Charging station planning method, device, electronic device and storage medium

Abstract

The present application provides a charging station planning method, device, electronic device, and storage medium. The method includes: obtaining vehicle driving data and at least one candidate charging station corresponding to a preset navigation route, and determining charging station attribute data corresponding to each candidate charging station; solving a pre-established objective function based on the vehicle driving data, the at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station under the constraints of a first constraint and a second constraint, determining at least one target charging station and a target charging power corresponding to each target charging station; and performing route planning based on the determined at least one target charging station and the target charging power corresponding to each target charging station. Through the technical solution of the present application, the effect of effectively selecting and planning charging stations for electric vehicles traveling long distances based on reference costs is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a charging station planning method, device, electronic device and storage medium. Background Art

[0002] Electric vehicles are becoming increasingly popular, but when driving long distances, drivers may become anxious about the problem of replenishing power.

[0003] Currently, most charging station recommendation systems simply present information about charging stations along the route, leaving drivers to make their own decisions. Some methods also recommend nearby charging stations based on the vehicle's remaining battery life, but these fail to factor in the vehicle's actual driving conditions. This can lead to inaccurate recommendations and poor charging planning, such as frequent charging that leaves the battery insufficient to reach the next charging station. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a charging station planning method, device, electronic device and storage medium to effectively plan the selection of charging stations for electric vehicles traveling long distances.

[0005] Based on the above objectives, the present application provides a charging station planning method, which includes:

[0006] Obtaining vehicle driving data and at least one candidate charging station corresponding to a preset navigation route, and determining charging station attribute data corresponding to each candidate charging station; wherein the charging station attribute data is used to characterize the attributes of the candidate charging station;

[0007] Based on the vehicle driving data, the at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station, a pre-established objective function is solved under the constraints of a first constraint and a second constraint to determine at least one target charging station and a target charging power corresponding to each target charging station; wherein the objective function is a function constructed with the goal of minimizing the reference cost, the first constraint is that the single driving distance corresponding to each candidate charging station is not greater than the corresponding single cruising range, and the second constraint is that the charging starting power corresponding to each candidate charging station is not greater than the target charging power corresponding to each candidate charging station; the charging starting power corresponding to the candidate charging station is the remaining power of the vehicle upon arrival at the candidate charging station;

[0008] Route planning is performed based on the determined at least one target charging station and the target charging power corresponding to each target charging station.

[0009] Based on the above objectives, the present application also provides a charging station planning device, which includes:

[0010] a data acquisition module, configured to acquire vehicle driving data and at least one candidate charging station corresponding to a preset navigation route, and determine charging station attribute data corresponding to each candidate charging station; wherein the charging station attribute data is used to characterize the attributes of the candidate charging station;

[0011] an objective function solving module, configured to solve a pre-established objective function based on the vehicle driving data, the at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station, under the constraints of a first constraint and a second constraint, to determine at least one target charging station and a target charging power corresponding to each target charging station; wherein the objective function is a function constructed with the goal of minimizing a reference cost, the first constraint is that the single driving distance corresponding to each candidate charging station is not greater than the corresponding single cruising range, and the second constraint is that the charging starting power corresponding to each candidate charging station is not greater than the target charging power corresponding to each candidate charging station; the charging starting power corresponding to the candidate charging station is the remaining power of the vehicle upon arrival at the candidate charging station;

[0012] The route planning module is used to plan a route based on at least one determined target charging station and a target charging power corresponding to each target charging station.

[0013] Based on the above purpose, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the charging station planning method provided in any embodiment of the present application.

[0014] Based on the above purpose, the present application also provides a computer-readable storage medium, which stores computer instructions, characterized in that the computer instructions are used to enable a computer to execute the charging station planning method provided in any embodiment of the present application.

[0015] As can be seen from the above, the charging station planning method provided in the present application can solve the pre-established objective function with the minimum reference cost as the goal under the constraints of the first constraint and the second constraint through vehicle driving data, each candidate charging station on the preset navigation route, and the charging station attribute data corresponding to each candidate charging station, so as to determine at least one target charging station and the target charging power corresponding to each target charging station according to the reference cost, and then perform path planning based on the determined at least one target charging station and the target charging power corresponding to each target charging station, so as to reasonably and effectively carry out charging station selection planning for electric vehicles traveling long distances based on the reference cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 A flowchart of a charging station planning method provided in an embodiment of the present application;

[0018] Figure 2 A flowchart of another charging station planning method provided in an embodiment of the present application;

[0019] Figure 3 A flowchart of another charging station planning method provided in an embodiment of the present application;

[0020] Figure 4 A schematic diagram of a charging station planning system provided in an embodiment of the present application;

[0021] Figure 5 A schematic diagram of the structure of a charging station planning device provided in an embodiment of the present application;

[0022] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0025] Figure 1This is a flowchart of a charging station planning method provided in an embodiment of the present application. This method is mainly applicable to the selection and planning of appropriate charging station planning schemes when electric vehicles travel long distances. This method can be executed by a charging station planning device, which can be implemented in software and / or hardware and can be configured in an electronic device. Figure 1 As shown, the method may specifically include the following steps:

[0026] S110: Acquire vehicle driving data and at least one candidate charging station corresponding to a preset navigation route, and determine charging station attribute data corresponding to each candidate charging station.

[0027] Vehicle driving data may include current and historical vehicle data, such as the current remaining battery charge and historical vehicle behavior data. A pre-planned navigation route may be a pre-planned driving route. Candidate charging stations may be charging stations within a pre-set distance along the entire pre-planned navigation route. Charging station attribute data characterizes the attributes of candidate charging stations, such as charging electricity price and charging power.

[0028] Specifically, the vehicle's driving data is obtained through the vehicle computer. The specific type of driving data obtained can be determined based on actual needs. Furthermore, based on the preset navigation route, charging stations within a preset distance around the entire preset navigation route are identified. For example, the preset distance can be 30 km, and the specific value can be set based on needs. The identified charging stations are then used as candidate charging stations.

[0029] For example, the charging stations within a preset distance around the entire navigation route are coded in order as 1, 2, ..., n, where n is the total number of charging stations. Candidate charging stations can be represented in the form of a set, for example: x1 = x 11 ,…,x 1i ,…,x 1n ], where x 11 …x 1n ∈Ω1, Ω1 is a 0, 1 variable. If the i-th charging station is selected, let x 1i =1, otherwise, let x 1i =0.

[0030] S120. Based on the vehicle driving data, at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station, a pre-established objective function is solved under the constraints of a first constraint and a second constraint to determine at least one target charging station and a target charging power corresponding to each target charging station.

[0031] Among them, the objective function is a function constructed with the goal of minimizing the reference cost. The first constraint condition is that the single driving distance corresponding to each candidate charging station is not greater than the corresponding single cruising range. The second constraint condition is that the charging starting power corresponding to each candidate charging station is not greater than the target charging power corresponding to each candidate charging station. The target charging power is used to represent the percentage of power after charging at the candidate charging station, that is, the percentage of the full power of the vehicle. Exemplarily, the target charging power can be expressed in the form of a set, for example: it can be x2=x 21 ,…,x 2i ,…,x 2n ], where x 21 …x 2i ∈Ω2, Ω2 is the feasible region of the target power, 0<Ω2<100%. The target charging station can be the charging station selected from the candidate charging stations for subsequent path planning.

[0032] Among them, the reference cost can be the additional cost of selecting a certain number of candidate charging stations for charging, which can be mileage cost, time cost, price cost, etc. The first constraint is used to ensure that the vehicle's power is sufficient for it to travel to the next candidate charging station. The second constraint is used to ensure that charging is performed in each candidate charging station, rather than discharging or inaction. The single driving distance can be the driving distance from the current position of the vehicle to the next selected candidate charging station and the driving distance between each two adjacent selected candidate charging stations. The single cruising range can be the maximum distance that the current power or the target charging power corresponding to each candidate charging station can support. The starting charging power corresponding to the candidate charging station is the remaining power of the vehicle when it arrives at the candidate charging station.

[0033] Specifically, based on the acquired vehicle driving data, at least one candidate charging station and the charging station attribute data corresponding to each candidate charging station are determined, and a pre-established objective function can be solved under the constraints of the first restriction condition and the second restriction condition. Then, the solution is used as the target charging station included in the subsequent path planning and the target charging power corresponding to each target charging station.

[0034] It is understandable that the first constraint, that the single trip distance corresponding to each candidate charging station is no greater than the corresponding single trip range, ensures that the vehicle can smoothly reach each candidate charging station and avoids running out of power along the way. The second constraint, that the starting charge level corresponding to each candidate charging station is no greater than the target charge level corresponding to each candidate charging station, ensures that the purpose of visiting each candidate charging station is to replenish energy rather than consume energy.

[0035] Based on the above example, if the reference cost includes the number of charging times, the pre-established objective function can be solved under the constraints of the first and second constraints in the following manner:

[0036] The number of charging times is determined according to the number of candidate charging stations selected from each candidate charging station; and under the constraints of the first constraint and the second constraint, an objective function constructed with the goal of minimizing the number of charging times is solved.

[0037] The selected candidate charging station may be a charging station to be charged selected from charging stations within a preset distance around the entire navigation route, and the number of charging times may be the number of the selected candidate charging stations.

[0038] Specifically, the number of selected candidate charging stations is used as the number of charging times, a type of reference cost. Based on this, the objective function can be transformed from one constructed with the goal of minimizing the reference cost to one constructed with the goal of minimizing the number of charging times. Furthermore, under the constraints of the first and second constraints, the objective function constructed with the goal of minimizing the number of charging times is solved.

[0039] For example, each candidate charging station can be represented as a set as x1=x 11 ,…,x 1i ,…,x 1n ], where n is the total number of candidate charging stations, x 11 …x 1n ∈Ω1, Ω1 is a 0, 1 variable. If the i-th candidate charging station is selected, let x 1i =1, otherwise, let x 1i =0.

[0040] So, the number of charging times

[0041] S130: Perform route planning based on the determined at least one target charging station and the target charging power corresponding to each target charging station.

[0042] Specifically, based on the determined at least one target charging station, the preset navigation route is replanned to obtain a new navigation route. Furthermore, the target charging power corresponding to each target charging station can be marked.

[0043] Optionally, each target charging station may be marked on the navigation map to remind the user to go to the target charging station for charging and charge the target charging amount corresponding to the target charging station.

[0044] The charging station planning method provided in this embodiment can solve a pre-established objective function with the goal of minimizing the reference cost under the constraints of a first constraint and a second constraint through vehicle driving data, candidate charging stations on a preset navigation route, and charging station attribute data corresponding to each candidate charging station, so as to determine at least one target charging station and a target charging power corresponding to each target charging station according to the reference cost. Then, based on the determined at least one target charging station and the target charging power corresponding to each target charging station, route planning is performed, so as to reasonably and effectively select charging stations for electric vehicles traveling long distances based on the reference cost.

[0045] Figure 2 This is a flowchart of another charging station planning method provided by an embodiment of the present application. Based on the above embodiments, optionally, when the reference cost includes the energy replenishment cost, an exemplary explanation of the solution method of the objective function is given. The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here. Figure 2 As shown, the method may specifically include the following steps:

[0046] S210: Acquire vehicle driving data and at least one candidate charging station corresponding to a preset navigation route, and determine charging station attribute data corresponding to each candidate charging station.

[0047] The vehicle driving data includes the vehicle's current remaining power and historical behavior data. The charging station attribute data includes the charging unit price. The vehicle may be an electric vehicle for long-distance driving. The current remaining power may be the percentage of the vehicle's remaining power. The charging unit price may be the price of charging a unit of power.

[0048] Specifically, based on the preset navigation route and the locations of each candidate charging station, the driving distance from the current location to the next selected candidate charging station can be determined. The driving distance between any two subsequent adjacent candidate charging stations can also be determined, and the energy consumed for each driving distance can be determined. Using the current remaining power and each target charging power as the minuend, and the energy consumed for the corresponding driving distance as the subtrahend, the remaining power after driving each driving distance can be obtained, i.e., the starting charging power.

[0049] Optionally, the charging price can be obtained for each candidate charging station within a preset distance around the entire preset navigation route, for example, it can be expressed in the form of a set as p = [p1, ..., p i ,…,p n ], where n is the total number of candidate charging stations, p i represents the charging unit price of the i-th candidate charging station.

[0050] S220: Determine the starting charging power corresponding to each candidate charging station based on the vehicle's current remaining power, historical behavior data, a preset navigation route, each candidate charging station, and the target charging power corresponding to each candidate charging station.

[0051] Based on the above example, optionally, the following steps may be used to determine the starting charging capacity of each candidate charging station:

[0052] Step 1: Based on the preset navigation route and each candidate charging station, determine the single driving distance corresponding to each candidate charging station.

[0053] The single driving distance may be the driving distance from the current position of the vehicle to the next selected candidate charging station or the driving distance between any two subsequent adjacent selected candidate charging stations.

[0054] Specifically, based on the preset navigation route and the location of each candidate charging station, the driving distance from the vehicle's current location to the next selected candidate charging station can be determined. The driving distance between any two subsequent adjacent selected candidate charging stations can also be determined, and these driving distances are used as single driving distances.

[0055] Step 2: Predict each single driving distance based on historical behavior data and a pre-built unit power consumption prediction model to determine the single energy consumption corresponding to each candidate charging station.

[0056] The unit power consumption prediction model may be a model for predicting the power consumed per unit distance traveled. Historical behavior data may include the vehicle's driving behavior data and / or experience behavior data in the past, where the past time may be a period of time before and adjacent to the current moment. Driving behavior data may include the frequency of sudden accelerations, the frequency of sudden decelerations, the average deceleration per kilometer, the average acceleration per kilometer, the average vehicle speed, and the like. Experience behavior data may include the average difference between the air conditioning target temperature and the external ambient temperature, the online rate of the entertainment system, the vehicle model, the average vehicle load, and the like.

[0057] Specifically, for each single driving distance, the single driving distance can be divided into multiple unit distances, and the energy consumption value corresponding to each unit distance can be determined through historical behavior data and a pre-built unit power consumption prediction model, and the sum of these energy consumption values can be used as the single energy consumption corresponding to the single driving distance.

[0058] Based on the above example, the following method can be used to predict the distance of each single trip based on historical behavior data and a pre-built unit power consumption prediction model to determine the single energy consumption corresponding to each candidate charging station:

[0059] For the single driving distance corresponding to each candidate charging station, the energy consumption value of each unit distance in the single driving distance is determined based on the historical behavior data and the unit power consumption prediction model, and the single energy consumption corresponding to the single driving distance is determined based on each energy consumption value; the sum of the single energy consumption corresponding to each single driving distance is used as the single energy consumption corresponding to the candidate charging station.

[0060] Specifically, the same method can be used to determine the single-trip energy consumption for each candidate charging station's single-trip distance. Therefore, we'll use one single-trip distance as an example for illustration. Based on historical behavior data, the behavior data for each unit distance within the single-trip distance can be predicted to obtain predicted behavior data. The predicted behavior data for each unit distance is input into the unit power consumption prediction model to predict the energy consumption value for each unit distance. The sum of these energy consumption values is then used as the single-trip energy consumption corresponding to that single-trip distance.

[0061] Based on the above example, the unit power consumption prediction model can be trained in the following way:

[0062] Obtain sample behavior data; divide the sample behavior data according to unit distance to obtain sample unit behavior data, and determine the sample unit energy consumption value corresponding to the sample unit behavior data; train the initial prediction model based on the sample unit behavior data and the sample unit energy consumption value to obtain a unit power consumption prediction model.

[0063] The sample behavior data may be data collected by a vehicle via a telematics-box (TBOX). The unit distance may be a preset distance, such as 1 km. The sample unit behavior data may be data obtained by dividing the sample behavior data by unit distance. The sample unit energy consumption value may be the electrical energy consumed by the vehicle traveling according to the sample unit behavior data. The initial prediction model may be a prediction model without any model parameter adjustment.

[0064] Specifically, the vehicle's sample behavior data is acquired through TBOX and divided according to unit distance, resulting in multiple sample unit behavior data. Furthermore, the electric energy consumed by the vehicle traveling according to each sample unit behavior data is determined, i.e., the sample unit energy consumption value corresponding to each sample unit behavior data. Furthermore, each sample unit behavior data and the corresponding sample unit energy consumption value are divided into a training set and a test set. The initial prediction model is trained using the training set, and the trained initial prediction model is tested using the test set to adjust the model parameters of the initial test model. The initial prediction model that passes the test is then used as the unit power consumption prediction model.

[0065] For example, the historical travel data of the vehicle is collected through TBOX and the data is stored. The data is desensitized according to security and compliance requirements, and the data is broken up as sample behavior data. The sample behavior data is divided into training set, validation set and test set according to 6:2:2. Furthermore, feature engineering is established, taking into account driving behavior habits, road congestion, and power consumption when using the in-car entertainment and air-conditioning systems. The sample behavior data is sliced and processed by 1 kilometer, including: calculating the frequency of sudden acceleration; calculating the frequency of sudden deceleration; calculating the average deceleration per kilometer; calculating the average acceleration per kilometer; calculating the average vehicle speed; calculating the average difference between the air-conditioning target temperature and the external ambient temperature (the target temperature is positive if it is lower than the ambient temperature, and negative if it is higher than the ambient dimension); calculating the online rate of the entertainment system; vehicle model (one-hot encoding); average vehicle load, etc. The xgboost model was used to fit the training set and the optimal parameters were set. The AUC (Area UnderCurve, area under the ROC curve) and feature weights were used as the basis for model iteration. The trained model achieved an AUC value of over 0.90 on the test set and was verified on the validation set to obtain a unit power consumption prediction model.

[0066] Based on the above example, the energy consumption value for each unit distance in a single driving distance can be determined optionally using the following method based on historical behavior data and a unit power consumption prediction model:

[0067] For each unit distance in a single driving distance, a preset number of historical unit behavior data corresponding to the unit distance is determined based on the historical behavior data; predicted unit behavior data corresponding to the unit distance is predicted based on the historical unit behavior data; the predicted unit behavior data is input into the unit power consumption prediction model to obtain the energy consumption value corresponding to the unit distance.

[0068] The historical unit behavior data may be data obtained by dividing the historical behavior data by unit distance, and the predicted unit behavior data may be predicted behavior data of unit distance.

[0069] Specifically, for each unit distance in a single driving distance, a preset number of historical unit behavior data are determined. The times corresponding to these historical unit behavior data are adjacent to each other, and the time corresponding to the last historical unit behavior data is adjacent to the time corresponding to the unit distance. Based on the preset number of historical unit behavior data, the behavior data corresponding to the unit distance can be predicted, i.e., the predicted unit behavior data. Furthermore, the predicted unit behavior data is input into the unit power consumption prediction model to predict the energy consumption value corresponding to the unit distance. Furthermore, the predicted unit behavior data can be used as historical unit behavior data to predict the predicted unit behavior data corresponding to the next unit distance.

[0070] For example, the data after 3 kilometers of each historical trip is obtained as a data set, and xgboost is used for time series modeling to obtain a time series prediction model. The data is sliced with a step length of 1 kilometer, and the data of the first 20 steps is used as input and the 21st step is used as output. The input data are the average frequency sequence of rapid acceleration per kilometer in the first 20 steps; the average frequency sequence of rapid deceleration per kilometer in the first 20 steps; the average acceleration sequence per kilometer in the first 20 steps; the average deceleration sequence per kilometer in the first 20 steps; the average vehicle speed sequence in the first 20 steps, etc. The time series prediction model is fitted, and the optimal parameters are set by adjustment. The AUC value and feature weight are used as the iteration basis of the time series prediction model to obtain the time series prediction model. For example: the iteration basis is that the AUC value on the test set is above 0.90. The time series prediction model obtained through training can use historical behavior data as model input to obtain predicted behavior data. In addition, the predicted behavior data can be added to the historical behavior data for subsequent predictions.

[0071] Step 3: Determine the charging starting power corresponding to each candidate charging station based on the current remaining power, the single energy consumption corresponding to each candidate charging station, and the target charging power.

[0072] Specifically, the current remaining power and each target charging power are used as the minuends, and each single energy consumption is used as the subtrahend. By subtracting them accordingly, we can obtain the remaining power after traveling each single driving distance, that is, the remaining power when arriving at each candidate charging station, that is, the starting charging power corresponding to each candidate charging station.

[0073] S230 determines the charging cost according to the starting charging power, target charging power, and charging unit price corresponding to each candidate charging station.

[0074] The charging cost is used to represent the electricity cost incurred when charging at each candidate charging station.

[0075] Specifically, the amount of energy required to recharge at each selected candidate charging station can be calculated based on the starting and target charging levels for each candidate charging station. For each selected candidate charging station, the corresponding recharge level is multiplied by the vehicle's full charge capacity, then multiplied by the corresponding charging unit price to determine the monetary cost of using that station. The monetary costs for each selected candidate charging station are then added together to determine the recharge cost.

[0076] For example, the energy cost can be Among them, the candidate charging station planning scheme x=[x1,x2],x1=x 11 ,…,x 1i ,…,x 1n ] is the set form of candidate charging stations, x2=x 21 ,…,x2i ,…,x 2n ] is the target charging power. i represents the initial charging capacity corresponding to the i-th charging station, p i is the charging price of the i-th charging station, and M is the full charge capacity of the vehicle.

[0077] S240: Under the constraints of the first constraint and the second constraint, solve the objective function constructed with the goal of minimizing the energy charging cost, and determine at least one target charging station and a target charging power corresponding to each target charging station.

[0078] Specifically, since the reference cost includes the energy replenishment cost, the objective function can be transformed from a function constructed with the goal of minimizing the reference cost to a function constructed with the goal of minimizing the energy replenishment cost. Furthermore, under the constraints of the first and second constraints, the objective function constructed with the goal of minimizing the energy replenishment cost is solved.

[0079] S250: Perform route planning based on the determined at least one target charging station and the target charging power corresponding to each target charging station.

[0080] The charging station planning method provided in this embodiment obtains the charging unit price corresponding to each candidate charging station, obtains historical behavior data, and determines the charging starting power corresponding to each candidate charging station. Then, based on the charging unit price, charging starting power set, and target charging power corresponding to each candidate charging station, the energy replenishment cost is determined. Under the constraints of the first and second constraints, an objective function constructed with the goal of minimizing the energy replenishment cost is solved to determine at least one target charging station and the target charging power corresponding to each target charging station through the energy replenishment cost. Charging station selection planning is performed for electric vehicles traveling long distances based on the amount of money spent on charging.

[0081] Figure 3 This is a flowchart of another charging station planning method provided by an embodiment of the present application. Based on the above embodiments, optionally, when the reference cost includes additional time, an exemplary method for solving the objective function is provided. The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here. Figure 3 As shown, the method may specifically include the following steps:

[0082] S310: Acquire vehicle driving data and at least one candidate charging station corresponding to a preset navigation route, and determine charging station attribute data corresponding to each candidate charging station.

[0083] Vehicle driving data includes the vehicle's current remaining battery charge and historical vehicle behavior data. Charging station attribute data includes charging distance, charging speed, and charging power. Charging distance can be the distance the vehicle deviates from the preset navigation route each time it travels to the next selected candidate charging station. Charging speed can be the driving speed corresponding to each charging distance. Charging power can be the power consumed during charging at each candidate charging station.

[0084] For example, based on the preset navigation route and the location of each candidate charging station, the distance of deviation from the preset navigation route each time traveling to each selected candidate charging station, i.e., the charging distance, can be determined, and based on the road conditions near each selected candidate charging station, the average speed on the route corresponding to each charging distance, i.e., the charging driving speed, can be obtained from the on-board third-party map interface.

[0085] S320: Determine the additional travel time based on the charging distance and charging speed corresponding to each candidate charging station.

[0086] The additional travel time is used to represent the time it takes to travel to and from each candidate charging station.

[0087] Specifically, for each candidate charging station's corresponding charging distance, divide the charging distance by the corresponding charging speed to calculate the extra one-way trip time to that candidate charging station. Multiply the extra one-way trip time by two to calculate the extra round-trip time. Then, add the extra round-trip times corresponding to each candidate charging station to calculate the extra trip time.

[0088] S330: Determine the starting charging power corresponding to each candidate charging station based on the vehicle's current remaining power, historical behavior data, a preset navigation route, each candidate charging station, and the target charging power corresponding to each candidate charging station.

[0089] It should be noted that the method of determining the charging starting power corresponding to each candidate charging station in S330 is the same as that in S220 and will not be repeated here.

[0090] S340: Determine the additional charging time based on the starting charging power, target charging power, and charging power corresponding to each candidate charging station.

[0091] The additional charging time is used to represent the time required to charge at each candidate charging station.

[0092] Specifically, the amount of energy required to recharge at each candidate charging station is calculated based on the starting charge and target charge. For each candidate charging station, the corresponding recharge capacity is multiplied by the vehicle's full charge capacity, and then divided by the corresponding charging power to obtain the time cost of charging at that station. The additional charging time is calculated by adding the time costs for each candidate charging station.

[0093] S350: The sum of the extra travel time and the extra charging time is taken as the extra time.

[0094] The extra time includes the extra time for the journey and the extra time for charging. The extra time may be the sum of the extra time for the journey and the extra time for charging.

[0095] For example, T(x) is the additional time, Among them, the candidate charging station planning scheme x=[x1,x2],x1=x 11 ,…,x 1i ,…,x 1n ] is the set form of candidate charging stations, x2=x 21 ,…,x 2i ,…,x 2n ] is the aggregate form of target charging power, s i is the charging distance corresponding to the i-th candidate charging station, v i is the driving speed corresponding to the i-th candidate charging station (which can be obtained in real time from the vehicle's three-party map interface), Remain i represents the starting charging capacity corresponding to the i-th candidate charging station, P i is the charging power of the i-th candidate charging station, and 1.2 is the charging loss coefficient.

[0096] S360: Under the constraints of the first constraint and the second constraint, solve the objective function constructed with the goal of minimizing the additional time.

[0097] Specifically, because the reference cost includes the extra time, the objective function can be transformed from a function constructed with the goal of minimizing the reference cost to a function constructed with the goal of minimizing the extra time. Furthermore, under the constraints of the first and second constraints, the objective function constructed with the goal of minimizing the extra time is solved.

[0098] S370: Perform route planning based on the determined at least one target charging station and the target charging power corresponding to each target charging station.

[0099] The charging station planning method provided in this embodiment obtains historical behavior data by obtaining the charging distance, charging speed, and charging power corresponding to each candidate charging station. Based on the charging distance and charging speed corresponding to each candidate charging station, the time spent on round-trip charging, i.e., the extra charging time, is calculated. Based on the starting charge level, target charge level, and charging power corresponding to each candidate charging station, the time spent charging at each candidate charging station, i.e., the extra charging time, is calculated. Based on the extra travel time and the extra charging time, the extra time is determined. Then, under the constraints of first and second constraints, an objective function constructed with the goal of minimizing the extra time is solved. Based on the extra time, at least one target charging station and the target charging power corresponding to each target charging station are determined. This allows for charging station selection and planning for long-distance electric vehicles, taking into account the extra time cost of charging.

[0100] Based on the above example, the following steps can be used to determine the single-trip mileage:

[0101] Step 1: Determine the single-trip battery life based on the vehicle's current remaining battery life and the target charging capacity corresponding to each candidate charging station.

[0102] The single-time endurance power may be the current remaining power and the target charging power corresponding to each candidate charging station.

[0103] Step 2: For each candidate charging station, determine the single-trip range corresponding to the candidate charging station based on historical behavior data and a pre-built unit power consumption prediction model.

[0104] Specifically, for each single endurance charge, the same method can be used to calculate the single endurance mileage corresponding to the single endurance charge. Therefore, one of the single endurance charges is used as an example for explanation. Based on historical behavior data and the pre-built unit power consumption prediction model, the electric energy consumed for each unit distance of driving can be predicted. These electric energies are added one by one until they are greater than or equal to the single endurance charge. If they are equal, the sum of the unit distances added at this time is used as the single endurance mileage corresponding to the single endurance charge; if they are greater than, the sum of the unit distances added the previous time is used as the single endurance mileage corresponding to the single endurance charge, that is, the single endurance mileage corresponding to the candidate charging station.

[0105] Optionally, the vehicle's corresponding user energy anxiety level can be determined based on the vehicle's historical charging history. This level indicates when the user typically charges. In this case, the single-trip battery life can be corrected to the difference between the current remaining battery level and the target charging level corresponding to each candidate charging station and the user energy anxiety level.

[0106] Optionally, the reference cost includes at least two sub-costs. The following steps can be performed to solve a pre-established objective function under the constraints of the first constraint and the second constraint to determine at least one target charging station and a target charging power corresponding to each target charging station:

[0107] Step 1: According to the pre-set sub-cost priority, the sub-cost with the highest sub-cost priority is used as the first sub-cost, and the first objective function is solved under the constraints of the first constraint condition and the second constraint condition to obtain the third constraint condition.

[0108] The sub-cost priority may include the priority of each sub-cost in the reference cost. The first objective function is a function constructed with the goal of minimizing the first sub-cost. The first sub-cost may be the sub-cost with the highest sub-cost priority. The third constraint may be a constraint added during the solution process based on the sub-cost priority.

[0109] Specifically, the sub-cost with the highest priority is used as the first sub-cost, and a first objective function is constructed with the goal of minimizing the first sub-cost. Furthermore, the first objective function can be solved under the constraints of the first and second constraints, and the solution obtained is used as the third constraint.

[0110] Step 2: The sub-cost with the sub-cost priority second only to the first sub-cost is taken as the second sub-cost, and it is determined whether the second sub-cost is the sub-cost with the lowest sub-cost priority.

[0111] Step 3: If yes, solve the second objective function under the constraints of the first constraint, the second constraint, and the third constraint to determine at least one target charging station and the target charging power corresponding to each target charging station.

[0112] The second objective function is a function constructed with the goal of minimizing the second sub-cost.

[0113] Specifically, if the second sub-cost has the lowest priority, then the second objective function, constructed with the goal of minimizing the second sub-cost, can be considered the last objective function to be solved. Therefore, the second objective function can be solved under the constraints of the first, second, and third constraints. The solution is then used as the target charging station and the target charging power corresponding to each target charging station in the subsequent route planning.

[0114] Step 4. If not, solve the second objective function under the constraints of the first constraint, the second constraint and the third constraint, update the third constraint, update the second sub-cost according to the sub-cost whose priority is second only to the second sub-cost, and return to the step of determining whether the second sub-cost is the sub-cost with the lowest sub-cost priority.

[0115] Specifically, if the second sub-cost is not the sub-cost with the lowest sub-cost priority, then it can be considered that the second objective function constructed with the second sub-cost as the minimum is not the last objective function to be solved. Therefore, the solution result can be added to the third constraint for the next objective function solution. Solve the second objective function under the constraints of the first constraint, the second constraint, and the third constraint, and add the solution result to the third constraint to update the third constraint. In addition, the sub-cost with the sub-cost priority second only to the second sub-cost is used as the new second sub-cost to update the second sub-cost, and the step of determining whether the second sub-cost is the sub-cost with the lowest sub-cost priority can be returned to execute until the second sub-cost is the sub-cost with the lowest sub-cost priority, and the target charging station and the target charging power corresponding to each target charging station are obtained.

[0116] For example, when the reference cost includes the recharging cost, the number of charging times, and the extra time, the objective function of the target charging station planning solution can be determined as:

[0117]

[0118] Where f(x) represents the target charging station planning scheme, C represents the energy replenishment cost, N represents the number of charging times, T represents the additional time, L(x) represents the single driving distance corresponding to each candidate charging station, mre(x) represents the single driving range corresponding to each candidate charging station, and Remain i represents the starting charging capacity corresponding to the i-th candidate charging station, and E(x) represents the target charging capacity corresponding to each candidate charging station.

[0119] For example, based on the above example, let the sub-cost priority be C>T>N. Then, we can first take C as the first sub-cost and construct the first objective function with the goal of minimizing C, that is:

[0120]

[0121] The solution can obtain the optimal value Cmin of C(x), and then use Cmin as the third constraint condition, and use the sub-cost T whose sub-cost priority is second only to the first sub-cost C as the second sub-cost, and construct the second objective function with the goal of minimizing T.

[0122] Since T is not the sub-cost with the lowest priority, the second objective function is solved under the constraints of the first constraint, the second constraint, and the third constraint, that is,

[0123]

[0124] Similarly, the optimal value Tmin of T(x) is obtained, and then Cmin and Tmin are used as the third constraint conditions at the same time, and the sub-cost N whose sub-cost priority is second only to the second sub-cost T is used as the new second sub-cost, and a new second objective function is constructed with the goal of minimizing N.

[0125] Since N is the sub-cost with the lowest sub-cost priority, the second objective function is solved under the constraints of the first constraint, the second constraint, and the third constraint, and finally the optimal solution under the optimal conditions is obtained, that is, at least one target charging station and the target charging power corresponding to each target charging station.

[0126] In some embodiments, a schematic diagram of a charging station planning system is shown as follows: Figure 4 As shown, it mainly includes: TBOX, HUT host (vehicle terminal), CP\SP (Content Provider\Service Provider) cloud platform and TSP (Telematics Service Provider).

[0127] Among them, TBOX includes a vehicle bus data acquisition module, the HUT host includes a vehicle application data acquisition module and an optimal charging result presentation module, the CP\SP cloud platform includes a road information real-time acquisition module and a charging pile information real-time acquisition module, and the TSP includes a vehicle data storage system, a cruising range prediction module, and an optimal charging station planning module.

[0128] like Figure 4 The system mainly consists of three parts: data collection, data storage and calculation, and result push and presentation:

[0129] Data Collection: The vehicle bus data acquisition module primarily collects vehicle driving status data from TBOX. The vehicle computer application data acquisition module collects vehicle computer map application data from the vehicle computer backend system. The road information real-time acquisition module and the charging pile information real-time acquisition module primarily obtain this information data in real time from the CP / SP cloud platform.

[0130] Data storage and computation: This functionality is handled by the cloud-based TSP platform. The vehicle data storage system stores both real-time and historical data. Real-time data includes charging station information, road information, and vehicle-side user route planning requests. Historical data includes vehicle status information generated by users' past driving history. The range prediction module and optimal charging station planning module implement the corresponding charging station planning method in any of the aforementioned embodiments.

[0131] Result presentation: This part of the function is to push the calculated optimal energy replenishment plan (target charging station planning scheme) to the vehicle terminal for display.

[0132] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate with each other to complete the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the charging station planning method.

[0133] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0134] Based on the same inventive concept, corresponding to any of the above embodiments and methods, this application also provides a charging station planning device. Figure 5 The charging station planning device includes: a data acquisition module 510, an objective function solving module 520 and a path planning module 530.

[0135] The data acquisition module 510 is configured to acquire vehicle driving data and at least one candidate charging station corresponding to a preset navigation route, and determine charging station attribute data corresponding to each candidate charging station; wherein the charging station attribute data is used to characterize the attributes of the candidate charging station; the objective function solving module 520 is configured to solve a pre-established objective function based on the vehicle driving data, the at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station, subject to first and second constraints, to determine at least one target charging station and a target charging capacity corresponding to each target charging station; wherein the objective function is constructed with the goal of minimizing the reference cost, the first constraint being that the single driving distance corresponding to each candidate charging station is not greater than the corresponding single cruising range, and the second constraint being that the charging starting capacity corresponding to each candidate charging station is not greater than the target charging capacity corresponding to each candidate charging station; the charging starting capacity corresponding to the candidate charging station is the remaining capacity of the vehicle upon arrival at the candidate charging station; and the route planning module 530 is configured to perform route planning based on the determined at least one target charging station and the target charging capacity corresponding to each target charging station.

[0136] The charging station planning device provided in this embodiment can solve a pre-established objective function with the goal of minimizing the reference cost under the constraints of a first constraint and a second constraint through vehicle driving data, candidate charging stations on a preset navigation route, and charging station attribute data corresponding to each candidate charging station, so as to determine at least one target charging station and a target charging power corresponding to each target charging station according to the reference cost. Then, path planning is performed based on the determined at least one target charging station and the target charging power corresponding to each target charging station, so as to reasonably and effectively plan the selection of charging stations for electric vehicles traveling long distances based on the reference cost.

[0137] Based on the above implementation, optionally, the reference cost includes a recharging cost, wherein the recharging cost is used to characterize the electricity cost incurred when charging at each of the candidate charging stations; the vehicle driving data includes the current remaining power of the vehicle and historical behavior data, and the charging station attribute data includes a charging unit price. The objective function solving module 520 is further used to determine the charging starting power corresponding to each candidate charging station based on the current remaining power of the vehicle, the historical behavior data, the preset navigation route, each candidate charging station and the target charging power corresponding to each candidate charging station; determine the recharging cost based on the charging starting power, target charging power and charging unit price corresponding to each candidate charging station; and solve the objective function constructed with the goal of minimizing the recharging cost under the constraints of the first and second constraints.

[0138] Based on the above implementation, optionally, the reference cost includes the number of charging times, and the objective function solving module 520 is further used to determine the number of charging times based on the number of candidate charging stations selected from each of the candidate charging stations; under the constraints of the first constraint and the second constraint, solve the objective function constructed with the minimum number of charging times as the goal.

[0139] Based on the above implementation, optionally, the reference cost includes additional time, wherein the additional time includes additional travel time and additional charging time, the additional travel time is used to represent the time for traveling to and from each candidate charging station, and the additional charging time is used to represent the time for charging at each candidate charging station; the vehicle driving data includes the current remaining power of the vehicle and historical behavior data, and the charging station attribute data includes charging distance, charging driving speed and charging power. The objective function solving module 520 is further used to calculate the target value based on the charging distance and charging driving speed corresponding to each candidate charging station. , determining an additional time for the journey; determining a starting charging power corresponding to each candidate charging station based on the vehicle's current remaining power, the historical behavior data, the preset navigation route, each candidate charging station, and a target charging power corresponding to each candidate charging station; determining an additional charging time based on the starting charging power, target charging power, and charging power corresponding to each candidate charging station; taking the sum of the additional time for the journey and the additional charging time as the additional time; and solving an objective function constructed with the goal of minimizing the additional time under the constraints of the first and second constraints.

[0140] On the basis of the above-mentioned implementation manner, optionally, the objective function solving module 520 is further used to determine the single driving distance corresponding to each candidate charging station based on the preset navigation route and each candidate charging station; predict each single driving distance based on the historical behavior data and a pre-built unit power consumption prediction model to determine the single energy consumption corresponding to each candidate charging station; and determine the charging starting power corresponding to each candidate charging station based on the current remaining power, the single energy consumption corresponding to each candidate charging station, and the target charging power.

[0141] On the basis of the above implementation manner, optionally, the objective function solving module 520 is further used to determine, for the single driving distance corresponding to each candidate charging station, the energy consumption value of each unit distance in the single driving distance according to the historical behavior data and the unit power consumption prediction model, and determine the single energy consumption corresponding to the single driving distance according to each energy consumption value; and use the sum of the single energy consumption corresponding to each single driving distance as the single energy consumption corresponding to the candidate charging station.

[0142] Based on the above implementation, optionally, the unit power consumption prediction model is trained according to the following method: obtaining sample behavior data; dividing the sample behavior data according to unit distance to obtain sample unit behavior data, and determining the sample unit energy consumption value corresponding to the sample unit behavior data; training the initial prediction model according to the sample unit behavior data and the sample unit energy consumption value to obtain the unit power consumption prediction model.

[0143] On the basis of the above-mentioned implementation manner, optionally, the objective function solving module 520 is further used to determine, for each unit distance in the single driving distance, a preset number of historical unit behavior data corresponding to the unit distance based on the historical behavior data; predict the predicted unit behavior data corresponding to the unit distance based on the historical unit behavior data; and input the predicted unit behavior data into the unit power consumption prediction model to obtain the energy consumption value corresponding to the unit distance.

[0144] On the basis of the above implementation manner, optionally, the device also includes: a single cruising range set prediction module, which is used to determine the single cruising range power corresponding to each candidate charging station based on the current remaining power of the vehicle and the target charging power corresponding to each candidate charging station; for the single cruising range power corresponding to each candidate charging station, the single cruising range corresponding to the candidate charging station is determined based on historical behavior data and a pre-built unit power consumption prediction model.

[0145] On the basis of the above implementation, optionally, the reference cost includes at least two sub-costs, and the objective function solving module 520 is further used to take the sub-cost with the highest sub-cost priority as the first sub-cost according to the pre-set sub-cost priority, solve the first objective function under the constraints of the first constraint and the second constraint, and obtain the third constraint; wherein, the first objective function is a function constructed with the goal of minimizing the first sub-cost; take the sub-cost with the sub-cost priority second only to the first sub-cost as the second sub-cost, and judge whether the second sub-cost is the sub-cost with the lowest sub-cost priority; if so, under the first constraint, The second objective function is solved under the constraints of the second constraint and the third constraint, and at least one target charging station and the target charging power corresponding to each target charging station are determined; wherein, the second objective function is a function constructed with the goal of minimizing the second sub-cost; if not, the second objective function is solved under the constraints of the first constraint, the second constraint and the third constraint, the third constraint is updated, the second sub-cost is updated according to the sub-cost whose priority is second only to the second sub-cost, and the step of determining whether the second sub-cost is the sub-cost with the lowest priority is returned to.

[0146] For the convenience of description, the above devices are described as being divided into various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0147] The apparatus of the above embodiment is used to implement the corresponding charging station planning method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0148] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the charging station planning method described in any of the above embodiments is implemented.

[0149] Figure 6 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0150] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0151] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0152] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0153] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0154] The bus 1050 comprises a pathway for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0155] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0156] The electronic device of the above embodiment is used to implement the corresponding charging station planning method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0157] Based on the same inventive concept, the present application also provides a vehicle, wherein the vehicle includes the electronic device as described in the above embodiment or the charging station planning device as described in the above embodiment.

[0158] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the charging station planning method described in any of the above embodiments.

[0159] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0160] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the charging station planning method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0161] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. Within the scope of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0162] In addition, for simplicity of description and discussion, and in order not to make the embodiment of the application difficult to understand, the known power supply / ground connection with integrated circuit (IC) chip and other components may or may not be shown in the accompanying drawings provided. In addition, the device can be shown in the form of a block diagram to avoid making the embodiment of the application difficult to understand, and this also takes into account the following fact, that is, the details of the embodiment of these block diagram devices are highly dependent on the platform to be implemented in the embodiment of the application (that is, these details should be fully within the scope of understanding of those skilled in the art). When specific details (for example, circuit) are set forth to describe exemplary embodiments of the application, it will be apparent to those skilled in the art that the embodiment of the application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0163] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the embodiments discussed.

[0164] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of this application.

Claims

1. A charging station planning method, characterized in that: include: Obtaining vehicle driving data and at least one candidate charging station corresponding to a preset navigation route, and determining charging station attribute data corresponding to each candidate charging station; wherein the charging station attribute data is used to characterize the attributes of the candidate charging station; Based on the vehicle driving data, the at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station, a pre-established objective function is solved under the constraints of a first constraint and a second constraint to determine at least one target charging station and a target charging power corresponding to each target charging station; wherein the objective function is a function constructed with the goal of minimizing the reference cost, the first constraint is that the single driving distance corresponding to each candidate charging station is not greater than the corresponding single cruising range, and the second constraint is that the charging starting power corresponding to each candidate charging station is not greater than the target charging power corresponding to each candidate charging station; the charging starting power corresponding to the candidate charging station is the remaining power of the vehicle upon arrival at the candidate charging station; Route planning is performed based on the determined at least one target charging station and the target charging power corresponding to each target charging station.

2. The method according to claim 1, characterized in that The reference cost includes a charging cost, wherein the charging cost is used to represent the electricity cost incurred when charging at each candidate charging station; the vehicle driving data includes the current remaining power of the vehicle and historical behavior data, and the charging station attribute data includes a charging unit price; and solving a pre-established objective function based on the vehicle driving data, the at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station, under the constraints of a first constraint and a second constraint, includes: Determining a charging starting power corresponding to each candidate charging station based on the vehicle's current remaining power, the historical behavior data, the preset navigation route, each candidate charging station, and a target charging power corresponding to each candidate charging station; Determining the energy replenishment cost based on the starting charging power, target charging power, and charging unit price corresponding to each candidate charging station; Under the constraints of the first restriction condition and the second restriction condition, an objective function constructed with the goal of minimizing the energy replenishment cost is solved.

3. The method according to claim 1, characterized in that The reference cost includes the number of charging times; and solving a pre-established objective function based on the vehicle driving data, the at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station, under the constraints of a first constraint and a second constraint, includes: determining the number of charging times according to the number of selected candidate charging stations from among the candidate charging stations; Under the constraints of the first restriction condition and the second restriction condition, an objective function constructed with the goal of minimizing the number of charging times is solved.

4. The method according to claim 1, wherein The reference cost includes additional time, wherein the additional time includes additional travel time and additional charging time, the additional travel time is used to represent the time for traveling to and from each candidate charging station, and the additional charging time is used to represent the time for charging at each candidate charging station; the vehicle driving data includes the current remaining power of the vehicle and historical behavior data, and the charging station attribute data includes charging distance, charging driving speed, and charging power; solving a pre-established objective function based on the vehicle driving data, the at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station under the constraints of a first constraint and a second constraint, includes: Determining the additional travel time based on the charging distance and charging speed corresponding to each candidate charging station; Determining a charging starting power corresponding to each candidate charging station based on the vehicle's current remaining power, the historical behavior data, the preset navigation route, each candidate charging station, and a target charging power corresponding to each candidate charging station; Determining an additional charging time based on the charging starting power, target charging power, and charging power corresponding to each candidate charging station; The sum of the extra travel time and the extra charging time is taken as the extra time; Under the constraints of the first restriction condition and the second restriction condition, an objective function constructed with the goal of minimizing the additional time is solved.

5. The method according to claim 2 or 4, characterized in that The determining of the starting charging power corresponding to each candidate charging station based on the current remaining power of the vehicle, the historical behavior data, the preset navigation route, each candidate charging station, and the target charging power corresponding to each candidate charging station includes: Determining a single driving distance corresponding to each candidate charging station based on the preset navigation route and each candidate charging station; Predicting each of the single driving distances based on the historical behavior data and a pre-built unit power consumption prediction model to determine the single energy consumption corresponding to each of the candidate charging stations; The charging starting power corresponding to each candidate charging station is determined according to the current remaining power, the single energy consumption corresponding to each candidate charging station, and the target charging power.

6. The method according to claim 5, characterized in that The predicting of each single driving distance based on the historical behavior data and a pre-built unit power consumption prediction model to determine the single energy consumption corresponding to each candidate charging station includes: For each single driving distance corresponding to the candidate charging station, determining an energy consumption value for each unit distance in the single driving distance based on the historical behavior data and the unit power consumption prediction model, and determining a single energy consumption corresponding to the single driving distance based on each energy consumption value; The sum of the single energy consumption corresponding to each single driving distance is used as the single energy consumption corresponding to the candidate charging station.

7. The method according to claim 6, characterized in that The unit power consumption prediction model is trained according to the following method: Obtain sample behavior data; Dividing the sample behavior data according to the unit distance to obtain sample unit behavior data, and determining a sample unit energy consumption value corresponding to the sample unit behavior data; An initial prediction model is trained according to the sample unit behavior data and the sample unit energy consumption value to obtain the unit power consumption prediction model.

8. The method according to claim 6, characterized in that The determining, based on the historical behavior data and the unit power consumption prediction model, the energy consumption value of each unit distance in the single driving distance includes: For each unit distance in the single driving distance, determining a preset number of historical unit behavior data corresponding to the unit distance based on the historical behavior data; predicting predicted unit behavior data corresponding to the unit distance based on the historical unit behavior data; The predicted unit behavior data is input into the unit power consumption prediction model to obtain the energy consumption value corresponding to the unit distance.

9. The method according to claim 1, characterized in that Also includes: Determining the single-trip battery life corresponding to each candidate charging station based on the vehicle's current remaining battery life and the target charging power corresponding to each candidate charging station; For the single cruising range power corresponding to each candidate charging station, the single cruising range corresponding to the candidate charging station is determined based on historical behavior data and a pre-built unit power consumption prediction model.

10. The method according to claim 1, characterized in that The reference cost includes at least two sub-costs. Solving a pre-established objective function under the constraints of the first constraint and the second constraint to determine at least one target charging station and a target charging power corresponding to each target charging station includes: According to the pre-set sub-cost priorities, the sub-cost with the highest sub-cost priority is used as the first sub-cost, and the first objective function is solved under the constraints of the first constraint condition and the second constraint condition to obtain the third constraint condition; wherein the first objective function is a function constructed with the goal of minimizing the first sub-cost; taking the sub-cost having a priority next to the first sub-cost as the second sub-cost, and determining whether the second sub-cost has the lowest priority among the sub-costs; If so, solving a second objective function under the constraints of the first constraint, the second constraint, and the third constraint to determine at least one target charging station and a target charging power corresponding to each target charging station; wherein the second objective function is a function constructed with the second sub-cost as the goal; If not, solve the second objective function under the constraints of the first constraint, the second constraint and the third constraint, update the third constraint, update the second sub-cost according to the sub-cost whose priority is second only to the second sub-cost, and return to the step of determining whether the second sub-cost is the sub-cost with the lowest priority among the sub-costs.

11. A charging station planning device, characterized in that: include: a data acquisition module, configured to acquire vehicle driving data and at least one candidate charging station corresponding to a preset navigation route, and determine charging station attribute data corresponding to each candidate charging station; wherein the charging station attribute data is used to characterize the attributes of the candidate charging station; an objective function solving module, configured to solve a pre-established objective function based on the vehicle driving data, the at least one candidate charging station, and the charging station attribute data corresponding to each candidate charging station, under the constraints of a first constraint and a second constraint, to determine at least one target charging station and a target charging power corresponding to each target charging station; wherein the objective function is a function constructed with the goal of minimizing a reference cost, the first constraint is that the single driving distance corresponding to each candidate charging station is not greater than the corresponding single cruising range, and the second constraint is that the charging starting power corresponding to each candidate charging station is not greater than the target charging power corresponding to each candidate charging station; the charging starting power corresponding to the candidate charging station is the remaining power of the vehicle upon arrival at the candidate charging station; The route planning module is used to plan a route based on at least one determined target charging station and a target charging power corresponding to each target charging station.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the charging station planning method according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the charging station planning method according to any one of claims 1 to 10.

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