Vehicle remaining charging duration prediction method, charging pile and storage medium

Abstract

The invention relates to the technical field of new energy vehicle charging, in particular to a vehicle remaining charging duration prediction method, a charging pile and a storage medium. The vehicle remaining charging duration prediction method comprises the steps of obtaining first charging feature information, second charging feature information and a charging mode, determining a target vehicle type of a charged vehicle according to the first charging feature information, and determining a target prediction model according to the target vehicle type and the charging mode, and predicting according to the second charging feature information and the target prediction model to obtain the residual charging duration of the charging vehicle. According to the embodiment of the invention, the prediction model is generated for each charging mode of each type of vehicle, and the prediction model corresponding to the current charging mode of the charging vehicle is utilized to predict the residual charging duration of the charging vehicle, so that the prediction accuracy of the residual charging duration is improved, and the use experience of a vehicle owner can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle charging, and in particular to a method for predicting the remaining charging time of a vehicle, a charging pile and a storage medium. Background Art

[0002] When current charging piles charge new energy vehicles, they can generally estimate the remaining charging time when the new energy vehicle is fully charged or charged to a certain level based on parameters such as the current power of the new energy vehicle and the current charging conditions, thereby facilitating car owners to reasonably arrange charging time and vehicle use time.

[0003] However, the charging efficiency of different models of vehicles generally varies, and even the charging efficiency of the same model of vehicle in different charging modes may vary. Since current charging piles have not adjusted the prediction strategy of the remaining charging time for these differences, the current charging piles' prediction of the remaining charging time of new energy vehicles is usually not accurate enough, which affects the owner's experience. Summary of the invention

[0004] An object of the present invention is to provide a method for predicting the remaining charging time of a vehicle, a charging pile and a storage medium, so as to solve the technical problem that the prediction of the remaining charging time of a vehicle is not accurate enough.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting the remaining charging time of a vehicle, comprising:

[0006] Acquiring first charging characteristic information, second charging characteristic information and a charging mode;

[0007] Determining a target model of a charging vehicle according to the first charging characteristic information;

[0008] Determining a target prediction model according to the target vehicle model and the charging mode;

[0009] The remaining charging time of the charging vehicle is predicted based on the second charging characteristic information and the target prediction model.

[0010] Optionally, determining a target prediction model according to the target vehicle model and the charging mode includes:

[0011] Determining a candidate prediction model according to the target vehicle model;

[0012] According to the charging mode, a target prediction model is determined among the candidate prediction models.

[0013] Optionally, predicting the remaining charging time of the charging vehicle according to the second charging characteristic information and the target prediction model includes:

[0014] Acquiring a battery temperature of the charging vehicle;

[0015] Determine whether the battery temperature is within a preset temperature range, and obtain a determination result;

[0016] The remaining charging time of the charging vehicle is predicted based on the judgment result, the second charging characteristic information and the target prediction model.

[0017] Optionally, the target prediction model includes a normal temperature charging prediction model, and the remaining charging time of the charging vehicle predicted according to the judgment result, the second charging characteristic information and the target prediction model includes:

[0018] If the judgment result is that the battery temperature is not within the preset temperature range, the second charging characteristic information is input into the normal temperature charging prediction model to predict the remaining charging time of the charging vehicle.

[0019] Optionally, the target prediction model further includes a high temperature charging prediction model, and the vehicle remaining charging time prediction method further includes:

[0020] determining a battery temperature rise rate of the charging vehicle;

[0021] Determine a target duration according to the battery temperature, the battery temperature rise rate and a first preset temperature threshold;

[0022] Determining whether the target charging time is less than the remaining charging time;

[0023] If it is less than the remaining charging time, the high-temperature charging time is predicted according to the high-temperature charging prediction model, and the remaining charging time is updated according to the high-temperature charging time and the target time.

[0024] Optionally, the predicting the remaining charging time of the charging vehicle according to the judgment result, the second charging characteristic information and the target prediction model includes:

[0025] If the judgment result is that the battery temperature is within the preset temperature range, judging whether the charging vehicle supports the battery preheating function according to the target vehicle model;

[0026] When the charging vehicle supports a battery preheating function, determining whether the charging vehicle is in a battery preheating state;

[0027] When the charging vehicle is in a battery preheating state, determining a battery preheating mode;

[0028] The remaining charging time of the charging vehicle is predicted according to the battery preheating mode, the second charging characteristic information and the target prediction model.

[0029] Optionally, the target prediction model includes a preheating prediction model and a normal temperature charging prediction model, and the remaining charging time of the charging vehicle predicted according to the battery preheating mode, the second charging characteristic information and the target prediction model includes:

[0030] Determining a target preheating prediction model according to the battery preheating mode, the target preheating prediction model comprising a first preheating prediction model and a second preheating prediction model;

[0031] Predicting the preheating time according to the first preheating prediction model;

[0032] updating second charging characteristic information when battery preheating is completed according to the second preheating prediction model;

[0033] Inputting the updated second charging characteristic information into the normal temperature charging prediction model to predict the normal temperature charging time;

[0034] The remaining charging time of the charging vehicle is predicted based on the normal temperature charging time and the preheating time.

[0035] Optionally, the target prediction model includes a first low-temperature charging prediction model, a second low-temperature charging prediction model and a normal temperature charging prediction model, and the vehicle remaining charging time prediction method further includes:

[0036] When the charging vehicle is not in a battery preheating state or does not support a battery preheating function, predicting a low-temperature charging duration according to the first low-temperature charging prediction model;

[0037] updating second charging characteristic information when low-temperature charging is completed according to the second low-temperature charging prediction model;

[0038] Inputting the updated second charging characteristic information into the normal temperature charging prediction model to predict the normal temperature charging time;

[0039] The remaining charging time of the charging vehicle is predicted based on the normal temperature charging time and the low temperature charging time.

[0040] Optionally, it is characterized in that the method for predicting the remaining charging time of a vehicle further comprises:

[0041] Determining the battery type of the charging vehicle according to the target vehicle model;

[0042] Obtaining a battery health status of the charging vehicle;

[0043] Determine a target compensation coefficient according to the battery health status and the battery type;

[0044] The remaining charging time is compensated according to the target compensation coefficient.

[0045] 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 method for predicting the remaining charging time of a vehicle as described above.

[0046] 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 remaining charging time prediction method as described above.

[0047] Compared with the prior art, the embodiment of the present invention provides a method for predicting the remaining charging time of a vehicle, a charging pile and a storage medium. The method for predicting the remaining charging time of a vehicle includes: obtaining first charging characteristic information, second charging characteristic information and a charging mode, determining a target vehicle model of the charging vehicle according to the first charging characteristic information, determining a target prediction model according to the target vehicle model and the charging mode, and predicting the remaining charging time of the charging vehicle according to the second charging characteristic information and the target prediction model. This embodiment generates a prediction model for each charging mode of each vehicle model, and uses the prediction model corresponding to the current charging mode of the charging vehicle to predict the remaining charging time of the charging vehicle, which is conducive to improving the prediction accuracy of the remaining charging time, thereby improving the user experience of the vehicle owner. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] 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.

[0049] Figure 1 A schematic diagram of an application scenario of a charging pile provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of a flow chart of a method for predicting the remaining charging time of a vehicle provided by an embodiment of the present invention;

[0051] Figure 3 A schematic diagram of the flow of S202 in a method for predicting the remaining charging time of a vehicle provided in an embodiment of the present invention;

[0052] Figure 4A schematic diagram of a flow chart of a method for predicting the remaining charging time of a vehicle provided by another embodiment of the present invention;

[0053] Figure 5 A schematic diagram of the flow of S2022 in a method for predicting remaining charging time of a vehicle provided in an embodiment of the present invention;

[0054] Figure 6 A schematic diagram of the structure of a vehicle remaining charging time prediction device provided by an embodiment of the present invention;

[0055] Figure 7 A schematic diagram of the structure of a second determination module in a vehicle remaining charging time prediction device provided by an embodiment of the present invention;

[0056] Figure 8 A schematic diagram of the structure of a vehicle remaining charging time prediction device provided by another embodiment of the present invention;

[0057] Fig. 9 A schematic diagram of the structure of a first determination module in a vehicle remaining charging time prediction device provided by an embodiment of the present invention;

[0058] Fig.10 A schematic diagram of the hardware structure of a charging pile provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] 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.

[0060] 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.

[0061] 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 including a power grid 100, a charging pile 200 and a charging vehicle 300.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] In some embodiments, the charging pile 200 is configured with one or more charging guns, which are interface devices connecting the charging pile 200 and the charging vehicle 300, and are mainly used to transfer electric energy to the charging vehicle 300 for charging. The charging gun usually has a plug and a connecting wire, one end of which is connected to the charging pile and the other end is inserted into the charging interface of the charging vehicle 300. According to different charging requirements and technical standards, the charging gun can be divided into a fast charging gun and a slow charging gun.

[0068] The fast charging gun is also called a DC fast charging gun, which is usually used in a fast charging station, has a large power output, and can quickly charge the power battery of the charging vehicle 300.

[0069] Slow charging guns are also called AC charging guns. They are usually used in home charging piles, commercial charging piles and public charging piles. They have low power and are suitable for charging with ordinary household power supply. The charging speed is relatively slow.

[0070] 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.

[0071] The charging process of the charging vehicle 300 may include a charging parameter configuration phase and a charging phase.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] See also Figure 2 , an embodiment of the present invention provides a method for predicting the remaining charging time of a vehicle, the method comprising:

[0083] S201, obtaining first charging characteristic information, second charging characteristic information and a charging mode;

[0084] In this step, the first charging characteristic information is information used to characterize the charging properties or battery properties of the charging vehicle, such as the maximum allowable charging voltage, current, charging power, etc. of the charging vehicle. The charging vehicle is a vehicle that is being charged by a charging pile. When the charging pile charges the charging vehicle, as mentioned 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 first charging characteristic information according to the charging interaction information.

[0085] The second charging characteristic information is information used to characterize the current charging state of the charging vehicle or the charging properties of the charging pile, wherein the current charging state of the charging vehicle includes the current output power of the charging pile, the current charge state of the charging vehicle, etc., and the charging properties of the charging pile include the maximum output power of the charging pile, etc.

[0086] The charging mode includes various power output modes under various charging types, among which the charging types include fast charging, slow charging, etc. The power output modes under fast charging include 30kw, 60kw, 120kw, 240kw, 380kw, etc., and the power output modes under slow charging include 3.3kw, 6.6kw, 7kw, etc.

[0087] S202, determining a target model of a charging vehicle according to the first charging characteristic information;

[0088] 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.

[0089] S203, determining a target prediction model according to the target vehicle model and charging mode;

[0090] In this step, the target prediction model is a prediction model corresponding to the current charging mode of the charging vehicle and used to predict the remaining charging time of the charging vehicle. The remaining charging time is the time it takes for the charging vehicle to charge from the current power to the preset power. The power is usually expressed by the state of charge (SOC), which refers to the ratio of the available power in the battery to the nominal capacity. Assuming that the current power of the charging vehicle is 20% and the preset power is 80%, the remaining charging time of the charging vehicle is the time it takes to charge from 20% to 80%. It is understandable that the preset power level can be set according to actual needs, including but not limited to 70%, 80%, 90%, 100%, etc., and one or more preset power levels can be set. When multiple preset power levels are set, the charging pile can predict the remaining charging time of the charging vehicle from the current power level to each preset power level according to the target prediction model. For example, the current power level is 20%, and the preset power levels are 80% and 100%. The charging pile can predict the remaining charging time of the charging vehicle from 20% to 80% and the remaining charging time from 20% to 100% according to the target prediction model.

[0091] The charging pile obtains the characteristic sampling information of the charging vehicle in advance when the charging vehicle of each model is in various charging modes, and classifies the characteristic sampling information according to the model and charging mode. For example, the characteristic sampling information of the model A1 in the charging mode B1 is classified into one category, the characteristic sampling information of the model A1 in the charging mode B2 is classified into another category, and the characteristic sampling information of the model A2 in the charging mode B3 is classified into another category, etc. Then, the charging pile can clean the classified characteristic sampling information to filter out the data with low correlation or invalidity with the remaining charging time prediction, so as to generate the prediction model more reliably in the future. Then, in each type of characteristic sampling information after cleaning, the charging pile can associate the specified characteristic sampling information with the remaining charging time label to obtain the sample data sets of various models in various charging modes. Finally, the charging pile can train the sample data set according to the preset machine learning algorithm such as 1D-CNN (one-dimensional convolutional neural network) algorithm to generate the prediction model for each model in each charging mode.

[0092] In some embodiments, the charging pile can input a portion of sample data into the corresponding prediction model, obtain the remaining charging time output by each prediction model, and calculate the error between the remaining charging time and the actual remaining charging time of the corresponding remaining charging time label, and test the performance of the prediction model based on the error. It can be understood that when the error is small, the charging pile can directly use the prediction model for prediction, and when the error is large, the charging pile can retrain the prediction model until the generated prediction model meets the prediction requirements.

[0093] In some embodiments, the charging pile can determine the candidate prediction model according to the target vehicle model, and determine the target prediction model among the candidate prediction models according to the charging mode.

[0094] In this embodiment, the candidate prediction models include prediction models generated for vehicles of the target vehicle type based on different charging modes, and the charging pile can select a prediction model that matches the current charging mode of the charging vehicle from the candidate prediction models as the target prediction model.

[0095] S204: Predict the remaining charging time of the charging vehicle according to the second charging characteristic information and the target prediction model.

[0096] Therefore, this embodiment generates a prediction model for each charging mode of each type of vehicle, and uses the prediction model corresponding to the current charging mode of the charging vehicle to predict the remaining charging time of the charging vehicle, which is beneficial to improving the prediction accuracy of the remaining charging time, thereby improving the user experience of the car owner.

[0097] In some embodiments, the charging pile obtains the battery temperature of the charging vehicle, determines whether the battery temperature is within a preset temperature range, obtains a judgment result, and predicts the remaining charging time of the charging vehicle based on the judgment result, the second charging characteristic information and the target prediction model.

[0098] In this embodiment, the preset temperature range is a relatively low temperature range for the power battery, for example, the preset temperature range is 0° C. to 15° C. The charging pile can select the optimal prediction strategy according to the battery temperature to predict the remaining charging time of the charging vehicle, which can improve the prediction accuracy of the remaining charging time.

[0099] In some embodiments, the target prediction model includes a normal temperature charging prediction model.

[0100] The normal temperature charging prediction model is a prediction model for predicting the normal temperature charging time of a charging vehicle. The normal temperature charging time is the time it takes for the charging vehicle to charge from the power at normal battery temperature to the preset power.

[0101] In some embodiments, if the judgment result is that the battery temperature is not within the preset temperature range, the second charging characteristic information is input into the normal temperature charging prediction model to predict the remaining charging time of the charging vehicle.

[0102] If the battery temperature of the charging vehicle does not fall within the preset temperature range, it means that the battery temperature is normal. At this time, the charging pile can directly predict the normal temperature charging time based on the currently acquired second charging characteristic information and the normal temperature charging prediction model and use the normal temperature charging time as the remaining charging time of the charging vehicle.

[0103] It is understandable that if a charging vehicle is charged after running for a long time and the ambient temperature is relatively high when the charging vehicle is charging, the battery temperature may be too high at a future point in time during the charging process of the charging vehicle. Too high a battery temperature will affect the charging efficiency of the charging vehicle. Therefore, the charging pile can predict the time point when the battery temperature is too high, and divide it into different charging time periods according to this time point, namely the charging time period before the battery temperature is too high and the charging time period after the battery temperature is too high, and then adjust the remaining charging time of the charging vehicle in real time according to the charging time of different charging time periods.

[0104] In some embodiments, the high temperature charging prediction model is a prediction model for predicting the high temperature charging time of a charging vehicle, where the high temperature charging time is the time it takes for the charging vehicle to charge from the power when the battery temperature is at a high temperature to a preset power.

[0105] In some embodiments, the charging pile can determine the battery temperature rise rate of the charging vehicle, determine the target time according to the battery temperature, the battery temperature rise rate and the first preset temperature threshold, and judge whether the target time is less than the remaining charging time. If it is less than the remaining charging time, the high-temperature charging time is predicted according to the high-temperature charging prediction model, and the remaining charging time is updated according to the high-temperature charging time and the target time.

[0106] In some embodiments, the charging pile can obtain the battery temperature of the charging vehicle at a certain interval, subtract the temperatures of two adjacent batteries to obtain a temperature difference, and then divide the temperature difference by the time interval between the two adjacent battery temperatures to obtain the battery temperature rise rate.

[0107] In some embodiments, the target duration is the time the charging pile predicts it will take to continuously charge from the current battery temperature until the battery temperature reaches a first preset temperature threshold. The charging pile can subtract the first preset temperature threshold from the current battery temperature to obtain a temperature difference, and then multiply the temperature difference by the battery temperature rise rate to obtain the target duration.

[0108] If the target duration is greater than or equal to the remaining charging time, it means that the temperature of the charging vehicle will not be too high during the remaining charging time when it is charged from the current power to the preset power. Therefore, the charging pile does not need to consider the impact of excessive temperature on the prediction of the remaining charging time, and can directly use the predicted remaining charging time.

[0109] If the target time is less than the remaining charging time, it means that the temperature may be too high during the remaining charging time when the charging vehicle is charged from the current power to the preset power. Therefore, the charging pile needs to consider the impact of excessive temperature on the prediction of the remaining charging time.

[0110] In some embodiments, the charging pile can input the charging characteristic information such as the state of charge and battery temperature when the battery temperature is too high into the high temperature charging prediction model to predict the high temperature charging duration, which is the time it takes for the charging vehicle to charge from the battery power when the battery temperature is too high to the preset power. The charging pile can obtain characteristic sampling information such as the state of charge and battery temperature in advance when each vehicle model is in each charging mode for high temperature charging, and associate the characteristic sampling information with the duration label to obtain a sample data set, and train the sample data set according to a preset machine learning algorithm to obtain a high temperature charging prediction model.

[0111] In some embodiments, the charging pile may update the remaining charging time by the sum of the high-temperature charging time and the target time.

[0112] For example, the current battery temperature is 40°C, the current power level is 25%, the preset power level is 80%, the first preset temperature threshold is 60°C, and the charging pile currently predicts that the remaining charging time is 2 hours. The charging pile predicts that the battery temperature will reach 60°C after the target time of 1 hour and 50 minutes. The charging pile predicts the high-temperature charging time it takes to charge the battery from the power at 60°C to the preset power level based on the high-temperature charging prediction model. Assuming that the high-temperature charging time is 20 minutes, the charging pile will add 20 minutes of high-temperature charging time to the target time of 1 hour and 50 minutes, and obtain an updated remaining charging time of 2 hours and 10 minutes.

[0113] Therefore, this embodiment can timely adjust the prediction strategy of the remaining charging time when it is foreseeable that the battery temperature is too high during the charging process, which is beneficial to improving the prediction reliability and accuracy of the remaining charging time.

[0114] It is understandable that when the ambient temperature is too low and the battery temperature is low, the chemical reaction rate of the power battery of the new energy vehicle will slow down, which may make the power battery unable to fully absorb the charging energy, resulting in insufficient charging, thus affecting the charging efficiency. In order to improve the charging efficiency of the power battery at low temperatures, some new energy vehicles support battery preheating. Through battery preheating, the temperature of the power battery can be adjusted to an appropriate range when the power battery temperature is too low, so as to improve the charging efficiency and safety. Since whether the new energy vehicle supports battery preheating or not and whether the new energy vehicle that supports battery preheating turns on the battery preheating or not will affect the temperature of the power battery, thereby affecting the charging rate and further affecting the remaining charging time, the charging pile can adopt different remaining charging time prediction strategies according to different situations.

[0115] In some embodiments, if the judgment result is that the battery temperature is within a preset temperature range, it is determined whether the charging vehicle supports the battery preheating function based on the target vehicle model. When the charging vehicle supports the battery preheating function, it is determined whether the charging vehicle is in a battery preheating state. When the charging vehicle is in a battery preheating state, the battery preheating mode is determined, and the remaining charging time of the charging vehicle is predicted based on the battery preheating mode, the second charging characteristic information and the target prediction model.

[0116] In this embodiment, the battery preheating function is a function for increasing the temperature of the battery to reach the optimal operating temperature range. The battery preheating function can be implemented through different battery preheating modes, including external heating mode, internal heating mode and internal and external heating mode, wherein the external heating mode refers to a mode in which the charging vehicle heats the power battery by starting a preheating device such as a PTC (Positive Temperature Coefficient) heating element, a heating film or a liquid circulation heating system, the internal heating mode refers to a mode in which the chemical substances inside the power battery are stimulated by alternating current to make the power battery itself heat up, and the internal and external heating mode refers to a mode in which the power battery is heated by both the external heating mode and the internal heating mode.

[0117] It is worth noting that when the charging vehicle is in the battery preheating state, the charging pile can charge the power battery of the charging vehicle regardless of the battery preheating mode currently used.

[0118] In some embodiments, the target prediction model includes a preheating prediction model and a normal temperature charging prediction model.

[0119] The preheating prediction model is a prediction model for predicting the preheating time of the charging vehicle. The preheating time is the time it takes for the battery of the charging vehicle to heat from the current temperature to the second preset temperature threshold. The second preset temperature threshold can be set according to actual needs. For example, the second preset temperature threshold is 15°C. The specific description of the normal temperature charging prediction model can refer to the above embodiment and will not be repeated here.

[0120] In some embodiments, the charging pile can determine a target preheating prediction model based on the battery preheating mode, the target preheating prediction model includes a first preheating prediction model and a second preheating prediction model, and the preheating time is predicted according to the first preheating prediction model. The second charging characteristic information when the battery preheating is completed is updated according to the second preheating prediction model, and the updated second charging characteristic information is input into the normal temperature charging prediction model to predict the normal temperature charging time. The remaining charging time of the charging vehicle is predicted based on the normal temperature charging time and the preheating time.

[0121] In this embodiment, the first preheating prediction model is used to predict the preheating time, and the second preheating prediction model is used to update the second charging characteristic information when the battery preheating is completed. The remaining charging time of the charging vehicle is the sum of the normal temperature charging time and the preheating time. If the charging vehicle is in the battery preheating state, the battery preheating is completed when the battery temperature of the charging vehicle reaches the second preset temperature threshold. The second preset temperature threshold is the battery temperature when the battery temperature is normal. When the battery preheating is completed, the battery temperature is normal.

[0122] The charging pile can obtain characteristic sampling information such as battery temperature, ambient temperature, state of charge, etc. when each vehicle model is in each battery preheating mode in the battery preheating state. On the one hand, the charging pile associates the characteristic sampling information with the duration label to obtain a sample data set, and trains the sample data set according to a preset machine learning algorithm to obtain a first preheating prediction model. On the other hand, the charging pile associates the characteristic sampling information with the state of charge label to obtain a sample data set, and trains the sample data set according to the preset machine learning algorithm to obtain a second preheating prediction model. The state of charge label is used to identify the state of charge when the battery temperature reaches a second preset temperature threshold.

[0123] The charging pile can extract charging characteristic information such as state of charge, charging power, etc. from the charging interaction information and input the charging characteristic information into the first preheating prediction model to predict the preheating time.

[0124] The charging pile can extract charging characteristic information such as charge state, battery temperature, ambient temperature, etc. from the charging interaction information and input the charging characteristic information into the second preheating prediction model to predict the charge state when the battery preheating is completed and update the second charging characteristic information according to the charge state, for example, updating the charge state currently obtained in the second charging characteristic information to the charge state when the battery preheating is completed.

[0125] It is understandable that since the preheating time of vehicles of the same model in different battery preheating modes is different, and different preheating times will affect the prediction of the remaining charging time, the charging pile can adopt different preheating time prediction strategies in combination with different battery preheating modes.

[0126] Therefore, this embodiment can predict the remaining charging time of the charging vehicle when the charging vehicle is in the battery preheating state, and can provide the car owner with a more comprehensive charging service, thereby improving the car owner's user experience.

[0127] It is understandable that when the battery temperature of the charging vehicle is low, if the charging vehicle is not in a battery preheating state or does not support the battery preheating function, the battery temperature of the charging vehicle gradually increases during the charging process of the charging pile. In the charging period when the battery temperature is low, the charging efficiency is low, and in the charging period when the battery temperature is high, the charging efficiency is high. Since the charging efficiency of different charging periods may be affected by different factors, the charging pile can predict the charging time of different charging periods respectively, and then predict the remaining charging time of the charging vehicle based on the charging time of each charging period.

[0128] In some embodiments, the target prediction model includes a first low temperature charging prediction model, a second low temperature charging prediction model, and a normal temperature charging prediction model.

[0129] In this embodiment, the first low-temperature charging prediction model is a prediction model for predicting the charging time (low-temperature charging time) of a charging vehicle during a charging period with a lower battery temperature, and the second low-temperature charging prediction model is a prediction model for updating the second charging characteristic information of the charging vehicle at the end of the charging period with a lower battery temperature (low-temperature charging is completed). The specific description of the normal temperature charging prediction model can refer to the above embodiment and will not be repeated here.

[0130] In some embodiments, if the charging vehicle is not in a battery preheating state or does not support the battery preheating function, the charging pile predicts the low-temperature charging time according to the first low-temperature charging prediction model, updates the second charging characteristic information when the low-temperature charging is completed according to the second low-temperature charging prediction model, and inputs the updated second charging characteristic information into the normal temperature charging prediction model to predict the normal temperature charging time, and predicts the remaining charging time of the charging vehicle based on the normal temperature charging time and the low temperature charging time.

[0131] In this embodiment, the charging pile can extract charging characteristic information such as state of charge, charging power, etc. from the charging interaction information and input the charging characteristic information into the first low-temperature charging prediction model to predict the low-temperature charging time. Then, the charging pile inputs the charging characteristic information such as state of charge, battery temperature, ambient temperature, etc. into the second low-temperature charging prediction model to predict the state of charge when the low-temperature charging is completed and update the second charging characteristic information according to the state of charge. For example, the state of charge currently obtained in the second charging characteristic information is updated to the state of charge when the low-temperature charging is completed, and the updated second charging characteristic information is input into the normal temperature charging prediction model to predict the normal temperature charging time. Finally, the charging pile can use the sum of the normal temperature charging time and the low temperature charging time as the remaining charging time of the charging vehicle.

[0132] Therefore, this embodiment can divide the charging process of the charging vehicle into different charging stages according to the battery temperature, predict the charging time of each charging stage respectively, and then predict the remaining charging time of the charging vehicle according to the charging time of each charging stage, which is conducive to more accurate prediction of the remaining charging time.

[0133] It is understandable that the charging efficiency of a charging vehicle is usually affected by static parameters such as the type and usage of the power battery of the charging vehicle. That is, different battery types and different usage conditions will also affect the charging pile's prediction of the remaining charging time. Therefore, the charging pile can correct the charging time based on the battery type and usage of the charging vehicle.

[0134] In some embodiments, the charging pile determines the battery type of the charged vehicle based on the target vehicle model, obtains the battery health status of the charged vehicle, determines the target compensation coefficient based on the battery health status and battery type, and compensates for the remaining charging time based on the target compensation coefficient.

[0135] For example, the remaining charging time is 2 hours, and the charging pile is configured with a preset mapping relationship table between different battery health states and different battery types and compensation coefficients. The charging pile can query the preset mapping relationship table according to the battery type and battery health status of the charged vehicle, and use the compensation coefficient corresponding to the battery type and battery health status of the charged vehicle in the preset mapping relationship table as the target compensation coefficient. Assuming the target compensation coefficient is 1.1, the charging pile can multiply the remaining charging time of 2 hours by the target compensation coefficient of 1.1 to obtain 2 hours and 12 minutes, and correct the remaining charging time of 2 hours to 2 hours and 12 minutes.

[0136] Therefore, this embodiment can correct the remaining charging time according to the battery condition of the charging vehicle, and can improve the prediction reliability and accuracy of the remaining charging time.

[0137] In some embodiments, see Figure 3 , S202 includes:

[0138] S2021. Inputting the first charging characteristic information into a vehicle type prediction model to obtain a vehicle type prediction result;

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 systems used are the same, when the A1 model vehicle and the B1 model vehicle are charging, the first charging feature information carried in the charging interaction information sent to the charging pile may be the same. After the first 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.

[0144] 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.

[0145] S2022. Determine the target vehicle model for charging according to the vehicle model test result.

[0146] 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.

[0147] Therefore, this embodiment can automatically identify the target model of the charging vehicle when charging the charging vehicle, which is conducive to subsequently locating the prediction model for predicting the remaining charging time of the charging vehicle in combination with the target model, thereby facilitating the reliable prediction of the remaining charging time of the charging vehicle.

[0148] In some embodiments, see Figure 4 Before executing S2021, the method for predicting the remaining charging time of a vehicle further includes:

[0149] S2023. Obtain characteristic sampling information of charging vehicles of each type;

[0150] 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.

[0151] S2024, associating the feature sampling information with the preset vehicle model label to obtain a sample data set;

[0152] 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:

[0153] {

[0154] "CC1":"XXX",

[0155] "CC2":XXX,

[0156] "CC3":XXX,

[0157] "CC4":XXX,

[0158] "CC5":XXX,

[0159] "VehicleLabel":"XXX"

[0160] }

[0161] Among them, CC1, CC2, CC3, CC4 and CC5 represent five characteristic parameters, and VehicleLabel represents the preset vehicle model label.

[0162] The charging pile aggregates the sample data for various vehicle models to obtain a sample data set.

[0163] S2025. Train the sample data set according to the preset machine learning algorithm to obtain a vehicle model prediction model.

[0164] 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.

[0165] 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.

[0166] In some embodiments, the vehicle model prediction result includes at least one vehicle model tag. Figure 5 , S2022 includes:

[0167] S20221. Determine the number of vehicle model labels of vehicle model prediction results;

[0168] 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.

[0169] S20222. Determine the target vehicle model for the charging vehicle based on the number of vehicle model labels and the vehicle model labels in the vehicle model prediction result.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] For example, suppose the vehicle model prediction model outputs the following vehicle model prediction results:

[0176] VehicleLabel_1:A1_120kWh

[0177] 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.

[0178] 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.

[0179] 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.

[0180] For example, suppose the vehicle model prediction model outputs the following vehicle model prediction results:

[0181] VehicleLabel_2: B1_70kWh

[0182] VehicleLabel_3: C1_100kWh

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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 includes the same battery pack capacity information and different battery pack capacity information, as long as all the battery pack capacity information is not all 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.

[0188] For another example, suppose the vehicle model prediction model outputs the following vehicle model prediction results:

[0189] VehicleLabel_4: D1_80kWh

[0190] VehicleLabel_5: E1_80kWh

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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:

[0197] Cap=Q1+Q2+Q3+……+Qn

[0198] Q1=v1*c1*t

[0199] Q2=v2*c2*t

[0200] Q3=v3*v3*t

[0201] Qn=vn*cn*t

[0202] 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.

[0203] 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:

[0204] Cap_av=(Cap1+Cap2+……+Capn) / n

[0205] 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.

[0206] 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.

[0207] In some embodiments, the charging pile matches the actual battery pack capacity with the battery pack capacity information corresponding to each vehicle model tag and determines the vehicle model tag corresponding to the successfully matched battery pack capacity information as the target vehicle model tag, and determines the target vehicle model of the charging vehicle based on the vehicle model information corresponding to the target vehicle model tag.

[0208] 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.

[0209] 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 120kWh, 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.

[0210] 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 model of the charged vehicle. This can avoid the charging pile reliably identifying the target model of the charged vehicle when it cannot directly obtain the battery pack capacity of the charged vehicle.

[0211] 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:

[0212] VehicleLabel_6: D1_80kWh

[0213] VehicleLabel_7: E1_80kWh

[0214] VehicleLabel_8: F1_90kWh

[0215] 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 vehicle model 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 charging vehicle based on the actual battery pack capacity, the charging pile can determine the charging curve characteristics of the charging vehicle and determine the target vehicle model of the charging vehicle based on the charging curve characteristics.

[0216] 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.

[0217] In some embodiments, the charging pile inputs the charging curve characteristics into the auxiliary prediction model, predicts the auxiliary prediction results, and determines the target model of the charging vehicle according to the auxiliary prediction results.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] For example, as mentioned above, the model prediction results include the following model labels:

[0222] VehicleLabel_4: D1_80kWh

[0223] VehicleLabel_5: E1_80kWh

[0224] Assume that the auxiliary prediction results include the following auxiliary prediction labels:

[0225] AuxiliaryLabel_1: E1

[0226] AuxiliaryLabel_2: G1

[0227] 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.

[0228] 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.

[0229] 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.

[0230] As another aspect of an embodiment of the present invention, an embodiment of the present invention provides a vehicle remaining charging time prediction device. The vehicle remaining charging time prediction device can be a software module, and the software module includes a number of instructions, which are stored in a memory, and the processor can access the memory and call the instructions for execution to complete the vehicle remaining charging time prediction method described in each of the above embodiments.

[0231] In some embodiments, the vehicle remaining charging time prediction device can be constructed by hardware devices, for example, the vehicle remaining charging time prediction device can be constructed by one or more chips, and each chip can work in coordination with each other to complete the vehicle remaining charging time prediction method described in each of the above embodiments. For another example, the vehicle remaining charging time prediction 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.

[0232] In some embodiments, see Figure 6 The vehicle remaining charging time prediction device 600 provided in the embodiment of the present invention includes a first acquisition module 601, a first determination module 602, a second determination module 603 and a prediction module 604.

[0233] The first acquisition module 601 is used to acquire the first charging characteristic information, the second charging characteristic information and the charging mode. The first determination module 602 is used to determine the target vehicle model of the charging vehicle according to the first charging characteristic information. The second determination module 603 is used to determine the target prediction model according to the target vehicle model and the charging mode. The prediction module 604 is used to predict the remaining charging time of the charging vehicle according to the second charging characteristic information and the target prediction model.

[0234] In some embodiments, see Figure 7 The second determination module 603 includes a first determination unit 6031 and a second determination unit 6032.

[0235] The first determination unit 6031 is used to determine a candidate prediction model according to a target vehicle model, and the second determination unit 6032 is used to determine a target prediction model from the candidate prediction models according to a charging mode.

[0236] In some embodiments, see Figure 8 The vehicle remaining charging time prediction device 600 also includes a third determination module 605, a second acquisition module 606, a fourth determination module 607 and a compensation module 608.

[0237] The third determination module 605 is used to determine the battery type of the charging vehicle according to the target vehicle model, the second acquisition module 606 is used to obtain the battery health status of the charging vehicle, the fourth determination module 607 is used to determine the target compensation coefficient according to the battery health status and battery type, and the compensation module 608 is used to compensate the remaining charging time according to the target compensation coefficient.

[0238] In some embodiments, see Fig. 9 , the first determination module 602 includes a prediction unit 6021 and a third determination unit 6022.

[0239] The prediction unit 6021 is used to input the first charging characteristic information into the vehicle type prediction model to predict the vehicle type prediction result. The third determination unit 6022 is used to determine the target vehicle type of the charging vehicle according to the vehicle type prediction result.

[0240] It should be noted that the above-mentioned vehicle remaining charging time prediction device can execute the vehicle remaining charging time prediction 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 embodiment of the vehicle remaining charging time prediction device, please refer to the vehicle remaining charging time prediction method provided in the embodiment of the present invention.

[0241] See also Fig.10 , Fig.10 The present invention provides a hardware structure diagram of a charging pile. Fig.10 As shown, the charging pile includes one or more processors 1001 and a memory 1002. Fig.10 A processor 1001 is taken as an example.

[0242] The processor 1001 is configured to support the computer device to perform the corresponding functions in the method in the above method embodiment. The processor 1001 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.

[0243] The memory 1002 is used to store program codes. The memory 1002 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 1002 may also include a combination of the above-mentioned types of memory.

[0244] The memory 1002 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 remaining charging time prediction method in the embodiment of the present invention. The processor 1001 executes various functional applications and data processing of the vehicle remaining charging time prediction method and the vehicle remaining charging time prediction device by running the non-volatile software programs, instructions and modules stored in the memory 1002, that is, realizes the functions of each module or unit of the vehicle remaining charging time prediction method and the vehicle remaining charging time prediction device provided in the above method embodiment.

[0245] The memory 1002 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 based on the use of the vehicle remaining charging time prediction device, etc. In some embodiments, the memory 1002 may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the vehicle remaining charging time prediction 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.

[0246] The one or more modules are stored in the memory 1002. When executed by the one or more processors 1001, the vehicle remaining charging time prediction method in any of the above-mentioned method embodiments is executed, for example, the method steps described in the above-mentioned method embodiments are executed to realize the functions of the modules described in the above-mentioned device embodiments.

[0247] 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.

[0248] It is understood by those skilled in the art 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.

[0249] 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 method for predicting the remaining charging time of a vehicle, characterized in that: include: Acquiring first charging characteristic information, second charging characteristic information and a charging mode; Determining a target model of a charging vehicle according to the first charging characteristic information; Determining a target prediction model according to the target vehicle model and the charging mode; The remaining charging time of the charging vehicle is predicted based on the second charging characteristic information and the target prediction model.

2. The method for predicting the remaining charging time of a vehicle according to claim 1, characterized in that: Determining the target prediction model according to the target vehicle model and the charging mode includes: Determining a candidate prediction model according to the target vehicle model; According to the charging mode, a target prediction model is determined among the candidate prediction models.

3. The method for predicting the remaining charging time of a vehicle according to claim 1, characterized in that: The predicting the remaining charging time of the charging vehicle according to the second charging characteristic information and the target prediction model includes: Acquiring a battery temperature of the charging vehicle; Determine whether the battery temperature is within a preset temperature range, and obtain a determination result; The remaining charging time of the charging vehicle is predicted based on the judgment result, the second charging characteristic information and the target prediction model.

4. The method for predicting the remaining charging time of a vehicle according to claim 3, characterized in that: The target prediction model includes a normal temperature charging prediction model, and the remaining charging time of the charging vehicle predicted according to the judgment result, the second charging characteristic information and the target prediction model includes: If the judgment result is that the battery temperature is not within the preset temperature range, the second charging characteristic information is input into the normal temperature charging prediction model to predict the remaining charging time of the charging vehicle.

5. The method for predicting the remaining charging time of a vehicle according to claim 4, characterized in that: The target prediction model also includes a high temperature charging prediction model, and the vehicle remaining charging time prediction method also includes: determining a battery temperature rise rate of the charging vehicle; Determine a target duration according to the battery temperature, the battery temperature rise rate and a first preset temperature threshold; Determining whether the target charging time is less than the remaining charging time; If it is less than the remaining charging time, the high-temperature charging time is predicted according to the high-temperature charging prediction model, and the remaining charging time is updated according to the high-temperature charging time and the target time.

6. The method for predicting the remaining charging time of a vehicle according to claim 3, characterized in that: The predicting of the remaining charging time of the charging vehicle according to the judgment result, the second charging characteristic information and the target prediction model includes: If the judgment result is that the battery temperature is within the preset temperature range, judging whether the charging vehicle supports the battery preheating function according to the target vehicle model; When the charging vehicle supports a battery preheating function, determining whether the charging vehicle is in a battery preheating state; When the charging vehicle is in a battery preheating state, determining a battery preheating mode; The remaining charging time of the charging vehicle is predicted according to the battery preheating mode, the second charging characteristic information and the target prediction model.

7. The method for predicting the remaining charging time of a vehicle according to claim 6, characterized in that: The target prediction model includes a preheating prediction model and a normal temperature charging prediction model, and the remaining charging time of the charging vehicle predicted according to the battery preheating mode, the second charging characteristic information and the target prediction model includes: Determining a target preheating prediction model according to the battery preheating mode, the target preheating prediction model comprising a first preheating prediction model and a second preheating prediction model; Predicting the preheating time according to the first preheating prediction model; updating second charging characteristic information when battery preheating is completed according to the second preheating prediction model; Inputting the updated second charging characteristic information into the normal temperature charging prediction model to predict the normal temperature charging time; The remaining charging time of the charging vehicle is predicted based on the normal temperature charging time and the preheating time.

8. The method for predicting the remaining charging time of a vehicle according to claim 6, characterized in that: The target prediction model includes a first low-temperature charging prediction model, a second low-temperature charging prediction model and a normal-temperature charging prediction model, and the vehicle remaining charging time prediction method also includes: When the charging vehicle is not in a battery preheating state or does not support a battery preheating function, predicting a low-temperature charging duration according to the first low-temperature charging prediction model; updating second charging characteristic information when low-temperature charging is completed according to the second low-temperature charging prediction model; Inputting the updated second charging characteristic information into the normal temperature charging prediction model to predict the normal temperature charging time; The remaining charging time of the charging vehicle is predicted based on the normal temperature charging time and the low temperature charging time.

9. The method for predicting the remaining charging time of a vehicle according to any one of claims 1 to 8, characterized in that: The method for predicting the remaining charging time of a vehicle further includes: Determining the battery type of the charging vehicle according to the target vehicle model; Obtaining a battery health status of the charging vehicle; Determine a target compensation coefficient according to the battery health status and the battery type; The remaining charging time is compensated according to the target compensation coefficient.

10. A charging pile, characterized in that: It includes a memory and a processor, 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 remaining charging time prediction method as described in any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the vehicle remaining charging time prediction method according to any one of claims 1 to 9.

Images