A battery swap scheduling method, device and server for battery swap vehicles

Abstract

The present application provides a battery swap scheduling method, device and server for battery swap vehicles. The battery swap scheduling method for battery swap vehicles provided in the present application can obtain the first distance between the target vehicle and each reachable station, the travel time of the target vehicle to each reachable station, the ready time of each battery in each reachable station, and the number of ready batteries expected to be held by each reachable station when the target vehicle arrives. It can start from multiple factors and use a certain factor or a combination of certain factors to obtain multiple sorting results, and then recommend battery swap stations based on multiple sorting results, which can provide more accurate and personalized battery swap station recommendations; in addition, when sorting the reachable stations, by predicting the ready time and future battery swap demand of each battery at each reachable station, battery resources can be arranged in advance, which helps to reduce the waiting time when the vehicle arrives at the battery swap station, and can improve the efficiency of battery swapping and user experience.

Description

Technical Field

[0001] The present application relates to the technical field of battery swapping scheduling, and in particular to a battery swapping scheduling method, device and server for a battery swapping vehicle. Background Art

[0002] As electric vehicles become more popular, electric vehicle owners have a growing demand for charging and battery life. In order to better meet this demand, battery swap stations have gradually become an important way to solve the problem of electric vehicle battery life. Through battery swap stations, car owners can quickly replace batteries and avoid long charging waits, thereby significantly improving travel convenience and efficiency.

[0003] Electric vehicles have become the main means of transportation in high-intensity scenarios such as closed scenarios, urban distribution scenarios, and trunk logistics. However, the demand for battery replacement in these scenarios is diverse and high-frequency, which brings great challenges to battery replacement scheduling. In order to meet these challenges, it is urgent to develop an efficient and accurate battery replacement scheduling solution to ensure that electric vehicles can maintain continuous and efficient operation in various complex scenarios. Summary of the invention

[0004] In view of this, the present application provides a battery swap scheduling method, device and server for battery swap vehicles, so as to provide an efficient and accurate battery swap scheduling solution to ensure that electric vehicles can maintain continuous and efficient operation in various complex scenarios.

[0005] Specifically, the present application is implemented through the following technical solutions:

[0006] A first aspect of the present application provides a battery swap scheduling method for a battery swap vehicle, the method being applied to a server, the method comprising:

[0007] Upon receiving a dispatch request, for each target vehicle indicated by the dispatch request, calculating a first distance between the target vehicle and each target site within the coverage area of ​​the target vehicle, and a travel time for the target vehicle to travel to the target site;

[0008] Determine the cruising range of the target vehicle at the current power level, and select, from the target sites, reachable sites that the target vehicle can reach before the power is exhausted, according to the cruising range and the first distance between the target vehicle and the target sites;

[0009] For each battery in each reachable site, determine the ready time of the battery, and determine the readiness information of the battery when the target vehicle arrives based on the ready time of the battery and the travel time of the target vehicle to the reachable site;

[0010] The battery swap demand prediction model is used to predict the battery swap demand of the reachable station in the next time period, and according to the battery swap demand and the readiness information of each battery when the target vehicle arrives, the number of ready batteries that the reachable station is expected to hold when the target vehicle arrives is determined;

[0011] For each of the preset multiple sorting strategies, all reachable sites are sorted according to at least one of the first distances of the target vehicle from each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries expected to be held by each reachable site when the target vehicle arrives, to obtain a sorting result corresponding to the sorting strategy; wherein the multiple sorting strategies use different information to sort all reachable sites from different aspects;

[0012] According to the sorting results corresponding to each sorting strategy, a battery swapping station is recommended for the target vehicle so that the target vehicle can go to the battery swapping station for battery swapping in the future.

[0013] A second aspect of the present application provides a battery swap scheduling device for a battery swap vehicle, the device comprising a determination module, a sorting module and a processing module; wherein:

[0014] The determination module is used to calculate, upon receiving a dispatch request, for each target vehicle indicated by the dispatch request, a first distance between the target vehicle and each target site within the coverage area of ​​the target vehicle, and a travel time for the target vehicle to travel to the target site;

[0015] The determination module is further configured to determine a cruising range of the target vehicle at a current power level, and to select, from the target sites, reachable sites that the target vehicle can reach before the power level is exhausted, based on the cruising range and a first distance between the target vehicle and each target site;

[0016] The determination module is further used to determine the ready time of each battery in each reachable site, and determine the ready information of the battery when the target vehicle arrives based on the ready time of the battery and the travel time of the target vehicle to the reachable site;

[0017] The determination module is further used to predict the battery replacement demand of the reachable site in the next time period by using the battery replacement demand prediction model, and determine the number of ready batteries that the reachable site is expected to hold when the target vehicle arrives based on the battery replacement demand and the readiness information of each battery when the target vehicle arrives;

[0018] The sorting module is used to sort all reachable sites for each of the preset multiple sorting strategies according to at least one of the first distance between the target vehicle and each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries expected to be held by each reachable site when the target vehicle arrives, to obtain a sorting result corresponding to the sorting strategy; wherein the multiple sorting strategies use different information to sort all reachable sites from different aspects;

[0019] The processing module is used to recommend a battery swap site for the target vehicle according to the sorting results corresponding to each sorting strategy, so that the target vehicle can go to the battery swap site for battery swap in the future.

[0020] The third aspect of the present application provides a server, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any one of the methods provided in the first aspect of the present application are implemented.

[0021] The battery swap scheduling method, device and server for battery swap vehicles provided in the present application, firstly, by predicting the cruising range of the target vehicle at the current power level, and obtaining the first distance between the target vehicle and the target site, and then comparing the first distance with the cruising range of the target vehicle at the current power level, the reachable site can be accurately found, and when recommending the battery swap site later, the recommendation is made based on the reachable site, so that the accuracy and effectiveness of the battery swap scheduling can be ensured, and the risk of the vehicle being unable to reach the battery swap site due to exhaustion of power can be effectively avoided. Secondly, when making recommendations based on reachable sites, by obtaining the first distance between the target vehicle and each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries expected to be held by each reachable site when the target vehicle arrives, so that, firstly, it is possible to start from multiple factors, use a certain factor or a combination of certain factors to obtain multiple sorting results, and then recommend battery swap sites based on multiple sorting results, which can provide more accurate and personalized battery swap site recommendations, and ensure that the target vehicle can successfully complete the battery swap operation; secondly, the preset multiple sorting strategies use different information to evaluate and sort sites, which can adapt to different actual scenarios. In addition, when sorting accessible stations, by predicting the ready time of each battery at each accessible station and the future battery swap demand, battery resources can be arranged in advance, battery usage and scheduling can be optimized, and battery shortages or excessive congestion at certain stations can be avoided, which helps to reduce the waiting time when vehicles arrive at the battery swap station and improve the efficiency of battery swap and user experience. Finally, combining real-time data and demand forecasts to dynamically adjust the battery swap plan can not only respond to real-time changes, but also predict potential demand changes, improving the robustness and reliability of the battery swap scheduling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of Embodiment 1 of the battery swap scheduling method for a battery swap vehicle provided in the present application;

[0023] Figure 2 A schematic diagram of a battery swap scheduling system shown in an exemplary embodiment of the present application;

[0024] Figure 3 A flow chart of Embodiment 2 of the battery swap scheduling method for a battery swap vehicle provided in the present application;

[0025] Figure 4 A flowchart of Embodiment 3 of the battery swap scheduling method for a battery swap vehicle provided in the present application;

[0026] Figure 5 A flowchart of a fourth embodiment of a battery swap scheduling method for a battery swap vehicle provided in the present application;

[0027] Figure 6A flowchart of Embodiment 5 of the battery swap scheduling method for a battery swap vehicle provided in the present application;

[0028] Figure 7 A flowchart of Embodiment 6 of the battery swap scheduling method for a battery swap vehicle provided in the present application;

[0029] Figure 8 The hardware structure diagram of the server where the battery swap scheduling device for the battery swap vehicle provided in this application is located;

[0030] Fig. 9 This is a structural schematic diagram of embodiment 1 of the battery swap scheduling device for a battery swap vehicle provided in this application. DETAILED DESCRIPTION

[0031] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0032] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0033] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0034] Specific embodiments are given below to introduce the technical solution of the present application in detail.

[0035] Figure 1 This is a flow chart of the first embodiment of the battery swap scheduling method for battery swap vehicles provided in this application. Please refer to Figure 1 The method provided in this embodiment is applied to a server, and the method may include:

[0036] S101. When a dispatch request is received, for each target vehicle indicated by the dispatch request, a first distance between the target vehicle and each target site within a coverage area of ​​the target vehicle and a travel time for the target vehicle to travel to the target site are calculated.

[0037] It should be noted that the battery swap scheduling method and device for battery swap vehicles provided in this application are applied to the server in the battery swap scheduling system.

[0038] Figure 2 This is a schematic diagram of a battery swap scheduling system shown in an exemplary embodiment of the present application. Figure 2 The battery swap dispatching system includes a server, multiple battery swap stations, controlled vehicles under the control of the server, and a database. The database is used to maintain the relevant data of the battery swap station and the relevant data of the controlled vehicles.

[0039] Furthermore, the battery swap scheduling method and device for a battery swap vehicle provided in the present application are applied to a single-vehicle multi-station scenario to recommend a battery swap station for the vehicle from multiple stations.

[0040] It should be noted that the single-bike multi-station scenarios include closed scenarios (such as mining areas, factories, large logistics parks, etc.), urban distribution scenarios (such as urban express delivery, instant delivery scenarios, etc.) and trunk line scenarios. The battery replacement needs in different scenarios show different characteristics.

[0041] Specifically, since closed scenes usually have limited space, dense vehicles, and concentrated demand for battery replacement, it is easy to cause congestion at the battery replacement station and long waiting time. At this time, users are more concerned about the battery replacement time. Furthermore, in the urban distribution scenario, the transportation and battery replacement process often encounter congestion, which puts higher requirements on the timeliness of transportation. At this time, users are more concerned about the battery replacement time. In addition, in the trunk line scenario, since trunk line transportation requires long-term continuous driving, drivers are more concerned about the power supply. If the scheduling is unreasonable, they may not be able to reach the next battery replacement station smoothly, resulting in vehicle failure or detention on the way. At this time, users are more concerned about whether they can reach the next battery replacement station smoothly.

[0042] The method provided in this embodiment comprehensively considers multi-dimensional information such as vehicle location, power status, battery swap station layout, traffic conditions, etc., and can achieve efficient and accurate battery swap scheduling, which can improve the vehicle's operating efficiency and user experience in various scenarios.

[0043] The method provided in this embodiment is introduced below:

[0044] Specifically, the dispatch request can be a battery replacement request from a vehicle, or a battery replacement request from an upper-level dispatch system or a periodically triggered battery replacement request. Furthermore, when the dispatch request is a battery replacement request from a vehicle, the dispatch request carries the vehicle's identification information, and the vehicle indicated by the identification information is the target vehicle; when the dispatch request is a battery replacement request from an upper-level dispatch system, at this time, the target vehicle is any vehicle under the control of the dispatch system; further, when the dispatch request is a periodically triggered battery replacement request, at this time, the target vehicle is any vehicle under the control of the dispatch system.

[0045] Specifically, the target site within the coverage range of the target vehicle refers to a battery swap station located within the coverage range of the target vehicle. The coverage range of the target vehicle may be a range defined by a circle with the target vehicle as the center and a preset coverage radius as the radius. The preset coverage radius is set according to actual needs and is not limited in this embodiment. For example, in one embodiment, the preset coverage radius is 5km, that is, the coverage range of the target vehicle is a range covered by a radius of 5km of the target vehicle.

[0046] In specific implementation, for each target vehicle indicated by the dispatch request, the coordinate range corresponding to the coverage range of the target vehicle can be determined based on the coordinates of the target vehicle and the preset coverage radius; further, based on the coordinates of each battery swap station under the control of this server and the coordinate range corresponding to the coverage range of the target vehicle, it is determined whether each battery swap station is within the coverage range of the target vehicle to determine whether the battery swap station is the target station within the coverage range of the target vehicle. Specifically, for example, when the coordinates of a battery swap station are within the coordinate range corresponding to the coverage range of the target vehicle, the battery swap station is the target station, otherwise it is not the target station.

[0047] Furthermore, after determining that there is a target site, the first distance between the target vehicle and the target site and the travel time of the target vehicle to the target site are calculated. In specific implementation, the first distance and travel time can be calculated based on the current position of the target vehicle and the position of the target site. For example, in one possible implementation, the first distance and travel time are obtained by calling an API (Application Programming Interface) provided by a map software or a navigation software. The specific implementation principle and implementation process of the map software or the navigation software to obtain the first distance and travel time can be found in the description of the relevant technology, which will not be repeated here.

[0048] S102, determining a cruising range of the target vehicle at a current power level, and screening, from the target sites, reachable sites that the target vehicle can reach before the power is exhausted, based on the cruising range and a first distance between the target vehicle and each target site.

[0049] Specifically, the cruising range of the target vehicle at the current power level refers to the maximum distance that the target vehicle can travel at the current power level. In one possible implementation, when determining the cruising range of the target vehicle at the current power level, the driving data of all vehicles can be obtained from the database, and then the driving data of all vehicles can be input into the static cruising range prediction model, so that the static cruising range prediction model can learn the relationship between power consumption and mileage, and output the power consumption per kilometer. Furthermore, in one possible implementation, the product of the current power level of the target vehicle and the rated energy can be calculated to obtain the current actual available power level of the target vehicle, and then the ratio of the actual available power level to the power consumption per kilometer is determined as the cruising range of the target vehicle at the current power level.

[0050] Specifically, when the cruising range of the target vehicle at the current power level is determined, the reachable sites that the target vehicle can reach before the power is exhausted can be screened from the target sites based on the cruising range and the first distance between the target vehicle and each target site.

[0051] In specific implementation, by comparing the cruising range with the first distance, the reachable sites can be screened out from the target sites. For example, when the first distance is greater than the cruising range, it means that the target vehicle cannot reach the target site before the battery is exhausted, and the target site is an unreachable site. On the contrary, when the first distance is less than or equal to the cruising range, it means that the target vehicle can reach the target site before the battery is exhausted, and the target site is a reachable site.

[0052] S103, for each battery in each reachable site, determine the ready time of the battery, and determine the readiness information of the battery when the target vehicle arrives based on the ready time of the battery and the travel time of the target vehicle to the reachable site.

[0053] Specifically, in a possible implementation, for each battery in each reachable site, the ready time of the battery may be determined according to the following method:

[0054] (1) For each first-type battery that is ready in each reachable site, the ready time of the first-type battery is determined as the current time.

[0055] (2) For each second type battery that is not ready in each reachable site, predict the charging time of the second type battery, and determine the ready time of the second type battery as the time that arrives after the charging time has passed from the current time.

[0056] It should be noted that the ready moment of a battery refers to the time point when the battery has completed charging and reaches a usable state. It is understandable that for each reachable site, there are ready batteries and unready batteries, wherein a ready battery refers to a battery that meets a preset ready condition, and an unready battery refers to a battery that does not meet the preset ready condition. It should be noted that the preset ready condition is set according to actual needs and is not limited in this embodiment. For example, in one embodiment, the preset ready condition is that when the battery power reaches 90% of the total power, the battery is a ready battery.

[0057] Furthermore, for the ready-to-use first-category battery, at the current moment, the battery has been fully charged and is ready for use. At this time, the current moment can be determined as the ready-to-use moment of the first-category battery.

[0058] Furthermore, for the second type of battery that is not ready, at the current moment, the battery has not completed charging and has not reached a usable state. At this time, the charging time of the second type of battery can be predicted, and the moment arrived after the charging time from the current moment is determined as the ready moment of the second type of battery.

[0059] Optionally, in a possible implementation, for any unready second-type battery, the charging time thereof may be determined according to the following formula:

[0060] ;

[0061] Among them, t is the charging time;

[0062] is the preset ready power;

[0063] is the current charge of the battery;

[0064] W is the charging power.

[0065] It should be noted that the charging power can be a preset empirical value, or it can be a value determined based on the historical charging data of the battery swap station or the historical charging data of the battery. In this embodiment, it is not limited to this.

[0066] Furthermore, for each battery in each reachable site, after determining the ready time of the battery, the readiness information of the battery when the target vehicle arrives can be determined based on the ready time of the battery and the travel time of the target vehicle to the reachable site.

[0067] It should be noted that the battery's readiness information when the target vehicle arrives is used to indicate whether the battery is ready when the target vehicle arrives at the reachable site. Readiness information includes ready and not ready. When the battery's readiness information is ready, it indicates that the battery is ready when the target vehicle arrives at the reachable site; when the battery's readiness information is not ready, it indicates that the battery is not ready when the target vehicle arrives at the reachable site.

[0068] In specific implementation, for a certain reachable site, the time after the current time plus the used time can be determined as the arrival time of the target vehicle at the reachable site; further, the arrival time of the target vehicle at the reachable site is compared with the ready time of each battery in the reachable site. When the ready time is earlier than the arrival time, the readiness information of the battery when the target vehicle arrives is determined to be ready; when the ready time is later than the arrival time, the readiness information of the battery when the target vehicle arrives is determined to be not ready.

[0069] Referring to the previous introduction, it can be understood that the batteries with ready information include batteries that are ready at the current moment and batteries that can be ready before the target vehicle arrives by charging (assuming that the battery starts to be charged at the current moment, it can be converted from a non-ready state to a ready state before the target vehicle arrives).

[0070] Combined with the previous introduction, for example, in one embodiment, the current time is 10:00, and the travel time for the target vehicle to reach a certain reachable site is 1 hour, that is, the arrival time of the target vehicle at the reachable site is 11:00. For the first type of battery that is ready at the current time, its ready information is ready; for the second type of battery that is not ready at the current time, if the second type of battery can be ready before 11:00, its ready information is ready, and if the second type of battery cannot be ready before 11:00, its ready information is not ready.

[0071] S104. Use the battery swap demand prediction model to predict the battery swap demand of the reachable site in the next time period, and determine the number of ready batteries that the reachable site is expected to have when the target vehicle arrives based on the battery swap demand and the readiness information of each battery when the target vehicle arrives.

[0072] Specifically, the battery swap demand prediction model is a model that can predict the battery swap demand of a reachable station in the next period of time. The battery swap demand in the next period of time is used to represent how many ready batteries will be swapped out in the next period of time.

[0073] Optionally, in a possible implementation, the battery swap demand prediction model can predict the battery swap demand of the reachable site in the next period based on the historical battery swap data of the reachable site. For example, the battery swap demand prediction model can be an LSTM model (Long Short-Term Memory, long short-term memory neural network), whose input is the historical battery swap volume of the reachable site in a specified time period before the current moment, and whose output is the battery swap demand of the reachable site in the next period.

[0074] It should be noted that the battery swap demand prediction model is pre-trained based on the historical battery swap data of the battery swap station, wherein the historical battery swap data of the battery swap station may include the number of batteries swapped out of the battery swap station at different times. Further, please refer to the description of the relevant prior art for the specific training method of the model, which will not be repeated here.

[0075] Furthermore, after using the battery swap demand prediction model to predict the battery swap demand of the reachable site in the next time period, the number of ready batteries that the reachable site is expected to have when the target vehicle arrives can be determined based on the battery swap demand and the readiness information of each battery when the target vehicle arrives.

[0076] Optionally, in a possible implementation, determining the number of ready batteries that the reachable station is expected to hold when the target vehicle arrives, based on the battery replacement demand and the readiness information of each battery when the target vehicle arrives, includes:

[0077] (1) According to the readiness information of each battery when the target vehicle arrives, the total number of ready batteries theoretically held by the reachable station when the target vehicle arrives is counted.

[0078] Specifically, referring to the previous description, the ready information of the batteries that are currently ready in the reachable site and the batteries that will be ready when the target vehicle arrives are all ready. In this step, for a certain reachable site, the number of batteries with ready information as ready in the reachable site can be counted, and this number is the total number of ready batteries that the reachable site theoretically holds when the target vehicle arrives.

[0079] (2) Based on the battery replacement demand, determine the number of batteries that can theoretically be replaced at the reachable station before the target vehicle arrives.

[0080] Specifically, in a possible implementation, for a certain reachable site, the battery replacement demand of the reachable site in the next time period can be directly determined as the number of batteries theoretically replaced at the site before the target vehicle arrives.

[0081] (3) Determine the difference between the total number and the swapped-out number as the number of ready batteries that the reachable station is expected to have when the target vehicle arrives.

[0082] Specifically, the following formula can be used to calculate the number of ready batteries that the reachable station is expected to have when the target vehicle arrives:

[0083] ;

[0084] Among them, E3 is the number of ready batteries that the reachable site is expected to have when the target vehicle arrives;

[0085] S is the total number of ready batteries theoretically held by the reachable site when the target vehicle arrives;

[0086] E2 is the number of batteries theoretically replaced at the reachable site before the target vehicle arrives.

[0087] S105. For each of the preset multiple sorting strategies, sort all reachable sites according to at least one of the first distances of the target vehicle from each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries that each reachable site is expected to have when the target vehicle arrives, to obtain a sorting result corresponding to the sorting strategy; wherein the multiple sorting strategies use different information to sort all reachable sites from different aspects.

[0088] Specifically, each of the multiple sorting strategies is different, and each sorting strategy will comprehensively consider and sort according to one or more of the above information. Through multiple sorting strategies, all reachable sites can be evaluated from different angles, for example, sites with short distances, short travel times, and more available batteries are given priority.

[0089] In specific implementation, for example, in one possible implementation, the reachable sites may be sorted based on distance; for another example, in one possible implementation, the reachable sites may be sorted based on travel time; for another example, in another possible implementation, the reachable sites may be sorted based on a combination of travel time and the number of ready batteries in the reachable sites.

[0090] S106. Recommend a battery swapping station for the target vehicle according to the sorting results corresponding to each sorting strategy, so that the target vehicle can go to the battery swapping station for battery swapping in the future.

[0091] As described above, each sorting strategy will generate a sorting result. In this step, a battery swapping station can be recommended for the target vehicle based on the sorting results corresponding to each sorting strategy.

[0092] Optionally, in a possible implementation, the sorting results corresponding to each sorting strategy can be returned to the target vehicle, so that the target vehicle displays multiple sorting results to the user, allowing the user to select a suitable sorting result according to actual battery replacement needs, and then select a suitable site from the selected sorting results.

[0093] Optionally, in another possible implementation, multiple ranking results may be integrated to obtain an integrated ranking result, and then the top k stations in the integrated ranking result may be recommended to the target vehicle. In a specific implementation, when multiple ranking results are integrated, the multiple ranking results may be integrated based on a voting mechanism or a weighted scoring mechanism.

[0094] Optionally, in another possible implementation, a battery swapping station can be recommended for the target vehicle according to the target vehicle's selection preference. For example, in one embodiment, the target vehicle's selection preference is to select the nearest one. In this case, when recommending a battery swapping station for the target vehicle, the battery swapping station can be selected based on the sorting result corresponding to the distance sorting strategy; for another example, in another embodiment, the target vehicle's selection preference is to give priority to stations with a short travel time. In this case, when recommending a battery swapping station for the target vehicle, the battery swapping station can be selected based on the sorting result corresponding to the travel time sorting strategy.

[0095] The battery swap scheduling method for battery swap vehicles provided in the present embodiment first predicts the range of the target vehicle at the current power level, and further, for each target site, obtains the first distance between the target vehicle and the target site, and compares the first distance with the range of the target vehicle at the current power level, so as to accurately find the reachable site. Subsequently, when recommending battery swap sites, the recommendations are made based on the reachable sites. In this way, the accuracy and effectiveness of the battery swap scheduling can be ensured, and the risk of the vehicle being unable to reach the battery swap site due to exhaustion of power can be effectively avoided. Secondly, when making recommendations based on reachable stations, by obtaining the first distance of the target vehicle from each reachable station, the time it takes for the target vehicle to travel to each reachable station, the ready time of each battery in each reachable station, and the number of ready batteries expected to be held at each reachable station when the target vehicle arrives, firstly, it is possible to start from multiple factors and use a certain factor or a combination of certain factors to obtain multiple sorting results, and then recommend battery swap stations based on multiple sorting results, which can provide more accurate and personalized recommendations for battery swap stations and ensure that the target vehicle can successfully complete the battery swap operation; secondly, the preset multiple sorting strategies use different information to evaluate and sort stations, which can adapt to different actual scenarios. For example, in the trunk line scenario, you can give priority to the station with the closest distance or the shortest time; in other scenarios, you can pay more attention to the availability and readiness of the battery. In addition, when sorting accessible stations, by predicting the ready time of each battery at each accessible station and the future battery swap demand, battery resources can be arranged in advance, battery usage and scheduling can be optimized, and battery shortages or excessive congestion at certain stations can be avoided, which helps to reduce the waiting time when vehicles arrive at the battery swap station and improve the efficiency of battery swap and user experience. Finally, combining real-time data and demand forecasts to dynamically adjust the battery swap plan can not only respond to real-time changes, but also predict potential demand changes, improving the robustness and reliability of the battery swap scheduling system.

[0096] Referring to the previous description, it can be understood that the battery swap scheduling method provided in this embodiment can accurately provide the optimal battery swap site recommendation for the target vehicle through multi-faceted information integration and prediction, which can effectively improve the battery swap efficiency, reduce waiting time, ensure the vehicle's endurance safety, and ultimately improve the operating efficiency and user experience of the entire battery swap scheduling system, and ensure that electric vehicles can maintain continuous and efficient operation in various complex scenarios.

[0097] Figure 3 This is a flow chart of Embodiment 2 of the battery swap scheduling method for battery swap vehicles provided in this application. Figure 3Based on the above embodiment, all reachable sites are sorted according to at least one of the first distances of the target vehicle from each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries that each reachable site is expected to have when the target vehicle arrives, including:

[0098] S301. Divide the reachable sites into first-category sites and second-category sites according to the number of ready batteries that each reachable site is expected to have when the target vehicle arrives; wherein the first-category sites are sites that have ready batteries when the target vehicle arrives; and the second-category sites are sites that do not have ready batteries when the target vehicle arrives.

[0099] Specifically, according to whether the reachable station has batteries that can be immediately replaced when the target vehicle arrives at each reachable station, the reachable stations are divided into two categories, one of which is: when the target vehicle arrives, there are stations with batteries that are ready and can be replaced immediately, and the other is: when the target vehicle arrives, there are no ready batteries.

[0100] For the convenience of explanation, the stations with ready batteries when the target vehicle arrives are recorded as first-category stations, and the stations with no ready batteries when the target vehicle arrives are recorded as second-category stations.

[0101] In specific implementation, for a certain reachable site, if the number of ready batteries that the reachable site is expected to hold when the target vehicle arrives is greater than 0, then the reachable site is considered to be a first-type site with ready batteries when the target vehicle arrives, and if the number of ready batteries that the reachable site is expected to hold when the target vehicle arrives is less than or equal to 0, then the reachable site is considered to be a second-type site with no ready batteries when the target vehicle arrives.

[0102] S302. Sort the first-category sites and the second-category sites respectively according to at least one of the following information: a first distance between the target vehicle and each reachable site, a travel time for the target vehicle to travel to each reachable site, a ready time of each battery in each reachable site, and a number of ready batteries that each reachable site is expected to have when the target vehicle arrives, to obtain a first sorting result corresponding to the first-category sites and a second sorting result corresponding to the second-category sites.

[0103] Specifically, the first type of sites can be sorted according to one or more of the following information: the first distance between the target vehicle and each reachable site, the time it takes for the target vehicle to travel to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries that each reachable site is expected to have when the target vehicle arrives, to obtain a first sorting result corresponding to the first type of sites; and the second type of sites can be sorted according to one or more of the following information: the first distance between the target vehicle and each reachable site, the time it takes for the target vehicle to travel to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries that each reachable site is expected to have when the target vehicle arrives, to obtain a second sorting result corresponding to the second type of sites.

[0104] In a specific implementation, when sorting the first type of sites and the second type of sites, the same information may be used and the sorting may be performed based on the same sorting strategy, or different information may be used and the sorting may be performed based on different sorting strategies, which is not limited in this embodiment. For example, in a possible implementation, for the first type of sites and the second type of sites, the first type of sites and the second type of sites are sorted respectively according to the rule of ascending distance arrangement based on the first distance between the target vehicle and each reachable site, to obtain a first sorting result corresponding to the first type of sites and a second sorting result corresponding to the second type of sites.

[0105] For another example, for the first type of site, there are a number of ready batteries. At this time, based on the first distance of the target vehicle from each reachable site, the first type of sites are sorted according to the rule of ascending distance sorting to obtain a first sorting result. Furthermore, for the second type of site, there are no ready batteries. At this time, based on the battery replacement time of the target vehicle to the site for battery replacement, the second type of sites can be sorted according to the rule of ascending battery replacement time sorting to obtain a second sorting result. For another example, in another possible implementation, for the first type of sites, they can be sorted according to the rule of ascending number of ready batteries, and for the second type of sites, they can be sorted according to the rule of ascending distance sorting.

[0106] S303: concatenate the second sorting result onto the first sorting result to obtain a comprehensive sorting result.

[0107] Specifically, since there is no ready battery at the second type of station when the vehicle arrives, the vehicle needs to wait when it arrives at the second type of station and cannot replace the battery immediately. Therefore, the second sorting result corresponding to the second type of station is spliced ​​after the first sorting result corresponding to the first type of station to obtain a comprehensive sorting result.

[0108] The method provided in this embodiment divides the reachable sites into the first category and the second category according to the number of ready batteries that each reachable site is expected to have when the target vehicle arrives, and sorts them respectively, and then splices the second sorting result corresponding to the second category site behind the first sorting result corresponding to the first category site. In this way, the battery replacement path selection of the target vehicle can be optimized, so that the target vehicle gives priority to those sites that already have ready batteries when the vehicle arrives, reducing the waiting time of the vehicle, and improving the battery replacement efficiency, thereby improving the overall operational efficiency.

[0109] Figure 4 This is a flow chart of the third embodiment of the battery swap scheduling method for battery swap vehicles provided in this application. Please refer to Figure 4 On the basis of the above embodiment, all reachable sites are sorted according to at least one of the first distance between the target vehicle and each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries expected to be held by each reachable site when the target vehicle arrives, to obtain a sorting result corresponding to the sorting strategy, including:

[0110] S401. For each reachable site, based on a preset first score calculation rule, calculate a first score of the reachable site according to a first distance between the target vehicle and the reachable site and a maximum distance between the target vehicle and each of the first distances between the reachable site; wherein the first score calculation rule is based on a score of the distance calculation site, and the first score of each reachable site is negatively correlated with the first distance between the target vehicle and the reachable site.

[0111] It should be noted that Figure 4 In the embodiment shown, all reachable sites are comprehensively sorted based on the first distance between the target vehicle and each reachable site and the number of ready batteries that each reachable site is expected to have when the target vehicle arrives, to obtain a sorting result. The specific sorting method is described in detail below.

[0112] Specifically, the first score calculation rule is used to assign a distance score to the site according to a first distance between the target vehicle and the reachable site (for the convenience of explanation, the distance score assigned based on the distance is recorded as the first score).

[0113] In other words, the first score calculation rule is used to characterize the functional relationship between the first score of the site and the first distance of the target vehicle from the site.

[0114] Furthermore, the first score assigned to each station based on the first score calculation rule is negatively correlated with the first distance between the target vehicle and the reachable station, that is, the greater the first distance between the target vehicle and the reachable station, the lower the first score of the reachable station.

[0115] It should be noted that the preset first score calculation rule is pre-set according to actual needs, and is not limited in this embodiment. For example, in a possible implementation, the first score calculation rule indicates that the distance and the first score are in a linear function relationship, when the distance is 0, the first score is 100 points, and when the distance is the maximum distance among the first distances between the target vehicle and each reachable site, the score is 0 points. At this time, the linear function can be fitted based on this.

[0116] Furthermore, for a certain reachable site, the first distance between the target vehicle and the reachable site may be substituted into the linear function to obtain a first score for the reachable site.

[0117] Combined with the previous introduction, the first score of each reachable site can be calculated according to the following formula:

[0118] ;

[0119] Among them, y i is the first score of the i-th reachable site;

[0120] x max is the maximum distance among the first distances between the target vehicle and each reachable site;

[0121] x i is the first distance between the target vehicle and the i-th reachable site.

[0122] S402. Based on a preset second score calculation rule, calculate a second score of the reachable site according to the number of ready batteries that the reachable site is expected to have when the target vehicle arrives; wherein the second score calculation rule calculates the score of the site based on the number of ready batteries.

[0123] Specifically, the second score calculation rule is used to assign a score to each station according to the number of ready batteries that each station is expected to have when the target vehicle arrives (for the convenience of explanation, the score assigned to the station based on the number of ready batteries is recorded as the second score). In other words, the second score calculation rule is used to characterize the functional relationship between the second score of the station and the number of ready batteries of the station (the number of ready batteries that the station is expected to have when the target vehicle arrives).

[0124] Furthermore, the second score assigned to the site based on the number of ready batteries is positively correlated with the number of ready batteries of the site, that is, the more accessible sites with more ready batteries, the higher the second score, and the fewer accessible sites with fewer ready batteries, the lower the second score.

[0125] It should be noted that the second score calculation rule is pre-set according to actual needs and is not limited in this embodiment. For example, in one possible implementation, the second score calculation rule indicates that the relationship between the number of ready batteries and the second score is a hyperbolic tangent curve, and a functional relationship between the number of ready batteries and the second score is established by the hyperbolic tangent curve, and the second score is assigned to each reachable site by the functional relationship.

[0126] Combined with the above introduction, the relationship between the number of ready batteries and the second score indicated in the second score calculation rule is a hyperbolic tangent curve. At this time, the second score can be calculated according to the following formula:

[0127] ;

[0128] Among them, y j is the second score of the jth reachable site;

[0129] a j is the number of ready batteries that the j-th reachable station is expected to have when the target vehicle arrives.

[0130] S403: Perform weighted processing on the first score and the second score according to a first weight corresponding to the first score calculation rule and a second weight corresponding to the second score calculation rule to obtain a comprehensive score of the reachable site.

[0131] Specifically, the first score and the second score of each reachable site may be weighted according to the following formula:

[0132] ;

[0133] Among them, F is the comprehensive score of the accessible site;

[0134] w1 is the first weight corresponding to the first scoring calculation rule;

[0135] w2 is the second weight corresponding to the second scoring calculation rule;

[0136] F1 is the first score of the reachable site;

[0137] F2 is the second score of the reachable site.

[0138] It should be noted that the first weight corresponding to the first scoring rule and the second weight corresponding to the second scoring rule are set according to actual needs, and are not limited in this embodiment. In specific implementation, the first weight of the first scoring rule and the second weight corresponding to the second scoring rule can be set based on the application scenario, that is, different weights can be set in different application scenarios. For example, in the trunk line scenario, the main focus is on the driver's anxiety about electricity consumption. It is necessary to ensure that the driver can arrive at the next station in sequence. At this time, more attention is paid to the distance. At this time, the first weight can be set to 0.7 and the second weight can be set to 0.3. For another example, in the campus scenario, the vehicles are dense and the demand for battery replacement is large. At this time, more attention is paid to the number of ready batteries. At this time, the first weight can be set to 0.4 and the second weight can be set to 0.6.

[0139] S404: Sort the first category sites and the second category sites respectively in descending order of comprehensive scores to obtain a first sorting result corresponding to the first category sites and a second sorting result corresponding to the second category sites.

[0140] Specifically, the reachable sites in the first category are sorted in descending order according to the comprehensive scores of the reachable sites to obtain a first sorting result. Further, the reachable sites in the second category are sorted in descending order according to the comprehensive scores of the reachable sites to obtain a second sorting result.

[0141] The method provided in this embodiment, when sorting the reachable sites, comprehensively considers the first distance of the target vehicle from each reachable site and the number of ready batteries that each reachable site is expected to have when the target vehicle arrives, and integrates the reachable sites, which can not only reduce the vehicle's driving distance and save time, but also ensure that the vehicle can immediately replace the battery when it arrives, reducing waiting time, thereby improving user experience and operational efficiency.

[0142] Figure 5 This is a flow chart of the fourth embodiment of the battery swap scheduling method for battery swap vehicles provided in this application. Please refer to Figure 5 On the basis of the above embodiment, all reachable sites are sorted according to at least one of the first distance between the target vehicle and each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries expected to be held by each reachable site when the target vehicle arrives, to obtain a sorting result corresponding to the sorting strategy, including:

[0143] S501 . For a first type of site, sort the first type of site in ascending order of travel time to obtain a first sorting result corresponding to the first type of site.

[0144] It should be noted that, in this embodiment, based on the travel time of the target vehicle to each reachable station and the ready time of each battery in each reachable station, all reachable stations are sorted from a time perspective (specifically, the reachable stations are sorted from the perspective of battery replacement time).

[0145] It should be noted that, referring to the previous introduction, the first type of station is a station with a ready battery when the target vehicle arrives, that is, when the target vehicle arrives at the station, it can immediately change the battery without waiting. Therefore, for the first type of station, the battery replacement time of the target vehicle to the first type of station can be characterized by its travel time. At this time, the first type of stations can be directly sorted based on the rule of ascending travel time to obtain the first sorting result corresponding to the first type of station.

[0146] In specific implementation, the sites with shorter travel time are placed in front, and the sites with longer travel time are placed in the back, and the first category of sites can be sorted in ascending order of travel time.

[0147] S502. For each second-category site, based on the travel time of the target vehicle to the second-category site and the ready time of each battery in the second-category site, determine the waiting time for the target vehicle to replace the battery at the second-category site, and determine the sum of the waiting time and the travel time of the target vehicle to the second-category site as the battery replacement time for the target vehicle to replace the battery at the second-category site.

[0148] S503. Sort the second-category sites in ascending order of battery replacement consumption to obtain a second sorting result corresponding to the second-category sites.

[0149] It should be noted that, referring to the previous introduction, the second type of site is a site where there is no ready battery when the target vehicle arrives, that is, when the target vehicle arrives at the site, there is no ready battery in the site, and the vehicle needs to wait until a battery is converted from unready to ready before it can be replaced. Therefore, for the second type of site, it is necessary to first determine the battery replacement time for the target vehicle to replace the battery at the second type of site, and then sort the second type of sites based on the rule of ascending order of battery replacement time.

[0150] Specifically, for each second-class station, the waiting time for the target vehicle to replace the battery at the second-class station can be determined first, and then the sum of the waiting time and the travel time of the target vehicle to the station can be determined as the battery replacement time for the target vehicle to replace the battery at the second-class station.

[0151] In specific implementation, when determining the waiting time for the target vehicle to replace the battery at the second-category station, the arrival time of the target vehicle at the second-category station can be determined based on the travel time of the target vehicle to the second-category station. Furthermore, the waiting time for the target vehicle to replace the battery at the second-category station can be determined based on the arrival time and the ready time of each battery in the second-category station.

[0152] In a specific implementation, the time indicated by the sum of the current time and the travel time can be determined as the arrival time. Furthermore, in a possible implementation, a candidate battery whose ready time is closest to the arrival time can be found from the batteries in the second type of site, and further, the duration indicated by the difference between the ready time and the arrival time of the candidate battery is determined as the waiting time.

[0153] In specific implementation, for example, in one embodiment, it takes one hour for the target vehicle to travel to a certain station in the second category, and half an hour to wait (that is, when the target vehicle arrives at the station, it needs to wait for half an hour before the unready battery can be converted to ready and the battery can be replaced). At this time, it takes one and a half hours for the target vehicle to arrive at the station for battery replacement.

[0154] Furthermore, after obtaining the battery replacement time, the stations with short battery replacement time can be placed in front, and the stations with long battery replacement time can be placed in the back, and the second category of stations can be sorted in ascending order of battery replacement time.

[0155] The method provided in this embodiment, when sorting the reachable sites, sorts the first type of sites based on the travel time, and sorts the second type of sites based on the battery replacement time. In this way, sorting the reachable sites from a time perspective (battery replacement time) allows car owners to give priority to those sites that require a shorter battery replacement time, thereby reducing the stay time and improving overall operating efficiency.

[0156] Figure 6 This is a flow chart of Embodiment 5 of the battery swap scheduling method for battery swap vehicles provided in this application. Figure 6 Based on the above embodiment, the step of determining the cruising range of the target vehicle at the current power level includes:

[0157] S601. Obtain driving data records of each controlled vehicle under the control of the server, and filter and block the driving data records to obtain multiple data blocks; wherein each data block records the driving data of a pair of vehicle battery combinations within a continuous time range.

[0158] Specifically, the database maintains the driving record data of each controlled vehicle under the control of the server. In this step, the driving data record of each controlled vehicle under the control of the server can be obtained from the database.

[0159] It should be noted that the vehicle's driving data record may include a vehicle identification code, a battery number and driving data, wherein the driving data may include the real-time mileage the vehicle has traveled, the vehicle's real-time acceleration, the vehicle's real-time speed while driving, the real-time battery power, the vehicle's real-time location, etc.

[0160] In specific implementation, in this step, the driving data records need to be cleaned and screened to remove abnormal values, complete missing data, standardize data formats, etc. Further, the screened driving data records are divided into blocks to obtain multiple data blocks.

[0161] It should be noted that each data block carries a vehicle identification code, a battery number, and driving data, which records the driving data of a pair of vehicle battery combinations (i.e., the vehicle battery combination indicated by the vehicle identification code and the battery code) within a continuous time range, and each data block describes the driving data of a specific vehicle during the period when a specific battery is powering a specific vehicle. In other words, each data block is used to characterize the driving performance of a specific vehicle under the power supply of a specific battery.

[0162] Specifically, the driving data recorded in each data block should be limited to a continuous time range. In specific implementation, for a pair of vehicle battery combinations, the driving data at similar times can be merged together to obtain the driving data of the pair of vehicle battery combinations within a continuous time range, thereby obtaining a data block.

[0163] For example, in one embodiment, from 8:00 to 12:00 in the morning, vehicle A uses battery X for driving, and the driving data records during this period are divided into one data block. For another example, from 12:00 to 13:00, the vehicle replaces the battery and uses battery Y, and from 13:00 to 17:00, vehicle A continues to use battery Y for driving. At this time, the driving data during the period from 12:00 to 17:00 is divided into the second data block.

[0164] S602. When there is a target data block of a specified vehicle battery combination within a specified time range in the data block, the target data block is input into a dynamic cruising range prediction model so that the dynamic cruising range prediction model learns the relationship between power consumption and mileage and outputs power consumption per kilometer; wherein the vehicle in the specified vehicle battery combination is the target vehicle, and the battery in the specified vehicle battery combination is the battery currently powering the target vehicle.

[0165] S603. When the target data block does not exist in the multiple data blocks, the multiple data blocks are simultaneously input into a static cruising range prediction model, so that the static cruising range prediction model learns the relationship between power consumption and mileage, and outputs power consumption per kilometer.

[0166] Specifically, when determining the cruising range of the target vehicle, the vehicle in the vehicle battery pack is designated as the target vehicle, and the batteries in the vehicle battery pack are designated as the batteries currently powering the target vehicle.

[0167] Furthermore, when determining the range of the target vehicle at the current power level, the power consumption per kilometer can be determined based on a dynamic range prediction model or a static range prediction model. Furthermore, the range of the target vehicle at the current power level can be determined based on the power consumption per kilometer, the current power level of the target vehicle, and the rated energy of the target vehicle.

[0168] Specifically, when there is a target data block among multiple data blocks, the dynamic cruising range prediction model is used to predict the power consumption per kilometer; when there is no target data block among multiple data blocks, the static cruising range prediction model is used to predict the power consumption per kilometer.

[0169] It should be noted that the input of the dynamic cruising range prediction model is the target data block, and the output is the power consumption per kilometer. It can learn the relationship between power consumption and mileage based on the linear regression algorithm, and then output the power consumption per kilometer based on this. Specifically, for the learning process of the dynamic cruising range prediction model, please refer to the relevant description of the relevant technology, which will not be repeated here. Furthermore, it can be understood that the dynamic cruising range prediction model predicts the power consumption per kilometer through the target vehicle’s own recent driving data. By considering the current usage status and driving mode of the target vehicle, the power consumption per kilometer can be predicted more accurately.

[0170] Specifically, the input of the static cruising range prediction model is all the data blocks, and the output is the power consumption per kilometer. The static cruising range prediction model learns the relationship between power consumption and mileage through a linear regression algorithm, and outputs the power consumption per kilometer. Specifically, for the learning process of the static cruising range prediction model, please refer to the relevant description of the relevant technology, which will not be repeated here. In addition, the static cruising range prediction model predicts the power consumption per kilometer through the driving data in all the data blocks. By using the driving data in multiple data blocks, from a global perspective, more diverse driving patterns and environmental variables can be learned, which can improve the robustness of the model. Especially when there is a lack of target data blocks corresponding to the target vehicle, the use of multiple data blocks can effectively supplement the problem of insufficient data.

[0171] Referring to the previous description, it can be seen that the dynamic cruising range prediction model can provide accurate predictions when the target data block exists, and has strong adaptability. The static cruising range prediction model can improve the robustness and accuracy of the model through a variety of data blocks, especially when the data is insufficient, and has a good supplementary effect. In this embodiment, the appropriate prediction model is selected according to the availability of the data block, which can maintain good performance in different situations and improve the accuracy and reliability of the cruising range prediction.

[0172] S604: Determine the cruising range of the target vehicle at the current power level according to the power consumption per kilometer, the current power level of the target vehicle, and the rated energy of the target vehicle.

[0173] Specifically, the current power of the target vehicle refers to the current power of the battery that supplies power to the target vehicle. In one embodiment, the current power is represented by the current SOC. Further, the rated energy of the target vehicle refers to the total capacity of the battery that supplies power to the target vehicle.

[0174] In specific implementation, the cruising range can be calculated by the following formula:

[0175] ;

[0176] Wherein, L is the cruising range of the target vehicle at the current power level;

[0177] SOC is the current charge of the target vehicle;

[0178] b is the preset safety margin;

[0179] E is the rated energy of the target vehicle;

[0180] h is the power consumption per kilometer.

[0181] It should be noted that the specific value of the safety margin is set in advance according to actual needs and is not limited in this embodiment.

[0182] In specific implementation, the safety margin can be set according to the application scenario. For example, in one possible implementation, different safety margins can be set in different application scenarios. For example, in one embodiment, in a park scenario, the vehicle is located in the park, at this time, the safety margin can be set to 0. For another example, in a trunk line scenario, it is necessary to determine whether the vehicle can reach the next battery swap station before the battery is exhausted. At this time, the safety margin can be set to 20%.

[0183] Combined with the first example above, for example, when the safety margin is 0, if the predicted power consumption per kilometer is 0.2kWh / km, the current power of the target vehicle is 40%, and the rated energy of the target vehicle is 60 kWh, in this step, the range of the target vehicle at the current power is determined to be 120 kilometers (where, ).

[0184] The method provided in this embodiment filters and blocks the driving data. Further, when a target data block exists, the cruising range is predicted based on a dynamic cruising range prediction model, and when no target data block exists, the cruising range of the vehicle is predicted based on a static cruising range prediction model. In this way, a suitable prediction model can be selected according to the availability of the data block, and good performance can be maintained in different situations, thereby improving the accuracy and reliability of the cruising range prediction.

[0185] Figure 7 This is a flow chart of Embodiment 6 of the battery swap scheduling method for battery swap vehicles provided in this application. Please refer to Figure 7 Based on the above embodiment, the step of predicting the charging time of the second type of battery may include:

[0186] S701. For each second-category battery that is not ready in each reachable site, fit a first function of how the battery charge changes with time during the charging process in the battery dimension and a second function of how the battery charge changes with time during the charging process in the site dimension based on the charging record of the second-category battery in a first specified time period before the current moment and the charging record of the reachable site in a second specified time period before the current moment.

[0187] Specifically, a first function of how the battery charge changes over time in the battery dimension during the charging process can be fitted based on the charging records of the second type of battery in the first specified time period before the current moment. Similarly, a second function of how the battery charge changes over time in the site dimension during the charging process can be fitted based on the charging records of each reachable site in the second specified time period before the current moment. Among them, the first function reflects the relationship between the battery charge and time during the charging process from the perspective of the second type of battery itself, which can reflect the charging performance of the second type of battery. Furthermore, the second function reflects the relationship between the battery charge and time during the charging process from the perspective of the reachable site, which can reflect the charging performance of the reachable site.

[0188] It should be noted that the charging records of the second type of batteries may include information such as the start time of charging, the initial power during charging, the power at each moment during the charging process, and the end power at the end of charging; further, the charging records of the accessible sites may include the charging activity information of all batteries in the battery swap station, and the charging activity information may include information such as the start time of charging, the end time of charging, the battery charging mode, and the power at different moments during the charging process.

[0189] Furthermore, the first designated time period and the second designated time period are set according to actual needs and are not limited in this embodiment.

[0190] It should be noted that the first designated time period and the second designated time period may be the same or different, and this embodiment does not limit this. The following description is made by taking the first designated time period of 10 days and the second designated time period of 5 days as an example.

[0191] In specific implementation, the time and power of the charging process can be used as a set of data pairs, and then multiple sets of data pairs can be used to fit the functional relationship of the battery power changing with time during the charging process.

[0192] For example, when fitting the first function, multiple data pairs can be obtained through the charging records of the battery in the past 10 days. Further, the first function of the battery power changing over time during the charging process in the site dimension is fitted through these multiple data pairs.

[0193] For another example, when fitting the second function, multiple sets of data pairs can be obtained through the charging records of the reachable stations in the past 5 days. Further, the second function of the battery power changing over time during the charging process in the battery dimension is fitted through these multiple sets of data pairs.

[0194] S702. Determine a first charge amount per unit time in a battery dimension by using the first function, and determine a second charge amount per unit time in a site dimension by using the second function.

[0195] Specifically, the slope of the first function per unit time in the charging process can be calculated, and the slope represents the charging amount per unit time in the battery dimension; further, all the slopes calculated using the first function are averaged by an average weighted method to obtain the charging amount per unit time in the battery dimension (recorded as the first charging amount); similarly, the slope of the second function per unit time in the charging process can be calculated, and the slope represents the charging amount per unit time in the site dimension; further, all the slopes calculated using the second function are averaged by an average weighted method to obtain the charging amount per unit time in the site dimension (recorded as the second charging amount).

[0196] It can be understood that, referring to the previous description, the first charge amount represents the charge amount per unit time in the battery dimension, and the second charge amount represents the charge amount per unit time in the site dimension.

[0197] S703: Perform weighted processing on the first charge amount and the second charge amount according to the first weight of the second type of battery at the current moment and the second weight of the reachable site at the current moment to obtain a weighted charge amount per unit time.

[0198] Specifically, the first charge amount and the second charge amount may be weighted by the following formula:

[0199] ;

[0200] Where T is the weighted charge per unit time;

[0201] is the first weight;

[0202] is the second weight;

[0203] is a first charge amount;

[0204] is the second charge amount.

[0205] It should be noted that the first weight and the second weight may be fixed, that is, the first weight and the second weight are fixed values ​​preset in advance. Specifically, when the first weight and the second weight are fixed, the specific values ​​of the first weight and the second weight are set according to actual needs, and are not limited in this embodiment. For example, in one possible implementation, the first weight and the second weight are the same, and the first weight and the second weight are both 0.5. For another example, in another possible implementation, the first weight is 0.4 and the second weight is 0.6.

[0206] Furthermore, the first weight and the second weight may also be dynamically updated. Furthermore, when the first weight and the second weight are dynamically updated, in a possible implementation, a method for determining the first weight at a current moment and the second weight at a current moment may include:

[0207] (1) Calculating a sum of a specified value and the number of batteries that have been charged at the reachable site within a third specified time period before the current moment; wherein the specified value is 1.

[0208] Specifically, the designated value and the third designated time period are set according to actual needs, and are not limited in this embodiment. For example, in one embodiment, the designated value is 1, and the third designated time period is 7 days before the current time.

[0209] Furthermore, the specified value and the sum of the number of batteries that have been charged at the reachable site within the third specified time period before the current moment can be calculated by the following formula:

[0210] ;

[0211] Wherein, n is the number of batteries that have been charged at the reachable site within the third specified time period before the current moment, that is, the number of batteries that have been charged at the reachable site within the third specified time period before the current moment.

[0212] (2) Determine the ratio of the number of batteries that have been charged at the reachable site within the third specified time period before the current moment to the sum value as the second weight at the current moment.

[0213] Specifically, in combination with the above description, the second weight can be calculated by the following formula:

[0214] ;

[0215] (3) The ratio of the specified value to the sum value is determined as the first weight at the current moment.

[0216] Specifically, in combination with the above description, the first weight can be calculated by the following formula:

[0217] ;

[0218] S704: Determine the charging time of the second type of battery according to the weighted charging capacity, the current capacity of the second type of battery, and the preset ready capacity.

[0219] Specifically, the difference between the preset ready power and the current power of the second type battery may be calculated, and then the ratio of the difference to the weighted charge capacity may be determined as the charging time of the second type battery.

[0220] As described above, the preset ready power is set according to actual needs and is not limited in this embodiment.

[0221] The method provided in this embodiment fits the function of the battery charge changing with time during the charging process from the vehicle dimension and the site dimension respectively. It can simultaneously consider the individual charging behavior of the battery and the overall charging performance of the site, and can combine the individual charging performance of the battery with the overall charging performance of the battery swap station. It can accurately analyze the differences between individual batteries and the overall charging environment, accurately determine the charging amount per unit time, and thus more accurately predict the charging time of the battery.

[0222] In addition, by assigning different weights to batteries and battery swap stations for weighted processing, we can flexibly respond to needs in different situations and improve the accuracy of predictions.

[0223] Furthermore, in the process of dynamically updating the weight, by calculating the sum of the designated value and the number of batteries that have been charged by the battery swap station in the third designated period before the current moment, the ratio of the number of batteries that have been charged by the battery swap station in the third designated period before the current moment to the sum is determined as the second weight at the current moment, and the ratio of the designated value to the sum is determined as the first weight at the current moment. In this way, the relative influence of the first weight (individual battery) and the second weight (overall charging trend) can be dynamically adjusted to ensure that the influence of the overall trend increases when the charging records are abundant, and when the charging records are few, more reliance is placed on the historical data of individual batteries to improve the accuracy of the prediction of charging time.

[0224] Corresponding to the aforementioned embodiment of a battery swap scheduling method for a battery swap vehicle, the present application also provides an embodiment of a battery swap scheduling device for a battery swap vehicle.

[0225] The present application discloses an embodiment of a battery swap scheduling device for a battery swap vehicle that can be applied on a server. The device embodiment can be implemented by software, or by hardware, or by a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, the processor of the server where it is located reads the corresponding computer program instructions in the non-volatile memory into the memory and runs them. From a hardware perspective, if Figure 8 As shown, it is a hardware structure diagram of the server where the battery swap scheduling device of the battery swap vehicle provided by this application is located, except Figure 8 In addition to the processor, memory, network interface, and non-volatile memory shown, the server where the device is located in the embodiment may also include other hardware according to the actual function of the battery swap scheduling device of the battery swap vehicle, which will not be elaborated here.

[0226] Fig. 9 This is a structural diagram of the first embodiment of the battery swap scheduling device for a battery swap vehicle provided by this application. Fig. 9 The device provided in this embodiment includes a determination module 910, a sorting module 920 and a processing module 930; wherein,

[0227] The determination module 910 is used to calculate, for each target vehicle indicated by the dispatch request, a first distance between the target vehicle and each target station within the coverage area of ​​the target vehicle and a travel time for the target vehicle to travel to the target station upon receiving the dispatch request;

[0228] The determination module 910 is further configured to determine the cruising range of the target vehicle at the current power level, and select, from the target sites, reachable sites that the target vehicle can reach before the power level is exhausted, according to the cruising range and the first distance between the target vehicle and the target sites.

[0229] The determination module 910 is further configured to determine the ready time of each battery in each reachable site, and determine the ready information of the battery when the target vehicle arrives based on the ready time of the battery and the travel time of the target vehicle to the reachable site;

[0230] The determination module 910 is further used to predict the battery replacement demand of the reachable site in the next time period by using the battery replacement demand prediction model, and determine the number of ready batteries that the reachable site is expected to hold when the target vehicle arrives based on the battery replacement demand and the readiness information of each battery when the target vehicle arrives;

[0231] The sorting module 920 is used to sort all reachable sites for each of the preset multiple sorting strategies according to at least one of the first distance between the target vehicle and each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries expected to be held by each reachable site when the target vehicle arrives, to obtain a sorting result corresponding to the sorting strategy; wherein the multiple sorting strategies use different information to sort all reachable sites from different aspects;

[0232] The processing module 930 is used to recommend a battery swap site for the target vehicle according to the sorting results corresponding to each sorting strategy, so that the target vehicle can go to the battery swap site for battery swap in the future.

[0233] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0234] Please continue to refer to Figure 8 The present application also provides a server, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any one of the methods provided in the first aspect of the present application are implemented.

[0235] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0236] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0237] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A battery swap scheduling method for a battery swap vehicle, characterized in that: The battery swap scheduling method for a battery swap vehicle is applied to a server, and the battery swap scheduling method for a battery swap vehicle includes: Upon receiving a dispatch request, for each target vehicle indicated by the dispatch request, calculating a first distance between the target vehicle and each target site within the coverage area of ​​the target vehicle, and a travel time for the target vehicle to travel to the target site; Determine the cruising range of the target vehicle at the current power level, and select, from the target sites, reachable sites that the target vehicle can reach before the power is exhausted, according to the cruising range and the first distance between the target vehicle and the target sites; Wherein, determining the cruising range of the target vehicle at the current power level includes: Acquire the driving data records of each controlled vehicle under the control of the server, and filter and block the driving data records to obtain multiple data blocks; wherein each data block records the driving data of a pair of vehicle battery combinations within a continuous time range; When there is a target data block of a designated vehicle battery combination within a designated time range in the data block, the target data block is input into a dynamic cruising range prediction model, so that the dynamic cruising range prediction model learns the relationship between power consumption and mileage, and outputs power consumption per kilometer; wherein the vehicle in the designated vehicle battery combination is the target vehicle, and the battery in the designated vehicle battery combination is the battery currently powering the target vehicle; When the target data block does not exist in the multiple data blocks, the multiple data blocks are simultaneously input into a static cruising range prediction model, so that the static cruising range prediction model learns the relationship between power consumption and mileage, and outputs power consumption per kilometer; Determine the cruising range of the target vehicle at the current power level according to the power consumption per kilometer, the current power level of the target vehicle, and the rated energy of the target vehicle; For each battery in each reachable site, determine the ready time of the battery, and determine the readiness information of the battery when the target vehicle arrives based on the ready time of the battery and the travel time of the target vehicle to the reachable site; The battery swap demand prediction model is used to predict the battery swap demand of the reachable station in the next time period, and according to the battery swap demand and the readiness information of each battery when the target vehicle arrives, the number of ready batteries that the reachable station is expected to hold when the target vehicle arrives is determined; For each of the preset multiple sorting strategies, all reachable sites are sorted according to at least one of the first distances of the target vehicle from each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries expected to be held by each reachable site when the target vehicle arrives, to obtain a sorting result corresponding to the sorting strategy; wherein the multiple sorting strategies use different information to sort all reachable sites from different aspects; According to the sorting results corresponding to each sorting strategy, a battery swapping station is recommended for the target vehicle so that the target vehicle can go to the battery swapping station for battery swapping in the future.

2. The method according to claim 1, characterized in that The step of sorting all reachable sites according to at least one of the following information: a first distance between the target vehicle and each reachable site, a travel time for the target vehicle to travel to each reachable site, a ready time of each battery in each reachable site, and a number of ready batteries that each reachable site is expected to have when the target vehicle arrives, comprises: According to the number of ready batteries that each reachable site is expected to have when the target vehicle arrives, the reachable sites are divided into first-category sites and second-category sites; wherein the first-category sites are sites that have ready batteries when the target vehicle arrives; and the second-category sites are sites that do not have ready batteries when the target vehicle arrives; According to at least one of the first distances of the target vehicle from each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries expected to be held by each reachable site when the target vehicle arrives, the first-category sites and the second-category sites are sorted respectively to obtain a first sorting result corresponding to the first-category sites and a second sorting result corresponding to the second-category sites; The second sorting result is concatenated onto the first sorting result to obtain a comprehensive sorting result.

3. The method according to claim 1, characterized in that According to the battery replacement demand and the readiness information of each battery when the target vehicle arrives, determining the number of ready batteries that the reachable station is expected to hold when the target vehicle arrives, including: According to the readiness information of each battery when the target vehicle arrives, the total number of ready batteries theoretically held by the reachable station when the target vehicle arrives is counted; According to the battery replacement demand, determine the number of batteries that are theoretically replaced at the reachable station before the target vehicle arrives; The difference between the total number and the swapped-out number is determined as the number of ready batteries that the reachable station is expected to hold when the target vehicle arrives.

4. The method according to claim 2, characterized in that: According to at least one of the first distances of the target vehicle from each reachable station, the travel time of the target vehicle to each reachable station, the ready time of each battery in each reachable station, and the number of ready batteries expected to be held by each reachable station when the target vehicle arrives, all reachable stations are sorted to obtain a sorting result corresponding to the sorting strategy, including: For each reachable site, based on a preset first score calculation rule, a first score of the reachable site is calculated according to the first distance between the target vehicle and the reachable site and the maximum distance between the target vehicle and each of the first distances between the reachable site and the target vehicle; wherein the first score calculation rule is based on the score of the distance calculation site, and the first score of each reachable site is negatively correlated with the first distance between the target vehicle and the reachable site; Based on a preset second score calculation rule, according to the number of ready batteries that the reachable site is expected to have when the target vehicle arrives, calculate a second score of the reachable site; wherein the second score calculation rule calculates the score of the site based on the number of ready batteries; The first score and the second score are weighted according to a first weight corresponding to the first scoring rule and a second weight corresponding to the second scoring rule to obtain a comprehensive score of the reachable site; The first category sites and the second category sites are sorted in descending order of comprehensive scores to obtain a first sorting result corresponding to the first category sites and a second sorting result corresponding to the second category sites.

5. The method according to claim 1, characterized in that The step of determining the ready time of each battery in each reachable site includes: For each ready first-type battery in each reachable site, determine the ready time of the first-type battery as the current time; For the unready second-type battery in each reachable site, the charging time of the second-type battery is predicted, and the ready time of the second-type battery is determined as the time arrived after the charging time from the current time.

6. The method according to claim 5, characterized in that The predicting the charging time of the second type of battery includes: For each unready second-category battery in each reachable site, a first function of the battery power changing with time during the charging process in the battery dimension and a second function of the battery power changing with time during the charging process in the site dimension are fitted according to the charging record of the second-category battery in the first specified time period before the current moment and the charging record of the reachable site in the second specified time period before the current moment respectively; Determine a first charge amount per unit time in a battery dimension by using the first function, and determine a second charge amount per unit time in a site dimension by using the second function; Performing weighted processing on the first charge amount and the second charge amount according to the first weight of the second type of battery at the current moment and the second weight of the reachable site at the current moment, to obtain a weighted charge amount per unit time; The charging time of the second type of battery is determined according to the weighted charging capacity, the current capacity of the second type of battery, and the preset ready capacity.

7. The method according to claim 2, characterized in that According to at least one of the first distances of the target vehicle from each reachable station, the travel time of the target vehicle to each reachable station, the ready time of each battery in each reachable station, and the number of ready batteries expected to be held by each reachable station when the target vehicle arrives, all reachable stations are sorted to obtain a sorting result corresponding to the sorting strategy, including: For the first type of sites, sort the first type of sites in ascending order of the time of arrival, to obtain a first sorting result corresponding to the first type of sites; For each second-category site, according to the travel time of the target vehicle to the second-category site and the ready time of each battery in the second-category site, the waiting time for the target vehicle to replace the battery at the second-category site is determined, and the sum of the waiting time and the travel time of the target vehicle to the second-category site is determined as the battery replacement time for the target vehicle to replace the battery at the second-category site; The second-category sites are sorted in ascending order of battery replacement consumption to obtain a second sorting result corresponding to the second-category sites.

8. The method according to claim 2, characterized in that: According to at least one of the first distances of the target vehicle from each reachable station, the travel time of the target vehicle to each reachable station, the ready time of each battery in each reachable station, and the number of ready batteries expected to be held by each reachable station when the target vehicle arrives, all reachable stations are sorted to obtain a sorting result corresponding to the sorting strategy, including: According to the first distance between the target vehicle and each reachable site, the first category sites and the second category sites are sorted in ascending order of distance to obtain a first sorting result corresponding to the first category sites and a second sorting result corresponding to the second category sites.

9. A battery swap dispatching device for a battery swapping vehicle, characterized in that: The device comprises a determination module, a sorting module and a processing module; wherein, The determination module is used to calculate, upon receiving a dispatch request, for each target vehicle indicated by the dispatch request, a first distance between the target vehicle and each target site within the coverage area of ​​the target vehicle, and a travel time for the target vehicle to travel to the target site; The determination module is further configured to determine a cruising range of the target vehicle at a current power level, and to select, from the target sites, reachable sites that the target vehicle can reach before the power level is exhausted, based on the cruising range and a first distance between the target vehicle and each target site; Wherein, determining the cruising range of the target vehicle at the current power level includes: Acquire the driving data records of each controlled vehicle under the control of the server, and filter and block the driving data records to obtain multiple data blocks; wherein each data block records the driving data of a pair of vehicle battery combinations within a continuous time range; When there is a target data block of a designated vehicle battery combination within a designated time range in the data block, the target data block is input into a dynamic cruising range prediction model, so that the dynamic cruising range prediction model learns the relationship between power consumption and mileage, and outputs power consumption per kilometer; wherein the vehicle in the designated vehicle battery combination is the target vehicle, and the battery in the designated vehicle battery combination is the battery currently powering the target vehicle; When the target data block does not exist in the multiple data blocks, the multiple data blocks are simultaneously input into a static cruising range prediction model, so that the static cruising range prediction model learns the relationship between power consumption and mileage, and outputs power consumption per kilometer; Determine the cruising range of the target vehicle at the current power level according to the power consumption per kilometer, the current power level of the target vehicle, and the rated energy of the target vehicle; The determination module is further used to determine the ready time of each battery in each reachable site, and determine the ready information of the battery when the target vehicle arrives based on the ready time of the battery and the travel time of the target vehicle to the reachable site; The determination module is further used to predict the battery replacement demand of the reachable site in the next time period by using the battery replacement demand prediction model, and determine the number of ready batteries that the reachable site is expected to hold when the target vehicle arrives based on the battery replacement demand and the readiness information of each battery when the target vehicle arrives; The sorting module is used to sort all reachable sites for each of the preset multiple sorting strategies according to at least one of the first distance between the target vehicle and each reachable site, the travel time of the target vehicle to each reachable site, the ready time of each battery in each reachable site, and the number of ready batteries expected to be held by each reachable site when the target vehicle arrives, to obtain a sorting result corresponding to the sorting strategy; wherein the multiple sorting strategies use different information to sort all reachable sites from different aspects; The processing module is used to recommend a battery swap site for the target vehicle according to the sorting results corresponding to each sorting strategy, so that the target vehicle can go to the battery swap site for battery swap in the future.

10. A server, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 8 when executing the program.

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