A battery swapping dispatching method, battery swapping station and vehicle

Abstract

This invention provides a battery swapping scheduling method, a battery swapping station, and a vehicle, relating to the field of battery scheduling technology. The method includes: acquiring the vehicle status of a target vehicle; predicting battery swapping demand based on the vehicle status; determining the battery supply capacity of the battery swapping station based on the battery status of the batteries within the station; and determining a battery swapping scheduling strategy for the target vehicle based on the battery swapping demand and the battery supply capacity. The battery swapping scheduling strategy includes a battery swapping sequence strategy and a battery swapping time strategy, ensuring that the battery swapping station can formulate a battery swapping scheduling strategy in advance based on the target vehicle's status, thereby improving battery swapping efficiency and rationally allocating battery resources.

Description

Technical Field

[0001] This invention relates to the field of battery scheduling technology, and more specifically, to a battery swapping scheduling method, a battery swapping station, and a vehicle. Background Technology

[0002] With the continuous growth of electric vehicle ownership, the problem of insufficient driving range has become increasingly prominent, gradually becoming a key bottleneck affecting their efficient operation. Due to current limitations in battery technology, electric vehicles require a long charging time, unlike gasoline vehicles which can be refueled quickly. Therefore, battery replacement has become a major solution for achieving rapid energy replenishment in electric vehicles.

[0003] In related technologies, battery swapping stations can only handle one electric vehicle's battery swapping request at a time, and cannot serve multiple vehicles waiting to be swapped simultaneously. The operation of battery swapping stations is relatively passive, only able to perform on-site scheduling and management of vehicles already in the station, and lacks an effective strategy to proactively respond to and optimize the scheduling of electric vehicles with battery swapping needs. Summary of the Invention

[0004] The problem addressed by this invention is how to proactively formulate battery swapping strategies based on demand.

[0005] To address the above problems, this invention provides a battery swapping scheduling method, a battery swapping station, and a vehicle.

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

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

[0008] Predict battery swapping demand based on the vehicle status;

[0009] The battery supply capacity of the battery swapping station is determined based on the battery status of the batteries within the station.

[0010] Based on the battery swapping demand and the battery supply capacity, a battery swapping scheduling strategy for the target vehicle is determined, wherein the battery swapping scheduling strategy includes a battery swapping sequence strategy and a battery swapping time strategy.

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

[0012] Optionally, before predicting battery swapping demand based on the vehicle status, the method further includes:

[0013] Construct a training set based on destination and driving route;

[0014] Create a route planning text template;

[0015] Based on the route planning text template, the artificial intelligence model is supervised and fine-tuned for the driving route task using 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 battery swapping demand based on the vehicle status includes:

[0017] The target route is obtained based on the current coordinates and the destination coordinates;

[0018] Based on the historical power consumption, the target route is predicted to obtain the power depletion point;

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

[0020] Optionally, the vehicle status also includes the current power consumption of the target vehicle's current journey, and before predicting the battery swapping demand based on the power depletion point and the destination coordinates, it further includes:

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

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

[0023] The historical power consumption within the sliding window is assigned a second power consumption weight 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] The future energy consumption of the target vehicle is obtained by processing the first weighted energy consumption and the second weighted energy consumption through a recurrent neural network.

[0025] The point at which the power is depleted is determined based on the future power consumption.

[0026] Optionally, the battery swapping demand includes the battery swapping time, and predicting the battery swapping demand based on the power depletion point and the destination coordinates includes:

[0027] When the point where the power is depleted is before the destination coordinates, the battery swapping station within the remaining distance is taken as the target battery swapping station, wherein the remaining distance includes the distance between the point where the power is depleted and the current location;

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

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

[0030] Optionally, determining the battery supply capacity of the battery swapping station based on the battery status of the batteries within the station includes:

[0031] Among all the target battery swapping stations, determine the current battery swapping station and obtain the battery status of the battery in the current battery swapping station, wherein the battery status includes the first battery model, the current battery capacity and the battery charging power;

[0032] Based on the battery status, batteries that meet the battery swapping standards within each preset time period are identified as target batteries.

[0033] The battery supply capacity is determined based on the number of target batteries.

[0034] Optionally, determining the battery swapping scheduling strategy for the target vehicle based on the battery swapping demand and the battery supply capacity includes:

[0035] The target battery for the first time period is determined based on 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 swapping time is located;

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

[0037] Secondly, the present invention also provides a battery swapping station, including a prediction module and a first radio frequency module;

[0038] The first radio frequency module is used to interact with the second radio frequency module installed in the target vehicle to obtain the vehicle status of the target vehicle;

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

[0040] Thirdly, the present invention also provides a vehicle, including 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 interact with the first radio frequency module located in the battery swapping station, upload vehicle status and receive battery swapping dispatch instructions, wherein the battery swapping dispatch instructions are generated according to the battery swapping dispatching strategy, and the battery swapping dispatching strategy is obtained by the battery swapping dispatching method of the first aspect.

[0042] The driving control module is used to control the vehicle to drive to a predetermined location according to the battery swapping dispatch instruction, switch the vehicle's gear 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 battery swapping state after the braking module releases the braking force, and to feed back the battery swapping state switching completion information to the battery swapping station through the second radio frequency module so that the battery swapping station can perform battery swapping operation on the vehicle.

[0044] The beneficial effects of the battery swapping dispatch method of the present invention are:

[0045] After determining specific battery swapping needs and battery supply capacity, the battery swapping needs are matched with available batteries to obtain battery swapping sequence and timing strategies, thereby deriving a battery swapping scheduling strategy and achieving intelligent battery scheduling and resource optimization. By analyzing the status information of target vehicles, their specific conditions can be understood. This provides an accurate data foundation for predicting subsequent battery swapping needs, enabling the planning of optimal battery swapping strategies in advance. Based on the understanding of vehicle status, the system predicts when target vehicles will need battery swapping and the possible timing. Preparing in advance before actual demand occurs allows for proactive allocation of battery swapping resources, reducing user waiting time and helping to balance the workload of various battery swapping stations, improving overall operational efficiency. Assessing the battery supply capacity within battery swapping stations ensures that while meeting all current battery swapping requests, sufficient backup batteries can be reserved to cope with future battery swapping needs. This reduces the risk of service interruptions due to insufficient batteries, ensuring the stability and reliability of the battery swapping service. Combining battery swapping needs and battery supply capacity, a battery swapping scheduling strategy that meets individual vehicle needs while also considering overall optimization can be formulated. This ensures that battery swapping stations can formulate battery swapping scheduling strategies in advance based on the status of the target vehicle, thereby improving battery swapping efficiency and rationally allocating battery resources. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the battery swapping scheduling method according to an embodiment of the present invention;

[0047] Figure 2 This is a detailed flowchart of step S200 of the battery swapping scheduling method according to an embodiment of the present invention.

[0048] Figure 3 This is a flowchart illustrating the process before step S230 in the battery swapping scheduling method of this embodiment of the invention.

[0049] Figure 4 This is a detailed flowchart of step S230 of the battery swapping scheduling method according to an embodiment of the present invention.

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

[0051] Figure 6 This is an example diagram of a vehicle according to an embodiment of the present invention. Detailed Implementation

[0052] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only 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. Furthermore, 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 "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0055] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

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

[0057] To address the problems existing in the aforementioned related technologies, this embodiment provides a battery swapping scheduling method, a battery swapping station, and a vehicle.

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

[0059] Step S100: Obtain the vehicle status of the target vehicle.

[0060] In one embodiment, the target vehicle refers to a vehicle that can communicate with the battery swapping station. When the vehicle communicates with the battery swapping station, the vehicle can upload its vehicle status (e.g., battery status), allowing the battery swapping station to obtain the vehicle's current status. Based on the vehicle status, the station can determine whether the vehicle needs a battery swap in the short term. If a battery swap is needed in the short term, the vehicle is designated as the target vehicle, and the station predicts the specific battery swapping demand based on the vehicle status.

[0061] The target vehicle can also refer to a vehicle with battery swapping needs. In this embodiment, only vehicles with battery swapping needs are considered and are taken as target vehicles. The target vehicle uploads its vehicle status so that the battery swapping station can determine the vehicle status for a future period of time based on the current vehicle status (e.g., battery status) and determine the specific battery swapping needs.

[0062] Step S200: Predict battery swapping demand based on the vehicle status.

[0063] In one embodiment, the battery swapping needs of the target vehicle in the future are predicted based on the vehicle status, thereby predicting the specific battery swapping needs. For example, the time node when the target vehicle needs to swap batteries and the location where the target vehicle needs to swap batteries are predicted. Based on the specific battery swapping needs, the battery swapping station to provide battery swapping services is determined for more accurate matching with the battery swapping station.

[0064] Step S300: Determine the battery supply capacity of the battery swapping station based on the battery status of the batteries in the battery swapping station.

[0065] In one embodiment, the load that the battery swapping station can withstand in each time period is determined based on the battery status (e.g., charge level and charging status) of the batteries in the swapping station, thereby measuring the battery supply capacity for better battery allocation. The load includes the maximum number of battery swaps the station can perform simultaneously and / or the maximum charging power it can withstand. The maximum number of battery swaps constrains the entry time of the target vehicle, while the maximum charging power constrains the future entry time periods of the target vehicle.

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

[0067] After determining specific battery swapping needs and battery supply capacity, these needs are matched with available batteries to formulate scheduling strategies, including swapping sequence and timing, enabling intelligent battery scheduling and resource optimization. Analysis of target vehicle status information provides a comprehensive understanding of each vehicle's specific situation, offering a data foundation for predicting battery swapping demand and allowing for advance planning of optimal swapping strategies. Based on this understanding of vehicle status, it's possible to predict when a vehicle will require a battery swap and the likely timing. This allows for proactive preparation before actual demand occurs, facilitating the allocation of battery swapping resources, reducing user wait times, and helping to balance the workload of various swapping stations, thereby improving operational efficiency.

[0068] Meanwhile, by assessing the battery supply capacity of each battery swapping station, it is ensured that not only current battery swapping requests can be met, but also sufficient backup batteries can be reserved to cope with future battery swapping needs. This reduces the risk of service interruptions due to insufficient batteries and guarantees the stability and reliability of the battery swapping service. Combining battery swapping demand and battery supply capacity, a battery swapping scheduling strategy that meets both individual vehicle needs and overall optimization can be formulated. In this way, battery swapping stations can formulate battery swapping scheduling strategies in advance based on the status of target vehicles, improve battery swapping efficiency, rationally allocate battery resources, and ultimately achieve the effect of proactively determining battery scheduling strategies based on demand, greatly improving the satisfaction of electric vehicle users and the overall operational efficiency.

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

[0070] Optionally, before predicting battery swapping demand based on the vehicle status, the method further includes:

[0071] Construct a training set based on destination and driving route.

[0072] Create a route planning text template.

[0073] Based on the route planning text template, the artificial intelligence model is supervised and fine-tuned for the driving route task using 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 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 origin, destination, route points, road type, and traffic conditions. This data is used to train a model to understand how various factors affect power consumption during driving.

[0075] Construct a route planning text template. This template defines a standard format for describing driving routes, ensuring consistency and readability for all data input into the AI ​​model. For example, the route planning text template could be expressed as: Please plan a driving route based on your current coordinates and destination coordinates.

[0076] Based on the aforementioned route planning text template, the artificial intelligence model is fine-tuned under supervised supervision for driving route tasks using the training set constructed above. This ultimately yields a model specifically optimized for driving route planning—the route planning model. This model can plan the most suitable driving route based on the specific circumstances of the target vehicle (such as its current location and destination) and output this route as the target route.

[0077] This approach considers both the vehicle's operational status and the impact of the actual driving environment, making battery swapping demand predictions more accurate. When vehicles travel along the planned route, their battery consumption can be more accurately estimated, allowing for advance determination of battery swapping demand and optimization of battery swapping scheduling strategies.

[0078] Optionally, such as Figure 2 As shown, predicting battery swapping demand based on the vehicle status includes:

[0079] Step S210: Obtain the target route based on the current coordinates and the destination coordinates.

[0080] Step S220: Based on the historical power consumption, predict the target route to obtain the power depletion point.

[0081] Step S230: Predict the battery swapping demand based on the power depletion 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 can involve using existing map services or navigation tools, or it can be based on artificial intelligence models (such as the route planning model obtained through supervised fine-tuning mentioned above) to plan the route. A reasonable driving path is planned as the target route based on the vehicle's current location and the user-set destination. The target route is used as the basis for subsequent analysis. The target route is predicted based on historical power consumption data to determine the point where the battery will run out. By analyzing the vehicle's power consumption records under similar driving conditions in the past, the remaining power at different locations on the target route can be estimated, and the specific locations where the vehicle may be unable to continue due to insufficient power can be identified, i.e., the point where the battery will run out. The battery runout point is combined with the destination coordinates to predict the battery swapping demand. For example, if the prediction results show that there is a risk of running out of power before reaching the destination, a corresponding battery swapping demand will be generated. The battery swapping demand is used to determine the location of nearby battery swapping stations, the estimated arrival time at the identified battery swapping station, and other information, so that the battery swapping station can take timely measures to pre-allocate batteries.

[0083] Optionally, such as Figure 3 As shown, the vehicle status also includes the current power consumption of the target vehicle's current journey, and before predicting the battery swapping demand based on the power depletion point and the destination coordinates, it also includes:

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

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

[0086] Step S203: Assign 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 through a recurrent neural network to obtain the future power consumption of the target vehicle.

[0088] Step S205: Determine the power depletion point based on 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 as time changes; when the range of the sliding window is distance, the sliding window is a window corresponding to the travel distance within a fixed time length that slides as time changes.

[0090] Specifically, the sliding window is an area that updates as the target vehicle moves. The size of this area can be determined based on a preset distance or a corresponding preset duration to match the specific driving conditions. Specifically, the range of the sliding window can be equal to the preset distance or preset duration, and the range of the sliding window (e.g., preset distance or preset duration) can be positively correlated with the route distance of the target route. When the route distance is long, the range of the sliding window is correspondingly long; when the route distance is short, the range of the sliding window is correspondingly short, to ensure the accuracy of predicted power consumption for different route distances. For example, when the route distance is 500 kilometers, if the range of the sliding window is determined by the preset duration, the range of the sliding window can be 15 minutes; if 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, if the range of the sliding window is determined by the preset duration, the range of the sliding window can be 5 minutes; and if the range is determined by the preset distance, the range of the sliding window can be 2.5 kilometers.

[0091] For current vehicle energy consumption data within the sliding window range, a first energy consumption weight is assigned to highlight its importance. Multiplying the current energy consumption by this weight yields a weighted current energy consumption value, called the first weighted energy consumption. For historical energy consumption data also within the sliding window range, a second energy consumption weight is assigned. This weight reflects the influence of historical energy consumption, which represents the energy consumption records of the vehicle under similar driving conditions in the past. Multiplying the historical energy consumption by the second weight yields a weighted historical energy consumption value, called the second weighted energy consumption. The sum of the first and second energy consumption weights equals 1, ensuring that the relative proportions of the energy consumption data remain unchanged after weighting, while allowing for adjustments to the relative importance of the two weights.

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

[0093] A recurrent neural network (RNN) is used to process the weighted first and second power consumption data. Because RNNs can remember previous data points, they are suitable for processing sequential data and are particularly effective at understanding trends in time series. In this embodiment, the RNN receives first-weighted power consumption (current power consumption information) and second-weighted power consumption (historical power consumption information) as input. By learning from these input data, the RNN can predict the power consumption of the target vehicle during future driving, i.e., future power consumption. Based on the future power consumption data predicted by the RNN, it can further predict when and where the target vehicle will run out of power during continued driving, i.e., the power depletion point. The power depletion point is used to help identify target battery swapping stations in advance or to remind the driver when and where a battery swapping is needed.

[0094] Recurrent Neural Networks (RNNs) are deep learning models specifically designed for processing sequential data. Unlike traditional feedforward neural networks, RNNs have recurrent connections, allowing information to circulate within the network, enabling it to capture temporal dependencies. In this embodiment, the RNN can receive a first weighted power consumption and a second weighted power consumption as input. Hidden layers containing multiple memory units (such as LSTM or GRU units) retain some information from previous states as they process data at each time step. This allows the RNN to understand power consumption patterns over a period of time. Based on the memory and computation results of the hidden layers, the power consumption value for a future time period is predicted through the output layer.

[0095] Optionally, such as Figure 4 As shown, the battery swapping demand includes the battery swapping time, and the prediction of the battery swapping demand based on the power depletion point and the destination coordinates includes:

[0096] Step S231: When the power depletion point is before the destination coordinates, the battery swapping station within the remaining distance is taken as the target battery swapping station, wherein the remaining distance includes the distance between the power depletion point and the current location.

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

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

[0099] When the predicted point of battery depletion occurs before the vehicle reaches its destination, it means the vehicle may be unable to continue driving due to insufficient battery power. To prevent this, a battery swapping plan needs to be planned in advance. In this case, all battery swapping stations along the distance from the current location to the point of battery depletion will be considered and designated as potential target stations. This ensures the vehicle can find a suitable battery swapping station before running out of power, thus avoiding mid-journey delays due to insufficient battery power.

[0100] When the predicted battery depletion point occurs after the vehicle has reached its destination, it indicates that the vehicle has sufficient charge to reach its destination smoothly. In this case, to optimize the battery swapping strategy, a series of battery swapping stations are selected as potential target stations. A preset number of battery swapping stations with the shortest detour distance are selected as target stations. The detour distance represents the additional distance the vehicle travels outside the target route to swap batteries; it is the shortest driving distance between the battery swapping station and the driving route, used to minimize battery loss during battery swapping. The preset number can be set according to actual needs, for example, selecting the 3 or 5 nearest battery swapping stations.

[0101] Once the exact location of the battery swapping station for vehicle battery replacement has been determined, the time required for the vehicle to travel from its current location to the target battery swapping station can be estimated based on factors such as the vehicle's current location, speed, and traffic conditions.

[0102] Optionally, determining the battery supply capacity of the battery swapping station based on the battery status of the batteries within the station includes:

[0103] Among all the target battery swapping stations, the current battery swapping station is determined, and the battery status of the battery in the current battery swapping station is obtained, wherein the battery status includes the first battery model, the current battery capacity, and the battery charging power.

[0104] Based on the battery status, batteries that meet the battery swapping standards within each preset time period are identified as target batteries.

[0105] The battery supply capacity is determined based on the number of target batteries.

[0106] The system determines the current battery swapping station that the target vehicle needs to reach from the target battery swapping stations. Once the specific battery swapping station is determined, the status information of all available batteries within that station is then determined. This information is used to plan the battery swapping strategy.

[0107] The first battery model indicates the type and model of the batteries provided at the battery swapping station. Different battery models may have different capacities, sizes, and other characteristics to match the appropriate battery for the target vehicle. The current battery charge level indicates the current remaining charge level of each battery. This is used to identify batteries that are fully charged and ready for immediate replacement, as well as batteries that still need charging. The battery charging power reflects the charging speed and is used to estimate the charging progress of the batteries.

[0108] At each preset time period (e.g., hourly, daily), the status information of all batteries in the current battery swapping station is checked. Based on the set battery swapping criteria (e.g., whether the battery charge is sufficient, whether the battery model matches the vehicle's requirements), batteries that meet the requirements are selected. These batteries that meet the battery swapping criteria are identified as target batteries, meaning they can be immediately used to replace vehicles that need battery swapping.

[0109] The system counts the number of target batteries available for use within each preset time period. By analyzing the number of target batteries, the battery supply capacity during that time period can be assessed, determining how many effective battery swapping services can be provided. If the number of target batteries is sufficient, it indicates that the battery swapping station has a strong battery supply capacity during the corresponding time period; conversely, if the number is insufficient, it indicates limited supply capacity, which may require adjustments to the charging strategy or an increase in spare battery inventory.

[0110] Optionally, determining the battery swapping scheduling strategy for the target vehicle based on the battery swapping demand and the battery supply capacity includes:

[0111] The target battery for the first time period is determined based on 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 swapping time is located.

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

[0113] The second battery model indicates the battery model that matches the target vehicle, ensuring the replacement battery is compatible with the vehicle's requirements. The first time period indicates a preset time period including the battery swap time. For example, if the battery swap time is at 2 PM and the preset time period is half an hour, then the first time period can be any 30-minute period between 1:30 PM and 2:30 PM, depending on the actual settings. Within the first time period including the battery swap time, a suitable target battery is identified—that is, a battery that not only matches the model but is also in a state where it can be immediately used for battery swapping.

[0114] When multiple vehicles need to swap batteries within the same time period, a matching battery is identified for each target vehicle. This means assigning a suitable battery to each target vehicle, ensuring its model and condition meet the swapping requirements. A reasonable battery swapping scheduling strategy is developed to ensure that each target vehicle can smoothly undergo battery swapping according to the predetermined time sequence. This strategy helps optimize resource utilization, reduce user waiting time, and improve the overall service efficiency and quality of the battery swapping station.

[0115] Based on the second battery model required by the target vehicle, a suitable target battery is determined within the first time period, including the battery swapping time. For multiple vehicles, each vehicle is matched with its corresponding target battery, and a detailed battery swapping scheduling strategy is developed based on the time and order in which the vehicles arrive at the battery swapping station. This ensures the efficiency and orderliness of the battery swapping process, improving user experience and service efficiency.

[0116] Secondly, embodiments of the present invention also provide a battery swapping station, including a prediction module and a first radio frequency module;

[0117] The first radio frequency module is used to interact with the second radio frequency module installed in the target vehicle to obtain the vehicle status of the target vehicle;

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

[0119] like Figure 5 As shown in the figure, an embodiment of the present invention provides a battery swapping station 500, which includes 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 swapping scheduling method as described above when the computer program is executed.

[0120] Alternatively, a battery swapping 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; the processor 520 is configured to perform the following operations when the computer program is executed:

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

[0122] Predict battery swapping demand based on the vehicle status;

[0123] The battery supply capacity of the battery swapping station is determined based on the battery status of the batteries within the station.

[0124] Based on the battery swapping demand and the battery supply capacity, a battery swapping scheduling strategy for the target vehicle is determined, wherein the battery swapping scheduling strategy includes a battery swapping sequence strategy and a battery swapping time strategy.

[0125] Thirdly, such as Figure 6 As shown, this embodiment of the invention also 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 interact with the first radio frequency module located in the battery swapping station, upload vehicle status and receive battery swapping dispatch instructions, wherein the battery swapping dispatch instructions are generated according to the battery swapping dispatching strategy, and the battery swapping dispatching strategy is obtained by the battery swapping dispatching method described in the first aspect.

[0127] The driving control module is used to control the vehicle to drive to a predetermined location according to the battery swapping dispatch instruction, switch the vehicle's gear 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 battery swapping state after the braking module releases the braking force, and to feed back the battery swapping state switching completion information to the battery swapping station through the second radio frequency module so that the battery swapping station can perform battery swapping operation on the vehicle.

[0129] After the target vehicle's second radio frequency module RFR2 interacts with the battery swapping station's first radio frequency module RFR1, it enters the battery swapping state. When the target vehicle enters the swapping station, the system automatically matches the target vehicle's VIN and selects the appropriate battery type. After the vehicle reaches a fixed location, the autonomous driving domain controller (ADCU) adjusts the vehicle's status to meet the battery swapping preparation requirements: shifting to neutral (N) and releasing the electronic parking brake (EPB). The vehicle controller (VCU) interacts with the second radio frequency module RFR2 to check the high voltage status of the vehicle's power supply. Upon receiving the relevant information, the first radio frequency module RFR1 initiates the battery swapping operation. After the battery swapping operation is completed, the autonomous driving domain controller (ADCU) controls the vehicle to drive it out of the swapping station.

[0130] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the battery swapping scheduling method described above.

[0131] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations:

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

[0133] Predict battery swapping demand based on the vehicle status;

[0134] The battery supply capacity of the battery swapping station is determined based on the battery status of the batteries within the station.

[0135] Based on the battery swapping demand and the battery supply capacity, a battery swapping scheduling strategy for the target vehicle is determined, wherein the battery swapping scheduling strategy includes a battery swapping sequence strategy and a battery swapping time strategy.

[0136] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0137] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A battery swapping dispatch method, characterized in that, include: Obtain the vehicle status of the target vehicle, wherein the vehicle status includes the current coordinates of the target vehicle, the historical power consumption of the target vehicle, the destination coordinates of the target vehicle, and the current power consumption of the target vehicle during its current trip; Predicting battery swapping demand based on the vehicle status includes: obtaining a target route based on the current coordinates and the destination coordinates; predicting the target route based on the historical power consumption to obtain the power depletion point; and predicting the battery swapping demand based on the power depletion point and the destination coordinates. The battery supply capacity of the battery swapping station is determined based on the battery status of the batteries within the station. Based on the battery swapping demand and the battery supply capacity, a battery swapping scheduling strategy for the target vehicle is determined, wherein the battery swapping scheduling strategy includes a battery swapping sequence strategy and a battery swapping time strategy. Before predicting the battery swapping demand based on the power depletion point and the destination coordinates, the method further includes: A corresponding sliding window is determined based on the route distance of the target route, wherein the sliding window is used to represent the range that moves with the current position of the target vehicle, and the range is determined based on at least one of a preset time and a preset distance; Assign a first power consumption weight to the current power consumption in the sliding window to obtain the first weighted power consumption; The historical power consumption within the sliding window is assigned a second power consumption weight to obtain a second weighted power consumption, wherein the sum of the first power consumption weight and the second power consumption weight is 1; The future energy consumption of the target vehicle is obtained by processing the first weighted energy consumption and the second weighted energy consumption through a recurrent neural network. The point at which the power is depleted is determined based on the future power consumption.

2. The battery swapping dispatch method according to claim 1, characterized in that, Before predicting battery swapping demand based on the vehicle status, the method further includes: Construct a training set based on destination and driving route; Create a route planning text template; Based on the route planning text template, the artificial intelligence model is supervised and fine-tuned for the driving route task using 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 and obtain the target route.

3. The battery swapping dispatch method according to claim 2, characterized in that, The battery swapping demand includes the battery swapping time, and the prediction of the battery swapping demand based on the power depletion point and the destination coordinates includes: When the point where the power is depleted is before the destination coordinates, the battery swapping station within the remaining distance is taken as the target battery swapping station, wherein the remaining distance includes the distance between the point where the power is depleted and the current location; When the point where the power is depleted is located at or after the destination coordinates, a preset number of battery swapping stations are selected as the target battery swapping stations, starting from the battery swapping station corresponding to the shortest detour distance, in ascending order of the detour distance. The detour distance includes the shortest driving distance between the battery swapping station and the driving route. Based on the target coordinates of the target battery swapping station, the time when the target vehicle arrives at the target coordinates is determined as the battery swapping time.

4. The battery swapping dispatch method according to claim 3, characterized in that, Determining the battery supply capacity of the battery swapping station based on the battery status of the batteries within the station includes: Among all the target battery swapping stations, determine the current battery swapping station and obtain the battery status of the battery in the current battery swapping station, wherein the battery status includes the first battery model, the current battery capacity and the battery charging power; Based on the battery status, batteries that meet the battery swapping standards within each preset time period are identified as target batteries. The battery supply capacity is determined based on the number of target batteries.

5. The battery swapping dispatch method according to claim 4, characterized in that, The step of determining the battery swapping scheduling strategy for the target vehicle based on the battery swapping demand and the battery supply capacity includes: The target battery for the first time period is determined based on 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 swapping time is located; When there are multiple target vehicles, each target vehicle is matched with the corresponding target battery, and the arrival time and order of the target vehicles at the battery swapping station are used as the battery swapping scheduling strategy.

6. A battery swapping station, characterized in that, Includes a prediction module and a first radio frequency module; The first radio frequency module is used to interact with the second radio frequency module installed in the target vehicle to obtain the vehicle status of the target vehicle, wherein the vehicle status includes the current coordinates of the target vehicle, the historical power consumption of the target vehicle, the destination coordinates of the target vehicle, and the current power consumption of the target vehicle during the current trip. The prediction module is used to predict battery swapping demand based on the vehicle status, including: obtaining a target route based on the current coordinates and the destination coordinates; predicting the target route based on the historical power consumption to obtain a power depletion point; predicting the battery swapping demand based on the power depletion point and the destination coordinates; determining the battery supply capacity of the battery swapping station based on the battery status of the batteries in the battery swapping station; and determining a battery swapping scheduling strategy for the target vehicle based on the battery swapping demand and the battery supply capacity, wherein the battery swapping scheduling strategy includes a battery swapping sequence strategy and a battery swapping time strategy. Before predicting the battery swapping demand based on the power depletion point and the destination coordinates, the method further includes: determining a corresponding sliding window based on the route distance of the target route, wherein the sliding window represents the range that moves with the current position of the target vehicle, the range being determined based on 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 through a recurrent neural network to obtain the future power consumption of the target vehicle; and determining the power depletion point based on the future power consumption.

7. A vehicle, characterized in that, This 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 interact with the first radio frequency module located in the battery swapping station, upload vehicle status and receive battery swapping dispatch instructions, wherein the battery swapping dispatch instructions are generated according to the battery swapping dispatching strategy, and the battery swapping dispatching strategy is obtained by the battery swapping dispatching method according to any one of claims 1 to 5; The driving control module is used to control the vehicle to drive to a predetermined location according to the battery swapping dispatch instruction, switch the vehicle's gear 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 battery swapping state after the braking module releases the braking force, and to feed back the battery swapping state switching completion information to the battery swapping station through the second radio frequency module so that the battery swapping station can perform battery swapping operation on the vehicle.

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