Vehicle model identification method, charging pile, and computer-readable storage medium

Abstract

The present invention relates to the field of charging piles, and particularly relates to a vehicle model identification method, a charging pile, and a computer-readable storage medium. The vehicle model identification method comprises: acquiring charging feature information of a charging vehicle; inputting the charging feature information into a vehicle model prediction model, and performing prediction to obtain a vehicle model prediction result; and on the basis of the vehicle model prediction result, determining a target vehicle model of the charging vehicle. Therefore, the present invention can automatically identify a target vehicle model of a charging vehicle when performing charging on the charging vehicle, such that a targeted charging strategy can be formulated for the charging vehicle in view of the charging control characteristics of the charging vehicle, so as to perform charging optimization, thereby facilitating efficient and high-quality charging for the charging vehicle.

Description

Vehicle type identification method, charging pile and computer readable storage medium

[0001] The present application claims priority to the Chinese patent application No. 202411374729.5, filed on September 29, 2024, and entitled "Vehicle type identification method, charging pile and computer readable storage medium", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the field of charging piles, in particular to a vehicle type identification method, a charging pile and a computer readable storage medium. BACKGROUND

[0003] At present, there are various types of electric vehicles on the market. The battery control characteristics and charging strategies of different electric vehicles are usually different. In the charging process of electric vehicles, the charging pile generally charges the charging vehicle according to the charging interaction data between the charging pile and the charging vehicle. This conventional charging method does not optimize the charging of the charging vehicle in combination with the vehicle characteristics of the charging vehicle itself, so the charging of the charging vehicle is slow. SUMMARY

[0004] An object of the present application is to provide a vehicle type identification method, a charging pile and a computer readable storage medium to solve the technical problem of slow charging of the charging vehicle.

[0005] In a first aspect, an embodiment of the present application provides a vehicle type identification method, comprising:

[0006] obtaining charging feature information of a charging vehicle;

[0007] inputting the charging feature information into a vehicle type prediction model to obtain a vehicle type prediction result;

[0008] determining a target vehicle type of the charging vehicle according to the vehicle type prediction result.

[0009] Optionally, the obtaining of the charging feature information of the charging vehicle comprises:

[0010] obtaining charging interaction information sent by the charging vehicle;

[0011] extracting the charging feature information of the charging vehicle from the charging interaction information.

[0012] Optionally, before the obtaining of the charging feature information of the charging vehicle, the vehicle type identification method further comprises:

[0013] obtaining feature sampling information of the charging vehicle of each vehicle type;

[0014] The feature sampling information is associated with a preset vehicle model label to obtain a sample data set;

[0015] The sample data set is trained according to a preset machine learning algorithm to obtain a vehicle model prediction model.

[0016] Optionally, the vehicle model prediction result includes at least one vehicle model label, and the target vehicle model of the charging vehicle is determined according to the vehicle model prediction result, including:

[0017] The number of vehicle model labels of the vehicle model prediction result is determined.

[0018] The target vehicle model of the charging vehicle is determined according to the number of vehicle model labels of the vehicle model prediction result and the vehicle model labels.

[0019] Optionally, the target vehicle model of the charging vehicle is determined according to the number of vehicle model labels of the vehicle model prediction result and the vehicle model labels, including:

[0020] It is judged whether the number of vehicle model labels is greater than 1 to obtain a judgment result.

[0021] The target vehicle model of the charging vehicle is determined according to the judgment result and the vehicle model labels of the vehicle model prediction result.

[0022] Optionally, the vehicle model label includes vehicle model information, and the target vehicle model of the charging vehicle is determined according to the judgment result and the vehicle model labels of the vehicle model prediction result, including:

[0023] If the judgment result is that the number of vehicle model labels is not greater than 1, the target vehicle model of the charging vehicle is determined according to the vehicle model information corresponding to the vehicle model label.

[0024] Optionally, the vehicle model label includes vehicle model information and battery pack capacity information, and the target vehicle model of the charging vehicle is determined according to the judgment result and the vehicle model labels of the vehicle model prediction result, including:

[0025] If the judgment result is that the number of vehicle model labels is greater than 1, the battery pack capacity information of each vehicle model label is extracted respectively, and the target vehicle model of the charging vehicle is determined according to the battery pack capacity information and the vehicle model information.

[0026] Optionally, the target vehicle model of the charging vehicle is determined according to the battery pack capacity information and the vehicle model information, including:

[0027] It is judged whether the battery pack capacity information is different.

[0028] If there is a difference, determining an actual battery pack capacity of the charging vehicle, determining a target vehicle model label according to the actual battery pack capacity, and determining a target vehicle model of the charging vehicle according to vehicle model information corresponding to the target vehicle model label.

[0029] If there is no difference, determining a charging curve feature of the charging vehicle, and determining a target vehicle model of the charging vehicle according to the charging curve feature.

[0030] Optionally, the determining of the actual battery pack capacity of the charging vehicle comprises:

[0031] calculating a battery charging amount required by the charging vehicle for charging a preset percentage of power;

[0032] averaging the battery charging amount to obtain an average power value;

[0033] determining the actual battery pack capacity of the charging vehicle according to the average power value and a preset value.

[0034] Optionally, the determining of the target vehicle model label according to the actual battery pack capacity comprises:

[0035] matching the actual battery pack capacity with battery pack capacity information corresponding to each vehicle model label;

[0036] determining a vehicle model label corresponding to the battery pack capacity information that is successfully matched as the target vehicle model label.

[0037] Optionally, the determining of the target vehicle model of the charging vehicle according to the charging curve feature comprises:

[0038] inputting the charging curve feature into an auxiliary prediction model to obtain an auxiliary prediction result;

[0039] determining the target vehicle model of the charging vehicle according to the auxiliary prediction result.

[0040] In a second aspect, an embodiment of the present application provides a charging pile, comprising a memory and a processor, the processor being electrically connected with the memory, used for executing one or more computer programs stored in the memory, and when the one or more computer programs are executed, causing the charging pile to implement the vehicle model identification method as described above.

[0041] In a third aspect, an embodiment of the present application provides a computer readable storage medium, the computer readable storage medium storing a computer program, the computer program comprising program instructions, the program instructions causing a processor to execute the vehicle model identification method as described above when the program instructions are executed by the processor.

[0042] Compared with the prior art, the embodiment of the present application provides a vehicle type identification method, a charging pile and a computer readable storage medium, the vehicle type identification method comprises: obtaining charging feature information of a charging vehicle, inputting the charging feature information into a vehicle type prediction model, obtaining a vehicle type prediction result by prediction, and determining a target vehicle type of the charging vehicle according to the vehicle type prediction result. Therefore, the embodiment can automatically identify the target vehicle type of the charging vehicle when charging the charging vehicle, which is beneficial to formulating a specific charging strategy for the charging vehicle to optimize charging according to the charging control characteristics of the charging vehicle, thereby being beneficial to charging the charging vehicle well and fast. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0044] Fig. 1 is a schematic diagram of an application scenario of a charging pile provided by the embodiment of the present application;

[0045] Fig. 2 is a flowchart of a vehicle type identification method provided by the embodiment of the present application;

[0046] Fig. 3 is a flowchart of a vehicle type identification method provided by another embodiment of the present application;

[0047] Fig. 4 is a flowchart of S203 in a vehicle type identification method provided by the embodiment of the present application;

[0048] Fig. 5 is a structural schematic diagram of a vehicle type identification device provided by the embodiment of the present application;

[0049] Fig. 6 is a structural schematic diagram of a first obtaining module in a vehicle type identification device provided by the embodiment of the present application;

[0050] Fig. 7 is a structural schematic diagram of a vehicle type identification device provided by another embodiment of the present application;

[0051] Fig. 8 is a structural schematic diagram of a determining module in a vehicle type identification device provided by the embodiment of the present application;

[0052] Fig. 9 is a hardware structural schematic diagram of a charging pile provided by the embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the objects, technical solutions and advantages of the present application clearer, the following further describes the present application with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0054] It should be noted that the various features of the embodiments of the present application can be combined with each other without conflict, and all fall within the scope of protection of the present application. In addition, although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a manner different from the module division in the device or the order in the flowchart. Furthermore, the "first", "second", "third" and the like used in the present application do not limit the data and execution order, but only distinguish the same items or similar items with basically the same function and effect.

[0055] Please refer to FIG. 1, the embodiment of the present application provides an application scenario schematic diagram of a charging pile, which includes a power grid 100, a charging pile 200 and a charging vehicle 300.

[0056] The power grid 100 is a power network for transmitting commercial power to the charging pile 200 through a power transmission line to supply power to the charging vehicle 300. The commercial power is a power frequency alternating current, which is generally represented by the commonly used three quantities of alternating current, i.e. voltage, current and frequency. Generally, the commercial power transmitted by the power grid 100 to the charging pile 200 is three-phase alternating current.

[0057] The charging pile 200 is a device for charging the charging vehicle 300 to supplement the electric quantity of the charging vehicle 300, and its working principle is to receive electric energy from the power grid 100, and then transmit the electric energy to the charging vehicle 300 through a charging line to realize charging of the charging vehicle 300. In some embodiments, the charging pile 200 can be any type of charging pile, such as a direct current charging pile, an alternating current charging pile, an alternating current-direct current integrated charging pile, etc.

[0058] The direct current charging pile uses direct current to charge the power battery of the charging vehicle 300, and this charging method is also called "fast charging". The direct current charging pile is electrically connected with the power grid 100, can receive three-phase 380V alternating current input from the power grid 100 and convert the alternating current into direct current, and then deliver the direct current to the power battery of the charging vehicle 300 through a standard direct current charging plug and a charging socket to realize direct current charging. The power supply characteristics of the direct current charging pile itself determine that it can output sufficient charging power, and the adjustment range of voltage and current is relatively large, thereby realizing fast charging. The direct current charging pile has the function of a charging machine, can monitor and control the state of the charged power battery in real time, and can also measure the charging electric quantity.

[0059] The AC charging pile is electrically connected with the power grid 100, and is configured to provide power output to the charging vehicle 300 using a single or double 220VAC / 380VAC AC output interface, so that the charging vehicle 300 uses the on-board charger to charge the power battery of the charging vehicle 300. This charging mode is also referred to as "slow charging". The output power of the AC charging pile is generally 5kW (220VAC) / 20kW (380VAC), but the actual charging power is limited by the on-board charger, and the on-board charging power of a small electric vehicle is generally between 2-3kW. The on-board charger of the charging vehicle 300 can convert the input AC power into DC power through filtering and rectification, and then store the DC power into the power battery of the charging vehicle 300, thereby charging the charging vehicle 300. This charging mode is mainly applied to small pure electric vehicles.

[0060] The input voltage of the AC-DC integrated charging pile is generally three-phase four-wire 380VAC±15%, and the frequency is 50Hz. The AC-DC integrated charging pile includes a DC output port and an AC output port. The DC output port outputs adjustable DC power to charge the power battery of the charging vehicle 300. The charging power is generally 10-40kW. The AC output port outputs 220VAC (5kW) / 380VAC (20kW) AC power to provide a charging power source for the on-board charger of the charging vehicle 300. The AC-DC integrated charging pile can provide a conventional charging mode through the AC output port and a fast charging mode through the DC output port. When there are more charging services during the day, the fast charging mode is used for fast charging. When there are fewer users of the charging station at night, the conventional charging mode can be used for slow charging. The AC-DC integrated charging pile can realize simultaneous charging of AC and DC, interlocking charging, and modular design, which is convenient for maintenance.

[0061] The charging vehicle 300 is in communication connection with the charging pile 200. On the one hand, the charging vehicle 300 can receive the DC power or AC power output by the charging pile 200. On the other hand, the charging vehicle 300 can interact with the charging pile 200. When the charging vehicle 300 interacts with the charging pile 200, the charging vehicle 300 and the charging pile 200 can transmit and receive various charging interaction information.

[0062] The charging process of the charging vehicle 300 can include a charging parameter configuration phase and a charging phase.

[0063] The charging pile 200 is physically connected with the charging vehicle 300 and is powered on, and enters the charging parameter configuration phase after checking that the voltage is normal. In this phase, the charging interaction information transmitted and received between the charging vehicle 300 and the charging pile 200 includes a handshake message, an identity authentication message, a charging parameter negotiation message, and a charging preparation message.

[0064] The handshake message is used to establish a communication connection between the charging pile 200 and the battery management system of the charging vehicle 300. The handshake message includes a handshake request message and a handshake response message. The handshake request message is sent by the charging pile 200 to the battery management system of the charging vehicle 300, which is used to determine whether the handshake is normal between the two parties, and indicates that the charging pile 200 is ready to configure the charging parameters. After the battery management system of the charging vehicle 300 receives the handshake request message, it sends a handshake response message to the charging pile 200, indicating that the battery management system of the charging vehicle 300 is ready to accept the charging parameter configuration. If the charging pile 200 receives the handshake response message, it determines that the handshake between the two parties is normal and establishes a communication connection between the two parties.

[0065] The identity authentication message is used to verify the identity of the charging pile 200. The identity authentication message includes an identity authentication request message and an identity authentication response message. The identity authentication request message is sent by the charging pile 200 to the battery management system of the charging vehicle 300, and the identity authentication request message includes the identity information of the charging pile 200. After the battery management system of the charging vehicle 300 receives the identity authentication request message, it sends an identity authentication response message to the charging pile 200, indicating that the battery management system of the charging vehicle 300 has verified the identity of the charging pile 200. After the charging pile 200 receives the identity authentication response message, the identity verification of the charging pile 200 is completed.

[0066] The charging parameter negotiation message is used to negotiate the charging parameters. The charging parameter negotiation message includes a charging parameter suggestion message and a charging parameter confirmation message. The charging parameter suggestion message is sent by the charging pile 200 to the battery management system of the charging vehicle 300, and the charging parameter suggestion message includes the charging parameters suggested by the charging pile 200. The charging parameters include maximum charging voltage, maximum charging current, minimum charging voltage, minimum charging current, charging mode, etc. After the battery management system of the charging vehicle 300 receives the charging parameter suggestion message, it sends a charging parameter confirmation message to the charging pile 200, indicating that the battery management system of the charging vehicle 300 confirms the received charging parameters. After the charging pile 200 receives the charging parameter confirmation message, the charging parameter negotiation is completed.

[0067] The charging preparation message is used to indicate that the charging pile 200 is ready. The charging preparation message is sent by the charging pile 200 to the battery management system of the charging vehicle 300, indicating that the charging pile 200 is ready to start charging.

[0068] In the charging phase, the charging pile 200 adjusts the charging voltage and charging current according to the charging requirements of the battery management system of the charging vehicle 300 to ensure that the charging process proceeds normally. The charging interaction information exchanged between the charging vehicle 300 and the charging pile 200 includes charging control messages, charging data messages, and charging fault messages.

[0069] The charging control message is used for controlling the charging process. The charging control message can include a start charging command message, a stop charging command message, and a charging status report message. The start charging command message is sent by the charging pile 200 to the battery management system of the charging vehicle 300, and is used to instruct the battery management system of the charging vehicle 300 to start charging. The stop charging command message is sent by the charging pile 200 to the battery management system of the charging vehicle 300, and is used to instruct the battery management system of the charging vehicle 300 to stop charging. The charging status report message is sent by the battery management system of the charging vehicle 300 to the charging pile 200, and is used to report the charging status of the battery management system of the charging vehicle 300.

[0070] The charging data message is used for transmitting charging data. The charging data message includes a charging voltage / current setting message and a battery voltage / current measurement message. The charging voltage / current setting message is sent by the charging pile 200 to the battery management system of the charging vehicle 300, and is used to set the charging voltage value and the charging current value. The battery voltage / current measurement message is sent by the battery management system of the charging vehicle 300 to the charging pile 200, and is used to report the battery voltage value and the battery current value of the charging vehicle 300.

[0071] The charging fault message is used for indicating charging-related faults. The charging fault message includes a charging fault message and a battery fault message. The charging fault message is sent by the charging pile 200 to the battery management system of the charging vehicle 300, and is used to indicate charging fault information. The battery fault message is sent by the battery management system of the charging vehicle 300 to the charging pile 200, and is used to indicate battery fault information.

[0072] The charging vehicle 300 includes any vehicle that can be driven by electric power, including but not limited to a pure electric vehicle, a hybrid electric vehicle, a fuel cell vehicle, etc.

[0073] Referring to FIG. 2, the embodiment of the present application provides a vehicle type identification method, which includes:

[0074] S201, obtaining charging feature information of a charging vehicle;

[0075] In this step, the charging vehicle is a vehicle that is currently using the charging pile for charging. When the charging pile is charging the charging vehicle, as described above, the charging pile can receive the charging interaction information sent by the charging vehicle based on the interaction between the charging pile and the charging vehicle, and obtain the charging feature information according to the charging interaction information. The charging feature information is information used to represent the charging properties or battery properties of the charging vehicle, such as charging power, etc.

[0076] It can be understood that the charging characteristic information of different charging vehicles can be the same or different. The charging characteristic information of charging vehicles of different brands can be different, the charging characteristic information of charging vehicles of different vehicle series of the same brand can be different, the charging characteristic information of charging vehicles of different vehicle models of the same vehicle series of the same brand can be different, and even the charging characteristic information of charging vehicles of the same vehicle series and the same vehicle model of the same brand can be different.

[0077] In some embodiments, the charging pile obtains the charging interaction information sent by the charging vehicle, and extracts the charging characteristic information of the charging vehicle from the charging interaction information.

[0078] In this embodiment, as described above, the charging interaction information can include a battery voltage / current measurement packet or other related packets carrying the charging characteristic information such as the battery voltage value. Therefore, when the charging pile receives the related packet, the charging characteristic information can be extracted from the related packet.

[0079] S202, input the charging characteristic information into the vehicle model prediction model to obtain a vehicle model prediction result;

[0080] In this step, the vehicle model prediction model is an algorithm model for predicting the vehicle model of the charging vehicle. The vehicle model prediction model can be any suitable type of algorithm model, including but not limited to a decision tree model, a random forest model, a logistic regression model, a neural network model, a support vector machine model, etc.

[0081] The vehicle model prediction result is a prediction result output by the vehicle model prediction model. In some embodiments, the vehicle model prediction result includes at least one vehicle model label. The vehicle model label is a classification label for uniquely determining the target vehicle of the target vehicle model. The vehicles of each vehicle model correspond to at most one vehicle model label.

[0082] Since the vehicle model label of the vehicle model prediction result can be one or more than one, the number of vehicle model labels will affect the determination strategy of the charging pile for determining the vehicle model of the charging vehicle. Therefore, when the charging pile determines the vehicle model of the charging vehicle according to the vehicle model prediction result, it is necessary to first count the number of vehicle model labels of the vehicle model prediction result.

[0083] It can be understood that when the vehicle model prediction result output by the vehicle model prediction model includes only one vehicle model label, the charging pile can directly determine the vehicle model of the charging vehicle according to the unique vehicle model label. When the vehicle model prediction result output by the vehicle model prediction model includes more than one vehicle model label, the charging pile fails to directly determine the vehicle model of the charging vehicle. At this time, the charging pile needs to further determine the target vehicle model of the charging vehicle.

[0084] For example, the A1 vehicle model of the A brand vehicle manufacturer and the B1 vehicle model of the B brand vehicle manufacturer both use the same battery management system produced by the same equipment manufacturer. Since the same battery management system is used, the charging feature information carried in the charging interaction information sent to the charging pile when the A1 vehicle model and the B1 vehicle model are charging may be the same. After the charging feature information is input into the vehicle model prediction model, the vehicle model prediction result output by the vehicle model prediction model may include the vehicle model label corresponding to the A1 vehicle model and the vehicle model label corresponding to the B1 vehicle model. At this time, the charging pile cannot directly determine whether the charging vehicle is the A1 vehicle model or the B1 vehicle model.

[0085] It can be understood that the prediction process of the vehicle model prediction model is a vehicle model initial identification stage. In the vehicle model initial identification stage, the charging pile can directly locate the vehicle model of the charging vehicle. For example, in the case where the vehicle model prediction result only includes one vehicle model label, the charging pile can also fail to directly locate the vehicle model of the charging vehicle. For example, in the case where the vehicle model prediction result includes more than one vehicle model label.

[0086] S203, determining the target vehicle model of the charging vehicle according to the pre-vehicle model result.

[0087] In this step, the target vehicle model is the vehicle model number of the charging vehicle. The vehicle model number is a number composed of pinyin letters and Arabic numerals specified for a type of vehicle for identifying the vehicle. It can be understood that one vehicle model can correspond to multiple vehicles, but one vehicle can only correspond to one vehicle model.

[0088] Therefore, the embodiment can automatically identify the target vehicle model of the charging vehicle when charging the charging vehicle, which is conducive to formulating a targeted charging strategy for the charging vehicle to optimize charging in combination with the charging control characteristics of the charging vehicle, thereby facilitating good and fast charging of the charging vehicle.

[0089] In some embodiments, referring to FIG. 3, before S201 is performed, the vehicle model identification method further includes:

[0090] S204, obtaining feature sampling information of the charging vehicle of each vehicle model;

[0091] In this step, the feature sampling information is charging feature information obtained by pre-sampling each vehicle model when the charging pile charges or tests each vehicle model. These vehicle models can include vehicles of different brands, vehicles of the same brand but different series, vehicles of the same brand, same series, and different models, vehicles of the same brand, same series, and same model but different production years, etc.

[0092] S205, associating the feature sampling information with a preset vehicle model label to obtain a sample data set;

[0093] In this step, the preset vehicle model label is a vehicle model label used for pre-classifying different vehicle models. The charging pile associates the feature sampling information sampled from each vehicle model with the preset vehicle model label of the corresponding vehicle model to obtain sample data for each vehicle model. For example, the sample data for one vehicle model is as follows: { "CC1":"XXX", "CC2":XXX, "CC3":XXX, "CC4":XXX, "CC5":XXX, "VehicleLabel":"XXX"}

[0094] In the formula, CC1, CC2, CC3, CC4, and CC5 represent five feature parameters, and VehicleLabel represents the preset vehicle model label.

[0095] The charging pile aggregates the sample data for various vehicle models to obtain a sample data set.

[0096] S206, training the sample data set according to a preset machine learning algorithm to obtain a vehicle model prediction model.

[0097] In this step, the type of the preset machine learning algorithm can be set according to actual needs, including but not limited to decision tree algorithm, random forest algorithm, logistic regression algorithm, neural network algorithm, support vector machine algorithm, etc. The above algorithms can be combined to form a machine learning algorithm library. The charging pile can call the machine learning algorithm library, adjust the optimal algorithm parameters according to the data characteristics of the sample data set, and then input the sample data set for training. After the training is completed, the machine learning algorithm library will give an optimal prediction model, and the charging pile will use the prediction model as the vehicle model prediction model.

[0098] Since the vehicle model prediction model is trained by the sample data set composed of sample data for various vehicle models, the vehicle model prediction model can cover vehicle model prediction for various vehicle models, and when a new vehicle model appears subsequently, the vehicle model prediction model can be further trained to expand the vehicle model, thereby facilitating the charging pile to accurately and reliably predict the vehicle model of the charging vehicle currently being charged.

[0099] In some embodiments, the vehicle model prediction result includes at least one vehicle model label. Please refer to FIG. 4, S203 includes:

[0100] S2031, determining the number of vehicle model labels in the vehicle model prediction result.

[0101] In this step, as described above, in the vehicle model initial identification stage, the number of vehicle model labels in the vehicle model prediction result output by the vehicle model prediction model is one or more than one.

[0102] S2032, determine the target vehicle type of the charging vehicle according to the number of vehicle labels in the vehicle type prediction result and the vehicle labels.

[0103] In this step, when the number of vehicle labels is more than one, the charging pile may not be able to distinguish the specific vehicle type of the charging vehicle, so after the vehicle type prediction model outputs the vehicle type prediction result, the charging pile needs to determine the number of vehicle labels in the vehicle type prediction result. When the number of vehicle labels is one, it is not necessary to enter the vehicle type identification stage, and when the number of vehicle labels is more than one, it is necessary to enter the vehicle type identification stage, so as to accurately determine the target vehicle type of the charging vehicle.

[0104] In some embodiments, the charging pile determines whether the number of vehicle labels is greater than one, obtains a determination result, and determines the target vehicle type of the charging vehicle according to the determination result and the vehicle labels in the vehicle type prediction result.

[0105] Therefore, the embodiment can flexibly select the prediction strategy of the target vehicle type according to the number of vehicle labels, which is beneficial to improve the reliability and accuracy of the vehicle type prediction of the charging vehicle.

[0106] In some embodiments, the vehicle label can include vehicle type information, and the vehicle type information is information used to uniquely determine the corresponding vehicle type. The vehicle label can also include vehicle type information and battery pack capacity information, and the battery pack capacity information is information used to represent the design capacity of the power battery pack of the vehicle type vehicle when it leaves the factory.

[0107] In some embodiments, if the determination result is that the number of vehicle labels is not greater than one, the charging pile determines the target vehicle type of the charging vehicle according to the vehicle type information corresponding to the vehicle label.

[0108] For example, it is assumed that the vehicle type prediction model outputs the following vehicle type prediction result:

[0109] VehicleLabel_1: A1_120kWh

[0110] In the vehicle label "VehicleLabel_1", A1 is the vehicle type information, and 120kWh is the battery pack capacity information of the A1 vehicle type vehicle.

[0111] Since the vehicle type prediction result only includes one vehicle label VehicleLabel_1, the charging pile can determine the vehicle type information A1 in the vehicle label as the target vehicle type of the charging vehicle.

[0112] In some embodiments, if the determination result is that the number of vehicle labels is greater than one, the battery pack capacity information of each vehicle label is extracted respectively, and the target vehicle type of the charging vehicle is determined according to the battery pack capacity information and the vehicle type information.

[0113] For example, assume that the vehicle type prediction model outputs the following vehicle type prediction results:

[0114] VehicleLabel_2: B1_70kWh

[0115] VehicleLabel_3: C1_100kWh

[0116] In the vehicle type label "VehicleLabel_2", B1 is the vehicle type information, and 70kWh is the battery pack capacity information of the B1 vehicle type vehicle. In the vehicle type label "VehicleLabel_3", C1 is the vehicle type information, and 100kWh is the battery pack capacity information of the C1 vehicle type vehicle.

[0117] Since the vehicle type prediction results include two vehicle type labels, VehicleLabel_2 and VehicleLabel_3, the number of vehicle type labels is 2, which is greater than 1. Therefore, the charging pile extracts the battery pack capacity information 70kWh and 100kWh corresponding to the vehicle type labels VehicleLabel_1 and VehicleLabel_2, respectively, and determines the target vehicle type of the charging vehicle according to the two battery pack capacity information and the corresponding vehicle type information.

[0118] Therefore, in the case where the vehicle type prediction results have multiple vehicle type labels, the embodiment can also clearly distinguish the target vehicle type of the charging vehicle, which is beneficial to improve the reliability and accuracy of vehicle type identification.

[0119] In some embodiments, the charging pile can determine whether the battery pack capacity information is different, and determine the target vehicle type of the charging vehicle according to the determination result.

[0120] The difference in battery pack capacity information can be that each of the multiple battery pack capacity information is different, or that there are the same battery pack capacity information and different battery pack capacity information in the multiple battery pack capacity information, as long as all the battery pack capacity information is not the same. For example, as described above, the charging pile extracts the battery pack capacity information 70kWh corresponding to the vehicle type label VehicleLabel_2 and the battery pack capacity information 100kWh corresponding to the vehicle type label VehicleLabel_3, respectively. At this time, the charging pile can determine that the battery pack capacity information is different.

[0121] For another example, assume that the vehicle type prediction model outputs the following vehicle type prediction results:

[0122] VehicleLabel_4: D1_80kWh

[0123] VehicleLabel_5: E1_80kWh

[0124] Since the battery pack capacity information corresponding to the vehicle labels VehicleLabel_4 and VehicleLabel_5 is both 80 kWh, the charging pile can determine that there is no difference in the battery pack capacity information.

[0125] If the battery pack capacity information is different, the charging pile can determine the actual battery pack capacity of the charging vehicle, determine the target vehicle label according to the actual battery pack capacity, and determine the target vehicle type of the charging vehicle according to the vehicle information corresponding to the target vehicle label.

[0126] If the battery pack capacity information is different, the charging pile can determine the actual battery pack capacity of the charging vehicle, determine the target vehicle label according to the actual battery pack capacity, and determine the target vehicle type of the charging vehicle according to the vehicle information corresponding to the target vehicle label.

[0127] In some embodiments, when determining the actual battery pack capacity of the charging vehicle, the charging pile calculates the battery charging capacity required for charging the charging vehicle by a preset percentage, averages the battery charging capacity, obtains an average value of the electric quantity, and determines the actual battery pack capacity of the charging vehicle according to the average value of the electric quantity and a preset value.

[0128] In this embodiment, the product of the preset value and the preset percentage is equal to 1. For example, when the preset percentage is one percent, the preset value is 100, and when the preset percentage is two percent, the preset value is 50. It can be understood that the one hundred percent electric quantity is the state of the charging vehicle being fully charged.

[0129] Taking one percent as the preset percentage as an example, first, since the charging current and the charging voltage during the charging process of the charging vehicle are real-time changes, the charging pile can integrate the battery charging capacity in the integral interval of the preset time length. For example, the integral interval is 1 second, and the charging pile can calculate the battery charging capacity charged every second, and then accumulate the battery charging capacity charged every second until the battery charging capacity changes by one percent. In some embodiments, the charging pile can calculate the battery charging capacity required for charging the charging vehicle by a preset percentage according to the following formula: Cap = Q1 + Q2 + Q3 + … + Qn Q1 = v1 * c1 * t Q2 = v2 * c2 * t Q3 = v3 * v3 * t Qn = vn * cn * t

[0130] Wherein, Cap represents the battery charging capacity required for charging the charging vehicle by a preset percentage, Qn represents the battery charging capacity charged in the nth integral interval, vn represents the charging voltage for charging the charging vehicle during the nth integral interval, cn represents the charging current for charging the charging vehicle during the nth integral interval, and t represents the time length of the integral interval.

[0131] Then, the charging pile can accumulate the battery charging capacity required for charging the charging vehicle to the preset percentage of the battery capacity according to the actual demand, and average the battery charging capacity to obtain an average battery capacity. In some embodiments, the charging pile can calculate the average battery capacity according to the following formula: Cap av = (Cap1 + Cap2 + … + Capn) / n

[0132] Finally, the charging pile multiplies the average battery capacity by a preset value to obtain the actual battery pack capacity of the charging vehicle. It can be understood that, as described above, the preset value can be set according to the preset percentage, as long as the product of the preset value and the preset percentage is equal to 1.

[0133] Therefore, the actual battery pack capacity of the charging vehicle is estimated by integrating the charging capacity, which avoids introducing estimation errors due to real-time changes in charging current and charging voltage during the charging process of the charging vehicle, so that the real battery pack capacity of the charging vehicle can be more accurately estimated, and the subsequent vehicle model prediction of the charging vehicle can be more reliably performed.

[0134] In some embodiments, the charging pile matches the actual battery pack capacity with the battery pack capacity information corresponding to each vehicle model label and determines that the vehicle model label corresponding to the matching successful battery pack capacity information is the target vehicle model label.

[0135] It can be understood that, if the actual battery pack capacity is consistent with the battery capacity information corresponding to a certain vehicle model label, the battery capacity information is determined to be the matching successful battery capacity information, or if the difference between the actual battery pack capacity and the battery capacity information corresponding to a certain vehicle model label is within a preset range, the battery capacity information is determined to be the matching successful battery capacity information.

[0136] For example, the actual battery pack capacity is 99 kWh, as described above, the vehicle model prediction result has vehicle model labels VehicleLabel_2 and VehicleLabel_3, and the battery pack capacity information corresponding to the two vehicle model labels is 70 kWh and 100 kWh, respectively. Since the difference between the actual battery pack capacity 99 kWh and 100 kWh is within the preset range ± 1 kWh, the charging pile can determine that 100 kWh is the matching successful battery capacity information, and the vehicle model information C1 corresponding to 100 kWh is determined as the target vehicle model of the charging vehicle.

[0137] Since the battery pack capacities of vehicles of different models are generally different, the charging pile can estimate the actual battery pack capacity of the charging vehicle to uniquely identify the target vehicle model of the charging vehicle, which can avoid the charging pile reliably identifying the target vehicle model of the charging vehicle when the charging pile cannot directly obtain the battery pack capacity of the charging vehicle.

[0138] It can be understood that although the battery pack capacity information is different, in the case that the vehicle type prediction model outputs multiple vehicle type labels, there may be a case that the battery pack capacity information corresponding to two or more vehicle type labels is the same. For example, the vehicle type prediction model outputs the following vehicle type prediction results: VehicleLabel_6: D1_80kWh VehicleLabel_7: E1_80kWh VehicleLabel_8: F1_90kWh

[0139] If the actual battery pack capacity estimated by the charging pile is 80kWh, the battery pack capacity information corresponding to the vehicle type labels VehicleLabel_6 and VehicleLabel_7 is 80kWh. At this time, the charging pile cannot directly determine whether the target vehicle type of the charging vehicle is D1 or E1, and therefore, in some embodiments, when the battery pack capacity information is different, but the charging pile cannot determine the target vehicle type of the charging vehicle according to the actual battery pack capacity, the charging pile can determine the charging curve characteristics of the charging vehicle, and determine the target vehicle type of the charging vehicle according to the charging curve characteristics.

[0140] In some embodiments, when determining the charging curve characteristics of the charging vehicle, the charging pile can obtain dynamic charging characteristic information at multiple time points during the process of charging the charging vehicle by a preset percentage of electric quantity (for example, ten percent), and fit the dynamic charging characteristic information into a charging curve according to any suitable curve fitting algorithm, so as to obtain the charging curve characteristics, wherein the dynamic charging characteristic information can include any suitable charging characteristic information such as required current.

[0141] In some embodiments, the charging pile inputs the charging curve characteristics into the auxiliary prediction model, obtains an auxiliary prediction result by prediction, and determines the target vehicle type of the charging vehicle according to the auxiliary prediction result.

[0142] In this embodiment, the auxiliary prediction model is an algorithm model used to assist the vehicle type prediction model in predicting the vehicle type of the charging vehicle. In some embodiments, the charging pile pre-obtains dynamic charging characteristic information of the charging vehicle during the charging process of each vehicle type, and fits the dynamic charging characteristic information into a sample charging curve according to any suitable curve fitting algorithm, then associates the sample charging curve with a preset vehicle type label to obtain a sample data set, and finally trains the sample data set according to a preset machine learning algorithm to obtain the auxiliary prediction model.

[0143] In some embodiments, the auxiliary prediction result includes at least one auxiliary prediction label, and the auxiliary prediction label includes vehicle type information. When the number of auxiliary prediction labels is 1, the charging pile can determine the target vehicle type of the charging vehicle according to the vehicle type information corresponding to the auxiliary prediction label.

[0144] In some embodiments, the charging pile can determine the target vehicle type of the charging vehicle according to the vehicle type label of the auxiliary prediction result and the vehicle type label of the vehicle type prediction result.

[0145] For example, as described above, the vehicle type prediction result includes the following vehicle type labels: VehicleLabel_4: D1_80kWh VehicleLabel_5: E1_80kWh

[0146] Suppose the auxiliary prediction result includes the following auxiliary prediction labels: AuxiliaryLabel_1: E1 AuxiliaryLabel_2: G1

[0147] The charging pile can extract the vehicle type information corresponding to each vehicle type label and the vehicle type information corresponding to each auxiliary prediction label, respectively, and sequentially compare each vehicle type information corresponding to the vehicle type information with the vehicle type information corresponding to each auxiliary prediction label, and take the vehicle type information that is consistent with the comparison as the target vehicle type of the charging vehicle. As described above, the charging pile can compare the vehicle type information D1 with the vehicle type information E1 and G1, respectively, at this time, the vehicle type information fails to be consistent with the comparison, then the charging pile can compare the vehicle type information E1 with the vehicle type information E1 and G1, respectively, at this time, the vehicle type information E1 is consistent with the comparison, and the charging pile can determine that the vehicle type information E1 is the target vehicle type of the charging vehicle.

[0148] Therefore, the embodiment can verify the vehicle type label of the auxiliary prediction result and the vehicle type label of the vehicle type prediction result, and determine the target vehicle type of the charging vehicle according to the verification result, so as to more accurately and reliably identify the target vehicle type of the charging vehicle.

[0149] It should be noted that in the above various embodiments, there is no certain sequence between the above steps, and those skilled in the art can understand from the description of the embodiments of the present application that the above steps can have different execution sequences in different embodiments, that is, they can be executed in parallel, or they can be executed in exchange, etc.

[0150] As another aspect of the embodiments of the present application, the embodiments of the present application provide a vehicle type identification device. The vehicle type identification device can be a software module, which includes a plurality of instructions stored in a memory, and a processor can access the memory to call and execute the instructions to complete the vehicle type identification method described in the above various embodiments.

[0151] In some embodiments, the vehicle type identification apparatus can be built by hardware devices, for example, the vehicle type identification apparatus can be built by one or more chips, and each chip can work in coordination with each other to complete the vehicle type identification method described in each of the above embodiments. For another example, the vehicle type identification apparatus can also be built by a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine), a programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or any combination of these components.

[0152] In some embodiments, referring to FIG. 5, the vehicle type identification apparatus 500 provided by the embodiment of the present application includes a first acquisition module 501, a prediction module 502, and a determination module 503.

[0153] The first acquisition module 501 is configured to acquire charging feature information of a charging vehicle, the prediction module 502 is configured to input the charging feature information into a vehicle type prediction model to predict a vehicle type prediction result, and the determination module 503 is configured to determine a target vehicle type of the charging vehicle according to the vehicle type prediction result.

[0154] Therefore, the embodiment can automatically identify the target vehicle type of the charging vehicle when charging the charging vehicle, which is beneficial to formulating a targeted charging strategy for the charging vehicle to optimize charging according to the charging control characteristics of the charging vehicle, thereby being beneficial to charging the charging vehicle well and quickly.

[0155] In some embodiments, referring to FIG. 6, the first acquisition module 501 includes an acquisition unit 5011 and an extraction unit 5012.

[0156] The acquisition unit 5011 is configured to acquire charging interaction information sent by the charging vehicle, and the extraction unit 5012 is configured to extract the charging feature information of the charging vehicle from the charging interaction information.

[0157] In some embodiments, referring to FIG. 7, the vehicle type identification apparatus 500 further includes a second acquisition module 504, an association module 505, and a training module 506.

[0158] The second acquisition module 504 is configured to acquire feature sampling information of the charging vehicle of each vehicle type, the association module 505 is configured to associate the feature sampling information with a preset vehicle type label to obtain a sample data set, and the training module 506 is configured to train the sample data set according to a preset machine learning algorithm to obtain the vehicle type prediction model.

[0159] In some embodiments, the vehicle type prediction result includes at least one vehicle type label, and referring to FIG. 8, the determination module 503 includes a first determination unit 5031 and a second determination unit 5032.

[0160] The first determining unit 5031 is configured to determine the number of vehicle type labels of the vehicle type prediction result, and the second determining unit 5032 is configured to determine the target vehicle type of the charging vehicle according to the number of vehicle type labels and the vehicle type label of the vehicle type prediction result.

[0161] In some embodiments, the vehicle type label includes vehicle type information and battery pack capacity information, and the second determining unit 5032 is specifically configured to: determine whether the number of vehicle type labels is greater than 1 to obtain a determination result, and determine the target vehicle type of the charging vehicle according to the determination result and the vehicle type label of the vehicle type prediction result.

[0162] It should be noted that the vehicle type recognition device described above can perform the vehicle type recognition method provided in the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method. Technical details not described in detail in the embodiments of the vehicle type recognition device can be referred to the vehicle type recognition method provided in the embodiments of the present application.

[0163] Please refer to FIG. 9, which is a schematic diagram of the hardware structure of a charging pile according to an embodiment of the present application. As shown in FIG. 9, the charging pile includes one or more processors 901 and memories 902, and one processor 901 is taken as an example in FIG. 9.

[0164] The processor 901 is configured to support the computer device to perform the corresponding functions in the methods in the method embodiments described above. The processor 901 can be a central processing unit (CPU), a network processor (NP), a hardware chip or any combination thereof. The hardware chip described above can be an application specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The PLD described above can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0165] The memory 902 is configured to store program codes and the like. The memory 902 can include a volatile memory (VM), for example, a random access memory (RAM); the memory can also include a non-volatile memory (NVM), for example, a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); and the memory 902 can further include a combination of the above-mentioned memories.

[0166] The memory 902 can be configured to store non-volatile software programs, non-volatile computer-executable programs and modules, such as program instructions / modules corresponding to the vehicle model identification method in the embodiments of the present application. The processor 901 executes various functional applications and data processing of the vehicle model identification method and the vehicle model identification apparatus by running the non-volatile software programs, instructions and modules stored in the memory 902, that is, realizes the functions of each module or unit of the vehicle model identification method and the vehicle model identification apparatus provided in the above method embodiments.

[0167] The memory 902 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function. The data storage area can store data created according to the use of the vehicle model identification apparatus, and the like. In some embodiments, the memory 902 can optionally include a memory remotely arranged with respect to the processor, and these remote memories can be connected to the vehicle model identification apparatus through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0168] The one or more modules are stored in the memory 902, and when executed by the one or more processors 901, perform the vehicle model identification method in any of the above method embodiments, for example, perform the method steps described in the above method embodiments, and realize the functions of the modules described in the above apparatus embodiments.

[0169] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program, the computer program including program instructions, the program instructions causing a computer to execute the method as described in the above embodiments when executed by the computer.

[0170] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, the program can include the processes of the above-mentioned embodiment methods. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like.

[0171] Finally, it should be pointed out that the present application can be implemented in many different forms and is not limited to the embodiments described in the specification, which are not intended to be additional limitations on the content of the present application. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. And under the idea of the present application, the above technical features continue to be combined with each other, and there are many other changes of different aspects of the present application as described above, which are considered to be within the scope of the present application. Further, for those skilled in the art, the above-mentioned embodiments can be improved or changed according to the above-mentioned description, and all these improvements and changes shall be within the scope of protection of the claims of the present application.

Claims

1. A vehicle model identification method characterized by comprising: The method comprises the following steps: obtaining charging characteristic information of a charging vehicle; inputting the charging characteristic information into a vehicle type prediction model to obtain a vehicle type prediction result; determining a target vehicle type of the charging vehicle according to the vehicle type prediction result.

2. The vehicle type identification method according to claim 1, characterized by, The step of obtaining the charging characteristic information of the charging vehicle comprises the following steps: obtaining charging interaction information sent by the charging vehicle; extracting the charging characteristic information of the charging vehicle from the charging interaction information.

3. The vehicle type identification method according to claim 1, characterized by, Before the step of obtaining the charging characteristic information of the charging vehicle, the vehicle type identification method further comprises the following steps: obtaining characteristic sampling information of the charging vehicle of each vehicle type; associating the characteristic sampling information with a preset vehicle type label to obtain a sample data set; training the sample data set according to a preset machine learning algorithm to obtain a vehicle type prediction model.

4. The vehicle type identification method according to claim 1, characterized by, The vehicle type prediction result comprises at least one vehicle type label, and the step of determining the target vehicle type of the charging vehicle according to the vehicle type prediction result comprises the following steps: determining the number of vehicle type labels of the vehicle type prediction result; determining the target vehicle type of the charging vehicle according to the number of vehicle type labels of the vehicle type prediction result and the vehicle type labels.

5. The vehicle type identification method according to claim 4, characterized by The step of determining the target vehicle type of the charging vehicle according to the number of vehicle type labels of the vehicle type prediction result and the vehicle type labels comprises the following steps: determining whether the number of vehicle type labels is greater than 1 to obtain a determination result; determining the target vehicle type of the charging vehicle according to the determination result and the vehicle type labels of the vehicle type prediction result.

6. The vehicle type identification method according to claim 5, characterized by The vehicle type label comprises vehicle type information, and the step of determining the target vehicle type of the charging vehicle according to the determination result and the vehicle type labels of the vehicle type prediction result comprises the following steps: if the determination result is that the number of vehicle type labels is not greater than 1, determining the target vehicle type of the charging vehicle according to the vehicle type information corresponding to the vehicle type label.

7. The vehicle type identification method according to claim 5, characterized by The vehicle type label comprises vehicle type information and battery pack capacity information, and the step of determining the target vehicle type of the charging vehicle according to the determination result and the vehicle type labels of the vehicle type prediction result comprises the following steps: if the determination result is that the number of vehicle type labels is greater than 1, extracting the battery pack capacity information of each vehicle type label respectively, and determining the target vehicle type of the charging vehicle according to the battery pack capacity information and the vehicle type information.

8. The vehicle type identification method according to claim 7, characterized by, The step of determining the target vehicle type of the charging vehicle according to the battery pack capacity information and the vehicle type information comprises the following steps: determining whether there is a difference in the battery pack capacity information; if there is a difference, determining the actual battery pack capacity of the charging vehicle, determining a target vehicle type label according to the actual battery pack capacity, and determining the target vehicle type of the charging vehicle according to the vehicle type information corresponding to the target vehicle type label; if there is no difference, determining the charging curve characteristics of the charging vehicle, and determining the target vehicle type of the charging vehicle according to the charging curve characteristics.

9. The vehicle type identification method according to claim 8, characterized by, The step of determining the actual battery pack capacity of the charging vehicle comprises the following steps: calculating the battery charging capacity required for charging a preset percentage of the battery of the charging vehicle; averaging the battery charging capacity to obtain an average capacity value; determining the actual battery pack capacity of the charging vehicle according to the average capacity value and a preset value.

10. The vehicle type identification method according to claim 8, characterized by The step of determining the target vehicle type label according to the actual battery pack capacity comprises the following steps: Match the actual battery pack capacity with the battery pack capacity information corresponding to each vehicle model label respectively; Determine the vehicle model label corresponding to the matched battery pack capacity information as the target vehicle model label.

11. The vehicle type identification method according to claim 8, characterized by, The method further comprises: Inputting the charging curve feature into an auxiliary prediction model to obtain an auxiliary prediction result; Determining the target vehicle model of the charging vehicle according to the auxiliary prediction result.

12. A charging station, characterized in that The charging pile comprises a memory and a processor, the processor is electrically connected with the memory, is used for executing one or more computer programs stored in the memory, and when the one or more computer programs are executed, makes the charging pile implement the vehicle model identification method in any one of claims 1 to 11.

13. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, the computer program comprises program instructions, and the program instructions make the processor execute the vehicle model identification method in any one of claims 1 to 11 when the processor executes the program instructions. The computer readable storage medium stores a computer program, the computer program comprises program instructions, and the program instructions make the processor execute the vehicle model identification method in any one of claims 1 to 11 when the processor executes the program instructions.

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