Battery swap scheduling method, battery swap station and vehicle

Abstract

The invention provides a battery swap scheduling method, a battery swap station and a vehicle, and relates to the technical field of battery scheduling, and the method comprises the steps: obtaining the vehicle state of a target vehicle; predicting a battery replacement demand according to the vehicle state; determining the battery supply capability of the battery swap station according to the battery state of the battery in the battery swap station; according to the battery replacement demand and the battery supply capacity, a battery replacement scheduling strategy of the target vehicle is determined, the battery replacement scheduling strategy comprises a battery replacement sequence strategy and a battery replacement time strategy, it is guaranteed that a battery replacement station can formulate the battery replacement scheduling strategy in advance according to the state of the target vehicle, the battery replacement efficiency is improved, and battery resources are reasonably allocated.

Description

Technical Field

[0001] The present invention relates to the field of battery scheduling technology, and in particular to a battery swap scheduling method, a battery swap station and a vehicle. Background Art

[0002] As the number of electric vehicles continues to grow, the problem of insufficient driving range has become increasingly prominent and has gradually become a key bottleneck affecting their efficient operation. Due to the development limitations of current battery technology, electric vehicles require a long time to charge and cannot be quickly recharged in a short time like gasoline vehicles. Therefore, replacing batteries for electric vehicles has become a major solution for achieving rapid energy replenishment.

[0003] In the related technology, the battery swap station can only handle the battery swap request of one electric vehicle at a time, and cannot serve multiple vehicles waiting for battery swap at the same time. The operation of the battery swap station is relatively passive, and it can only perform on-site dispatch management for vehicles that have entered the battery swap station, but lacks an effective strategy that can actively respond to and optimize the dispatch of electric vehicles with battery swap needs. Summary of the invention

[0004] The problem solved by the present invention is how to actively formulate a battery replacement strategy based on demand.

[0005] In order to solve the above problems, the present invention provides a battery swap scheduling method, a battery swap station and a vehicle.

[0006] In a first aspect, the present invention provides a battery swap scheduling method, comprising:

[0007] Get the vehicle status of the target vehicle;

[0008] predicting the need for battery replacement based on the vehicle status;

[0009] Determining the battery supply capacity of the battery swap station according to the battery status of the batteries in the battery swap station;

[0010] The battery replacement scheduling strategy of the target vehicle is determined according to the battery replacement demand and the battery supply capacity, wherein the battery replacement scheduling strategy includes a battery replacement sequence strategy and a battery replacement time strategy.

[0011] Optionally, the vehicle status includes current coordinates of the target vehicle, historical power consumption of the target vehicle, and destination coordinates of the target vehicle.

[0012] Optionally, before predicting the battery replacement demand according to the vehicle state, the method further includes:

[0013] Build a training set based on destinations and driving routes;

[0014] Build a route planning text template;

[0015] Based on the route planning text template, the artificial intelligence model is supervised and fine-tuned on the driving route task through the training set to obtain a route planning model, wherein the route planning model is used to plan the driving route of the target vehicle to obtain the target route.

[0016] Optionally, predicting the battery replacement demand according to the vehicle state includes:

[0017] Obtaining a target route according to the current coordinates and the destination coordinates;

[0018] According to the historical power consumption, predicting the target route to obtain a power exhaustion point;

[0019] The battery replacement demand is predicted based on the power depletion point and the destination coordinates.

[0020] Optionally, the vehicle state further includes the current power consumption of the target vehicle during its current trip, and before predicting the battery replacement demand according to the power exhaustion point and the destination coordinates, further includes:

[0021] Determining a corresponding sliding window according to the route distance of the target route, wherein the sliding window is used to represent a range that moves with the current position of the target vehicle, and the range is determined according to at least one of a preset duration and a preset distance;

[0022] Assigning a first power consumption weight to the current power consumption in the sliding window to obtain a first weighted power consumption;

[0023] Assigning a second power consumption weight to the historical power consumption in the sliding window to obtain a second weighted power consumption, wherein the sum of the first power consumption weight and the second power consumption weight is 1;

[0024] Processing the first weighted power consumption and the second weighted power consumption by a recursive neural network to obtain the future power consumption of the target vehicle;

[0025] The power depletion point is determined based on the future power consumption.

[0026] Optionally, the battery replacement demand includes a battery replacement time, and the predicting the battery replacement demand according to the power exhaustion point and the destination coordinates includes:

[0027] When the power exhaustion point is before the destination coordinates, taking the power exchange station within the remaining distance as the target power exchange station, wherein the remaining distance includes the distance between the power exhaustion point and the current position;

[0028] When the power exhaustion point is located at or after the destination coordinates, starting from the battery swap station corresponding to the shortest detour distance, a preset number of battery swap stations are selected in ascending order of the detour distance as the target battery swap station, wherein the detour distance includes the shortest driving distance between the battery swap station and the driving route;

[0029] Based on the target coordinates of the target battery swap station, the time when the target vehicle arrives at the target coordinates is determined as the battery swap time.

[0030] Optionally, determining the battery supply capacity of the battery swap station according to the battery status of the batteries in the battery swap station includes:

[0031] Determine a current battery swap station among all the target battery swap stations, and obtain the battery status of the battery in the current battery swap station, wherein the battery status includes a first battery model, a current battery charge, and a battery charging power;

[0032] Determine, according to the battery status, a battery that meets the battery replacement standard within each preset time period as a target battery;

[0033] The battery supply capacity is determined according to the number of the target batteries.

[0034] Optionally, determining the battery replacement scheduling strategy of the target vehicle according to the battery replacement demand and the battery supply capacity includes:

[0035] Determine the target battery within the first time period according to the second battery model, wherein the second battery model includes the battery model of the target vehicle, and the first time period includes the preset time period in which the battery replacement time is located;

[0036] When there are multiple target vehicles, each target vehicle is matched with the corresponding target battery, and the time and order of arrival of the target vehicles at the battery swap station are used as the battery swap scheduling strategy.

[0037] In a second aspect, the present invention further provides a battery swap station, comprising a prediction module and a first radio frequency module;

[0038] The first radio frequency module is used to exchange information with a second radio frequency module disposed in the target vehicle to obtain the vehicle status of the target vehicle;

[0039] The prediction module is used to predict the battery replacement demand based on the vehicle status; determine the battery supply capacity of the battery replacement station based on the battery status of the batteries in the battery replacement station; and determine the battery replacement scheduling strategy of the target vehicle based on the battery replacement demand and the battery supply capacity, wherein the battery replacement scheduling strategy includes a battery replacement sequence strategy and a battery replacement time strategy.

[0040] In a third aspect, the present invention further provides a vehicle, comprising a second radio frequency module, a driving control module, a braking module and a vehicle controller;

[0041] The second radio frequency module is used to exchange information with the first radio frequency module provided in the battery swap station, upload the vehicle status and receive the battery swap scheduling instruction, wherein the battery swap scheduling instruction is generated according to the battery swap scheduling strategy, and the battery swap scheduling strategy is obtained by the battery swap scheduling method of the first aspect;

[0042] The driving control module is used to control the vehicle to travel to a predetermined position according to the battery swap scheduling instruction, switch the gear of the vehicle to a preset gear, and control the braking module to release the braking force;

[0043] The vehicle controller is used to switch the vehicle state to the battery replacement state after the braking module releases the braking force, and to feed back the battery replacement state switching completion information to the battery replacement station through the second radio frequency module, so that the battery replacement station can perform a battery replacement operation on the vehicle.

[0044] The beneficial effects of the battery swap scheduling method of the present invention are:

[0045] After determining the specific battery swap demand and battery supply capacity, match the battery swap demand with the available batteries to obtain the battery swap sequence strategy and battery swap time strategy, thereby obtaining the battery swap scheduling strategy and realizing intelligent battery scheduling and resource optimization. By analyzing the status information of the target vehicle, the specific situation of the target vehicle can be mastered. It can provide an accurate data basis for the subsequent prediction of battery swap demand and be used to plan the optimal battery swap strategy in advance. Based on the understanding of the vehicle status, it is predicted that the target vehicle will need to swap batteries and the possible time point. Be prepared before the actual demand occurs, actively arrange battery swap resources, reduce user waiting time, and help balance the workload of each battery swap station and improve overall operational efficiency. Evaluate the battery supply capacity in the battery swap station, while meeting all current battery swap requests, and reserve enough spare batteries to cope with future battery swap needs. Reduce the risk of service interruption due to insufficient batteries, and ensure the stability and reliability of battery swap services. Combining battery swap demand and battery supply capacity, a battery swap scheduling strategy that meets both individual vehicle needs and global optimization can be formulated. Ensure that the battery swap station can formulate a battery swap scheduling strategy in advance according to the status of the target vehicle, improve the battery swap efficiency, and rationally allocate battery resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a flow chart of a battery swap scheduling method according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of a detailed flow chart of step S200 of the battery swap scheduling method according to an embodiment of the present invention;

[0048] Figure 3 This is a flow chart of the battery swap scheduling method before step S230 according to an embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of a detailed flow chart of step S230 of the battery swap scheduling method according to an embodiment of the present invention;

[0050] Figure 5 This is an example diagram of a battery swap station according to an embodiment of the present invention;

[0051] Figure 6 An exemplary diagram of a vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0053] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0054] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0055] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0056] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes and are not intended to limit the scope of these messages or information.

[0057] In response to the problems existing in the above-mentioned related technologies, this embodiment provides a battery swap scheduling method, a battery swap station and a vehicle.

[0058] like Figure 1 As shown, an embodiment of the present invention provides a battery swap scheduling method, including:

[0059] Step S100, obtaining the vehicle status of the target vehicle.

[0060] In one embodiment, the target vehicle refers to a vehicle that can communicate with the battery swap station. When the vehicle communicates with the battery swap station, the vehicle can upload the vehicle status (e.g., battery status) so that the battery swap station can obtain the current vehicle status of the vehicle, thereby determining whether the vehicle needs to be replaced in a short time based on the vehicle status. If the vehicle needs to be replaced in a short time, the vehicle is used as the target vehicle, and the specific battery replacement demand is predicted based on the vehicle status.

[0061] The target vehicle can also represent a vehicle with a battery replacement demand. In this embodiment, only vehicles with a battery replacement demand are considered as target vehicles. The target vehicle uploads the vehicle status so that the battery replacement station can determine the vehicle status in the future based on the current vehicle status (such as battery status) and determine the specific battery replacement demand.

[0062] Step S200, predicting the battery replacement demand based on the vehicle status.

[0063] In one embodiment, the battery swapping demand of a target vehicle in the future is predicted based on the vehicle status to achieve prediction of specific battery swapping demand, for example, prediction of the time node at which the target vehicle needs to swap batteries, prediction of the location at which the target vehicle needs to swap batteries, etc., and the battery swapping station to provide battery swapping services is determined based on the specific battery swapping demand for more accurate matching with the battery swapping station.

[0064] Step S300, determining the battery supply capacity of the battery swap station according to the battery status of the batteries in the battery swap station.

[0065] In one embodiment, the load that the battery swap station can bear in each time period is determined based on the battery status (e.g., power level and charging status) of the batteries in the battery swap station, thereby measuring the battery supply capacity for better battery allocation, wherein the load includes the maximum number of battery swaps that the battery swap station can perform at the same time and / or the maximum charging power that it can bear. The target vehicle's entry time point is constrained by the maximum number of battery swaps, and the target vehicle's entry time period in the future is constrained by the maximum charging power.

[0066] Step S400: Determine a battery replacement scheduling strategy for the target vehicle based on the battery replacement demand and the battery supply capacity, wherein the battery replacement scheduling strategy includes a battery replacement sequence strategy and a battery replacement time strategy.

[0067] After determining the specific battery swapping needs and battery supply capacity, these needs are matched with the available batteries to develop a scheduling strategy including the order and time of battery swapping, and to achieve intelligent battery scheduling and resource optimization. By analyzing the status information of the target vehicle, we can fully understand the specific situation of each vehicle, provide a data basis for predicting battery swapping needs, and plan the optimal battery swapping strategy in advance. Based on the understanding of the vehicle status, it is possible to predict when the vehicle will need to swap batteries and the possible time points. This allows preparations to be made before the actual demand occurs, proactively arrange battery swapping resources, reduce user waiting time, and help balance the workload of each battery swapping station and improve operational efficiency.

[0068] At the same time, by evaluating the battery supply capacity of each battery swap station, it is ensured that not only all current battery swap requests can be met, but also enough spare batteries can be reserved to cope with future battery swap needs, reducing the risk of service interruption due to insufficient batteries, and ensuring the stability and reliability of battery swap services. Combining battery swap demand and battery supply capacity, a battery swap scheduling strategy can be formulated that meets the individual needs of vehicles while taking into account global optimization. In this way, the battery swap station can formulate a battery swap scheduling strategy in advance according to the status of the target vehicle, improve the efficiency of battery swapping, and reasonably allocate battery resources, ultimately achieving the effect of actively determining the battery scheduling strategy according to demand, greatly improving the satisfaction of electric vehicle users and the overall operating efficiency.

[0069] Optionally, the vehicle status includes current coordinates of the target vehicle, historical power consumption of the target vehicle, and destination coordinates of the target vehicle.

[0070] Optionally, before predicting the battery replacement demand according to the vehicle state, the method further includes:

[0071] Construct a training set based on destinations and driving routes.

[0072] Build a route planning text template.

[0073] Based on the route planning text template, the artificial intelligence model is supervised and fine-tuned on the driving route task through the training set to obtain a route planning model, wherein the route planning model is used to plan the driving route of the target vehicle to obtain the target route.

[0074] In one embodiment, a training set based on the destination and driving route is constructed. This training set contains a large amount of driving data under different conditions, including but not limited to information such as the departure point, destination, passing places, road types, and traffic conditions. These data are used to train the model to understand how various factors affect the power consumption during driving.

[0075] Build a route planning text template. This template defines a standard format for describing driving routes, making all data input into the AI ​​model consistent and readable. For example, a route planning text template can be expressed as: Please plan a driving route based on the current coordinates and the destination coordinates.

[0076] Based on the route planning text template, the artificial intelligence model is fine-tuned on the driving route task through the training set constructed above, and finally a model specifically optimized for driving route planning is obtained, namely the route planning model. This model can plan the most suitable driving route according to the specific conditions of the target vehicle (such as current location, destination), and output this route as the target route.

[0077] This method not only takes into account the operating status of the vehicle itself, but also combines the impact of the actual driving environment, making the battery swap demand forecast closer to the actual situation. When the vehicle is driving along the planned target route, its power consumption can be estimated more accurately, so as to determine the battery swap demand in advance and optimize the battery swap scheduling strategy.

[0078] Alternatively, if Figure 2 As shown, predicting the battery replacement demand according to the vehicle state includes:

[0079] Step S210, obtaining a target route according to the current coordinates and the destination coordinates.

[0080] Step S220: predicting the target route based on the historical power consumption to obtain a power exhaustion point.

[0081] Step S230, predicting the battery replacement demand based on the power exhaustion point and the destination coordinates.

[0082] The target route is obtained based on the current coordinates of the target vehicle and the coordinates of the destination. This process may involve the use of existing map services or navigation tools, or the route may be planned based on an artificial intelligence model (such as the route planning model obtained by the above-mentioned supervised fine-tuning). A reasonable driving path is planned as the target route based on the current position of the vehicle and the destination set by the user. The target route is used as the basis for subsequent analysis, and the target route is predicted based on historical power consumption data to determine the power exhaustion point. By analyzing the power consumption records of the vehicle under the same or similar driving conditions in the past, the remaining power at different locations on the target route can be estimated, and the specific location where the vehicle may not be able to continue moving forward due to insufficient power, that is, the power exhaustion point, can be found. The power exhaustion point is combined with the destination coordinates to predict the demand for battery replacement. For example, if the prediction results show that there is a risk of power exhaustion before reaching the destination, a corresponding demand for battery replacement will be generated. The demand for battery replacement is used to determine the location of nearby battery replacement stations, the estimated time of arrival at the determined battery replacement station, and other information, so that the battery replacement station can take timely measures to schedule the battery in advance.

[0083] Alternatively, if Figure 3 As shown, the vehicle state also includes the current power consumption of the target vehicle during its current trip, and before predicting the battery replacement demand based on the power exhaustion point and the destination coordinates, it also includes:

[0084] Step S201, determining a corresponding sliding window according to the route distance of the target route, wherein the sliding window is used to represent a range that moves with the current position of the target vehicle, and the range is determined according to at least one of a preset duration and a preset distance.

[0085] Step S202: assigning a first power consumption weight to the current power consumption in the sliding window to obtain a first weighted power consumption.

[0086] Step S203: assigning a second power consumption weight to the historical power consumption in the sliding window to obtain a second weighted power consumption, wherein the sum of the first power consumption weight and the second power consumption weight is 1.

[0087] Step S204: Process the first weighted power consumption and the second weighted power consumption by a recursive neural network to obtain the future power consumption of the target vehicle.

[0088] Step S205: determining the power exhaustion point according to the future power consumption.

[0089] When the range of the sliding window is time, the sliding window includes a window of a fixed time length that slides over time; when the range of the sliding window is distance, the sliding window is a window corresponding to the driving distance within a fixed time length that slides over time.

[0090] Specifically, the sliding window is an area that is updated as the target vehicle moves, and the size of the area can be determined according to a preset distance or a corresponding preset duration to match the specific conditions of the vehicle's travel. Specifically, the range of the sliding window can be equal to the preset distance or the preset duration, and the range of the sliding window (such as the preset distance or the preset duration) can be positively correlated with the route distance of the target route. When the route distance is longer, the range of the sliding window is correspondingly longer, and when the route distance is shorter, the range of the sliding window is correspondingly shorter, which is used to meet the accuracy of predicting power consumption for different route distances. For example, when the route distance is 500 kilometers, the range of the sliding window is determined by the preset duration, and the range of the sliding window can be 15 minutes; when the range is determined by the preset distance, the range of the sliding window can be 10 kilometers; when the route distance is 100 kilometers, the range of the sliding window is determined by the preset duration, and the range of the sliding window can be 5 minutes; when the range is determined by the preset distance, the range of the sliding window is 2.5 kilometers.

[0091] For the current power consumption data of the vehicle within the sliding window, the first power consumption weight is assigned to highlight the importance of the current power consumption. By multiplying the current power consumption by this weight, the current power consumption value after weighted processing can be obtained, which is called the first weighted power consumption. For the historical power consumption data that is also within the sliding window, the second power consumption weight is assigned. This weight is used to reflect the degree of influence of historical power consumption. The historical power consumption represents the power consumption record of the vehicle under the same or similar driving conditions in the past. By multiplying the historical power consumption by the second power consumption weight, the weighted historical power consumption value can be obtained, which is called the second weighted power consumption. The sum of the first power consumption weight plus the second power consumption weight is equal to 1, which ensures that the power consumption data can still maintain the relative proportion unchanged after weighting, but at the same time allows the importance between the two to be adjusted.

[0092] Optionally, the first power consumption weight is greater than or equal to the second power consumption weight.

[0093] The weighted first power consumption and second power consumption data are processed by a recurrent neural network (RNN). Because RNN can remember previous data points, it is suitable for processing sequence data and has a better effect on understanding trends in time series. In this embodiment, RNN receives the first weighted power consumption (current power consumption information) and the second weighted power consumption (historical power consumption information) as input. By learning these input data, RNN can predict the power consumption of the target vehicle in the future driving process, that is, the future power consumption. Based on the future power consumption data predicted by RNN, it is possible to further predict when and where the target vehicle will run out of power during continued driving, that is, the power depletion point. The power depletion point is used to help determine the target battery swap station in advance or remind the driver when and where the battery needs to be swapped.

[0094] Recursive neural network is a deep learning model specially designed for processing sequence data. Unlike traditional feedforward neural networks, RNN has cyclic connections, allowing information to flow in a loop within the network, so that the network can capture temporal dependencies. In this embodiment, RNN can receive a first weighted power consumption and a second weighted power consumption as input. Through a hidden layer containing multiple memory units (such as LSTM units or GRU units), the hidden layer retains some information of the previous state when processing the data of each time step. It is used to enable RNN to understand the power consumption change pattern over a period of time. Based on the memory and calculation results of the hidden layer, the power consumption value in a certain time period in the future is predicted through the output layer.

[0095] Alternatively, if Figure 4 As shown, the battery replacement demand includes the battery replacement time, and the battery replacement demand is predicted according to the power exhaustion point and the destination coordinates, including:

[0096] Step S231, when the power exhaustion point is before the destination coordinates, taking the battery swap station within the remaining distance as the target battery swap station, wherein the remaining distance includes the distance between the power exhaustion point and the current position.

[0097] Step S232, when the power exhaustion point is located at the destination coordinates or after the destination coordinates, starting from the battery swap station corresponding to the shortest detour distance, a preset number of battery swap stations are selected as the target battery swap stations in ascending order of the detour distance, wherein the detour distance includes the shortest driving distance between the battery swap station and the driving route.

[0098] Step S233, based on the target coordinates of the target battery swap station, determine the time when the target vehicle arrives at the target coordinates as the battery swap time.

[0099] When the predicted power exhaustion point occurs before the vehicle reaches its destination, it means that the vehicle may not be able to continue driving due to insufficient power before reaching the destination. To prevent this from happening, a battery replacement plan needs to be planned in advance. At this time, all battery replacement stations within the distance from the current location to the power exhaustion point will be considered and used as potential target battery replacement stations. This ensures that the vehicle can find a suitable battery replacement station for battery replacement before the power is exhausted, thus avoiding the problem of mid-trip stagnation caused by insufficient power.

[0100] When the predicted power exhaustion point occurs after the vehicle reaches the destination, it means that the vehicle has enough power to reach the destination smoothly. In this case, in order to optimize the battery swap strategy, a series of battery swap stations will be selected as potential target battery swap stations. The preset number of battery swap stations with the shortest detour distance is used as the target battery swap station, where the detour distance is used to indicate the additional distance the vehicle travels outside the target route for battery swapping, that is, the shortest driving distance between the battery swap station and the driving route, which is used to minimize the power loss caused by battery swapping. The preset number can be set according to actual needs, for example, selecting the nearest 3 or 5 battery swap stations.

[0101] After determining the specific location of the battery swap station for the vehicle, the time required for the vehicle to reach the target battery swap station from the current location can be estimated based on factors such as the vehicle's current location, driving speed, and traffic conditions.

[0102] Optionally, determining the battery supply capacity of the battery swap station according to the battery status of the batteries in the battery swap station includes:

[0103] Determine a current battery swap station among all the target battery swap stations, and obtain the battery status of the battery in the current battery swap station, wherein the battery status includes a first battery model, a current battery charge, and a battery charging power.

[0104] According to the battery status, the battery that meets the battery replacement standard in each preset time period is determined as the target battery.

[0105] The battery supply capacity is determined according to the number of the target batteries.

[0106] Determine the current battery swap station that the target vehicle is going to reach from the target battery swap station. Once the specific battery swap station is determined, the status information of all available batteries in the battery swap station is further determined. This is used to plan the battery swap strategy.

[0107] The first battery model indicates the battery type and model of the batteries provided in the battery swap station. Different models of batteries may have different capacities, sizes and other characteristics, which are used to match the battery suitable for the target vehicle. The battery current charge indicates the current remaining charge level of each battery. It is used to determine the batteries that are fully charged and can be used for replacement immediately, and the batteries that still need to be charged. The battery charging power reflects the speed at which the battery is charged and is used to estimate the charging progress of the battery.

[0108] At each preset time period (e.g. every hour, every day, etc.), check the status information of all batteries in the current battery swap station. According to the set battery swap standards (such as whether the battery power is sufficient, whether the battery model matches the vehicle requirements, etc.), select the batteries that meet the requirements. These batteries that meet the battery swap standards are identified as target batteries, which means they can be immediately used to replace vehicles that need battery swaps.

[0109] Count how many target batteries are available in each preset time period. By analyzing the number of target batteries, the battery supply capacity in that time period can be evaluated. That is, determine how many effective battery replacement services can be provided. If the number of target batteries is sufficient, it means that the battery supply capacity of the battery swap station is strong in the corresponding time period; conversely, if the number is insufficient, it indicates that the supply capacity is limited, and it may be necessary to adjust the charging strategy or increase the spare battery inventory.

[0110] Optionally, determining the battery replacement scheduling strategy of the target vehicle according to the battery replacement demand and the battery supply capacity includes:

[0111] The target battery within the first time period is determined according to a second battery model, wherein the second battery model includes the battery model of the target vehicle, and the first time period includes the preset time period where the battery replacement time is located.

[0112] When there are multiple target vehicles, each target vehicle is matched with the corresponding target battery, and the time and order of arrival of the target vehicles at the battery swap station are used as the battery swap scheduling strategy.

[0113] The second battery model represents a battery model that matches the target vehicle, and is used to enable the replaced battery to adapt to the requirements of the vehicle. The first time period represents a preset time period including the battery replacement time. For example, when the battery replacement time is at 2 pm and the preset time period is half an hour, the first time period can be any 30-minute time period between 1:30 pm and 2:30 pm, depending on the actual setting. Within the first time period including the battery replacement time, determine a target battery that meets the conditions, that is, a battery that not only matches the model but is also in a state that can be used immediately for battery replacement.

[0114] When multiple vehicles need to swap batteries in the same time period, for each target vehicle, find a target battery that matches it. That is, assign a suitable battery to each target vehicle, whose model and status meet the battery swap requirements. Develop a reasonable battery swap scheduling strategy so that each target vehicle can smoothly swap batteries in the predetermined time sequence. This strategy helps optimize resource utilization, reduce user waiting time, and improve the service efficiency and quality of the entire battery swap station.

[0115] According to the second battery model required by the target vehicle, determine the appropriate target battery in the first period including the battery swap time. For multiple vehicles, match each vehicle with its corresponding target battery, and formulate a detailed battery swap scheduling strategy based on the time and order of vehicle arrival at the battery swap station. Ensure the efficiency and orderliness of the battery swap process, and improve user experience and service efficiency.

[0116] In a second aspect, an embodiment of the present invention further provides a battery swap station, including a prediction module and a first radio frequency module;

[0117] The first radio frequency module is used to exchange information with a second radio frequency module disposed in the target vehicle to obtain the vehicle status of the target vehicle;

[0118] The prediction module is used to predict the battery replacement demand based on the vehicle status; determine the battery supply capacity of the battery replacement station based on the battery status of the batteries in the battery replacement station; and determine the battery replacement scheduling strategy of the target vehicle based on the battery replacement demand and the battery supply capacity, wherein the battery replacement scheduling strategy includes a battery replacement sequence strategy and a battery replacement time strategy.

[0119] like Figure 5 As shown, an embodiment of the present invention provides a battery swap station 500, including a memory 510 and a processor 520; the memory 510 is used to store a computer program; the processor 520 is used to implement the battery swap scheduling method as described above when executing the computer program.

[0120] In other words, a battery swap station 500 includes a memory 510 and a processor 520 coupled to the memory 510; the memory 510 is configured to store a computer program; and the processor 520 is configured to perform the following operations when executing the computer program:

[0121] Get the vehicle status of the target vehicle;

[0122] predicting the need for battery replacement based on the vehicle status;

[0123] Determining the battery supply capacity of the battery swap station according to the battery status of the batteries in the battery swap station;

[0124] The battery replacement scheduling strategy of the target vehicle is determined according to the battery replacement demand and the battery supply capacity, wherein the battery replacement scheduling strategy includes a battery replacement sequence strategy and a battery replacement time strategy.

[0125] Thirdly, Figure 6 As shown, an embodiment of the present invention further provides a vehicle, including a second radio frequency module, a driving control module, a braking module and a vehicle controller;

[0126] The second radio frequency module is used to exchange information with the first radio frequency module provided in the battery swap station, upload the vehicle status and receive the battery swap scheduling instruction, wherein the battery swap scheduling instruction is generated according to the battery swap scheduling strategy, and the battery swap scheduling strategy is obtained by the battery swap scheduling method described in the first aspect;

[0127] The driving control module is used to control the vehicle to travel to a predetermined position according to the battery swap scheduling instruction, switch the gear of the vehicle to a preset gear, and control the braking module to release the braking force;

[0128] The vehicle controller is used to switch the vehicle state to the battery replacement state after the braking module releases the braking force, and to feed back the battery replacement state switching completion information to the battery replacement station through the second radio frequency module, so that the battery replacement station can perform a battery replacement operation on the vehicle.

[0129] After the target vehicle's second RF module RFR2 interacts with the first RF module RFR1 of the battery swap station, it enters the battery swap state; when the target vehicle enters the battery swap station, it automatically matches the target vehicle VIN and selects the battery type that matches the target vehicle. After the vehicle drives to a fixed position, the autonomous driving domain controller ADCU adjusts the vehicle status to meet the battery swap preparation state: switches to N gear and releases the electronic parking brake system EPB. The vehicle controller VCU interacts with the second RF module RFR2 to perform high voltage in the vehicle power state. After receiving the relevant information, the first RF module RFR1 performs the battery swap operation. After the battery swap operation is completed, the autonomous driving domain controller ADCU controls the vehicle to drive the vehicle out of the battery swap station.

[0130] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the battery swap scheduling method as described above is implemented.

[0131] In other words, a non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following operations:

[0132] Get the vehicle status of the target vehicle;

[0133] predicting the need for battery replacement based on the vehicle status;

[0134] Determining the battery supply capacity of the battery swap station according to the battery status of the batteries in the battery swap station;

[0135] The battery replacement scheduling strategy of the target vehicle is determined according to the battery replacement demand and the battery supply capacity, wherein the battery replacement scheduling strategy includes a battery replacement sequence strategy and a battery replacement time strategy.

[0136] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0137] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A battery swap scheduling method, characterized in that: include: Get the vehicle status of the target vehicle; predicting the need for battery replacement based on the vehicle status; Determining the battery supply capacity of the battery swap station according to the battery status of the batteries in the battery swap station; The battery replacement scheduling strategy of the target vehicle is determined according to the battery replacement demand and the battery supply capacity, wherein the battery replacement scheduling strategy includes a battery replacement sequence strategy and a battery replacement time strategy.

2. The battery swap scheduling method according to claim 1, characterized in that: The vehicle status includes current coordinates of the target vehicle, historical power consumption of the target vehicle, and destination coordinates of the target vehicle.

3. The battery swap scheduling method according to claim 2, characterized in that: Before predicting the battery replacement demand according to the vehicle state, the method further includes: Build a training set based on destinations and driving routes; Build a route planning text template; Based on the route planning text template, the artificial intelligence model is supervised and fine-tuned on the driving route task through the training set to obtain a route planning model, wherein the route planning model is used to plan the driving route of the target vehicle to obtain the target route.

4. The battery swap scheduling method according to claim 3, characterized in that: The predicting of battery replacement demand according to the vehicle state includes: Obtaining a target route according to the current coordinates and the destination coordinates; According to the historical power consumption, predicting the target route to obtain a power exhaustion point; The battery replacement demand is predicted based on the power depletion point and the destination coordinates.

5. The battery swap scheduling method according to claim 4, characterized in that: The vehicle state also includes the current power consumption of the current trip of the target vehicle, and before predicting the battery replacement demand according to the power exhaustion point and the destination coordinates, it also includes: Determining a corresponding sliding window according to the route distance of the target route, wherein the sliding window is used to represent a range that moves with the current position of the target vehicle, and the range is determined according to at least one of a preset duration and a preset distance; Assigning a first power consumption weight to the current power consumption in the sliding window to obtain a first weighted power consumption; Assigning a second power consumption weight to the historical power consumption in the sliding window to obtain a second weighted power consumption, wherein the sum of the first power consumption weight and the second power consumption weight is 1; Processing the first weighted power consumption and the second weighted power consumption by a recursive neural network to obtain the future power consumption of the target vehicle; The power depletion point is determined based on the future power consumption.

6. The battery swap scheduling method according to claim 4, characterized in that: The battery replacement demand includes a battery replacement time, and the predicting of the battery replacement demand according to the power exhaustion point and the destination coordinates includes: When the power exhaustion point is before the destination coordinates, taking the power exchange station within the remaining distance as the target power exchange station, wherein the remaining distance includes the distance between the power exhaustion point and the current position; When the power exhaustion point is located at or after the destination coordinates, starting from the battery swap station corresponding to the shortest detour distance, a preset number of battery swap stations are selected in ascending order of the detour distance as the target battery swap station, wherein the detour distance includes the shortest driving distance between the battery swap station and the driving route; Based on the target coordinates of the target battery swap station, the time when the target vehicle arrives at the target coordinates is determined as the battery swap time.

7. The battery swap scheduling method according to claim 6, characterized in that: The determining the battery supply capacity of the battery swap station according to the battery status of the batteries in the battery swap station includes: Determine a current battery swap station among all the target battery swap stations, and obtain the battery status of the battery in the current battery swap station, wherein the battery status includes a first battery model, a current battery charge, and a battery charging power; Determine, according to the battery status, a battery that meets the battery replacement standard within each preset time period as a target battery; The battery supply capacity is determined according to the number of the target batteries.

8. The battery swap scheduling method according to claim 7, characterized in that: Determining the battery replacement scheduling strategy of the target vehicle according to the battery replacement demand and the battery supply capacity includes: Determine the target battery within the first time period according to the second battery model, wherein the second battery model includes the battery model of the target vehicle, and the first time period includes the preset time period in which the battery replacement time is located; When there are multiple target vehicles, each target vehicle is matched with the corresponding target battery, and the time and order of arrival of the target vehicles at the battery swap station are used as the battery swap scheduling strategy.

9. A battery swap station, characterized in that: including a prediction module and a first radio frequency module; The first radio frequency module is used to exchange information with a second radio frequency module disposed in the target vehicle to obtain the vehicle status of the target vehicle; The prediction module is used to predict the battery replacement demand based on the vehicle status; determine the battery supply capacity of the battery replacement station based on the battery status of the batteries in the battery replacement station; and determine the battery replacement scheduling strategy of the target vehicle based on the battery replacement demand and the battery supply capacity, wherein the battery replacement scheduling strategy includes a battery replacement sequence strategy and a battery replacement time strategy.

10. A vehicle, characterized in that: It includes a second radio frequency module, a driving control module, a braking module and a vehicle controller; The second radio frequency module is used to exchange information with the first radio frequency module provided in the battery swap station, upload the vehicle status and receive the battery swap scheduling instruction, wherein the battery swap scheduling instruction is generated according to the battery swap scheduling strategy, and the battery swap scheduling strategy is obtained by the battery swap scheduling method according to any one of claims 1 to 8; The driving control module is used to control the vehicle to travel to a predetermined position according to the battery swap scheduling instruction, switch the gear of the vehicle to a preset gear, and control the braking module to release the braking force; The vehicle controller is used to switch the vehicle state to the battery replacement state after the braking module releases the braking force, and to feed back the battery replacement state switching completion information to the battery replacement station through the second radio frequency module, so that the battery replacement station can perform a battery replacement operation on the vehicle.

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