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

Abstract

The invention relates to the field of charging piles, in particular to a vehicle type recognition method, a charging pile and a computer readable storage medium. The vehicle type identification method comprises the steps of obtaining charging feature information of a charging vehicle, inputting the charging feature information into a vehicle type prediction model, performing prediction to obtain a vehicle type prediction result, and determining a target vehicle type of the charging vehicle according to the vehicle type prediction result. Therefore, the target vehicle type of the charging vehicle can be automatically identified when the charging vehicle is charged, the charging control characteristics of the charging vehicle can be combined to formulate a targeted charging strategy for the charging vehicle to carry out charging optimization, and the charging vehicle can be charged well and quickly.

Description

Technical Field

[0001] The present invention relates to the field of charging piles, and in particular to a vehicle type identification method, a charging pile and a computer-readable storage medium. Background Art

[0002] There are many types of electric vehicles on the market. Different types of electric vehicles usually have different battery control characteristics and charging strategies. During the charging process of electric vehicles, charging piles generally charge the charging vehicles based on the charging interaction data between the charging piles and the charging vehicles. This conventional charging method does not optimize the charging of the charging vehicles based on the vehicle characteristics of the charging vehicles themselves, so the charging vehicles charge slowly. Summary of the invention

[0003] An object of the present invention 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 charging vehicles.

[0004] In a first aspect, an embodiment of the present invention provides a vehicle type identification method, comprising:

[0005] Obtain charging characteristic information of the charging vehicle;

[0006] Inputting the charging characteristic information into a vehicle type prediction model to obtain a vehicle type prediction result;

[0007] The target vehicle type of the charging vehicle is determined according to the vehicle type prediction result.

[0008] Optionally, the acquiring charging characteristic information of the charging vehicle includes:

[0009] Acquiring charging interaction information sent by the charging vehicle;

[0010] The charging characteristic information of the charging vehicle is extracted from the charging interaction information.

[0011] Optionally, before acquiring the charging characteristic information of the charging vehicle, the vehicle type identification method further includes:

[0012] Obtain characteristic sampling information of charging vehicles of each type;

[0013] Associating the feature sampling information with a preset vehicle model label to obtain a sample data set;

[0014] The sample data set is trained according to a preset machine learning algorithm to obtain a vehicle model prediction model.

[0015] Optionally, the vehicle type prediction result includes at least one vehicle type label, and determining the target vehicle type of the charging vehicle according to the vehicle type prediction result includes:

[0016] Determine the number of vehicle model labels of the vehicle model prediction result;

[0017] The target vehicle model of the charging vehicle is determined according to the number of vehicle model labels and the vehicle model labels of the vehicle model prediction result.

[0018] Optionally, determining the target vehicle model of the charging vehicle according to the number of vehicle model labels and the vehicle model labels of the vehicle model prediction result includes:

[0019] Determine whether the number of vehicle model labels is greater than 1, and obtain a determination result;

[0020] The target vehicle model of the charging vehicle is determined according to the judgment result and the vehicle model label of the vehicle model prediction result.

[0021] Optionally, the vehicle model label includes vehicle model information, and determining the target vehicle model of the charging vehicle according to the vehicle model label of the judgment result and the vehicle model prediction result includes:

[0022] If the judgment result is that the number of the 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 labels.

[0023] Optionally, the vehicle model label includes vehicle model information and battery pack capacity information, and determining the target vehicle model of the charging vehicle according to the vehicle model label of the judgment result and the vehicle model prediction result includes:

[0024] If the judgment result is that the number of the 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.

[0025] Optionally, determining the target vehicle model of the charging vehicle according to the battery pack capacity information and the vehicle model information includes:

[0026] Determining whether there is a difference in the battery pack capacity information;

[0027] If there is a difference, the actual battery pack capacity of the charging vehicle is determined, the target vehicle model label is determined according to the actual battery pack capacity, and the target vehicle model of the charging vehicle is determined according to the vehicle model information corresponding to the target vehicle model label;

[0028] If there is no difference, a charging curve characteristic of the charging vehicle is determined, and a target vehicle model of the charging vehicle is determined according to the charging curve characteristic.

[0029] Optionally, determining the actual battery pack capacity of the charging vehicle includes:

[0030] Calculating the battery charging power required to charge the charging vehicle by a preset percentage;

[0031] Averaging the charged power of the battery to obtain an average power value;

[0032] The actual battery pack capacity of the charging vehicle is determined based on the average power value and a preset value.

[0033] Optionally, determining the target vehicle model label according to the actual battery pack capacity includes:

[0034] Matching the actual battery pack capacity with the battery pack capacity information corresponding to each vehicle model label;

[0035] The vehicle model label corresponding to the successfully matched battery pack capacity information is determined as the target vehicle model label.

[0036] Optionally, determining the target vehicle model of the charging vehicle according to the charging curve characteristics includes:

[0037] Inputting the charging curve characteristics into an auxiliary prediction model to predict and obtain an auxiliary prediction result;

[0038] A target vehicle type of the charging vehicle is determined according to the auxiliary prediction result.

[0039] In a second aspect, an embodiment of the present invention provides a charging pile, comprising a memory and a processor, wherein the processor is electrically connected to the memory and is used to execute one or more computer programs stored in the memory, and when executing the one or more computer programs, the charging pile implements the vehicle model identification method as described above.

[0040] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the vehicle model identification method as described above.

[0041] Compared with the prior art, the embodiment of the present invention provides a vehicle type identification method, a charging pile and a computer-readable storage medium. The vehicle type identification method includes: obtaining charging characteristic information of a charging vehicle, inputting the charging characteristic information into a vehicle type prediction model, predicting a vehicle type prediction result, and determining a target vehicle type of the charging vehicle according to the vehicle type prediction result. Therefore, the present embodiment can automatically identify the target vehicle type of the charging vehicle when charging the charging vehicle, which is conducive to formulating a targeted charging strategy for the charging vehicle in combination with the charging control characteristics of the charging vehicle for charging optimization, thereby facilitating charging the charging vehicle well and quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0043] Figure 1 A schematic diagram of an application scenario of a charging pile provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a flow chart of a vehicle type identification method provided by an embodiment of the present invention;

[0045] Figure 3 A schematic flow chart of a vehicle type identification method provided by another embodiment of the present invention;

[0046] Figure 4 A schematic diagram of the flow of S203 in a vehicle type identification method provided by an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of the structure of a vehicle type identification device provided by an embodiment of the present invention;

[0048] Figure 6 A schematic diagram of the structure of a first acquisition module in a vehicle type identification device provided by an embodiment of the present invention;

[0049] Figure 7 A schematic structural diagram of a vehicle type identification device provided by another embodiment of the present invention;

[0050] Figure 8 A schematic diagram of the structure of a determination module in a vehicle type identification device provided by an embodiment of the present invention;

[0051] Fig. 9 A schematic diagram of the hardware structure of a charging pile provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0053] It should be noted that, if there is no conflict, the various features in the embodiments of the present invention can be combined with each other, all within the scope of protection of the present invention. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order from the module division in the device or the flow chart. Furthermore, the words "first", "second", "third", etc. used in the present invention do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0054] See also Figure 1 , an embodiment of the present invention provides a schematic diagram of an application scenario of a charging pile, the application scenario includes a power grid 100, a charging pile 200 and a charging vehicle 300.

[0055] The power grid 100 is a power network used to transmit the mains electricity to the charging pile 200 through a transmission line to supply power to the charging vehicle 300. The mains electricity is industrial frequency alternating current, which is generally characterized by the three common quantities of alternating current, namely voltage, current and frequency. Generally, the mains electricity transmitted from the power grid 100 to the charging pile 200 is three-phase alternating current.

[0056] The charging pile 200 is a device for charging the charging vehicle 300 to replenish the power of the charging vehicle 300. 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 the charging line to charge the charging vehicle 300. In some embodiments, the charging pile 200 can be any type of charging pile, such as a DC charging pile, an AC charging pile, an AC / DC integrated charging pile, etc.

[0057] The DC charging pile uses DC power to perform DC charging on the power battery of the charging vehicle 300. This charging method is also called "fast charging". The DC charging pile is electrically connected to the power grid 100, and can receive three-phase 380V AC power input from the power grid 100 and convert the AC power into DC power, which is then transmitted to the power battery of the charging vehicle 300 for charging through a standard DC charging plug and charging socket, thereby achieving DC charging. The power supply characteristics of the DC charging pile itself determine that it can output sufficient charging power, and the adjustment range of voltage and current is relatively large, thereby achieving fast charging. The DC charging pile has a charger function, which can monitor and control the status of the charged power battery in real time, and can also measure the charging power.

[0058] The AC charging pile is electrically connected to the power grid 100, and is used to use a single or dual 220VAC / 380VAC AC output interface to provide power output to the charging vehicle 300, so that the charging vehicle 300 can use the on-board charger to charge the power battery of the charging vehicle 300. This charging method is also called "slow charging". The output power of the AC charging pile is generally 5kW (220VAC) / 20kW (380VAC), but the actual charging power is restricted by the on-board charger. Generally, the on-board charging power of a small electric vehicle is between 2 and 3kW. The on-board charger of the charging vehicle 300 can convert the input AC power into DC power by filtering and rectifying, and then store the DC power in the power battery of the charging vehicle 300, thereby charging the charging vehicle 300. This charging method is mainly used for small pure electric vehicles.

[0059] The input voltage of the AC / DC integrated charging pile generally adopts three-phase four-wire 380VAC±15%, frequency 50Hz, and the AC / DC integrated charging pile includes a DC output port and an AC output port, wherein 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, and the AC output port outputs 220VAC (5kW) / 380VAC (20kW) AC power to provide charging power for the on-board charger of the charging vehicle 300. The AC / DC integrated charging pile can provide conventional charging through the AC output port and fast charging through the DC output port. When there are many charging services during the day, fast charging is used for fast charging. When there are few users at the charging station at night, conventional charging can be used for slow charging. The AC / DC integrated charging pile can realize both AC and DC charging at the same time, and interlocking charging. It adopts modular design for easy maintenance.

[0060] The charging vehicle 300 is communicatively connected with the charging pile 200. On the one hand, the charging vehicle 300 can receive direct current or alternating current 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, various charging interaction information can be sent and received between the charging vehicle 300 and the charging pile 200.

[0061] The charging process of the charging vehicle 300 may include a charging parameter configuration phase and a charging phase.

[0062] The charging pile 200 is physically connected to the charging vehicle 300 and powered on, and after checking that the voltage is normal, it enters the charging parameter configuration stage. In this stage, the charging interaction information sent and received between the charging vehicle 300 and the charging pile 200 includes handshake messages, identity authentication messages, charging parameter negotiation messages and charging preparation messages.

[0063] The handshake message is used to establish a communication connection between the battery management system of the charging pile 200 and 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 to determine whether the handshake between the two parties is normal, and indicates that the charging pile 200 is ready to configure the charging parameters. After receiving the handshake request message, the battery management system of the charging vehicle 300 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, the charging pile 200 determines that the handshake between the two parties is normal and establishes a communication connection between the two parties.

[0064] 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. 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 authentication of the charging pile 200 is completed.

[0065] The charging parameter negotiation message is used to negotiate 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. The charging parameter suggestion message includes the charging parameters suggested by the charging pile 200. The charging parameters include the maximum charging voltage, the maximum charging current, the minimum charging voltage, the minimum charging current, the charging mode and other parameters. After receiving the charging parameter suggestion message, the battery management system of the charging vehicle 300 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.

[0066] The charging ready message is used to indicate that the charging pile 200 is ready. The charging ready 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.

[0067] During 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 sent and received between the charging vehicle 300 and the charging pile 200 includes charging control messages, charging data messages, and charging fault messages.

[0068] The charging control message is used to control the charging process. The charging control message may 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 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 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 to report the charging status of the battery management system of the charging vehicle 300.

[0069] The charging data message is used to transmit 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 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 to report the battery voltage value and the battery current value of the charging vehicle 300.

[0070] The charging fault message is used to indicate 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 to indicate charging fault information, and the battery fault message is sent by the battery management system of the charging vehicle 300 to the charging pile 200 to indicate battery fault information.

[0071] The charging vehicle 300 includes any vehicle that can be driven by electricity, including but not limited to pure electric vehicles, hybrid electric vehicles, fuel cell vehicles, etc.

[0072] See also Figure 2 The embodiment of the present invention provides a vehicle type recognition method, the vehicle type recognition method comprising:

[0073] S201, obtaining charging characteristic information of a charging vehicle;

[0074] In this step, the charging vehicle is a vehicle being charged by a charging pile. When the charging pile charges the charging vehicle, as mentioned above, the charging pile can receive charging interaction information sent by the charging vehicle based on the interaction between the charging pile and the charging vehicle, and obtain charging characteristic information according to the charging interaction information. The charging characteristic information is information used to characterize the charging properties or battery properties of the charging vehicle, such as charging power, etc.

[0075] It is understandable that the charging characteristic information of different charging vehicles may be the same or different. The charging characteristic information of charging vehicles of different brands may be different, the charging characteristic information of charging vehicles of the same brand but different car series may also be different, the charging characteristic information of charging vehicles of the same brand, the same car series but different models may also be different, and even the charging characteristic information of charging vehicles of the same brand, the same car series and the same model may also be different.

[0076] In some embodiments, the charging pile obtains charging interaction information sent by the charging vehicle, and extracts charging feature information of the charging vehicle from the charging interaction information.

[0077] In this embodiment, as mentioned above, the charging interaction information may include a battery voltage / current measurement message or other related messages, and the related messages carry charging characteristic information such as the battery voltage value. Therefore, when the charging pile receives the related message, the charging characteristic information can be extracted from the related message.

[0078] S202, inputting the charging characteristic information into a vehicle model prediction model to obtain a vehicle model prediction result;

[0079] In this step, the vehicle model prediction model is an algorithm model used to predict 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.

[0080] Model prediction result The prediction result output by the model prediction model. In some embodiments, the model prediction result includes at least one model label, which is a classification label for uniquely identifying the target model of the vehicle, and each model of the vehicle corresponds to at most one model label.

[0081] Since the model label of the model prediction result can be one or more than one, the number of model labels will affect the charging pile's strategy for determining the model of the charging vehicle. Therefore, when the charging pile determines the model of the charging vehicle based on the model prediction result, it is necessary to first count the number of model labels of the model prediction result.

[0082] It can be understood that when the vehicle model prediction result output by the vehicle model prediction model only includes one vehicle model label, the charging pile can directly determine the vehicle model of the charging vehicle based on 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.

[0083] For example, the A1 model vehicle of brand A vehicle manufacturer and the B1 model vehicle of brand B vehicle manufacturer both use the same battery management system produced by the same equipment manufacturer. Since the battery management system used is the same, when the A1 model vehicle and the B1 model vehicle are charging, the charging feature information carried in the charging interaction information sent to the charging pile may be the same. After the charging feature information is input into the model prediction model, the model prediction result output by the model prediction model may include the model label corresponding to the A1 model vehicle and the model label corresponding to the B1 model vehicle. At this time, the charging pile cannot directly determine whether the charged vehicle is an A1 model vehicle or a B1 model vehicle.

[0084] It can be understood that the prediction process of the vehicle type prediction model is the initial identification stage of the vehicle type. In the initial identification stage of the vehicle type, the charging pile can directly locate the model of the charging vehicle, for example, when the vehicle type prediction result only includes one vehicle type label. The charging pile may also not be able to directly locate the model of the charging vehicle, for example, when the vehicle type prediction result includes more than one vehicle type label.

[0085] S203: Determine the target vehicle model for the charging vehicle according to the pre-model test result.

[0086] In this step, the target vehicle model is the vehicle model of the charging vehicle, and the vehicle model is a number consisting of pinyin letters and Arabic numerals assigned to a type of vehicle for identification. It can be understood that one vehicle model can correspond to multiple vehicles, but one vehicle can only correspond to one vehicle model.

[0087] Therefore, this embodiment can automatically identify the target model of the charging vehicle when charging the charging vehicle, which is conducive to formulating targeted charging strategies for the charging vehicle in combination with the charging control characteristics of the charging vehicle for charging optimization, thereby facilitating charging the charging vehicle well and quickly.

[0088] In some embodiments, see Figure 3 Before executing S201, the vehicle type identification method further includes:

[0089] S204, obtaining characteristic sampling information of charging vehicles of each type;

[0090] In this step, the characteristic sampling information is the charging characteristic information obtained by sampling each type of vehicle in advance when the charging pile is charging or testing each type of vehicle. These types of vehicles may include vehicles of different brands, vehicles of the same brand but different series, vehicles of the same brand but different models, vehicles of the same brand, same series, and same model but different production years, etc.

[0091] S205, associating the feature sampling information with the preset vehicle model label to obtain a sample data set;

[0092] In this step, the preset vehicle model label is a vehicle model label used to pre-classify different vehicle models. The charging pile associates the characteristic sampling information obtained 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 of one vehicle model is as follows:

[0093]

[0094] Among them, CC1, CC2, CC3, CC4 and CC5 represent five characteristic 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. Train the sample data set according to the preset machine learning algorithm to obtain a vehicle model prediction model.

[0097] In this step, the type of 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. These 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 this prediction model as the vehicle model prediction model.

[0098] Since the vehicle type prediction model is trained by a sample data set consisting of sample data of various vehicle types, the vehicle type prediction model can cover the vehicle type prediction of various vehicle types, and when new vehicle types appear in the future, the vehicle type prediction model can be further trained to expand the vehicle type, thereby facilitating the charging pile to make accurate and reliable vehicle type predictions for the currently charging vehicle.

[0099] In some embodiments, the vehicle model prediction result includes at least one vehicle model tag. Figure 4 , S203 includes:

[0100] S2031, determining the number of vehicle model labels of vehicle model prediction results;

[0101] In this step, as mentioned above, in the initial vehicle model identification stage, the vehicle model prediction result output by the vehicle model prediction model has one or more vehicle model labels.

[0102] S2032. Determine the target vehicle model for the charging vehicle according to the number of vehicle model labels and the vehicle model labels in the vehicle model prediction result.

[0103] In this step, when the number of vehicle model labels is more than one, the charging pile may not be able to clearly distinguish the specific model of the charging vehicle. Therefore, after the vehicle model prediction model outputs the vehicle model prediction result, the charging pile needs to determine the number of vehicle model labels of the vehicle model prediction result. When the number of vehicle model labels is one, there is no need to enter the vehicle model precise identification stage, and when the number of vehicle model labels is more than one, it is necessary to enter the vehicle model precise identification stage to accurately locate the target vehicle model of the charging vehicle.

[0104] In some embodiments, the charging pile determines whether the number of vehicle model labels is greater than 1, obtains a determination result, and determines a target vehicle model for the charging vehicle based on the determination result and the vehicle model label of the vehicle model prediction result.

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

[0106] In some embodiments, the vehicle model label may include vehicle model information, which is information used to uniquely identify the corresponding vehicle model. The vehicle model label may also include vehicle model information and battery pack capacity information, which is information used to indicate the design capacity of the power battery pack of the vehicle model corresponding to the vehicle model label when it leaves the factory.

[0107] In some embodiments, if the judgment result is that the number of vehicle model tags is not greater than 1, the charging pile determines the target vehicle model of the charging vehicle according to the vehicle model information corresponding to the vehicle model tag.

[0108] For example, suppose the vehicle model prediction model outputs the following vehicle model prediction results:

[0109] VehicleLabel_1:A1_120kWh

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

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

[0112] In some embodiments, 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.

[0113] For example, suppose the vehicle model prediction model outputs the following vehicle model prediction results:

[0114] VehicleLabel_2: B1_70kWh

[0115] VehicleLabel_3: C1_100kWh

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

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

[0118] Therefore, this embodiment can clearly distinguish the target vehicle model of the charging vehicle even when there are multiple vehicle model labels in the vehicle model prediction results, which is beneficial to improving the reliability and accuracy of vehicle model identification.

[0119] In some embodiments, the charging station can determine whether there is a difference in the battery pack capacity information, and determine the target model of the charged vehicle based on the determination result.

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

[0121] For another example, suppose the vehicle model prediction model outputs the following vehicle model prediction results:

[0122] VehicleLabel_4: D1_80kWh

[0123] VehicleLabel_5: E1_80kWh

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

[0125] If there is a difference in the battery pack capacity information, the charging pile can determine the actual battery pack capacity of the charged vehicle, determine the target vehicle model label based on the actual battery pack capacity, and determine the target vehicle model based on the model information corresponding to the target vehicle model label.

[0126] If there is no difference in the battery pack capacity information, the charging pile can determine the charging curve characteristics of the charging vehicle, determine the target vehicle model label based on the charging curve characteristics, and determine the target vehicle model based on the vehicle model information corresponding to the target vehicle model label.

[0127] In some embodiments, when determining the actual battery pack capacity of a charging vehicle, the charging station calculates the battery charging power required to charge the charging vehicle for each preset percentage, averages the battery charging power to obtain an average power value, and determines the actual battery pack capacity of the charging vehicle based on the average power value and the 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 1%, the preset value is 100, and when the preset percentage is 2%, the preset value is 50. It can be understood that 100% power means that the charging vehicle is fully charged.

[0129] Taking the preset percentage of 1% as an example, first, since the charging current and charging voltage during the charging process of the charging vehicle change in real time, the charging pile can integrate the battery charging power in an integration interval of a preset time length. For example, if the integration interval is 1 second, the charging pile can calculate the battery charging power charged per second, and then accumulate the battery charging power charged per second until the battery charging power changes by 1%. In some embodiments, the charging pile can calculate the battery charging power required for each preset percentage charge of the charging vehicle according to the following formula:

[0130] Cap=Q1+Q2+Q3+……+Qn

[0131] Q1=v1*c1*t

[0132] Q2=v2*c2*t

[0133] Q3=v3*v3*t

[0134] Qn=vn*cn*t

[0135] Among them, Cap represents the battery charging capacity required to charge the charging vehicle to a preset percentage, Qn represents the battery charging capacity charged in the nth integration interval, vn represents the charging voltage used to charge the charging vehicle during the nth integration interval, cn represents the charging current used to charge the charging vehicle during the nth integration interval, and t represents the duration of the integration interval.

[0136] Next, the charging pile can accumulate multiple battery charging capacities required to charge the charging vehicle to a preset percentage according to actual needs, and average the battery charging capacities to obtain an average power value. In some embodiments, the charging pile can calculate the average power value according to the following formula:

[0137] Cap_av=(Cap1+Cap2+……+Capn) / n

[0138] Finally, the charging pile multiplies the average power by the preset value to obtain the actual battery pack capacity of the charging vehicle. It is understandable that, as mentioned 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.

[0139] Therefore, this embodiment estimates the actual battery pack capacity of the charging vehicle by integrating the charging electricity, thereby avoiding the introduction of estimation errors due to real-time changes in charging current and charging voltage during the charging process of the charging vehicle, thereby being able to more accurately estimate the actual battery pack capacity of the charging vehicle, which in turn is conducive to more reliable subsequent prediction of the model of the charging vehicle.

[0140] 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 successfully matched battery pack capacity information is the target vehicle model label.

[0141] 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 successfully matched 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 successfully matched battery capacity information.

[0142] For example, the actual battery pack capacity is 99kWh. As mentioned above, the vehicle model labels of the vehicle model prediction results are VehicleLabel_2 and VehicleLabel_3. The battery pack capacity information corresponding to the two vehicle model labels are 70kWh and 100kWh respectively. Since the difference between the actual battery pack capacity of 99kWh and 100kWh is within the preset range of ±1kWh, the charging pile can determine that 100kWh is the successfully matched battery pack capacity information, and use the vehicle model information C1 corresponding to 100kWh as the target vehicle model for the charging vehicle.

[0143] 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 charged vehicle to uniquely locate the target model of the charged vehicle. This can avoid the situation where the charging pile cannot directly obtain the battery pack capacity of the charged vehicle and reliably identify the target model of the charged vehicle.

[0144] It is understandable that although there are differences in the battery pack capacity information, when the vehicle model prediction model outputs multiple vehicle model labels, there may be a situation where the battery pack capacity information corresponding to two or more vehicle model labels is the same. For example, the vehicle model prediction model outputs the following vehicle model prediction results:

[0145] VehicleLabel_6: D1_80kWh

[0146] VehicleLabel_7: E1_80kWh

[0147] VehicleLabel_8: F1_90kWh

[0148] If the actual battery pack capacity estimated by the charging pile is 80kWh, and the battery pack capacity information corresponding to the vehicle model labels VehicleLabel_6 and VehicleLabel_7 are both 80kWh, the charging pile cannot directly determine whether the target vehicle model of the charged vehicle is D1 or E1. Therefore, in some embodiments, when there is a difference in the battery pack capacity information, but the charging pile cannot determine the target vehicle model of the charged vehicle based on the actual battery pack capacity, the charging pile can determine the charging curve characteristics of the charged vehicle and determine the target vehicle model of the charged vehicle based on the charging curve characteristics.

[0149] In some embodiments, when determining the charging curve characteristics of a charging vehicle, the charging pile can obtain dynamic charging characteristic information at multiple time points during the process of continuously charging the charging vehicle to a preset percentage of electricity (for example, ten percent), and fit the dynamic charging characteristic information into a charging curve according to any suitable curve fitting algorithm, thereby obtaining the charging curve characteristics, wherein the dynamic charging characteristic information may include any suitable charging characteristic information such as demand current.

[0150] In some embodiments, the charging pile inputs the charging curve characteristics into the auxiliary prediction model, predicts to obtain an auxiliary prediction result, and determines the target model of the charging vehicle according to the auxiliary prediction result.

[0151] 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-acquires the dynamic charging characteristic information of each vehicle type during the charging process and fits the dynamic charging characteristic information into a sample charging curve according to any appropriate 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.

[0152] In some embodiments, the auxiliary prediction result includes at least one auxiliary prediction tag, the auxiliary prediction tag includes vehicle model information, and when the number of auxiliary prediction tags is 1, the charging pile can determine the target vehicle model of the charging vehicle based on the vehicle model information corresponding to the auxiliary prediction tag.

[0153] In some embodiments, the charging pile can determine the target model of the charging vehicle based on the model label of the auxiliary prediction result and the model label of the model prediction result.

[0154] For example, as mentioned above, the model prediction results include the following model labels:

[0155] VehicleLabel_4: D1_80kWh

[0156] VehicleLabel_5: E1_80kWh

[0157] Assume that the auxiliary prediction results include the following auxiliary prediction labels:

[0158] AuxiliaryLabel_1: E1

[0159] AuxiliaryLabel_2: G1

[0160] The charging pile can extract the vehicle model information corresponding to each vehicle model tag and the vehicle model information corresponding to each auxiliary prediction tag, and compare each vehicle model information corresponding to the vehicle model information with the vehicle model information corresponding to each auxiliary prediction tag in turn, and use the vehicle model information that matches as the target vehicle model of the charging vehicle. As mentioned above, the charging pile can compare the vehicle model information D1 with the vehicle model information E1 and G1 respectively. At this time, the vehicle model information fails to match. Then, the charging pile can compare the vehicle model information E1 with the vehicle model information E1 and G1 respectively. At this time, the vehicle model information E1 matches, and the charging pile can determine that the vehicle model information E1 is the target vehicle model of the charging vehicle.

[0161] Therefore, this embodiment can use the vehicle model label of the auxiliary prediction result and the vehicle model label of the vehicle model prediction result for verification, and determine the target vehicle model of the charging vehicle according to the verification result, so that the target vehicle model of the charging vehicle can be identified more accurately and reliably.

[0162] It should be noted that, in each of the above-mentioned embodiments, there is not necessarily a certain order between the above-mentioned steps. A person of ordinary skill in the art can understand, based on the description of the embodiments of the present invention, that in different embodiments, the above-mentioned steps may have different execution orders, that is, they may be executed in parallel, may be executed interchangeably, and so on.

[0163] As another aspect of an embodiment of the present invention, an embodiment of the present invention provides a vehicle type identification device, wherein the vehicle type identification device may be a software module, the software module includes a plurality of instructions, which are stored in a memory, and a processor may access the memory and call the instructions for execution to complete the vehicle type identification method described in each of the above embodiments.

[0164] In some embodiments, the vehicle type identification device can be constructed by hardware devices, for example, the vehicle type identification device can be constructed 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 device can also be constructed 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.

[0165] In some embodiments, see Figure 5 The vehicle type identification device 500 provided in the embodiment of the present invention includes a first acquisition module 501, a prediction module 502 and a determination module 503.

[0166] The first acquisition module 501 is used to acquire the charging characteristic information of the charging vehicle, the prediction module 502 is used to input the charging characteristic information into the vehicle model prediction model to predict the vehicle model prediction result, and the determination module 503 is used to determine the target vehicle model of the charging vehicle according to the vehicle model prediction result.

[0167] Therefore, this embodiment can automatically identify the target model of the charging vehicle when charging the charging vehicle, which is conducive to formulating targeted charging strategies for the charging vehicle in combination with the charging control characteristics of the charging vehicle for charging optimization, thereby facilitating charging the charging vehicle well and quickly.

[0168] In some embodiments, see Figure 6The first acquisition module 501 includes an acquisition unit 5011 and an extraction unit 5012.

[0169] The acquisition unit 5011 is used to acquire the charging interaction information sent by the charging vehicle, and the extraction unit 5012 is used to extract the charging characteristic information of the charging vehicle from the charging interaction information.

[0170] In some embodiments, see Figure 7 The vehicle type identification device 500 also includes a second acquisition module 504 , an association module 505 and a training module 506 .

[0171] The second acquisition module 504 is used to obtain feature sampling information of charging vehicles of each model, the association module 505 is used to associate the feature sampling information with the preset model label to obtain a sample data set, and the training module 506 is used to train the sample data set according to the preset machine learning algorithm to obtain a model prediction model.

[0172] In some embodiments, the vehicle model prediction result includes at least one vehicle model tag. Figure 8 , the determination module 503 includes a first determination unit 5031 and a second determination unit 5032.

[0173] The first determination unit 5031 is used to determine the number of vehicle model labels of the vehicle model prediction result, and the second determination unit 5032 is used to determine the target vehicle model of the charging vehicle according to the number of vehicle model labels and the vehicle model labels of the vehicle model prediction result.

[0174] In some embodiments, the vehicle model label includes vehicle model information and battery pack capacity information. The second determination unit 5032 is specifically used to: determine whether the number of vehicle model labels is greater than 1, obtain a determination result, and determine the target vehicle model of the charging vehicle based on the determination result and the vehicle model label of the vehicle model prediction result.

[0175] It should be noted that the vehicle type identification device can execute the vehicle type identification method provided in the embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in the vehicle type identification device embodiment, please refer to the vehicle type identification method provided in the embodiment of the present invention.

[0176] See also Fig. 9 , Fig. 9 The present invention provides a hardware structure diagram of a charging pile. Fig. 9 As shown, the charging pile includes one or more processors 901 and a memory 902. Fig. 9 A processor 901 is taken as an example.

[0177] The processor 901 is configured to support the computer device to perform the corresponding functions in the method in the above method embodiment. The processor 901 can be a central processing unit (CPU), a network processor (NP), a hardware chip or any combination thereof. The above hardware chip can be an application specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0178] The memory 902 is used to store program codes, etc. The memory 902 may include a volatile memory (VM), such as a random access memory (RAM); the memory may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); the memory 902 may also include a combination of the above-mentioned types of memory.

[0179] The memory 902 can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the vehicle type identification method in the embodiment of the present invention. The processor 901 executes various functional applications and data processing of the vehicle type identification method and vehicle type identification device 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 type identification method and vehicle type identification device provided in the above method embodiment.

[0180] The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function. The data storage area may store data created according to the use of the vehicle type identification device, etc. In some embodiments, the memory 902 may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the vehicle type identification device via 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 combinations thereof.

[0181] The one or more modules are stored in the memory 902. When executed by the one or more processors 901, the vehicle model identification method in any of the above method embodiments is executed, for example, the method steps described in the above method embodiments are executed to realize the functions of the modules described in the above device embodiments.

[0182] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the method described in the above embodiment.

[0183] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0184] Finally, it should be noted that the present invention can be implemented in many different forms and is not limited to the embodiments described in this specification. These embodiments are not intended to be additional limitations on the content of the present invention. The purpose of providing these implementations is to make the understanding of the disclosure of the present invention more thorough and comprehensive. And under the idea of ​​the present invention, the above-mentioned technical features continue to be combined with each other, and there are many other changes in different aspects of the present invention as described above, which are all considered to be within the scope of the present invention. Further, for those of ordinary skill in the art, improvements or changes can be made according to the above description, and all these improvements and changes should belong to the scope of protection of the claims attached to the present invention.

Claims

1. A vehicle type identification method, characterized in that: include: Obtain charging characteristic information of the charging vehicle; Inputting the charging characteristic information into a vehicle type prediction model to obtain a vehicle type prediction result; The target vehicle type of the charging vehicle is determined according to the vehicle type prediction result.

2. The vehicle type identification method according to claim 1, characterized in that: The acquiring of charging characteristic information of the charging vehicle includes: Acquiring charging interaction information sent by the charging vehicle; The charging characteristic information of the charging vehicle is extracted from the charging interaction information.

3. The vehicle type identification method according to claim 1, characterized in that: Before acquiring the charging characteristic information of the charging vehicle, the vehicle type identification method further includes: Obtain characteristic sampling information of charging vehicles of each type; Associating the feature sampling information with a preset vehicle model label to obtain a sample data set; The sample data set is trained according to a preset machine learning algorithm to obtain a vehicle model prediction model.

4. The vehicle type identification method according to claim 1, characterized in that: The vehicle type prediction result includes at least one vehicle type label, and determining the target vehicle type of the charging vehicle according to the vehicle type prediction result includes: Determine the number of vehicle model labels of the vehicle model prediction result; The target vehicle model of the charging vehicle is determined according to the number of vehicle model labels and the vehicle model labels of the vehicle model prediction result.

5. The vehicle type identification method according to claim 4, characterized in that: The step of determining the target vehicle model of the charging vehicle according to the number of vehicle model labels and the vehicle model labels of the vehicle model prediction result comprises: Determine whether the number of vehicle model labels is greater than 1, and obtain a determination result; The target vehicle model of the charging vehicle is determined according to the judgment result and the vehicle model label of the vehicle model prediction result.

6. The vehicle type identification method according to claim 5, characterized in that: The vehicle model label includes vehicle model information, and determining the target vehicle model of the charging vehicle according to the vehicle model label of the judgment result and the vehicle model prediction result includes: If the judgment result is that the number of the 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 labels.

7. The vehicle type identification method according to claim 5, characterized in that: The vehicle model label includes vehicle model information and battery pack capacity information, and the vehicle model label according to the judgment result and the vehicle model prediction result determines the target vehicle model of the charging vehicle, including: If the judgment result is that the number of the 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.

8. The vehicle type identification method according to claim 7, characterized in that: Determining the target vehicle model of the charging vehicle according to the battery pack capacity information and the vehicle model information includes: Determining whether there is a difference in the battery pack capacity information; If there is a difference, the actual battery pack capacity of the charging vehicle is determined, the target vehicle model label is determined according to the actual battery pack capacity, and the target vehicle model of the charging vehicle is determined according to the vehicle model information corresponding to the target vehicle model label; If there is no difference, a charging curve characteristic of the charging vehicle is determined, and a target vehicle model of the charging vehicle is determined according to the charging curve characteristic.

9. The vehicle type identification method according to claim 8, characterized in that: Determining the actual battery pack capacity of the charging vehicle includes: Calculating the battery charging power required to charge the charging vehicle by a preset percentage; Averaging the charged power of the battery to obtain an average power value; The actual battery pack capacity of the charging vehicle is determined based on the average power value and a preset value.

10. The vehicle type identification method according to claim 8, characterized in that: Determining the target vehicle model label according to the actual battery pack capacity includes: Matching the actual battery pack capacity with the battery pack capacity information corresponding to each vehicle model label; The vehicle model label corresponding to the successfully matched battery pack capacity information is determined as the target vehicle model label.

11. The vehicle type identification method according to claim 8, characterized in that: Determining the target vehicle model of the charging vehicle according to the charging curve characteristics includes: Inputting the charging curve characteristics into an auxiliary prediction model to predict and obtain an auxiliary prediction result; A target vehicle type of the charging vehicle is determined according to the auxiliary prediction result.

12. A charging pile, characterized in that: It includes a memory and a processor, wherein the processor is electrically connected to the memory and is used to execute one or more computer programs stored in the memory, and when executing the one or more computer programs, the charging pile implements the vehicle type identification method as described 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, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the vehicle model identification method according to any one of claims 1 to 11.

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