Vehicle charging and discharging method, electronic equipment and computer readable storage medium

Abstract

The invention relates to the field of vehicle charging and discharging, in particular to a vehicle charging and discharging method, electronic equipment and a computer readable storage medium. The charging and discharging method is applied to the electronic equipment, the electronic equipment is in communication connection with a charging pile of a vehicle, and the charging and discharging method comprises the following steps: collecting operation behavior information of a user on a life electric appliance; inputting the operation behavior information into a preset travel time prediction model to obtain first driving travel time of the user; determining a first charging and discharging strategy of the vehicle based on the first driving travel time; and transmitting the first charging and discharging strategy to the charging pile, wherein the charging pile is used for charging and discharging the vehicle based on the first charging and discharging strategy. The charging and discharging strategy can be flexibly formulated based on the user demand, and the vehicle demand of the user is met.

Description

Technical Field

[0001] The present application relates to the field of vehicle charging and discharging, and specifically to a vehicle charging and discharging method, an electronic device, and a computer-readable storage medium. Background Art

[0002] With the continuous development of new energy technology, new energy vehicles have gradually been recognized by the market and favored by consumers for their many advantages such as fast start-up, zero emissions, low noise and low energy consumption.

[0003] When controlling the charging and discharging of new energy vehicles, a fixed charging and discharging mode is generally used to charge and discharge the vehicle, but the charging and discharging mode is not flexible and cannot meet the user's vehicle needs.

[0004] For example, if the user needs to go out immediately, a slow charging speed may result in insufficient battery power to meet the endurance requirements. If the vehicle is charged too quickly or overcharged or over-discharged, the battery life of the vehicle will be greatly shortened. Summary of the invention

[0005] In view of the above, the embodiments of the present application provide a vehicle charging and discharging method, an electronic device, and a computer-readable storage medium, which can flexibly formulate a charging and discharging strategy based on user needs to meet the user's vehicle needs.

[0006] The embodiment of the present application provides a vehicle charging and discharging method, which is applied to an electronic device, the electronic device is communicatively connected to a charging pile of the vehicle, and the charging and discharging method includes:

[0007] Collect information about users’ operation behaviors on household appliances;

[0008] Inputting the operation behavior information into a preset travel time prediction model to obtain the first driving travel time of the user;

[0009] Determining a first charging and discharging strategy for the vehicle based on the first driving travel time;

[0010] The first charging and discharging strategy is transmitted to the charging pile, and the charging pile is used to charge and discharge the vehicle based on the first charging and discharging strategy.

[0011] The embodiment of the present application can accurately predict the user's first driving trip time from the user's operating behavior information on household appliances, and formulate a first charging and discharging strategy for the vehicle based on the user's first driving trip time, so that the charging and discharging strategy of the vehicle is more intelligent and meets the user's vehicle needs. It can not only ensure the vehicle's endurance requirements when the user is driving, but also, compared with the fast charging mode, the embodiment of the present application can make full use of the time period from the current time to the first driving trip time for charging and discharging, thereby improving the battery life.

[0012] In some embodiments, the operation behavior information includes multiple pieces of operation data of the user on the household appliance, and the travel time prediction model is used to perform the following steps:

[0013] Acquire the type of the household appliance operated by each piece of operation data among the plurality of operation data;

[0014] Based on the operation data of the user operating the same type of household appliances, the driving travel time is predicted to obtain the second driving travel time corresponding to each type of household appliances;

[0015] The first driving travel time of the user is determined based on the second driving travel time corresponding to each type of the household electrical appliances.

[0016] In some embodiments, based on the operation data of the user operating the same type of household appliances, obtaining the second driving travel time corresponding to each type of household appliance includes:

[0017] Predicting a driving travel time based on each piece of operation data, and obtaining a third driving travel time corresponding to each piece of operation data;

[0018] Based on the type of the household appliance operated by each piece of operation data, classify the third driving travel time corresponding to each piece of operation data to obtain a set of driving travel times corresponding to each type of household appliance;

[0019] The third driving travel times belonging to the same driving travel time set are merged to obtain the second driving travel time.

[0020] In some embodiments, determining the first driving travel time of the user based on the second driving travel time corresponding to each type of the household appliance includes:

[0021] Obtaining the weight corresponding to each type of household appliance;

[0022] The first driving travel time is obtained based on the weight corresponding to each type of household electrical appliances and the second driving travel time corresponding to each type of household electrical appliances.

[0023] In some embodiments, the operation behavior information includes at least one piece of operation data of the user on the household appliance, and after collecting the operation behavior information of the user on the household appliance, the following further includes:

[0024] If the operation data is detected in a preset database, marking the training state of the operation data as known information, the preset database including samples for training the travel time prediction model;

[0025] If the operation data is not detected in the preset database, marking the training state of the operation data as unknown information;

[0026] storing the operation data and the training status of the operation data in the preset database;

[0027] The travel time prediction model is retrained based on the operation data stored in the preset database and the training status.

[0028] In some embodiments, determining a first charging and discharging strategy for the vehicle based on the first driving travel time includes:

[0029] Acquiring historical charging and discharging data of the vehicle;

[0030] Inputting the historical charge and discharge data into a preset charge and discharge strategy formulation model to obtain a second charge and discharge strategy for the vehicle;

[0031] Based on the first driving travel time, the second charging and discharging strategy is adjusted to obtain a first charging and discharging strategy for the vehicle.

[0032] In some embodiments, the charge and discharge strategy formulation model is used to perform the following steps:

[0033] Based on the historical charge and discharge data, predict a plurality of third charge and discharge strategies;

[0034] Obtaining the probability of improving the life of the vehicle battery by each of the third charging and discharging strategies;

[0035] Based on the probability of improving the life of the vehicle battery by the third charging and discharging strategy, selecting a third charging and discharging strategy that meets a preset battery life improvement condition;

[0036] Based on the third charge-discharge strategy that meets the preset battery life improvement condition, the second charge-discharge strategy is generated.

[0037] In some embodiments, based on the first driving travel time, adjusting the second charging and discharging strategy to obtain the first charging and discharging strategy of the vehicle includes:

[0038] Get real-time electricity prices;

[0039] Based on the first driving travel time and the real-time electricity price, the second charging and discharging strategy is adjusted to obtain a first charging and discharging strategy for the vehicle.

[0040] An embodiment of the present application also provides an electronic device, which includes a processor and a memory, wherein the memory is used to store instructions, and the processor is used to call the instructions in the memory so that the electronic device executes the above-mentioned vehicle charging and discharging method.

[0041] An embodiment of the present application also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned vehicle charging and discharging method. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of an application scenario of a charging and discharging system provided according to an embodiment of the present application.

[0043] Figure 2 The present invention is a flowchart of a charging and discharging method according to an embodiment of the present application.

[0044] Figure 3 The flowchart is a sub-step flowchart of step 202 provided according to an embodiment of the present application.

[0045] Figure 4 A flowchart of the steps for retraining a travel time prediction model according to an embodiment of the present application.

[0046] Figure 5 The flowchart is a sub-step flowchart of step 203 provided according to an embodiment of the present application.

[0047] Figure 6 The figure is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.

[0048] Main component symbols

[0049] Electronic devices 100

[0050] Memory 20

[0051] Processor 30

[0052] Household appliances 200

[0053] Charging pile 300

[0054] Battery Management System 400

[0055] Home Power Management System 500 DETAILED DESCRIPTION

[0056] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the implementation methods of the present application and the features in the implementation methods can be combined with each other without conflict.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present application. The described implementations are only part of the implementations of the present application, rather than all the implementations.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0059] It should be further noted that, in this article, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0060] In this application, "at least one" means one or more, and "more than one" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0061] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0062] The embodiments of the present application provide a vehicle charging and discharging method, an electronic device, and a computer-readable storage medium, which are described in detail below.

[0063] refer to Figure 1 As shown, Figure 1 A schematic diagram of an application scenario of a charging and discharging system according to an embodiment of the present application.

[0064] The charging and discharging system may include an electronic device 100 , a household appliance 200 , and a charging pile 300 .

[0065] The electronic device 100 is communicatively connected to the household electrical appliances 200 and the charging station 300 .

[0066] The life appliances 200 include terminals for personal use such as mobile phones and computers, and household appliances such as air conditioners, washing machines, and hair dryers, but are not limited thereto.

[0067] The electronic device 100 is used to execute a charging and discharging method, which includes: first, collecting user operation behavior information on the household appliance 200, inputting the operation behavior information into a preset travel time prediction model to obtain the user's first driving travel time, and then determining a first charging and discharging strategy for the vehicle based on the first driving travel time; transmitting the first charging and discharging strategy to the charging pile 300, and the charging pile 300 is used to charge and discharge the vehicle according to the first charging and discharging strategy.

[0068] The electronic device 100 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a processor, a microprogrammed control unit (MCU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc. For example, the electronic device 100 can be an integrated home control device to collect user operation behavior information on the household appliance 200, and the electronic device 100 and the household appliance 200 can adopt the Internet of Things technology.

[0069] The home control device can also adopt edge computing technology. Compared with cloud technology, the embodiment of the present application adopts edge computing technology to improve the security of operation behavior information and protect user privacy.

[0070] In addition, if Figure 1 As shown, the charging and discharging system may also include a device that stores the charging and discharging data of the vehicle. For example, the device may be a battery management system 400 of the vehicle and a home power management system 500, so that the electronic device 100 can collect the charging and discharging data of the vehicle, but is not limited to this.

[0071] It should be noted that Figure 1 The scenario diagram of the system shown is only an example. The charging and discharging system and scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the charging and discharging system and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0072] Figure 2 This is a flowchart of the steps of an embodiment of the vehicle charging and discharging method of the present application. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0073] The vehicle charging and discharging method can be used in the electronic device 100 , and the electronic device 100 is also at least communicatively connected to the charging post 300 .

[0074] See also Figure 2 As shown, the vehicle charging and discharging method may include the following steps.

[0075] Step 201, collecting user operation behavior information on household electrical appliances.

[0076] The operation behavior information is used to describe the user's operation on the household electrical appliance 200 .

[0077] The operation behavior information may include at least one piece of operation data of the user on the household appliance 200 .

[0078] The life appliances 200 may include an alarm clock, a television, a tablet, a refrigerator, a hair dryer, etc. The data fed back by the life appliances 200 can be used to predict the user's driving travel time.

[0079] For example, if the household appliance 200 is an alarm clock, the operation data can be used to describe the user's itinerary. For example, the operation data can include the alarm time set by the user. The time consumed by the user from getting up to going out generally fluctuates within a certain range, so the user's driving travel time can be predicted based on the alarm time.

[0080] If the household appliance 200 is a television, the operation data can be used to describe the user's television viewing habits, such as the user's television viewing time and viewing content, etc. The user may have the habit of watching news or weather programs before going out every morning, so the TV start time, viewing content and viewing time can be used to predict the user's driving travel time.

[0081] If the household appliance 200 is a tablet, the user can input household-related expenses, inventory of household items, family schedules, scheduled driving time, appointment time, etc. on the tablet. These operating data can reflect the user's driving time.

[0082] If the household appliance 200 is a refrigerator, the operation data may include the time when the family takes out or stores items from the refrigerator, and the amount of items remaining in the refrigerator. For example, the user may drink fresh milk regularly before going out in the morning, check the ingredients in the refrigerator before cooking dinner, and go out to purchase items when the amount of items remaining reaches a certain range.

[0083] If the household appliance 200 is a hair dryer, the operation data includes the time and length of the user's hair drying. For example, the user may dry his hair before going out or after taking a shower. If the user dry his hair before going out, it may mean that the user will drive in a few tens of minutes. If the user dry his hair after just returning from driving, it means that the user may dry his hair after taking a shower and may not go out for a long time.

[0084] The operating data of the above-mentioned household appliances 200 can be transmitted to the electronic device 100 of the embodiment of the present application via wired or wireless means, so that the electronic device 100 can estimate the user's driving travel time, and thus formulate a charging and discharging strategy based on the user's driving travel time to facilitate meeting the user's home and travel time.

[0085] The above-mentioned household appliances 200 and their corresponding operation data are only examples. In actual applications, more or less household appliances 200 and operation data may be included. For example, the electronic device 100 may also be connected to a water heater, an air conditioner, an electric light, an electric fan, etc. for communication.

[0086] After acquiring the operation behavior information, the electronic device 100 may also pre-process the operation behavior information so that the operation behavior information meets the format requirements of the travel time prediction model for input data.

[0087] Then, the operation data that does not meet the preset travel time evaluation conditions is eliminated from the operation behavior information. For example, the operation data is data generated by a person who does not have the right to drive (such as a guest, a minor, etc.) operating the household appliance 200. If a certain operation data does not meet the preset travel time evaluation conditions, it means that the operation data cannot reflect the user's driving travel time. Eliminating this type of operation data can improve the accuracy of the subsequent travel time prediction model in estimating the user's travel time.

[0088] Step 202: Input the operation behavior information into a preset travel time prediction model to obtain the user's first driving travel time.

[0089] The travel time prediction model is used to describe the relationship between the user's operating behavior information and the user's first driving travel time.

[0090] The travel time prediction model may be an artificial intelligence model, which may be trained based on the user's historical operating behavior information to obtain the user's habits, and further obtain the relationship between each operating behavior information and the user's first driving travel time.

[0091] The travel time prediction model may also be a data table, which stores a mapping relationship between each type of operation data in the operation behavior information and the first driving travel time, which is not limited in this embodiment of the present application.

[0092] For example, if the operation behavior information includes operation data of the timed reminder type, the operation data is that the alarm clock rings at seven in the morning, and the travel time prediction model reflects that the user generally goes out between fifty and sixty minutes after the alarm clock rings, therefore, it can be predicted that the user's first driving trip time is between 7:50 and 8:00.

[0093] In some embodiments, when the collected operation behavior information includes a piece of operation data, the travel time prediction model can predict the first driving travel time based on the piece of operation data.

[0094] When the collected operation behavior information includes multiple operation data, the travel time prediction model can select one operation data from the multiple operation data, such as each operation data has a priority, select the operation data with a high priority, and predict the first driving travel time based on the selected operation data.

[0095] For example, the operation behavior information includes the alarm set by the user and the user's refrigerator switch data. The priority of the alarm set by the user is before the refrigerator switch data. The first driving trip time can be predicted based on the alarm set by the user.

[0096] In other embodiments, reference Figure 3 As shown, when the operation behavior information includes multiple operation data, the preset travel time prediction model can be used to perform the following steps:

[0097] Step 301 : obtaining the type of the household appliance 200 operated by each piece of operation data among a plurality of operation data.

[0098] In some embodiments, the travel time prediction model may include a classification module, which is an artificial intelligence model. The electronic device 100 can input operation data into the classification module, and the classification module can calculate the probability of the operation data operating each type of household appliance 200 respectively, and then determine the type of household appliance 200 operated by the operation data based on the probability of the operation data operating each type of household appliance 200.

[0099] For example, the types of household appliances 200 include type A, type B and type C. The classification module calculates that the probability that the operation data data1 operates a household appliance 200 of type A is pA, the probability that the operation data data1 operates a household appliance 200 of type B is pB, and the probability that the operation data data1 operates a household appliance 200 of type C is pC. A maximum value can be selected from pA, pB and pC, and the type of household appliance 200 corresponding to the maximum value is used as the type of household appliance 200 operated by the operation data.

[0100] Alternatively, pA, pB, and pC are compared with a certain threshold, and the type of the household appliance 200 corresponding to the probability exceeding the threshold is used as the type of the household appliance 200 operated by the operation data.

[0101] The above-mentioned method of obtaining the type of the household appliance 200 operated by each piece of operation data is only an example, which can be set according to needs in actual application, and the embodiment of the present application is not limited to this.

[0102] The types of the household appliances 200 can be divided according to the functions provided by the household appliances 200, and the specific functions can be set according to the requirements. For example, the types of the household appliances 200 may include hair dryers, refrigerators, air conditioners, appliances that can provide schedule reminders, such as tablets, appliances that can provide timing functions, such as alarm clocks, etc., but are not limited thereto.

[0103] It can be understood that some household appliances 200 can provide multiple functions, and the type of the household appliance 200 operated by the operation data can be determined based on the function provided by the household appliance 200 when the operation data operates the household appliance 200.

[0104] For example, the user can input a planned schedule on the tablet, such as going out for a date at a certain time. The type of household appliance 200 operated by the operation data is an appliance that provides schedule reminders. The tablet can also provide an alarm function, such as ringing an alarm at 7 o'clock in the morning. The type of household appliance 200 operated by the operation data is an appliance that provides a timing function.

[0105] Step 302 : predicting the driving time based on the operation data of the user operating the same type of household appliances, and obtaining a second driving time corresponding to each type of household appliance.

[0106] For example, the user sets an alarm for 7 a.m. and 7:30 a.m. The operation behavior information includes two operation data of ringing at 7 a.m. and 7:30 a.m., and the operation data both belong to electrical appliances with timing functions. Therefore, the second driving trip time corresponding to the household appliance 200 with timing functions can be predicted based on the two operation data of ringing at 7 a.m. and 7:30 a.m.

[0107] If the operation behavior information also includes the operation data that the user blew his hair at 7:10 and the duration of blowing his hair was 5 minutes, the electronic device 100 can predict the second driving travel time corresponding to the blow dryer based on the operation data.

[0108] In some embodiments, step 302 may include:

[0109] Step 3021: predicting the driving travel time based on each piece of operation data, and obtaining a third driving travel time corresponding to each piece of operation data.

[0110] For example, based on the travel time prediction model, it can be determined that after the alarm rings in the morning, the user generally drives one hour later. Therefore, the third driving travel time obtained based on the operation data of the alarm ringing at 7 o'clock in the morning is 8 o'clock, and the third driving travel time obtained based on the operation data of the alarm ringing at 7:30 in the morning is 8:30.

[0111] Step 3022: Classify the third driving travel time corresponding to each piece of operation data based on the type of the household appliance operated by each piece of operation data, and obtain a set of driving travel times corresponding to each type of household appliance.

[0112] That is, if a plurality of operation data are used to operate the same type of household electrical appliances 200 , the third driving travel time predicted based on the plurality of operation data belongs to the same driving travel time set.

[0113] For example, the household appliances 200 operated by the two operation data of ringing at 7 a.m. and ringing at 7:30 a.m. are both household appliances 200 with a timing function. Therefore, the driving travel time set corresponding to the household appliances 200 with a timing function includes: a third driving travel time predicted based on the operation data of ringing at 7 a.m. (e.g., 8 o'clock) and a third driving travel time predicted based on the operation data of ringing at 7:30 a.m. (e.g., 8:30).

[0114] Step 3023: Merge the third driving travel times belonging to the same driving travel time set to obtain the second driving travel time corresponding to each type of household electrical appliance.

[0115] For example, the earliest or latest time point may be selected from the third driving travel time belonging to the same driving travel time set as the second driving travel time corresponding to each type of the household appliance 200 .

[0116] For another example, the driving travel time set determined based on the ringing time includes eight o'clock and eight-thirty. If the operation data of the user using the hair dryer at seven-thirty is received, it can be known that the user may get up when the ringing time is seven o'clock. Therefore, eight o'clock can be used as the second driving travel time corresponding to the household appliance 200 with a timing function.

[0117] The above merging method is only an example and can be set according to actual application requirements in actual applications. The embodiments of the present application are not limited to this.

[0118] Step 303: Determine the user's first driving travel time based on the second driving travel time corresponding to each type of household electrical appliance.

[0119] In some embodiments, step 303 may include: obtaining the weight corresponding to each type of household appliance 200; and obtaining the first driving travel time based on the weight corresponding to each type of household appliance 200 and the second driving travel time corresponding to each type of household appliance 200.

[0120] The weight corresponding to each type of household appliance 200 can be set according to demand. For example, the weight of an appliance that provides schedule reminders is greater than the weight of an appliance with a timing function, but the invention is not limited thereto.

[0121] Furthermore, based on the weight corresponding to each type of household appliance 200 and the second driving travel time corresponding to each type of household appliance 200, obtaining the first driving travel time can include: determining the type of household appliance 200 with the largest weight among the weights corresponding to each type of household appliance 200, and using the second driving travel time corresponding to that type of household appliance 200 as the first driving travel time.

[0122] For example, the weight of the appliance for schedule reminder is the largest, and the second driving travel time corresponding to the appliance for schedule reminder can be used.

[0123] For another example, the weight corresponding to each type of household electrical appliance 200 and the second driving travel time corresponding to each type of household electrical appliance 200 are weightedly summed to obtain the first driving travel time.

[0124] In some embodiments, after collecting the user's operating behavior information on the household appliance 200, the electronic device 100 may also retrain the travel time prediction model based on the newly collected operating behavior information.

[0125] The newly collected operation behavior information includes at least one piece of operation data of the user on the household appliance 200 .

[0126] For example, refer to Figure 4 As shown in Figure 2, the retraining steps of the travel time prediction model include:

[0127] Step 401: Check whether there is newly collected operation data in the preset database.

[0128] The preset database includes training samples for training the travel time prediction model.

[0129] If the newly collected operation data is detected in the preset database, it means that the training data set of the travel time prediction model includes the operation data and the travel time prediction model has been trained based on the operation data, and step 402 is executed.

[0130] If the newly collected operation data is not detected in the preset database, it means that the training data set of the travel time prediction model does not include the operation data, and the travel time prediction model has not been trained based on the operation data, and step 403 is executed.

[0131] Step 402: Mark the training status of the operation data as known information.

[0132] Step 403: Mark the training status of the operation data as unknown information.

[0133] Step 404: store the operation data and the training status of the operation data in a preset database.

[0134] Step 405: retrain the travel time prediction model based on the operation data and training status stored in the preset database.

[0135] For example, deep learning can be used to preset the operation behavior information and training status stored in the database to retrain the travel time prediction model.

[0136] The training algorithms used for known information and unknown information may be different. Retraining the known information can improve the accuracy of the travel time prediction model for this type of operation data. Retraining the unknown information can increase the diversity of the types of operation data that the travel time prediction model can handle.

[0137] After the first driving travel time is obtained, step 203 may be executed.

[0138] Step 203: Determine a first charging and discharging strategy for the vehicle based on the first driving travel time.

[0139] The first driving travel time may reflect the predicted time during which the vehicle can be charged or discharged at the charging station 300 .

[0140] The first charging and discharging strategy can reflect the user's charging time, discharging time, electrical parameters used for charging, such as voltage, current, power, total charging power, etc., and electrical parameters used for discharging.

[0141] In some embodiments, the electronic device 100 may establish a constrained optimization model, wherein the constrained optimization model uses the charging and discharging strategy as a decision variable, and the objective function is to maximize the battery life under the charging and discharging strategy. The charging and discharging strategy is used before the first driving trip time so that the battery capacity of the vehicle can reach the total power required by the user for the driving trip as a constraint condition, and the constrained optimization model is solved to obtain the first charging and discharging strategy.

[0142] Furthermore, the constraint conditions may also include the maximum voltage and current that the vehicle's battery can withstand. The constraint conditions of the constraint optimization model can be set according to actual application requirements, and the embodiments of the present application are not limited to this.

[0143] In some embodiments, the electronic device 100 may obtain a second charging and discharging strategy, and then adjust the second charging and discharging strategy based on the first driving travel time to obtain the first charging and discharging strategy.

[0144] The embodiment of the present application combines the first driving travel time and the second charging and discharging strategy to obtain a first charging and discharging strategy for the vehicle, which can meet the user's requirements for vehicle endurance when traveling.

[0145] The second charging and discharging strategy may be a charging and discharging strategy pre-stored in the electronic device 100, or may be determined based on historical charging and discharging data of the vehicle.

[0146] For example, refer to Figure 5 As shown, step 203 may also include:

[0147] Step 501, obtaining historical charging and discharging data of the vehicle.

[0148] Reference again Figure 1 As shown, the electronic device 100 can obtain the historical charging and discharging data of the vehicle from the battery management system 400 , the home power management system 500 and / or the charging pile 300 .

[0149] The historical charge and discharge data is used to describe the change relationship between the battery capacity and time each time the vehicle battery is charged and discharged in history, as well as the charge and discharge current, voltage, power, etc.

[0150] In some embodiments, after acquiring the historical charge and discharge data, the format of the charge and discharge data may be preprocessed so that the charge and discharge data meets the preset charge and discharge data format, so as to facilitate the subsequent charge and discharge strategy formulation model to process the charge and discharge data. For example, the charge and discharge data is presented in the form of a charge and discharge curve, the horizontal axis of the charge and discharge curve may be time, and the vertical axis may be the current, voltage and capacity of the battery.

[0151] Then, the curve segments that do not extend the battery life, such as fast charging and short-time charging, are eliminated from the charge and discharge curve, so that the acquired charge and discharge data can help to promote the improvement of the battery life of the vehicle, thereby facilitating the subsequent formulation of a charge and discharge strategy that is conducive to improving the battery life.

[0152] After the historical charging and discharging data of the vehicle is collected, the preset charging and discharging strategy formulation model can be retrained based on the newly collected historical charging and discharging data of the vehicle to improve the accuracy of the charging and discharging strategy formulation model.

[0153] Step 502 , inputting the historical charging and discharging data of the vehicle into a preset charging and discharging strategy formulation model to obtain a second charging and discharging strategy for the vehicle.

[0154] The charging and discharging strategy formulation model can be an artificial intelligence model.

[0155] Among them, the charging and discharging strategy formulation model can be used to perform the following steps:

[0156] Step 5021: predict multiple third charging and discharging strategies based on historical charging and discharging data.

[0157] The historical charge and discharge data can reflect the relationship between the voltage, current, capacity, etc. of the vehicle's battery and changes over time.

[0158] The third charge and discharge strategy is used to describe the charging time, discharging time, and the trends of the current, voltage, power and capacity used in charging and discharging over time. The charge and discharge strategy can also be presented in the form of a charge and discharge curve.

[0159] For example, the historical charge and discharge data may include a plurality of historical charge and discharge curves, and the electronic device 100 may form a plurality of third charge and discharge strategies based on a combination of the plurality of charge and discharge curves, such as splicing.

[0160] Step 5022, obtaining the probability of each third charging and discharging strategy improving the life of the vehicle battery.

[0161] The probability of improving the battery life created by different third charge and discharge strategies is different. Therefore, the probability of improving the battery life of each charge and discharge strategy can be calculated based on the charge and discharge strategy formulation model.

[0162] Step 5023, based on the probability of improving the life of the vehicle battery by the third charging and discharging strategy, select the third charging and discharging strategy that meets the preset battery life improvement conditions.

[0163] The preset battery life improvement condition may include that the life improvement probability exceeds a preset probability threshold, or, among the third charge and discharge strategies, the third charge and discharge strategy with the life improvement probability ranking in the top several, but is not limited thereto.

[0164] Step 5024: Generate a second charge-discharge strategy based on the third charge-discharge strategy that meets the preset battery life improvement condition.

[0165] In some embodiments, the electronic device 100 may organize and merge the third charge and discharge strategies. For example, in the second charge and discharge strategy, one third charge and discharge strategy is used for charge and discharge in a certain time period, and another third charge and discharge strategy is used for charge and discharge in another time period.

[0166] Alternatively, a weighted average processing is performed on the voltage, current, etc. of charging and discharging in each time period of the third charging and discharging strategy that meets the preset battery life improvement condition to obtain the second charging and discharging strategy.

[0167] The embodiment of the present application combines the charging and discharging strategy with the probability of improving the life of the vehicle battery to generate a second charging and discharging strategy, so that the second charging and discharging strategy can maximize the battery service life of the vehicle.

[0168] Step 503: Based on the first driving travel time, adjust the second charging and discharging strategy to obtain the first charging and discharging strategy of the vehicle.

[0169] For example, in the second charging and discharging strategy, it takes eight hours to charge the vehicle battery, but the first driving trip time is six hours later. In this case, the electronic device 100 can perform a comprehensive calculation of the two to obtain the first charging and discharging strategy of the vehicle to find the best balance between the two. For example, the second charging and discharging strategy may be modified based on the first driving trip time to advance the charging completion time to five hours and forty-five minutes to meet the user's driving trip requirements.

[0170] The embodiment of the present application combines the first driving travel time and the second charging and discharging strategy to obtain a first charging and discharging strategy for the vehicle, thereby not only meeting the user's requirements for vehicle endurance, but also minimizing the negative impact of the charging and discharging process on battery life.

[0171] In some embodiments, step 503 may include: the electronic device 100 may also obtain the real-time electricity price; based on the first driving travel time and the real-time electricity price, adjust the second charging and discharging strategy to obtain the first charging and discharging strategy of the vehicle.

[0172] Furthermore, the electronic device may adjust the charging time period in the second charging and discharging strategy to a time period with a lower electricity price in the real-time electricity price, and the time period is before the first driving travel time, so as to obtain the first charging and discharging strategy.

[0173] The electronic device may also add a discharge time period in the second charge and discharge strategy so that the vehicle can power household electrical appliances, and the discharge time period is a time period with a higher electricity price in the real-time electricity price.

[0174] The embodiment of the present application can reduce the charging cost of the vehicle by generating a first charging and discharging strategy for the vehicle in combination with the real-time electricity price.

[0175] For example, the electronic device 100 may select a time period with a lower real-time electricity price before the first driving trip time as the charging time period of the second charging and discharging strategy, thereby obtaining the first charging and discharging strategy for the vehicle.

[0176] Assume that the first driving travel time indicates that the user will drive out in ten hours. During these ten hours, the real-time electricity price is: 0.8 yuan per kilowatt-hour for the 0th to 5th hour, and 0.5 yuan per kilowatt-hour for the 5th to 10th hour. The second charging and discharging strategy indicates that charging is required from the 0th to 5th hour. The electronic device 100 can adjust the charging time of the second charging and discharging strategy to charge in the time period of the 5th to 10th hour to save electricity costs.

[0177] For another example, when the current time is far from the first driving time, the electronic device 100 can select a time period with higher electricity prices before the first driving time, discharge during the time period with higher electricity prices, thereby providing power for the household appliances 200, and charge during the time period with lower electricity prices, thereby meeting the user's travel needs.

[0178] That is, the electronic device 100 adjusts the charge and discharge curve of the second charge and discharge strategy during the period of higher electricity prices to the discharge curve, and the electronic device 100 can also adjust the charge and discharge curve of the second charge and discharge strategy during the period of lower electricity prices to the charge curve.

[0179] For example, if the user goes out after three days and the vehicle battery still has power remaining, the second charging and discharging strategy indicates that the vehicle battery can continue to be charged for several hours to fully charge the battery. The electronic device 100 can adjust the second charging and discharging strategy so that the vehicle discharges when the electricity price is high to power the household appliances 200. When the electricity price is low, the second charging and discharging strategy is used to continue charging the vehicle battery.

[0180] After acquiring the first charging and discharging strategy, the electronic device 100 may execute step 204 .

[0181] Step 204 , transmitting the first charging and discharging strategy to the charging pile 300 , and the charging pile 300 is used to charge and discharge the vehicle based on the first charging and discharging strategy.

[0182] The embodiment of the present application can collect the user's operating behavior information on the household appliances 200. The user's operating behavior information on the household appliances 200 can reflect the user's living habits. The user's first driving trip time can be predicted based on the user's living habits, and the vehicle's first charging and discharging strategy can be formulated based on the user's first driving trip time, so that the vehicle's charging and discharging strategy is more intelligent and meets the user's vehicle needs.

[0183] Moreover, the embodiment of the present application predicts the second driving travel time based on the operating data of the same type of household appliances 200. The operating data of the same type of household appliances 200 can reflect the same type of living habits, for example, waking up habits, washing habits, etc., so that the second driving travel time corresponding to different types of living habits can be predicted. Then, the second driving travel times corresponding to different types of living habits are integrated to obtain the first driving travel time. The first driving travel time can be predicted by combining the user's daily life behavior in multiple dimensions, thereby improving the accuracy of the first driving travel time.

[0184] In addition, the embodiment of the present application takes into account the impact of the charging and discharging strategy on the battery life when formulating the first charging and discharging strategy for the vehicle. Therefore, the embodiment of the present application can ensure the battery life as much as possible while ensuring the user's use of the vehicle.

[0185] The present application also provides an electronic device, Figure 6 This is a schematic diagram of an embodiment of an electronic device of the present application.

[0186] The electronic device 100 includes a memory 20, a processor 30, and a computer program 40 stored in the memory 20 and executable on the processor 30. When the processor 30 executes the computer program 40, the steps in the above-mentioned vehicle charging and discharging method embodiment are implemented, for example Figure 2 Steps 201 to 204 are shown.

[0187] Exemplarily, the computer program 40 may also be divided into one or more modules / units, which are stored in the memory 20 and executed by the processor 30. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 40 in the electronic device 100.

[0188] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 100 and does not constitute a limitation of the electronic device 100. The electronic device 100 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 100 may also include input and output devices, network access devices, buses, etc.

[0189] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, a single-chip microcomputer, or the processor 30 may also be any conventional processor, etc.

[0190] The memory 20 can be used to store the computer program 40 and / or the module / unit. The processor 30 realizes various functions of the electronic device 100 by running or executing the computer program and / or the module / unit stored in the memory 20 and calling the data stored in the memory 20. The memory 20 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0191] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0192] In the several embodiments provided in this application, it should be understood that the disclosed electronic device and method can be implemented in other ways. For example, the electronic device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation.

[0193] In addition, each functional unit in each embodiment of the present application may be integrated into the same processing unit, each unit may exist physically separately, or two or more units may be integrated into the same unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0194] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or electronic devices stated in the electronic device claim can also be implemented by the same unit or electronic device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0195] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the above embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present application.

Claims

1. A method for charging and discharging a vehicle, characterized in that: Applied to an electronic device, the electronic device is communicatively connected to a charging pile of the vehicle, and the charging and discharging method comprises: Collect information about users’ operation behaviors on household appliances; Inputting the operation behavior information into a preset travel time prediction model to obtain the first driving travel time of the user; Determining a first charging and discharging strategy for the vehicle based on the first driving travel time; The first charging and discharging strategy is transmitted to the charging pile, and the charging pile is used to charge and discharge the vehicle based on the first charging and discharging strategy.

2. The vehicle charging and discharging method according to claim 1, characterized in that: The operation behavior information includes a plurality of operation data of the user on the household electrical appliances, and the travel time prediction model is used to perform the following steps: Acquire the type of the household appliance operated by each piece of operation data among the plurality of operation data; Based on the operation data of the user operating the same type of household appliances, the driving travel time is predicted to obtain the second driving travel time corresponding to each type of household appliances; The first driving travel time of the user is determined based on the second driving travel time corresponding to each type of the household electrical appliances.

3. The vehicle charging and discharging method according to claim 2, characterized in that: The method predicts the driving travel time based on the operation data of the user operating the same type of household appliances to obtain the second driving travel time corresponding to each type of household appliances, including: Predicting a driving travel time based on each piece of operation data, and obtaining a third driving travel time corresponding to each piece of operation data; Based on the type of the household appliance operated by each piece of operation data, classify the third driving travel time corresponding to each piece of operation data to obtain a set of driving travel times corresponding to each type of household appliance; The third driving travel times belonging to the same driving travel time set are merged to obtain the second driving travel time.

4. The vehicle charging and discharging method according to claim 2, characterized in that: The determining the first driving travel time of the user based on the second driving travel time corresponding to each type of the household electrical appliances includes: Obtaining the weight corresponding to each type of household appliance; The first driving travel time is obtained based on the weight corresponding to each type of household electrical appliances and the second driving travel time corresponding to each type of household electrical appliances.

5. The vehicle charging and discharging method according to claim 1, characterized in that: The operation behavior information includes at least one piece of operation data of the user on the household appliance. After the operation behavior information of the user on the household appliance is collected, the following further includes: If the operation data is detected in a preset database, marking the training state of the operation data as known information, the preset database including samples for training the travel time prediction model; If the operation data is not detected in the preset database, marking the training state of the operation data as unknown information; storing the operation data and the training status of the operation data in the preset database; The travel time prediction model is retrained based on the operation data stored in the preset database and the training status.

6. The vehicle charging and discharging method according to any one of claims 1 to 5, characterized in that: The determining, based on the first driving travel time, a first charging and discharging strategy of the vehicle includes: Acquiring historical charging and discharging data of the vehicle; Inputting the historical charge and discharge data into a preset charge and discharge strategy formulation model to obtain a second charge and discharge strategy for the vehicle; Based on the first driving travel time, the second charging and discharging strategy is adjusted to obtain a first charging and discharging strategy for the vehicle.

7. The vehicle charging and discharging method according to claim 6, characterized in that: The charging and discharging strategy formulation model is used to perform the following steps: Based on the historical charge and discharge data, predict a plurality of third charge and discharge strategies; Obtaining the probability of improving the life of the vehicle battery by each of the third charging and discharging strategies; Based on the probability of improving the life of the vehicle battery by the third charging and discharging strategy, selecting a third charging and discharging strategy that meets a preset battery life improvement condition; Based on the third charge-discharge strategy that meets the preset battery life improvement condition, the second charge-discharge strategy is generated.

8. The vehicle charging and discharging method according to claim 6, characterized in that: The adjusting the second charging and discharging strategy based on the first driving travel time to obtain the first charging and discharging strategy of the vehicle includes: Get real-time electricity prices; Based on the first driving travel time and the real-time electricity price, the second charging and discharging strategy is adjusted to obtain a first charging and discharging strategy for the vehicle.

9. An electronic device, comprising a processor and a memory, characterized in that: The memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the vehicle charging and discharging method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the vehicle charging and discharging method according to any one of claims 1 to 8.

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