ambient temperature, the estimated current battery temperature of each battery is obtained.

[0079] Step S102: Obtain the operating status of each charging compartment of the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment, wherein the operating status includes at least one of the following: idle state, preheating state, charging state, and other states.

[0080] In some embodiments, the execution data includes at least a heating state and the execution power and remaining duration corresponding to the heating state, or an unheated state.

[0081] In some embodiments, a heating chamber is provided between adjacent charging compartments, and the heating module is installed inside the heating chamber. Specifically, multiple heating chambers are evenly distributed around each charging compartment on its upper, lower, left, and right sides. The heating chambers are positioned between two adjacent charging compartments, allowing one heating chamber to heat two adjacent charging compartments on either side of it. Specifically, the charging components of the charging compartment are located on the front or rear side of the charging compartment to charge the battery.

[0082] In some embodiments, an indicator device is also provided on the outside of the charging compartment to display the operating status of the charging compartment. Specifically, the indicator device can be a color display, with green corresponding to the idle state, yellow corresponding to the preheating state, blue corresponding to the charging state, and red corresponding to other states. The indicator device can also be a display of color and flashing frequency. The selection of the indicator device and its corresponding display here is only an illustrative example. In actual testing, those skilled in the art can select according to actual needs, as long as the operating status of the charging compartment can be displayed through the indicator device. Further details are omitted here.

[0083] Step S103: Input the user's charging needs, ambient temperature, operating status of each charging compartment, and execution data of multiple heating modules around each charging compartment into the preheating energy consumption management model to obtain the location of the charging compartment corresponding to each charging battery and the operating parameters of the heating modules around each charging compartment. The operating parameters include at least the start heating time, heating duration, and heating power.

[0084] like Figure 3 As shown, in some embodiments, before inputting the user's charging needs, ambient temperature, operating status of each charging compartment, and execution data of multiple heating modules around each charging compartment into the preheating energy consumption management model, the method further trains the preheating energy consumption management model through the following steps:

[0085] Multiple training sample sets are obtained under different ambient temperatures. The training sample sets include at least training samples with different charging parameters. The charging parameters include at least the expected start charging time, the number of training samples, the amount of charge to be generated for each training sample, the material of each training sample, and the current temperature of each training sample.

[0086] Based on the material of each training sample, the current temperature of each training sample, the amount of charge to be charged of each training sample, and the preset historical charging database, the appropriate charging temperature and the expected charging temperature rise corresponding to the material of each training sample are obtained.

[0087] Based on the ambient temperature, the current temperature of each training sample, the expected charging rise temperature of each training sample, and the suitable charging temperature of each training sample, the preheating temperature of each training sample is determined.

[0088] Based on the obtained operating status of each charging compartment in the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment, the location of each charging compartment in the idle state is determined and the internal temperature of each charging compartment in the idle state is selectively obtained and / or predicted. The execution data includes at least the heating state and the execution power and remaining time corresponding to the heating state, or the unheated state.

[0089] Based on the location of each charging compartment in an idle state, the internal temperature of each charging compartment and / or the predicted internal temperature, the preheating temperature of each training sample and the expected start charging time, multiple distribution schemes are obtained for each training sample set. The distribution scheme includes at least the location of the charging compartment corresponding to each training sample in the training sample set and the operating parameters of the heating module around the charging compartment.

[0090] Based on the multiple distribution schemes corresponding to each training sample set, the preheating energy consumption of each distribution scheme is obtained.

[0091] The distribution scheme with the lowest preheating energy consumption is selected as the location of the charging compartment corresponding to each training sample in the training sample set and the operating parameters of the heating module around each charging compartment. The operating parameters include at least the start heating time, heating duration and heating power.

[0092] In some embodiments, obtaining the suitable charging temperature and expected charging rise temperature corresponding to the material of each training sample based on the material of each training sample, the current temperature of each training sample, the amount of charge to be charged of each training sample, and a preset historical charging database includes:

[0093] Based on the material of each training sample, the amount of electricity to be charged for each training sample, and the preset historical charging database, the estimated charging time for each training sample is obtained.

[0094] Based on the current temperature of each training sample, the expected charging time of each training sample, and the preset historical charging database, the expected charging temperature rise of each training sample is determined.

[0095] Based on the material of each training sample and the preset historical charging database, the appropriate charging temperature corresponding to the training sample of each material is obtained.

[0096] In some embodiments, the method further constructs a preset historical charging database through the following steps:

[0097] Battery parameters of each rechargeable battery were collected under different ambient temperatures, wherein the battery parameters include at least the material of the rechargeable battery;

[0098] The charging data of each rechargeable battery is collected under different ambient temperatures. The charging data includes at least the current battery level, the battery temperature corresponding to each battery level during the charging process, and the charging time.

[0099] Based on the battery parameters and charging data of each rechargeable battery under different ambient temperatures, a preset historical charging database is constructed.

[0100] In some embodiments, obtaining the estimated charging time for each training sample based on its material, the amount of power to be charged, and a preset historical charging database includes:

[0101] Based on the material of each training sample, at least one set of charging data corresponding to the material of each training sample is obtained from a preset historical charging database.

[0102] Based on the various amounts of electricity to be charged in the training samples, at least one set of charging data corresponding to each amount of electricity to be charged is obtained;

[0103] Cluster analysis is performed based on at least one set of charging data corresponding to each charge quantity to be charged, to obtain the material of each training sample and the charging data corresponding to each charge quantity to be charged for the material, and based on the charging data, the expected charging time of each training sample is obtained.

[0104] In some embodiments, determining the expected charging rise temperature of each training sample based on the current temperature of each training sample, the expected charging duration of each training sample, and a preset historical charging database includes:

[0105] Based on the expected charging time of each training sample and the preset historical charging database, at least one set of battery temperature data in the preset historical charging database is obtained, corresponding to the material and expected charging time of each training sample during the charging process.

[0106] Cluster analysis is performed on the current temperature of each training sample and the battery temperature data of at least one set corresponding to each training sample to obtain the battery temperature rise data corresponding to the expected charging time of each training sample, and the battery temperature rise data corresponding to the expected charging time of each training sample is used as the expected charging temperature rise of each training sample.

[0107] In some embodiments, obtaining the appropriate charging temperature for each training sample based on its material and a preset historical charging database includes:

[0108] Based on the material of each training sample and the preset historical charging database, multiple sets of charging data corresponding to different ambient temperatures for each material are obtained. The historical charging data includes at least the current charge level, the battery temperature corresponding to each charge level during the charging process, and the charging time.

[0109] Cluster analysis was performed on multiple sets of charging data under different ambient temperatures for each material to determine the charging efficiency of each material under different ambient temperatures.

[0110] The ambient temperature corresponding to the highest charging efficiency data for each material is used as the suitable charging temperature for the training sample of that material.

[0111] In some embodiments, determining the preheating temperature of each training sample based on the ambient temperature, the current temperature of each training sample, the expected charging rise temperature of each training sample, and the suitable charging temperature of each training sample includes obtaining the preheating temperature of the training sample using the following formula: Preheating temperature of training sample = Suitable charging temperature of training sample v Expected charging rise temperature of training sample * Weighting coefficient of expected charging rise temperature and ambient temperature - (Maximum value of ambient temperature or current temperature of training sample) * (Weighting coefficient of selected temperature). The choice of formula for the preheating temperature of training sample is merely illustrative. In actual testing, those skilled in the art can choose according to actual needs, as long as it can achieve the determination of the preheating temperature of each training sample based on the ambient temperature, the current temperature of each training sample, the expected charging rise temperature of each training sample, and the suitable charging temperature of each training sample. Further details are omitted here.

[0112] In some embodiments, determining the location of each charging compartment in an idle state and selectively acquiring and / or predicting the internal temperature of each charging compartment in an idle state based on the obtained operating status of each charging compartment in the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment includes:

[0113] Based on the obtained operating status of each charging compartment in the preheating energy consumption management system, the location of each charging compartment in the idle state of the preheating energy consumption management system is determined.

[0114] Based on the location of each charging compartment in the idle state, the execution data of multiple heating modules around each charging compartment in the idle state is selected, wherein the execution data includes at least the heating state and the execution power and remaining time corresponding to the heating state, or the unheated state.

[0115] If multiple heating modules around the charging compartment that is in an idle state are not in a heated state, then the internal temperature of the charging compartment is obtained;

[0116] If at least one of the multiple heating modules around the charging compartment that is in an idle state is in a heating state, then the execution power and remaining heating time of all heating modules around the charging compartment that are in a heating state are obtained, and the temperature is predicted based on the current temperature inside the charging compartment, the execution power of all heating modules around the charging compartment that are in a heating state, and the remaining heating time, to obtain the predicted temperature inside the charging compartment.

[0117] In some embodiments, the process of obtaining multiple distribution schemes for each training sample set based on the location of each charging compartment in an idle state, the internal temperature of each charging compartment and / or the predicted internal temperature, the preheating temperature of each training sample, and the expected start charging time includes:

[0118] Based on the preheating temperature corresponding to each training sample and the internal temperature and / or predicted internal temperature of each charging compartment, the charging compartment with the smallest temperature difference between its internal temperature and / or predicted internal temperature and the preheating temperature of the training sample is determined, and the charging compartment is taken as the charging compartment corresponding to the training sample.

[0119] Repeat the allocation between training samples and charging pods to obtain the charging pods corresponding to each training sample in each training sample set;

[0120] Based on the preheating temperature of each training sample in each training sample set and the internal temperature and / or predicted internal temperature of the charging compartment corresponding to each training sample, the operating parameters of the heating module around the charging compartment corresponding to each training sample are determined, wherein the operating parameters include at least the start heating time, heating duration and heating power.

[0121] And / or,

[0122] Each training sample in each training sample set is randomly assigned to an idle charging compartment.

[0123] Based on the preheating temperature of each training sample and the internal temperature and / or predicted internal temperature of the charging compartment corresponding to each training sample, the operating parameters of the heating module around the charging compartment corresponding to each training sample are determined.

[0124] Furthermore, if the heating duration exceeds the expected charging start duration, the operating parameters of the heating modules surrounding the charging compartment corresponding to the training sample are adjusted.

[0125] In some embodiments, the method of obtaining multiple distribution schemes corresponding to each training sample set can also be obtained through a trained neural network model or through a preheating battery reference table. The choice of method for obtaining multiple distribution schemes corresponding to each training sample set is only an example. In actual testing, those skilled in the art can choose according to actual needs. As long as it can be achieved by using the position of each charging compartment in an idle state, the internal temperature of each charging compartment and / or the predicted internal temperature, the preheating temperature corresponding to each training sample, and the expected start charging time, multiple distribution schemes corresponding to each training sample set can be obtained. Further details are omitted here.

[0126] In some embodiments, after training samples are allocated to an idle charging hopper, the method further includes:

[0127] Based on the historical preheating data of the charging compartments in an idle state corresponding to the training samples and the preheating temperature of the training samples, determine whether the charging compartments in an idle state corresponding to the training samples can reach the preheating temperature of the training samples:

[0128] If the determination is yes, then the location of the charging compartment corresponding to the training sample will be taken as the location of the charging compartment corresponding to the training sample.

[0129] If the determination is negative, then any of the remaining charging compartments that are not assigned to training samples and are in an idle state are reassigned to the training sample, the historical preheating data of the charging compartment is obtained, and it is re-determined whether the charging compartment can reach the preheating temperature of the training sample. Based on the determination result, the location of the charging compartment corresponding to the training sample is selectively used as the location of the charging compartment corresponding to the training sample.

[0130] In some embodiments, obtaining the preheating energy consumption of each distribution scheme based on the multiple distribution schemes corresponding to each training sample set includes obtaining the preheating energy consumption of each distribution scheme through the following formula: Preheating energy consumption of each distribution scheme = sum of energy consumption of heating modules around the charging compartment corresponding to each training sample, and energy consumption of heating modules around the charging compartment corresponding to each training sample = sum of heating time * heating power of each heating module. The selection of the formula for the preheating energy consumption of each distribution scheme here is only an illustrative example. In actual testing, those skilled in the art can choose according to actual needs, as long as it can achieve the goal of obtaining the preheating energy consumption of each distribution scheme through the multiple distribution schemes corresponding to each training sample set. It will not be elaborated here.

[0131] In the above embodiments, by using the training model, based on the material of the training sample, the amount of power to be charged, and a preset historical charging database, the suitable charging temperature and the expected charging rise temperature corresponding to each training sample are obtained. Then, based on the ambient temperature, the suitable charging temperature, and the expected charging rise temperature, the preheating temperature of each training sample is determined. Then, based on the operating status of each charging compartment and the execution data of the heating modules around each charging compartment, the location of the charging compartment in an idle state is determined, and the internal temperature and / or predicted internal temperature of the charging compartment are selectively obtained. Then, combined with the expected start charging time of each training sample, multiple distribution schemes corresponding to each training sample set are obtained. Then, the preheating energy consumption of each distribution scheme is obtained, and the distribution scheme with the lowest preheating energy consumption is selected as the location of the charging compartment corresponding to each training sample in that training sample set and the operating parameters of the heating modules around each charging compartment. This ensures that the battery is charged at the corresponding suitable charging temperature and the preheating energy consumption is minimized, ensuring that the preheating energy consumption management system has the lowest preheating energy consumption, improving the charging efficiency of the battery, and thus improving the utilization rate of the heat heated by each heating module of the preheating energy consumption management system.

[0132] In some embodiments, the step of inputting the user's charging needs, ambient temperature, operating status of each charging compartment, and execution data of multiple heating modules around each charging compartment into the preheating energy consumption management model to obtain the location of the charging compartment corresponding to each charging battery and the operating parameters of the heating modules around each charging compartment includes:

[0133] Based on the material of each battery in the charging demand, the current battery temperature of each battery, the amount of charge to be charged of each battery, and the preset historical charging database, the appropriate charging temperature and the expected charging temperature rise corresponding to the material of each battery are obtained.

[0134] Based on the ambient temperature, the current battery temperature of each battery, the expected charging temperature rise of each battery, and the suitable charging temperature of each battery, the preheating temperature of each battery is determined.

[0135] Based on the obtained operating status of each charging compartment in the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment, the location of each charging compartment in the idle state is determined and the internal temperature of each charging compartment in the idle state is selectively obtained and / or predicted. The execution data includes at least the heating state and the execution power and remaining time corresponding to the heating state, or the unheated state.

[0136] Based on the location of each charging compartment in an idle state, the internal temperature of each charging compartment and / or the predicted internal temperature, the preheating temperature of each battery, and the expected start charging time in the charging demand, multiple distribution schemes corresponding to the user's charging demand are obtained. The distribution scheme includes at least the location of the charging compartment corresponding to each battery in the charging demand and the operating parameters of each heating module around the charging compartment.

[0137] Based on the multiple distribution schemes corresponding to the user's charging needs, the preheating energy consumption of each distribution scheme is obtained.

[0138] The distribution scheme with the lowest preheating energy consumption is selected as the location of the charging compartment corresponding to each battery in the charging demand and the operating parameters of each heating module around each charging compartment, and output. The operating parameters include at least the start heating time, heating duration and heating power.

[0139] In some embodiments, the method further validates the training results of the trained preheating energy management model through the following steps:

[0140] Multiple sets of verification samples are obtained under different ambient temperatures. The verification sample sets include at least verification samples with different charging parameters. The charging parameters include at least the expected start charging time, the number of verification samples, the amount of power to be charged for each verification sample, the material of each verification sample, and the current temperature of each verification sample.

[0141] The multiple sets of verification sample sets and the corresponding ambient temperatures of each set of verification sample sets are input into the trained preheating energy consumption management model to obtain the location of the charging compartment, the preheating temperature, and the operating parameters of the heating modules around each charging compartment for each verification sample in each set of verification sample sets.

[0142] Multiple sets of verification sample sets, the ambient temperature corresponding to each set of verification sample sets, the location of the charging compartment corresponding to each verification sample in each set of verification sample sets, and the operating parameters of the heating modules around each charging compartment are simulated to obtain the preheating simulation data corresponding to the charging compartment of each verification sample in each set of verification sample sets.

[0143] Based on the preheating simulation data of the charging compartment corresponding to each verification sample in each set of verification samples, the internal temperature of the charging compartment corresponding to each verification sample in each set of verification samples is obtained when the expected charging start time is reached.

[0144] Based on the charging compartment temperature and preheating temperature corresponding to each verification sample in each verification sample set, the preheating completion rate and energy consumption redundancy rate of each verification sample set are obtained:

[0145] If the preheating completion rate reaches a preset preheating completion rate threshold, and the energy redundancy rate is lower than a preset energy redundancy rate threshold, then the preheating energy consumption management model is determined to be trained successfully.

[0146] If the preheating completion rate does not reach the preset preheating completion rate threshold, or if the energy redundancy rate is higher than the preset energy redundancy rate threshold, then the preheating energy consumption management model is determined to have failed to train, and the preheating energy consumption management model is retrained.

[0147] In some embodiments, the simulation adopts the simulation technology in the prior art. The choice of simulation here is only an example. In actual testing, those skilled in the art can choose according to actual needs. As long as it can simulate multiple sets of verification sample sets, the ambient temperature corresponding to each set of verification sample sets, the position of the charging compartment corresponding to each verification sample in each set of verification sample sets, and the preheating temperature of each charging compartment, and obtain the preheating simulation data corresponding to the charging compartment corresponding to each verification sample in each set of verification sample sets, it is acceptable. Further details are not provided here.

[0148] In some embodiments, the preheating completion rate threshold can be 80% or 85%, and the energy redundancy rate threshold can be 5% or 6%. The setting of the preheating completion rate threshold and the energy redundancy rate threshold here is only an illustrative example. In actual testing, those skilled in the art can set them according to actual needs, and they will not be elaborated here.

[0149] Step S104: Feed back the location of the charging compartment corresponding to each charging battery to the user and perform a preheating operation based on the operating parameters of the heating modules around each charging compartment.

[0150] In some embodiments, after feeding back the location of the charging compartment corresponding to each rechargeable battery to the user and performing a preheating operation based on the operating parameters of the heating modules around each charging compartment, the method further includes:

[0151] In response to the preheating operation performed by the heating modules around each charging compartment, the preheating temperature corresponding to each charging compartment and the internal temperature data of the charging compartment when the heating modules around each charging compartment stop running are obtained.

[0152] If the heating modules around each charging compartment stop operating and the temperature inside the charging compartment is lower than the preheating temperature corresponding to the charging compartment, and no battery is placed in the charging compartment, then the heating modules around the charging compartment will continue to perform heating and the temperature inside the charging compartment will be monitored in real time.

[0153] If the temperature inside the charging compartment reaches the preheating temperature of the charging compartment, the heating modules around the charging compartment will be controlled to stop heating.

[0154] In some embodiments, after the heating module around the charging compartment continues to perform heating, the method further includes:

[0155] The heating data collected from the heating modules around each charging compartment to continue heating includes at least heating power and heating time.

[0156] Based on the heating data of the heating modules around each charging compartment continuing to perform heating, the redundant energy consumption for continued heating of each charging compartment is obtained;

[0157] Based on the redundant energy consumption of continued heating in each charging compartment, the average redundant energy consumption corresponding to the user's charging needs is obtained.

[0158] If the average redundant energy consumption is higher than the preset redundant energy consumption threshold, the trained preheating energy consumption management model will be retrained.

[0159] In some embodiments, the redundancy energy consumption threshold can be 1KJ or 0.5KJ. The setting of the redundancy energy consumption threshold here is only an illustrative example. In actual testing, those skilled in the art can set it according to actual needs, which will not be elaborated here.

[0160] In some embodiments, the heating module may be a PTC heater. The selection of the heating module described here is only an illustrative example. In actual testing, those skilled in the art can select according to actual needs, as long as the charging compartment can be heated. Further details are omitted here.

[0161] In some embodiments, after the temperature inside the charging compartment reaches the preheating temperature of the charging compartment, the method further includes:

[0162] Acquire the starting temperature data of the battery in each charging compartment when it begins charging, the preheating temperature of each charging compartment, and the ending temperature data when the battery finishes charging.

[0163] Based on the preheating temperature of each charging compartment, the start temperature data when the battery starts charging, and the end temperature data when the battery finishes charging, the preheating completion rate and heat preservation completion rate of each charging compartment are obtained.

[0164] Based on the preheating completion rate and heat preservation completion rate of each charging compartment, the average preheating completion rate and average heat preservation completion rate corresponding to the user's charging needs are obtained.

[0165] Based on the average preheating completion rate or the average insulation completion rate, the trained preheating energy consumption management model is selectively retrained.

[0166] In some embodiments, selectively retraining the trained preheating energy consumption management model based on the average preheating completion rate or the average insulation completion rate includes:

[0167] If the average preheating completion rate exceeds a preset average preheating completion rate threshold, and the average insulation completion rate exceeds a preset average insulation completion rate threshold, then the preheating energy consumption management model will not be retrained; otherwise, the preheating energy consumption management model will be retrained.

[0168] In some embodiments, the average preheating completion rate threshold can be 85% or 90%, and the average insulation completion rate threshold can be 75% or 80%. The setting of the average preheating completion rate threshold and the average insulation completion rate threshold here is only an illustrative example. In actual testing, those skilled in the art can set them according to actual needs, and they will not be elaborated here.

[0169] According to the embodiments of this disclosure, the following technical effects are achieved: By inputting the obtained charging demand, ambient temperature, charging compartment operating status, and heating module execution data into the preheating energy consumption management model, the location of the charging compartment corresponding to each rechargeable battery and the operating parameters of the heating module around the charging compartment are obtained. The charging compartment location is then fed back to the user, and a preheating operation is performed according to the corresponding operating parameters. This achieves the determination of the charging compartment and heating module operating parameters corresponding to each battery with the lowest energy consumption distribution scheme based on a comprehensive consideration of battery condition, ambient temperature, charging compartment operating status, and heating module execution data. This achieves rational use of energy consumption, avoids resource waste, and enables targeted preheating of batteries according to demand, allowing batteries to be charged at a suitable temperature, ensuring battery charging safety, and thereby improving battery charging efficiency. This avoids the technical problem in the prior art where a uniform heating temperature and heating time are generally used, which cannot guarantee that the preheating temperature of the charging compartment is suitable for the rechargeable battery, or even causes the battery to be charged at an excessively high temperature, which may damage the battery.

[0170] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that, in order to achieve the effects of this disclosure, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of this disclosure.

[0171] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0172] The above is an introduction to the method embodiments. The following system embodiments will further illustrate the solution described in this disclosure.

[0173] Figure 2 A block diagram of a preheating energy consumption management system according to an embodiment of the present disclosure is shown. Figure 2As shown, the preheating energy consumption management system 200 includes at least a plurality of charging chambers 203, a preheating energy consumption management model 202, and a control device 201. Heating chambers 204 are arranged between adjacent charging chambers 203, and heating modules 2041 are installed in the heating chambers 204. The control device 201 includes a processor 2011 and a memory 2012. The memory 2012 can be configured to store program code 2013 for executing the low-temperature charging preheating energy consumption management method of the above method embodiments. The processor 2011 can be configured to execute the program code 2013 in the memory 2012. The program code 2013 includes, but is not limited to, program code 2013 for executing the low-temperature charging preheating energy consumption management method of the above method embodiments. For ease of explanation, only the parts related to the embodiments of this disclosure are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of this disclosure. The control device 201 can be a control device 201 formed by various electronic devices. In one embodiment, a description of the specific functions can be found in steps S101 to S104.

[0174] The aforementioned preheating energy consumption management system 200 is used to perform... Figure 1 The embodiments of the low-temperature charging preheating energy consumption management method shown are similar in technical principle, technical problem solved and technical effect produced. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the preheating energy consumption management system 200 can be referred to the contents described in the embodiments of the low-temperature charging preheating energy consumption management method, and will not be repeated here.

[0175] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment of this disclosure can also be implemented by instructing related hardware through computer program code 2013. The computer program code 2013 can be stored in a computer-readable storage medium. When executed by processor 2011, the computer program code 2013 can implement the steps of the various method embodiments described above. The computer program code 2013 includes computer program code 2013, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code 2013, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory 2012, a read-only memory 2012, a random access memory 2012, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0176] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0177] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0178] According to embodiments of the present disclosure, the preheating energy management system 200 of the present disclosure further includes a computer-readable storage medium.

[0179] In one embodiment of the computer-readable storage medium according to this disclosure, the computer-readable storage medium may be configured to store program code 2013 for performing the low-temperature charging preheating energy consumption management method of the above-described method embodiments. This program code 2013 may be loaded and executed by a processor 2011 to implement the above-described low-temperature charging preheating energy consumption management method. For ease of explanation, only the parts related to the embodiments of this disclosure are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this disclosure. The computer-readable storage medium may be a memory 2012 device comprising various electronic devices. Optionally, in the embodiments of this disclosure, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0180] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the apparatus of this disclosure, the physical devices corresponding to these modules may be the processor 2011 itself, or a part of the software, hardware, or a combination of software and hardware within the processor 2011. Therefore, the number of modules shown in the figures is merely illustrative.

[0181] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0182] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0183] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0184] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0185] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0186] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0187] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0188] The specific embodiments described above do not constitute a limitation on the scope of protection of this 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 substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for managing energy consumption during low-temperature charging preheating, characterized in that, The method is applied to a preheating energy consumption management system, the system including at least several charging compartments, each charging compartment being surrounded by multiple heating modules, and the method includes the following steps: In response to the user's charging needs, the ambient temperature at the location of the preheating energy consumption management system is obtained, wherein the charging needs include at least the expected start charging time, the number of charging batteries, and the battery parameters of each battery. The operating status of each charging compartment in the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment are obtained. The operating status includes at least one of the following: idle state, preheating state, charging state, and other states. The user's charging needs, ambient temperature, operating status of each charging compartment, and execution data of multiple heating modules around each charging compartment are input into the preheating energy consumption management model to obtain the location of the charging compartment corresponding to each charging battery and the operating parameters of the heating modules around each charging compartment. The operating parameters include at least the start heating time, heating duration, and heating power. The location of the charging compartment corresponding to each rechargeable battery is fed back to the user, and a preheating operation is performed based on the operating parameters of the heating modules around each charging compartment. Before inputting the user's charging needs, ambient temperature, operating status of each charging compartment, and execution data of multiple heating modules around each charging compartment into the preheating energy consumption management model, the method further trains the preheating energy consumption management model through the following steps: Multiple training sample sets are obtained under different ambient temperatures. The training sample sets include at least training samples with different charging parameters. The charging parameters include at least the expected start charging time, the number of training samples, the amount of charge to be generated for each training sample, the material of each training sample, and the current temperature of each training sample. Based on the material of each training sample, the current temperature of each training sample, the amount of charge to be charged of each training sample, and the preset historical charging database, the appropriate charging temperature and the expected charging temperature rise corresponding to the material of each training sample are obtained. Based on the ambient temperature, the current temperature of each training sample, the expected charging rise temperature of each training sample, and the suitable charging temperature of each training sample, the preheating temperature of each training sample is determined. Based on the obtained operating status of each charging compartment in the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment, the location of each charging compartment in the idle state is determined and the internal temperature of each charging compartment in the idle state is selectively obtained and / or predicted. The execution data includes at least the heating state and the execution power and remaining time corresponding to the heating state, or the unheated state. Based on the location of each charging compartment in an idle state, the internal temperature of each charging compartment and / or the predicted internal temperature, the preheating temperature of each training sample and the expected start charging time, multiple distribution schemes are obtained for each training sample set. The distribution scheme includes at least the location of the charging compartment corresponding to each training sample in the training sample set and the operating parameters of the heating module around the charging compartment. Based on the multiple distribution schemes corresponding to each training sample set, the preheating energy consumption of each distribution scheme is obtained. The distribution scheme with the lowest preheating energy consumption is selected as the location of the charging compartment corresponding to each training sample in the training sample set and the operating parameters of the heating module around each charging compartment. The operating parameters include at least the start heating time, heating duration and heating power.

2. The method according to claim 1, characterized in that, A heating chamber is provided between adjacent charging compartments, and the heating module is installed inside the heating chamber. The process of determining the location of each charging compartment in an idle state and selectively acquiring and / or predicting the internal temperature of each charging compartment in an idle state, based on the operating status of each charging compartment in the preheating energy consumption management system and the execution data of multiple heating modules around each charging compartment, includes: Based on the obtained operating status of each charging compartment in the preheating energy consumption management system, the location of each charging compartment in the idle state of the preheating energy consumption management system is determined. Based on the location of each charging compartment in the idle state, the execution data of multiple heating modules around each charging compartment in the idle state are selected, wherein the execution data includes at least the heating state and the execution power and remaining time corresponding to the heating state, or the unheated state. If multiple heating modules around the charging compartment that is in an idle state are not in a heated state, then the internal temperature of the charging compartment is obtained; If at least one of the multiple heating modules around the charging compartment that is in an idle state is in a heating state, then the execution power and remaining heating time of all heating modules around the charging compartment that are in a heating state are obtained, and the temperature is predicted based on the current temperature inside the charging compartment, the execution power of all heating modules around the charging compartment that are in a heating state, and the remaining heating time, to obtain the predicted temperature inside the charging compartment.

3. The method according to claim 2, characterized in that, The process of obtaining the suitable charging temperature and expected charging temperature rise corresponding to the material of each training sample based on the material of each training sample, the current temperature of each training sample, the amount of charge to be charged of each training sample, and a preset historical charging database includes: Based on the material of each training sample, the amount of electricity to be charged for each training sample, and the preset historical charging database, the estimated charging time for each training sample is obtained. Based on the current temperature of each training sample, the expected charging time of each training sample, and the preset historical charging database, the expected charging temperature rise of each training sample is determined. Based on the material of each training sample and the preset historical charging database, the appropriate charging temperature corresponding to the training sample of each material is obtained.

4. The method according to claim 2, characterized in that, The method also verifies the training results of the preheating energy management model through the following steps: Multiple sets of verification samples are obtained under different ambient temperatures. The verification sample sets include at least verification samples with different charging parameters. The charging parameters include at least the expected start time of charging, the number of verification samples, the amount of power to be charged for each verification sample, the material of each verification sample, and the current temperature of each verification sample. The multiple sets of verification sample sets and the corresponding ambient temperatures of each set of verification sample sets are input into the trained preheating energy consumption management model to obtain the location of the charging compartment, the preheating temperature, and the operating parameters of the heating modules around each charging compartment for each verification sample in each set of verification sample sets. Multiple sets of verification sample sets, the ambient temperature corresponding to each set of verification sample sets, the location of the charging compartment corresponding to each verification sample in each set of verification sample sets, and the operating parameters of the heating modules around each charging compartment are simulated to obtain the preheating simulation data corresponding to the charging compartment of each verification sample in each set of verification sample sets. Based on the preheating simulation data of the charging compartment corresponding to each verification sample in each set of verification samples, the internal temperature of the charging compartment corresponding to each verification sample in each set of verification samples is obtained when the expected charging start time is reached. Based on the charging compartment temperature and preheating temperature corresponding to each verification sample in each verification sample set, the preheating completion rate and energy consumption redundancy rate of each verification sample set are obtained: If the preheating completion rate reaches a preset preheating completion rate threshold, and the energy redundancy rate is lower than a preset energy redundancy rate threshold, then the preheating energy consumption management model is determined to be trained successfully. If the preheating completion rate does not reach the preset preheating completion rate threshold, or if the energy redundancy rate is higher than the preset energy redundancy rate threshold, then the preheating energy consumption management model is determined to have failed to train, and the preheating energy consumption management model is retrained.

5. The method according to claim 4, characterized in that, After feeding back the location of the charging compartment corresponding to each rechargeable battery to the user and performing a preheating operation based on the operating parameters of the heating modules around each charging compartment, the method further includes: In response to the preheating operation performed by the heating modules around each charging compartment, the preheating temperature corresponding to each charging compartment and the internal temperature data of the charging compartment when the heating modules around each charging compartment stop running are obtained. If the heating modules around each charging compartment stop operating and the temperature inside the charging compartment is lower than the preheating temperature corresponding to the charging compartment, and no battery is placed in the charging compartment, then the heating modules around the charging compartment will continue to perform heating and the temperature inside the charging compartment will be monitored in real time. If the temperature inside the charging compartment reaches the preheating temperature of the charging compartment, the heating modules around the charging compartment will be controlled to stop heating.

6. The method according to claim 5, characterized in that, After the heating module around the charging compartment continues to perform heating, the method further includes: The heating data collected from the heating modules around each charging compartment to continue heating includes at least heating power and heating time. Based on the heating data of the heating modules around each charging compartment continuing to perform heating, the redundant energy consumption for continued heating of each charging compartment is obtained; Based on the redundant energy consumption of continued heating in each charging compartment, the average redundant energy consumption corresponding to the user's charging needs is obtained. If the average redundant energy consumption is higher than the preset redundant energy consumption threshold, the trained preheating energy consumption management model will be retrained.

7. The method according to claim 6, characterized in that, After the temperature inside the charging compartment reaches the preheating temperature of the charging compartment, the method further includes: Acquire the starting temperature data of the battery in each charging compartment when it begins charging, the preheating temperature of each charging compartment, and the ending temperature data when the battery finishes charging. Based on the preheating temperature of each charging compartment, the start temperature data when the battery starts charging, and the end temperature data when the battery finishes charging, the preheating completion rate and heat preservation completion rate of each charging compartment are obtained. Based on the preheating completion rate and heat preservation completion rate of each charging compartment, the average preheating completion rate and average heat preservation completion rate corresponding to the user's charging needs are obtained. Based on the average preheating completion rate or the average insulation completion rate, the trained preheating energy consumption management model is selectively retrained.

8. The method according to claim 3, characterized in that, The method also constructs a preset historical charging database through the following steps: Battery parameters of each rechargeable battery were collected under different ambient temperatures, wherein the battery parameters include at least the material of the rechargeable battery; The charging data of each rechargeable battery is collected under different ambient temperatures. The charging data includes at least the current battery level, the battery temperature corresponding to each battery level during the charging process, and the charging time. Based on the battery parameters and charging data of each rechargeable battery under different ambient temperatures, a preset historical charging database is constructed.

9. A preheating energy consumption management system, characterized in that, The system includes at least several charging compartments, a preheating energy consumption management model, and a control device. A heating compartment is provided between adjacent charging compartments, and a heating module is installed in the heating compartment. The control device includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores program code that can be executed by the at least one processor, and the program code is executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-8.

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