Battery swap station state-based battery swap guidance method and device, and medium

Abstract

The invention discloses a battery swap guidance method and device based on a battery swap station state and a medium. The method comprises the steps of determining available battery swap capacities corresponding to a plurality of prediction moments of a battery swap station; determining site information corresponding to the battery swap station according to the available battery swap capacity corresponding to the battery swap station at the prediction moment; acquiring a current geographic position corresponding to the user to be subjected to battery replacement, and determining a supply space corresponding to the user according to the current geographic position; and pushing the station information corresponding to each battery swap station in the supply space to the battery swap user so as to realize battery swap guidance for the battery swap user through the station information.

Description

[0001] This application claims priority based on the invention patent application filed with the Chinese Patent Office on December 29, 2023, with the application number 202311872323.5 and the invention title "A Battery Swapping Guidance Method, Device, and Medium Based on the Status of Battery Swap Stations". Technical Field

[0002] This application relates to the field of battery swapping technology, and specifically to a battery swapping guidance method, device, and medium based on the status of battery swap stations. Background Art

[0003] Battery swap stations are an important part of the charging infrastructure for electric vehicles and can provide fast and convenient battery charging services for electric vehicle users. However, in actual use, due to the limited number of battery swap stations and various problems that may occur during the battery swapping process, users may face problems such as long waiting times or inability to find available batteries. Therefore, it is necessary to provide accurate battery swapping guidance for users by real-time monitoring and analyzing the status information of battery swap stations to improve the user's battery swapping experience. Summary of the Invention

[0004] To solve the above problems, this application proposes a battery swapping guidance method based on the status of battery swap stations, including:

[0005] Determine the available battery swapping capacity of the battery swap station at several predicted times;

[0006] Determine the station information corresponding to the battery swap station according to the available battery swapping capacity of the battery swap station at the predicted time;

[0007] Obtain the current geographical location of the user waiting for battery swapping, and determine the supply space corresponding to the user according to the current geographical location;

[0008] Push the station information corresponding to each battery swap station within the supply space to the battery swapping user, so as to realize the battery swapping guidance for the battery swapping user through the station information.

[0009] By determining the available battery swapping capacity of the battery swap station at several predicted times in the embodiments of this application, users can understand the available battery swap stations and the available battery swapping capacity of the battery swap stations in advance, so as to better plan the battery swapping journey, reduce waiting time and uncertainty, and improve the overall battery swapping experience of users. By obtaining the current geographical location of the user, the supply space of the user can be determined, and the station information of the battery swap stations that meet the conditions can be pushed to the user, providing personalized battery swapping guidance for the user and improving user satisfaction.

[0010] To solve the above problems, this application proposes a battery swapping guidance method based on the status of battery swap stations, including:

[0011] Collect historical battery swapping data generated by each battery swapping station. For each battery swapping station, based on the historical battery swapping data, predict the battery swapping status of the battery swapping station at several future prediction times, and generate a battery swapping status curve corresponding to the battery swapping station; wherein, the historical battery swapping data includes the current time and the historical battery swapping locations, historical battery swapping times, and historical battery swapping amounts before the current time.

[0012] Obtain the battery swapping upper limit data corresponding to the battery swapping station at the prediction time, and determine the battery swapping capacity corresponding to the battery swapping station at the prediction time according to the battery swapping status curve and the battery swapping upper limit data.

[0013] Obtain the current battery bin situation of the battery swapping station, and generate site information corresponding to the battery swapping station according to the battery swapping capacity and the current battery bin situation.

[0014] Obtain the current geographical location of the user to be battery swapped, and determine the supply space corresponding to the user according to the current geographical location.

[0015] Push the site information corresponding to each battery swapping station within the supply space to the battery swapping user, so as to realize the battery swapping guidance for the battery swapping user through the site information.

[0016] In the embodiments of the present application, by predicting the battery swapping status and providing site information, users can understand the available battery swapping stations and the capacity of the battery swapping stations in advance, so as to better plan the battery swapping journey, reduce waiting time and uncertainty, and improve the overall battery swapping experience of users. By obtaining the current geographical location of the user, the supply space of the user can be determined, and the site information of the eligible battery swapping stations can be pushed to the user, providing personalized battery swapping guidance for the user and improving user satisfaction.

[0017] In one implementation manner of the present application, the method further includes:

[0018] Obtain the current battery bin situation and the current time of the battery swapping station; wherein, the current battery bin situation includes the situation of fully charged batteries and the situation of charging batteries.

[0019] Based on the battery swapping status data corresponding to the battery swapping station and the situation of fully charged batteries of the battery swapping station, determine the remaining available battery situation corresponding to the battery swapping station at the prediction time.

[0020] Obtain the time length from the current time to the prediction time, and update the available battery situation within the time length according to the situation of charging batteries to obtain the predicted battery bin situation corresponding to the battery swapping station at the prediction time.

[0021] Generate site information corresponding to the battery swapping station according to the battery swapping capacity and the predicted battery bin situation.

[0022] Preferably, according to the charging battery condition, update the available battery condition within the time length to obtain the predicted battery bin condition corresponding to the swap station at the predicted moment, specifically including:

[0023] Based on the charging battery condition of the swap station and the time length, determine the available battery condition of the newly charged batteries;

[0024] Based on the remaining available battery condition and the available battery condition of the newly charged batteries, determine the predicted battery bin condition corresponding to the swap station at the predicted moment.

[0025] The embodiment of the present application can accurately predict the available battery condition of the swap station at the future predicted moment through the fully charged batteries at the current moment and the battery swapping state data at the predicted moment. Moreover, considering the influence of the charging battery condition on the available battery condition within the time length from the current moment to the predicted moment, by determining the available battery condition of the newly charged batteries, the battery resource status of the swap station at the predicted moment can be more comprehensively reflected, thereby providing more reliable battery swapping guidance for users.

[0026] In an implementation manner of the present application, determining the available battery swapping capacity corresponding to the swap station at several predicted moments includes:

[0027] Collect the historical battery swapping data generated by each swap station. For each swap station, according to the historical battery swapping data, predict the battery swapping state of the swap station at several future predicted moments to generate the corresponding battery swapping state data of the swap station;

[0028] Obtain the battery swapping upper limit data corresponding to the swap station at the predicted moment. According to the battery swapping state data and the battery swapping upper limit data, determine the available battery swapping capacity corresponding to the swap station at the predicted moment;

[0029] Preferably, the historical battery swapping data includes the current moment, as well as the historical battery swapping location, historical battery swapping time, and historical battery swapping amount before the current moment. For each swap station, according to the historical battery swapping data, predict the battery swapping state of the swap station at several future predicted moments to generate the corresponding battery swapping state data of the swap station, specifically including:

[0030] Through a preset time series decomposition method, decompose the historical battery swapping data to obtain the decomposed time series components;

[0031] Use the time series components as training samples for model training to obtain an autoregressive moving average model;

[0032] Predict the battery swapping status of the battery swapping station at several future prediction times through the autoregressive moving average model, and obtain the predicted number of battery swaps corresponding to the prediction times.

[0033] Generate the battery swapping status data corresponding to the battery swapping station according to the predicted number of battery swaps corresponding to several future prediction times.

[0034] In the embodiment of the present application, by performing time series decomposition and model training on historical battery swapping data, the predicted number of battery swaps of the battery swapping station at several future times can be accurately predicted. Based on these prediction results, users can know the availability of the battery swapping station in advance, so as to provide accurate battery swapping guidance for users, reduce waiting time and the number of people queuing, and improve the battery swapping experience of users.

[0035] In an implementation manner of the present application, for each battery swapping station, predict the battery swapping status of the battery swapping station at several future prediction times according to the historical battery swapping data, and generate a battery swapping status curve corresponding to the battery swapping station, specifically including:

[0036] Perform time series decomposition on the historical battery swapping data through a preset time series decomposition method to obtain the decomposed time series components; wherein, the time series components include a trend component, a seasonal component, and a random component.

[0037] Use the time series components as training samples for model training to obtain an autoregressive moving average model.

[0038] Predict the battery swapping status of the battery swapping station at several future prediction times through the autoregressive moving average model, and obtain the predicted number of battery swaps corresponding to the prediction times.

[0039] Fit and generate a battery swapping status curve corresponding to the battery swapping station according to the predicted number of battery swaps corresponding to several future prediction times.

[0040] In the embodiment of the present application, by performing time series decomposition and model training on historical battery swapping data, the predicted number of battery swaps of the battery swapping station at several future times can be accurately predicted. Based on these prediction results, users can know the availability of the battery swapping station in advance, so as to provide accurate battery swapping guidance for users, reduce waiting time and the number of people queuing, and improve the battery swapping experience of users.

[0041] In an implementation manner of the present application, generate the battery swapping status data corresponding to the battery swapping station according to the predicted number of battery swaps corresponding to several future prediction times, specifically including:

[0042] Determine the current number of battery swaps corresponding to the battery swapping station at the current time according to the historical battery swapping data.

[0043] Determine a corresponding first time interval according to the current number of battery replacements; wherein, the first time interval is the interval between several future prediction moments, and the current number of battery replacements is negatively correlated with the first time interval;

[0044] Obtain the battery replacement status data corresponding to the battery replacement station according to the predicted number of battery replacements corresponding to several future prediction moments.

[0045] In the embodiment of the present application, the first time interval is determined according to the current number of battery replacements. The first time interval is used to determine how often to evaluate the battery replacement situation of the battery replacement station in the future, and the first time interval is negatively correlated with the current number of battery replacements. That is to say, the larger the current number of battery replacements of the battery replacement station at the current moment, the busier the battery replacement station is, and the more likely it is to have a shortage of battery supply. To provide a more accurate battery replacement status prompt for users, it is necessary to increase the prediction frequency. In this way, through reasonable time interval evaluation and preparation, the battery replacement station can provide a faster and more convenient battery replacement service and improve the accuracy of the prediction results.

[0046] In an implementation manner of the present application, obtaining the battery replacement status data corresponding to the battery replacement station according to the predicted number of battery replacements corresponding to several future prediction moments specifically includes:

[0047] For each prediction moment, determine the predicted number of battery replacements corresponding to the prediction moment, and respectively determine the second time interval corresponding to the predicted number of battery replacements; wherein, the predicted number of battery replacements is negatively correlated with the second time interval;

[0048] Adjust the time interval between any two prediction moments from the first time interval corresponding to the current number of battery replacements to the second time interval corresponding to the previous prediction moment among the two prediction moments to obtain several adjusted prediction moments;

[0049] Re-predict the battery replacement status of the battery replacement station at the adjusted several prediction moments to obtain the predicted number of battery replacements corresponding to the re-predicted prediction moments;

[0050] Regenerate the battery replacement status data corresponding to the battery replacement station according to the re-predicted predicted number of battery replacements.

[0051] In the embodiment of the present application, considering that the battery replacement situation is affected by various variables and uncertain factors, it is difficult to reflect the actual operation situation of the actual battery replacement station by evaluating the battery replacement situation of the battery replacement station at a fixed first time interval. Therefore, by adjusting the second time interval between several future prediction moments in real time according to the predicted number of battery replacements obtained by estimation, it can better adapt to the situation of battery replacement stations with different numbers of battery replacements and improve the accuracy of the estimation results.

[0052] In an implementation manner of the present application, obtaining the battery replacement upper limit data corresponding to the battery replacement station specifically includes:

[0053] Obtaining the environmental data corresponding to the battery replacement station on the battery replacement day when the prediction moment is located, and determining the battery replacement demand parameter corresponding to the battery replacement station according to the environmental data; wherein, the battery replacement demand parameter is used to evaluate the battery demand status of the user;

[0054] Compensating the battery replacement upper limit data according to the battery replacement demand parameter to obtain the compensated battery replacement upper limit data.

[0055] The embodiment of the present application takes into account the influence of the environment on the demand for battery replacement services by users. In the case of bad weather conditions, the battery replacement station may need to increase the battery inventory to meet the needs of users. In this way, by determining the battery replacement demand parameter corresponding to the battery replacement station through environmental data, and then compensating the battery replacement upper limit data of the battery replacement station according to the battery replacement demand parameter, it is possible to realize the advance scheduling and allocation of battery replacement resources, and ensure the normal operation and service quality of the battery replacement station.

[0056] In an implementation manner of the present application, obtaining the current geographical location corresponding to the user to be battery replaced, and determining the supply space corresponding to the user according to the current geographical location specifically includes:

[0057] Locating a number of historical battery replacement stations used by the user to be battery replaced according to the historical battery replacement locations, and determining the corresponding historical battery replacement times of each historical battery replacement station;

[0058] Selecting the designated historical battery replacement stations with the historical battery replacement times greater than a preset value from a number of historical battery replacement stations;

[0059] Obtaining the battery replacement station location corresponding to the designated historical battery replacement station, and determining the target battery replacement distance corresponding to the user to be battery replaced according to the battery replacement station location;

[0060] Obtaining the current geographical location corresponding to the user to be battery replaced, and generating the supply space corresponding to the user to be battery replaced according to the current geographical location and the target battery replacement distance.

[0061] The embodiment of the present application can determine the battery replacement stations that the user is more inclined to use by screening the designated historical battery replacement stations with the historical battery replacement times greater than a preset value, and determine the battery replacement range where each battery replacement station that the user goes to when replacing the battery is located through the battery replacement station location of the above-mentioned designated historical battery replacement stations, and then determine the target battery replacement distance that the user can receive. In this way, when determining the supply space according to the current geographical location of the user, the actual needs of the user can be taken into account, and the battery replacement stations within the distance range that the user can receive can be pushed to the user.

[0062] In one implementation of the present application, determining the target battery replacement distance corresponding to the user to be battery replaced according to the location of the battery replacement station specifically includes:

[0063] Generating a circumcircle where the specified historical battery replacement station is located according to the location of the specified historical battery replacement station;

[0064] Determining the radius corresponding to the circumcircle and using the radius as the target battery replacement distance of the user to be battery replaced.

[0065] In the embodiment of the present application, generating a circumcircle based on the location of the specified historical battery replacement station and determining the target battery replacement distance can predict that the user has a battery replacement demand within the circumcircle area. In this way, determining the target battery replacement distance of the user according to the radius of the circumcircle can recommend available battery replacement stations within the distance acceptable to the user, improving user satisfaction.

[0066] In one implementation of the present application, determining the available battery replacement capacity corresponding to the battery replacement station at the predicted moment according to the battery replacement status data and the battery replacement upper limit data specifically includes:

[0067] Determining the predicted number of battery replacements corresponding to the battery replacement station at several future predicted moments according to the battery replacement status data;

[0068] For each predicted moment, determining the difference between the battery replacement upper limit data and the predicted number of battery replacements and using the difference as the available battery replacement capacity corresponding to the battery replacement station at the predicted moment.

[0069] In the embodiment of the present application, predicting the future number of battery replacements according to the battery replacement status data can understand the battery replacement demand situation of the user in advance. Thus, according to the difference between the battery replacement upper limit data and the predicted number of battery replacements, a more accurate prediction of the available battery replacement capacity can be provided for the battery replacement station, avoiding the situation that the user waits too long or cannot complete the battery replacement due to insufficient available battery replacement capacity.

[0070] The embodiment of the present application provides a battery replacement guidance device based on the status of the battery replacement station, characterized in that the device includes:

[0071] At least one processor;

[0072] And a memory communicatively connected to the at least one processor;

[0073] Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0074] Collect the historical battery swapping data generated by each battery swapping station. For each battery swapping station, based on the historical battery swapping data, predict the battery swapping status of the battery swapping station at several future prediction times, and generate the battery swapping status data corresponding to the battery swapping station;

[0075] Obtain the battery swapping upper limit data corresponding to the battery swapping station at the prediction time. Based on the battery swapping status data and the battery swapping upper limit data, determine the available battery swapping capacity corresponding to the battery swapping station at the prediction time;

[0076] Determine the station information corresponding to the battery swapping station according to the available battery swapping capacity corresponding to the battery swapping station at the prediction time;

[0077] Obtain the current geographical location of the user waiting for battery swapping, and determine the supply space corresponding to the user according to the current geographical location;

[0078] Push the station information corresponding to each battery swapping station in the supply space to the battery swapping user, so as to realize the battery swapping guidance for the battery swapping user through the station information.

[0079] An embodiment of the present application provides a non-volatile computer storage medium, storing computer-executable instructions, and the computer-executable instructions are set as:

[0080] Collect the historical battery swapping data generated by each battery swapping station. For each battery swapping station, based on the historical battery swapping data, predict the battery swapping status of the battery swapping station at several future prediction times, and generate the battery swapping status data corresponding to the battery swapping station;

[0081] Obtain the battery swapping upper limit data corresponding to the battery swapping station at the prediction time. Based on the battery swapping status data and the battery swapping upper limit data, determine the available battery swapping capacity corresponding to the battery swapping station at the prediction time;

[0082] Determine the station information corresponding to the battery swapping station according to the available battery swapping capacity corresponding to the battery swapping station at the prediction time;

[0083] Obtain the current geographical location of the user waiting for battery swapping, and determine the supply space corresponding to the user according to the current geographical location;

[0084] Push the station information corresponding to each battery swapping station in the supply space to the battery swapping user, so as to realize the battery swapping guidance for the battery swapping user through the station information. Description of the Drawings

[0085] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0086] Figure 1 It is a schematic flowchart of a battery swapping guidance method based on the state of a battery swapping station provided by an embodiment of the present application;

[0087] Figure 2 It is a specific schematic flowchart of a battery swapping guidance method based on the state of a battery swapping station provided by an embodiment of the present application;

[0088] Figure 3 It is a schematic structural diagram of a battery swapping guidance device based on the state of a battery swapping station provided by an embodiment of the present application. Detailed implementation manners

[0089] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0090] The technical solutions provided by each embodiment of the present application will be described in detail below in conjunction with the drawings.

[0091] As Figure 1 shown, a battery swapping guidance method based on the state of a battery swapping station provided by an embodiment of the present application includes:

[0092] S101: Determine the available battery swapping capacity corresponding to the battery swapping station at the predicted moment.

[0093] Due to the limited driving range and long charging time of electric vehicles, battery replacement is a fast and convenient charging method. A battery swapping station usually has multiple sets of batteries. Users can remove the vehicle's battery and replace it with a fully charged battery pack to achieve the purpose of fast charging. Since the business of battery swapping stations is relatively busy, if the future battery swapping situation of the station cannot be provided to users, it may lead to a long waiting time for users due to insufficient available batteries. Therefore, to improve the customer experience, it is necessary to collect the historical battery swapping data generated by each battery swapping station. Among them, the historical battery swapping data includes the current time, as well as the historical battery swapping location, historical battery swapping time, and historical battery swapping amount before the current time. For each battery swapping station, based on the historical battery swapping data, the battery swapping status of the station at several future prediction times is predicted, and the corresponding battery swapping status data of the station is generated. The battery swapping status data is used to represent the expected number of battery swaps at the station at different future prediction times, including various visualization display methods, such as battery swapping status curves, battery swapping status data tables, bar charts, etc. The number of battery swaps refers to the number of battery swaps generated during the time period between the prediction time and the previous prediction time, that is, the total number of batteries that the battery swapping station needs to provide externally. The higher the number of battery swaps, the busier the period is indicated.

[0094] Due to the limited driving range and long charging time of electric vehicles, battery replacement is a fast and convenient charging method. A battery swapping station usually has multiple sets of batteries. Users can remove the vehicle's battery and replace it with a fully charged battery pack to achieve the purpose of fast charging. Since the business of battery swapping stations is relatively busy, if the future battery swapping situation of the station cannot be provided to users, it may lead to a long waiting time for users due to insufficient available batteries. Therefore, to improve the customer experience, it is necessary to collect the historical battery swapping data generated by each battery swapping station. Among them, the historical battery swapping data includes the current time, as well as the historical battery swapping location, historical battery swapping time, and historical battery swapping amount before the current time. For each battery swapping station, based on the historical battery swapping data, the battery swapping status of the station at several future prediction times is predicted, and the corresponding battery swapping status curve of the station is generated. The battery swapping status curve is used to represent the number of battery swaps at the station at different future prediction times. The number of battery swaps refers to the number of battery swaps generated during the time period between the prediction time and the previous prediction time. The higher the number of battery swaps, the busier the period is indicated.

[0095] Preferably, through a preset time series decomposition method, the historical battery swapping data is decomposed into time series components. Among them, the time series decomposition method can adopt an additive model, and the time series components include a trend component, a seasonal component, and a random component. The trend component represents the overall change trend of the battery swapping times, the seasonal component represents the periodic change of the battery swapping times in different seasons, and the random component represents the random fluctuations except for the trend and seasonality. The time series components corresponding to the above-mentioned collected historical battery swapping data are used as training samples for model training to obtain an Auto-Regressive Moving Average Model (ARMA). Through the ARMA autoregressive moving average model, based on the historical battery swapping data observed in the past, the battery swapping status of the battery swapping station at several future prediction times is predicted to obtain the predicted battery swapping times corresponding to the prediction times. According to the predicted battery swapping times corresponding to several future prediction times, the battery swapping status data corresponding to the battery swapping station is generated. This data can reflect the change situation of the battery swapping times of the battery swapping station in a future period of time.

[0096] Under normal circumstances, the time interval between different future prediction times is fixed, and generally, the predicted battery swapping times of the battery swapping station are predicted once per hour. However, when evaluating the operation of the battery swapping station at a fixed interval, the impact of the battery swapping demand on the prediction accuracy is not considered. The larger the number of battery swaps, the greater the battery swapping volume of the battery swapping station, and the greater the probability of insufficient battery supply. If the battery swapping times of the battery swapping station are still predicted at an interval of one hour, some users may refer to the predicted battery swapping times corresponding to the previous prediction time and choose to go to the battery swapping station for battery swapping. However, at the actual battery swapping time, due to the large time interval, the battery swapping station may be busy and the battery supply may be insufficient.

[0097] In one embodiment, to improve the accuracy of predicting the battery swapping times, it is necessary to determine the current battery swapping times corresponding to the battery swapping station according to the historical battery swapping data. According to the current battery swapping times, the corresponding first time interval is determined. According to the predicted battery swapping times corresponding to several future prediction times, the battery swapping status data corresponding to the battery swapping station is obtained. Among them, the first time interval is the interval between several future prediction times, and the current battery swapping times are negatively correlated with the first time interval. The higher the current battery swapping times, the busier the battery swapping station, and the greater the probability of insufficient battery supply at the battery swapping station. At this time, it is necessary to shorten the evaluation time interval, and the first time interval is smaller. At this time, the first time interval between different prediction times is a fixed value.

[0098] However, considering that the battery swapping situation is affected by multiple variables and uncertain factors, it is difficult to reflect the actual operation of the actual battery swapping station by evaluating the battery swapping situation of the battery swapping station at a fixed first time interval. Therefore, it is necessary to adjust the fixed first time interval according to the predicted number of battery swaps to adapt to the actual battery swapping situation of the battery swapping station.

[0099] In one embodiment, for each predicted time, determine the predicted number of battery swaps corresponding to the predicted time, and respectively determine the second time interval corresponding to the predicted number of battery swaps. Among them, each predicted time corresponds to a second time interval, and the predicted number of battery swaps is negatively correlated with the second time interval. Then, adjust the time interval between any two predicted times from the first time interval corresponding to the current number of battery swaps to the second time interval corresponding to the previous predicted time among the two predicted times, so as to obtain several adjusted predicted times. For example, for predicted times A and B, the first time interval between them is a fixed value, that is, one hour. However, after predicting the number of battery swaps corresponding to times A and B respectively according to the first time interval, determine the second time interval corresponding to the predicted number of battery swaps at time A, which is half an hour. At this time, it is necessary to adjust the first time interval between A and B to the second time interval. This can adjust the time interval according to the real-time operation of the battery swapping station and improve the prediction accuracy. After adjusting the time interval between the predicted times, it is necessary to re-predict the battery swapping status of the battery swapping station at the adjusted several predicted times to obtain the predicted number of battery swaps corresponding to the re-predicted predicted times. Then, according to the re-predicted number of battery swaps, re-generate the battery swapping status data corresponding to the battery swapping station.

[0100] The battery storage capacity of the battery swapping station is certain. After predicting the predicted number of battery swaps corresponding to different future times, it is necessary to obtain the battery swapping upper limit data of the battery swapping station at the predicted time, so as to determine the available battery swapping capacity of the battery swapping station at the predicted time according to the battery swapping status data and the battery swapping upper limit data. The available battery swapping capacity refers to the number of available batteries in the battery swapping station. Generally, the available batteries need to ensure that the battery power is full. It should be noted that the battery swapping upper limit data at a certain predicted time can be determined by the battery storage capacity in the battery swapping station and the total number of battery swaps before this predicted time. Each battery swap will reduce the battery storage capacity by one. Of course, when determining the battery swapping upper limit data, it is also necessary to consider the battery replenishment supply situation. When a battery is fully charged and incorporated into the available storage batteries, the battery swapping upper limit data also needs to be updated.

[0101] Specifically, obtain the current battery compartment situation of the battery swapping station and the current time. Among them, the current battery compartment situation includes the fully charged battery situation and the charging battery situation. A fully charged battery is a battery that can supply power externally. Based on the battery swapping status data corresponding to the battery swapping station and the fully charged battery situation of the battery swapping station, the remaining available battery situation corresponding to the battery swapping station at the predicted time can be determined. However, as time goes by, the batteries in the charging state will gradually become fully charged and return to the power supply state. Therefore, it is necessary to obtain the time length from the current time to the predicted time, and update the available battery situation within the time length according to the charging battery situation to obtain the predicted battery compartment situation corresponding to the battery swapping station at the predicted time. Generate the station information corresponding to the battery swapping station through the battery swapping capacity and the predicted battery compartment situation.

[0102] Preferably, based on the charging battery situation of the battery swapping station and the time length, determine the available battery situation of the newly fully charged batteries. For example, there are three batteries in the charging state at the battery swapping station at 12 o'clock, with battery powers of 80%, 70%, and 50% respectively. Charging the batteries at the preset charging power can achieve a charging speed of 10% per hour. Then, by 3 pm, there are two newly fully charged available batteries, and these two batteries can participate in battery swapping. Therefore, based on the remaining available battery situation and the available battery situation of the newly fully charged batteries, the predicted battery compartment situation corresponding to the battery swapping station at the predicted time can be determined.

[0103] In one embodiment, according to the battery swapping status data, determine the predicted number of battery swapping times corresponding to the battery swapping station at several future predicted times. Then, for each predicted time, determine the difference between the battery swapping upper limit data and the predicted number of battery swapping times, and use the difference as the available battery swapping capacity corresponding to the battery swapping station at the predicted time. It should be noted that the available battery swapping capacity refers to the number of batteries with a battery power of 100%.

[0104] It should be noted that in cold weather, the driving range of electric vehicles may decrease, resulting in users needing to change batteries more frequently. Therefore, in adverse weather conditions, the battery swap station may need to increase its battery inventory to meet user demand. Thus, it is necessary to adjust the battery swap upper limit data of the battery swap station in combination with the environmental data at the prediction moment, that is, obtain the environmental data corresponding to the battery swap station on the battery swap day when the prediction moment is located, and determine the battery swap demand parameter corresponding to the battery swap station according to the environmental data. The battery swap demand parameter is used to evaluate the battery demand status of users, and the relationship between the environmental data and the battery swap demand parameter can be evaluated through the actual operation of the battery swap station. After obtaining the battery swap demand parameter, it is necessary to compensate the battery swap upper limit data according to the battery swap demand parameter to obtain the compensated battery swap upper limit data. It should be noted that the battery swap demand parameter is mainly affected by temperature factors. When it is judged from the environmental data that the temperature is low, the corresponding battery swap demand parameter at this time is a value greater than 1. After compensating the battery swap upper limit data according to the battery swap demand parameter, the battery swap upper limit of the battery swap station can be correspondingly increased, so as to provide more available batteries for customers.

[0105] In addition, by predicting the status of the battery swap station, potential problems and faults can be discovered in a timely manner. For example, if the prediction result shows that the number of battery swaps at a certain battery swap station abnormally decreases at a certain future prediction moment, it may mean that there is a fault in the equipment of the battery swap station or the battery supply is insufficient. Managers can take timely measures to solve the problem and ensure the normal operation of the battery swap station.

[0106] S102: Determine the site information corresponding to the battery swap station according to the available battery swap capacity corresponding to the battery swap station at the prediction moment.

[0107] After predicting the available battery swap capacity corresponding to several future moments, if the above information is to be pushed to users, it is also necessary to combine the current situation of the battery swap station at the current moment for users to understand the charging situation of the current battery compartment, estimate the available battery status by themselves, and select the most suitable battery swap time for themselves. Therefore, it is necessary to generate the site information corresponding to the battery swap station according to the available battery swap capacity corresponding to the battery swap station at the prediction moment.

[0108] S103: Obtain the current situation of the battery compartments of the battery swap station, and generate the site information corresponding to the battery swap station according to the battery swap capacity and the current situation of the battery compartments.

[0109] After predicting the battery swap capacity corresponding to several future moments, if the above information is to be pushed to users, it is also necessary to combine the current situation of the battery swap station at the current moment for users to understand the charging situation of the current battery compartment, estimate the available battery status by themselves, and select the most suitable battery swap time for themselves. Therefore, it is necessary to obtain the current situation of the battery compartments of the battery swap station. The current situation of the battery compartments includes the current power of each available battery, and generate the site information corresponding to the battery swap station according to the battery swap capacity and the current situation of the battery compartments.

[0110] S103: Obtain the current geographical location corresponding to the user to be battery-swapped, and determine the supply space corresponding to the user according to the current geographical location.

[0111] Considering that some users have fixed usage habits. For example, they are accustomed to going to a battery swap station at a relatively short distance or a battery swap station with more affordable prices for battery swapping. Therefore, when pushing battery swap stations to the user to be battery-swapped, it is necessary to analyze the range of the user's frequently used battery swap stations in combination with their historical battery swapping data. In this way, when pushing battery swap stations according to the user's current location, information on battery swap stations within the acceptable distance can be provided.

[0112] In one embodiment, locate several historical battery swap stations used by the user to be battery-swapped according to the historical battery swapping locations, and determine the corresponding historical battery swapping times for each historical battery swap station. However, some of the above historical battery swap stations may not be the frequently used battery swap stations of the battery-swapping user, but only battery swapping behaviors occur because the user to be battery-swapped occasionally arrives at a certain location. Therefore, after determining the corresponding historical battery swapping times for each historical battery swap station, it is necessary to screen out the designated historical battery swap stations with historical battery swapping times greater than the preset value from several historical battery swap stations. The designated historical battery swap stations obtained at this time are the frequently used battery swap stations of the user to be battery-swapped. It is necessary to obtain the location of the designated historical battery swap station, and determine the target battery swapping distance corresponding to the user to be battery-swapped according to the location of the battery swap station. The target battery swapping distance is the distance of the battery swap station that the user can accept when swapping batteries determined according to the battery swapping habits of the user to be battery-swapped. Obtain the current geographical location corresponding to the user to be battery-swapped. According to the current geographical location and the target battery swapping distance, that is, with the current geographical location as the center and the target battery swapping distance as the radius, the range where the generated circle is located at this time is the supply space corresponding to the user to be battery-swapped. Subsequently, when pushing battery swap station information to the user, it is necessary to refer to the situation of battery swap stations within this supply space to recommend battery swap stations to the user to be battery-swapped.

[0113] Among them, when determining the target battery swapping distance, in order to expand the screening range of battery swap stations as much as possible, it is necessary to generate a supply space with as large a range as possible according to the designated historical battery swap station. Therefore, according to the location of the designated historical battery swap station, generate the circumscribed circle where the designated historical battery swap station is located, and then determine the radius corresponding to the circumscribed circle, and use the radius as the target battery swapping distance of the user to be battery-swapped.

[0114] S104: Push the station information corresponding to each battery swap station within the supply space to the user to be battery-swapped, so as to realize the battery swapping guidance for the user to be battery-swapped through the station information.

[0115] After determining the supply space corresponding to the user to be battery-swapped, it is necessary to push the site information corresponding to each battery swap station in the supply space to the battery-swapping user through methods such as the application on the mobile terminal and the in-vehicle application. In this way, after receiving the site information, the battery-swapping user can select a battery swap station with sufficient battery swap capacity and a relatively reasonable distance according to the site information for battery swapping. Thus, the battery swapping guidance for the battery-swapping user is realized, the battery swapping waiting time of the user is reduced, and the user experience is improved.

[0116] Figure 2 A battery swapping guidance method based on the state of the battery swap station provided by an embodiment of the present application includes:

[0117] S201: Collect historical battery swapping data generated by each battery swap station. For each battery swap station, predict the battery swapping state of the battery swap station at several future prediction moments according to the historical battery swapping data, and generate a battery swapping state curve corresponding to the battery swap station; wherein, the historical battery swapping data includes the current moment, the historical battery swapping location, historical battery swapping time, and historical battery swapping amount before the current moment.

[0118] S202: Obtain the battery swapping upper limit data corresponding to the battery swap station at the prediction moment, and determine the battery swapping capacity corresponding to the battery swap station at the prediction moment according to the battery swapping state curve and the battery swapping upper limit data.

[0119] S203: Obtain the current battery bin situation of the battery swap station, and generate site information corresponding to the battery swap station according to the battery swapping capacity and the current battery bin situation.

[0120] S204: Obtain the current geographical location corresponding to the user to be battery-swapped, and determine the supply space corresponding to the user according to the current geographical location.

[0121] S205: Push the site information corresponding to each battery swap station in the supply space to the battery-swapping user, so as to realize the battery swapping guidance for the battery-swapping user through the site information.

[0122] The above is the method embodiment proposed by the present application. Based on the same idea, some embodiments of the present application also provide the device and non-volatile computer storage medium corresponding to the above method.

[0123] Figure 3 A structural schematic diagram of a battery swapping guidance device based on the state of the battery swap station provided by an embodiment of the present application. As Figure 3 shown, it includes:

[0124] Collect historical battery swapping data generated by each battery swap station. For each battery swap station, predict the battery swapping state of the battery swap station at several future prediction moments according to the historical battery swapping data, and generate battery swapping state data corresponding to the battery swap station.

[0125] Obtain the battery replacement upper limit data corresponding to the battery replacement station at the prediction moment, and determine the available battery replacement capacity corresponding to the battery replacement station at the prediction moment according to the battery replacement status data and the battery replacement upper limit data;

[0126] Determine the station information corresponding to the battery replacement station according to the available battery replacement capacity corresponding to the battery replacement station at the prediction moment;

[0127] Obtain the current geographical location of the user to be battery replaced, and determine the supply space corresponding to the user according to the current geographical location;

[0128] Push the station information corresponding to each battery replacement station in the supply space to the battery replacement user, so as to realize the battery replacement guidance for the battery replacement user through the station information.

[0129] An embodiment of the present application provides a non-volatile computer storage medium, storing computer-executable instructions, and the computer-executable instructions are set as:

[0130] Collect the historical battery replacement data generated by each battery replacement station. For each battery replacement station, predict the battery replacement status of the battery replacement station at several future prediction moments according to the historical battery replacement data, and generate the battery replacement status data corresponding to the battery replacement station;

[0131] Obtain the battery replacement upper limit data corresponding to the battery replacement station at the prediction moment, and determine the available battery replacement capacity corresponding to the battery replacement station at the prediction moment according to the battery replacement status data and the battery replacement upper limit data;

[0132] Determine the station information corresponding to the battery replacement station according to the available battery replacement capacity corresponding to the battery replacement station at the prediction moment;

[0133] Obtain the current geographical location of the user to be battery replaced, and determine the supply space corresponding to the user according to the current geographical location;

[0134] Push the station information corresponding to each battery replacement station in the supply space to the battery replacement user, so as to realize the battery replacement guidance for the battery replacement user through the station information.

[0135] Each embodiment in the present application is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0136] The device and medium provided by the embodiment of the present application correspond one-to-one with the method. Therefore, the device and medium also have beneficial technical effects similar to the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be repeated here.

[0137] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0138] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the processes and / or blocks Figure 1 one or more of the blocks.

[0139] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more of the processes and / or blocks Figure 1 one or more of the blocks.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the processes and / or blocks Figure 1 one or more of the blocks.

[0141] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0142] The memory may include non-permanent memory in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0143] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0144] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0145] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for guiding battery swapping based on the state of a battery swapping station, characterized in that, The method includes: Determining the available battery replacement capacity of the battery replacement station at a plurality of predicted times; Determining the station information corresponding to the battery replacement station according to the available battery replacement capacity of the battery replacement station at the predicted time; Obtaining the current geographical location of the user to be battery replaced, and determining the supply space corresponding to the user according to the current geographical location; Pushing the station information corresponding to each battery replacement station within the supply space to the battery replacement user, so as to implement battery replacement guidance for the battery replacement user through the station information.

2. The method for guiding battery swapping based on the state of a battery swapping station according to claim 1, wherein The method further includes: Obtaining the current battery bin situation and the current time of the battery replacement station; wherein, the current battery bin situation includes the situation of fully charged batteries and the situation of charging batteries; Based on the battery replacement status data corresponding to the battery replacement station and the situation of fully charged batteries of the battery replacement station, determining the remaining available battery situation of the battery replacement station at the predicted time; Obtaining the time length from the current time to the predicted time, and updating the available battery situation within the time length according to the situation of charging batteries, so as to obtain the predicted battery bin situation of the battery replacement station at the predicted time; Generating the station information corresponding to the battery replacement station according to the battery replacement capacity and the predicted battery bin situation; Preferably, updating the available battery situation within the time length according to the situation of charging batteries to obtain the predicted battery bin situation of the battery replacement station at the predicted time specifically includes: Determining the situation of newly charged available batteries based on the situation of charging batteries of the battery replacement station and the time length; Based on the remaining available battery situation and the situation of newly charged available batteries, determining the predicted battery bin situation of the battery replacement station at the predicted time.

3. The method for guiding battery swapping based on the state of a battery swapping station according to claim 1, wherein Determining the available battery replacement capacity of the battery replacement station at a plurality of predicted times includes: Collecting the historical battery replacement data generated by each battery replacement station, and for each battery replacement station, predicting the battery replacement status of the battery replacement station at a plurality of future predicted times according to the historical battery replacement data, and generating the battery replacement status data corresponding to the battery replacement station; Obtaining the battery replacement upper limit data of the battery replacement station at the predicted time, and determining the available battery replacement capacity of the battery replacement station at the predicted time according to the battery replacement status data and the battery replacement upper limit data; Preferably, the historical battery replacement data includes the current time, the historical battery replacement location, the historical battery replacement time and the historical battery replacement amount before the current time. For each battery replacement station, predicting the battery replacement status of the battery replacement station at a plurality of future predicted times according to the historical battery replacement data, and generating the battery replacement status data corresponding to the battery replacement station specifically includes: Performing time series decomposition on the historical battery replacement data through a preset time series decomposition method to obtain the decomposed time series components; Using the time series components as training samples for model training to obtain an autoregressive moving average model; Predicting the battery replacement status of the battery replacement station at a plurality of future predicted times through the autoregressive moving average model to obtain the predicted battery replacement times corresponding to the predicted time; Generate the battery swapping status data corresponding to the battery swapping station according to the predicted battery swapping times corresponding to a plurality of future prediction times.

4. The method for guiding battery swapping based on the state of a battery swapping station according to claim 3, wherein Generating the battery swapping status data corresponding to the battery swapping station according to the predicted battery swapping times corresponding to a plurality of future prediction times specifically includes: Determine the current battery swapping times corresponding to the battery swapping station at the current time according to the historical battery swapping data; Determine the corresponding first time interval according to the current battery swapping times; wherein, the first time interval is the interval between a plurality of future prediction times, and the current battery swapping times is negatively correlated with the first time interval; Obtain the battery swapping status data corresponding to the battery swapping station according to the predicted battery swapping times corresponding to a plurality of future prediction times.

5. A method for guiding battery swapping based on the state of a battery swapping station according to claim 4, wherein Obtaining the battery swapping status data corresponding to the battery swapping station according to the predicted battery swapping times corresponding to a plurality of future prediction times specifically includes: For each prediction time, determine the predicted battery swapping times corresponding to the prediction time, and respectively determine the corresponding second time interval of the predicted battery swapping times; wherein, the predicted battery swapping times is negatively correlated with the second time interval; Adjust the time interval between any two prediction times from the first time interval corresponding to the current battery swapping times to the second time interval corresponding to the previous prediction time among the two prediction times to obtain the adjusted plurality of prediction times; Re-predict the battery swapping status of the battery swapping station at the adjusted plurality of prediction times to obtain the predicted battery swapping times corresponding to the re-predicted prediction times; Regenerate the battery swapping status data corresponding to the battery swapping station according to the re-predicted predicted battery swapping times.

6. The method for guiding battery swapping based on the state of a battery swapping station according to claim 3, wherein Obtain the battery swapping upper limit data corresponding to the battery swapping station, specifically including: Obtain the environmental data corresponding to the battery swapping station on the battery swapping day where the prediction time is located, and determine the battery swapping demand parameter corresponding to the battery swapping station according to the environmental data; wherein, the battery swapping demand parameter is used to evaluate the battery demand status of users; Compensate the battery swapping upper limit data according to the battery swapping demand parameter to obtain the compensated battery swapping upper limit data.

7. A battery swapping guidance method based on the state of a battery swapping station according to claim 1, wherein, Obtain the current geographical location of the user to be battery swapped, and determine the supply space corresponding to the user according to the current geographical location, specifically including: Locate a plurality of historical battery swapping stations used by the user to be battery swapped according to the historical battery swapping locations, and determine the corresponding historical battery swapping times of each historical battery swapping station; Screen out the designated historical battery swapping stations from the plurality of historical battery swapping stations, where the historical battery swapping times are greater than a preset value; Obtain the location of the designated historical battery swapping station, and determine the target battery swapping distance corresponding to the user to be battery swapped according to the location of the battery swapping station; Obtain the current geographical location of the user to be battery swapped, and generate the supply space corresponding to the user to be battery swapped according to the current geographical location and the target battery swapping distance.

8. The method for guiding battery swapping based on the status of a battery swapping station according to claim 7, wherein Determine the target battery swapping distance corresponding to the user to be battery swapped according to the location of the battery swapping station, specifically including: Generate a circumcircle where the designated historical battery swapping station is located according to the location of the designated historical battery swapping station; Determine the radius corresponding to the circumcircle, and use the radius as the target battery swapping distance of the user to be battery swapped.

9. The method for guiding battery swapping based on the state of a battery swapping station according to claim 3, wherein Determine the available battery swapping capacity corresponding to the battery swapping station at the predicted moment according to the battery swapping status data and the battery swapping upper limit data, specifically including: Determine the predicted number of battery swapping times corresponding to the battery swapping station at a plurality of future predicted moments according to the battery swapping status data; For each predicted moment, determine the difference between the battery swapping upper limit data and the predicted number of battery swapping times, and use the difference as the available battery swapping capacity corresponding to the battery swapping station at the predicted moment.

10. A battery swapping guidance device based on the state of a battery swapping station, characterized in that, The device includes: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a battery swapping guidance method based on the status of a battery swapping station according to any one of claims 1-9.

11. A non-volatile computer storage medium stores computer-executable instructions, characterized in that, The computer-executable instructions are set as: A battery swapping guidance method based on the status of a battery swapping station according to any one of claims 1-9.

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