Method and device for predicting remaining charging time of vehicle, vehicle and storage medium

Abstract

The invention relates to a method and device for predicting the remaining charging time of a vehicle, the vehicle and a storage medium, and the method comprises the steps: obtaining first data corresponding to the vehicle and second data corresponding to a charging pile under the condition that a connection is established between the vehicle and the charging pile, the first data being used for representing the battery information of the vehicle, and the second data being used for representing the battery information of the vehicle; the second data is used for representing the upper limit capability of the charging pile; according to the first data, the second data and cloud data, first remaining charging time is obtained, the first remaining charging time is a remaining time initial value needed by the battery to complete charging, and the cloud data comprises regression model parameters; and displaying the first residual charging time. By combining the first data, the second data and the cloud data, the remaining time initial value of the battery can be simply and effectively obtained.

Description

Technical Field

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

[0002] With the development of electric vehicles, people have a higher and higher demand for electric vehicle charging. Among them, when charging electric vehicles through charging piles, it is a basic requirement for in-vehicle control systems such as battery management systems (BMS) to obtain the remaining charging time and display it. Therefore, how to simply and effectively obtain the remaining charging time of the battery is a technical problem that needs to be solved urgently. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a method, device, vehicle and storage medium for predicting the remaining charging time of a vehicle.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for predicting remaining charging time of a vehicle is provided, comprising:

[0005] When a connection is established between the vehicle and the charging pile, first data corresponding to the vehicle and second data corresponding to the charging pile are acquired, wherein the first data is used to characterize battery information of the vehicle, and the second data is used to characterize the upper limit capacity of the charging pile;

[0006] Acquire a first remaining charging time according to the first data, the second data and cloud data, where the first remaining charging time is an initial value of the remaining time required for the battery to complete charging, and the cloud data includes regression model parameters;

[0007] The first remaining charging time is displayed.

[0008] Optionally, the obtaining a first remaining charging time according to the first data, the second data and cloud data includes:

[0009] Sending the first data and the second data to the cloud;

[0010] Determine that an initial value calculation result sent by the cloud is received within a first time period, and use the initial value calculation result as the first remaining charging time, the start time of the first time period is the time when the first data and the second data are sent, and the initial value calculation result is comprehensively obtained by the cloud based on the first data, the second data and the regression model parameters.

[0011] Optionally, determining that the initial value calculation result sent by the cloud is not received within the first time period, and obtaining the regression model parameter transmitted by the cloud;

[0012] The first remaining charging time is determined based on the regression model parameters, the first data, and the second data.

[0013] Optionally, the determining that an initial value calculation result sent by the cloud is received within a first time period, and taking the initial value calculation result as the first remaining charging time, includes:

[0014] Determine that an initial value calculation result sent by the cloud is received within a first time period, and determine that the initial value calculation result meets a preset condition, and use the initial value calculation result as the first remaining charging time.

[0015] Optionally, after obtaining the first remaining charging time according to the first data, the second data and cloud data, the method further includes:

[0016] Acquiring a starting SOC and an ending SOC of the battery, and segmenting the charging process of the battery based on the starting SOC and the ending SOC to obtain at least one SOC segment;

[0017] Obtaining the charging time of each SOC segment according to the starting SOC, the ending SOC and the charging current corresponding to each SOC segment;

[0018] Acquire a total charging time based on the charging time of the plurality of SOC segments;

[0019] A second remaining charging time is determined according to the total charging time and the first remaining charging time, and the second remaining charging time is displayed.

[0020] Optionally, the method further comprises:

[0021] Obtaining a battery starting temperature corresponding to each of the SOC segments;

[0022] finding a current request value of the vehicle corresponding to the battery starting temperature;

[0023] One is selected from the current request value, the capacity value of the charging pile and the actual capacity value of the charging pile as the charging current corresponding to the SOC segment.

[0024] Optionally, the method further comprises:

[0025] The charging time and the battery starting temperature corresponding to the current SOC segment are input into the temperature model to obtain the battery ending temperature corresponding to the current SOC segment, and the battery ending temperature is used as the battery starting temperature of the next SOC segment.

[0026] Optionally, the first data includes at least one of a battery temperature, a battery SOC and a charging upper limit SOC, and the upper limit of the charging pile includes at least one of a maximum power, a maximum current and a maximum voltage.

[0027] According to a second aspect of an embodiment of the present disclosure, there is provided a device for predicting remaining charging time of a vehicle, comprising:

[0028] A first acquisition module is configured to acquire first data corresponding to the vehicle and second data corresponding to the charging pile when a connection is established between the vehicle and the charging pile;

[0029] a second acquisition module, configured to acquire a first remaining charging time according to the first data, the second data and cloud data, wherein the first remaining charging time is an initial value of the remaining time required for the battery to complete charging, and the cloud data includes regression model parameters;

[0030] The display module is configured to display the first remaining charging time.

[0031] According to a third aspect of an embodiment of the present disclosure, a vehicle is provided, comprising:

[0032] processor;

[0033] a memory for storing processor-executable instructions;

[0034] Wherein, the processor is configured to:

[0035] When a connection is established between the vehicle and the charging pile, obtaining first data corresponding to the vehicle and second data corresponding to the charging pile;

[0036] Acquire a first remaining charging time according to the first data, the second data and cloud data, where the first remaining charging time is an initial value of the remaining time required for the battery to complete charging, and the cloud data includes regression model parameters;

[0037] The first remaining charging time is displayed.

[0038] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the method for predicting the remaining charging time of a vehicle provided in the first aspect of the present disclosure are implemented.

[0039] When the embodiment of the present disclosure detects that a connection is established between a vehicle and a charging pile, first data corresponding to the vehicle and second data corresponding to the charging pile are obtained, wherein the first data is used to characterize the battery information of the vehicle, and the second data is used to characterize the upper limit capacity of the charging pile. On this basis, a first remaining charging time is obtained according to the first data, the second data and the cloud data, and the first remaining charging time is displayed. Here, the first remaining charging time is the initial value of the remaining time required for the vehicle's battery to complete charging. The cloud data may include regression model parameters. By combining the first data, the second data and the cloud data, the embodiment of the present disclosure can simply and effectively obtain the first remaining charging time.

[0040] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0042] Figure 1 The present invention is a flowchart of a method for predicting the remaining charging time of a vehicle according to an exemplary embodiment.

[0043] Figure 2 The present invention is a flowchart of obtaining a first remaining charging time in a method for predicting a remaining charging time of a vehicle according to an exemplary embodiment.

[0044] Figure 3 The present invention is an example diagram showing the remaining charging time in a method for predicting the remaining charging time of a vehicle according to an exemplary embodiment.

[0045] Figure 4 The present invention is a flowchart of another method for predicting the remaining charging time of a vehicle according to an exemplary embodiment.

[0046] Figure 5 The figure is an example diagram of remaining time following in another method for predicting remaining charging time of a vehicle according to an exemplary embodiment.

[0047] Figure 6 The present invention is a block diagram of a device for predicting remaining charging time of a vehicle according to an exemplary embodiment.

[0048] Figure 7 It is a schematic diagram of a functional block diagram of a vehicle showing an exemplary embodiment. DETAILED DESCRIPTION

[0049] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0050] It should be noted that all actions of acquiring signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0051] In the description of the present disclosure, the terms used, such as "first", "second", etc., are used to distinguish similar objects and are not necessarily understood as a specific order or sequence. In addition, in the description with reference to the accompanying drawings, the same symbols in different drawings represent the same elements unless otherwise stated.

[0052] Although operations or steps are described in a specific order in the drawings in the embodiments of the present disclosure, it should not be understood that it is required to perform these operations or steps in the specific order shown or in a serial order, or to perform all the operations or steps shown to obtain the desired results. In the embodiments of the present disclosure, these operations or steps can be performed in series; these operations or steps can also be performed in parallel; or some of these operations or steps can be performed.

[0053] As the production and sales volume and ownership of new energy vehicles increase year by year, the contradiction of energy replenishment for new energy vehicles is becoming increasingly acute. Improving the accuracy of calculating the remaining charging time can help car owners to arrange their travel plans reasonably. Due to the complexity of electric vehicle charging characteristics, the variability of the charging process, and the uncertainty of external factors (such as ambient temperature, charging current of charging piles), the accurate prediction of the remaining charging time is relatively complex and difficult. Among them, the remaining charging time has become an important indicator in charging management technology because it can intuitively display the time users need to wait. In related technologies, the method for estimating the remaining charging time of power batteries has problems such as complex estimation process and low accuracy of estimated results.

[0054] Figure 1 is a flow chart of a method for predicting the remaining charging time of a vehicle according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps.

[0055] In step S110, when a connection is established between the vehicle and the charging pile, first data corresponding to the vehicle and second data corresponding to the charging pile are acquired.

[0056] In the disclosed embodiment, the first data may be current battery information, which may also be referred to as vehicle-side data or vehicle data, and the first data may include battery temperature, battery SOC (State of Charge) and charging upper limit SOC. Here, the charging upper limit SOC may be the maximum SOC.

[0057] Optionally, the second data can be used to characterize the upper limit capacity of the charging pile, which can also be called pile end data or charging pile data, wherein the upper limit of the charging pile may include at least one of the maximum power, maximum current and maximum voltage. In other words, the pile end upper limit capacity may include upper limits such as power / current / voltage.

[0058] As an optional manner, the embodiment of the present disclosure can detect whether a connection is established between the power battery of the vehicle and the charging pile. Specifically, detecting whether a connection is established between the power battery of the vehicle and the charging pile can be determining whether a connection is established between the vehicle and the charging pile.

[0059] Optionally, the embodiment of the present disclosure may also detect whether the battery of the vehicle starts to be charged based on the charging pile. If it is determined that the battery starts to be charged based on the charging pile, it may be determined that a connection is successfully established between the vehicle and the charging pile.

[0060] In step S120, a first remaining charging time is obtained according to the first data, the second data and the cloud data.

[0061] In some embodiments, the vehicle may include a battery management system (BMS), which may establish a wired / wireless connection with a cloud (server) during operation. Thereafter, the BMS may receive cloud data from the cloud, which may include regression model parameters.

[0062] On this basis, the battery management system can verify the regression model parameters. Specifically, the battery management system can determine whether the regression model parameters meet the specified conditions. If it is determined that the regression model parameters meet the specified conditions, the embodiment of the present disclosure can record the regression model parameters in the battery management system.

[0063] Here, the main purpose of determining whether the regression model parameters meet the specified conditions is to determine whether the regression model parameters transmitted by the cloud are reasonable. If it is determined that the regression model parameters transmitted by the cloud are reasonable, the regression model parameters are recorded. If it is determined that the regression model parameters transmitted by the cloud are unreasonable, the embodiment of the present disclosure can instruct the cloud to transmit new regression model parameters.

[0064] In the disclosed embodiment, the regression model parameters may be model parameters of the remaining time acquisition model, wherein the remaining time acquisition model may be a regression model, which may be a fitting formula pre-trained and obtained based on historical data or sample data, and the model parameters of the remaining time acquisition model may be coefficients / parameters of the fitting formula. In other words, the regression model parameters may be parameters / coefficients constituting the regression model.

[0065] In addition, the input data of the remaining time acquisition model may be the first data and the second data, and the output data may be the first remaining charging time. Here, the remaining time acquisition model may be a model obtained by training a regression model based on a large amount of data, which can be used to obtain the remaining time required for charging to be completed.

[0066] It should be noted that since the computing power of the cloud is relatively high and the computing power of the vehicle is relatively low, the regression model parameters sent by the cloud can be parameters obtained by simplifying the model parameters of the remaining time acquisition model.

[0067] In some embodiments, after acquiring the first data and the second data, the embodiment of the present disclosure may send the first data and the second data to the cloud, and determine whether the initial value calculation result sent by the cloud is received within the first time period. If the vehicle receives the initial value calculation result sent by the cloud within the first time period, the initial value calculation result may be used as the first remaining charging time. The start time of the first preset time period may be the time when the first data and the second data are sent.

[0068] In other words, after the vehicle sends the first data and the second data to the cloud, a timer may be started. When the timer exceeds the preset time, if the initial value calculation result sent by the cloud is still not received, it means that the data reception from the cloud fails. On the contrary, if the vehicle receives the initial value calculation result from the cloud before the timer exceeds the preset time, the initial value calculation result may be used as the first remaining charging time.

[0069] In the disclosed embodiment, the initial value calculation result can be obtained by the cloud based on the first data, the second data and the regression model parameters. As can be seen from the above introduction, the regression model parameters can be the model parameters of the remaining time acquisition model. After receiving the first data and the second data sent by the vehicle, the cloud can input the first data and the second data into the remaining time acquisition model, and obtain the initial value calculation results corresponding to the first data and the second data based on the regression model parameters of the remaining time acquisition model.

[0070] In the disclosed embodiment, the end where the vehicle is located can be called the vehicle end, that is, the vehicle can also be called the vehicle end, the end where the cloud server is located can be called the cloud end, that is, the cloud server can also be called the cloud end, and the end where the charging pile is located can be called the pile end, that is, the charging pile can also be called the pile end.

[0071] In other embodiments, if the vehicle does not receive the initial value calculation result sent by the cloud within the first time period, the embodiment of the present disclosure can obtain the pre-recorded regression model parameters. On this basis, the first remaining charging time is determined based on the pre-recorded regression model parameters, the first data and the second data, and the way the vehicle side calculates the first remaining charging time is similar to the way the cloud side calculates. The difference is that the computing power of the vehicle side is lower than that of the cloud side, so the complexity of the regression model parameters used by the vehicle side in the process of calculating the first remaining charging time may be lower than that of the cloud side.

[0072] Optionally, the parameters used in the process of calculating the first remaining charging time on the vehicle side may also be less than those on the cloud side. For example, the vehicle side can obtain the first remaining charging time through the first regression model parameters based on the battery temperature, battery SOC, and the maximum power of the charging pile. For another example, the cloud side can obtain the first remaining charging time through the second regression model parameters based on the battery temperature, battery SOC, charging upper limit SOC, and the maximum power, maximum current, and maximum voltage of the charging pile. Among them, the complexity of the first regression model parameters is lower than that of the second regression model parameters.

[0073] In other embodiments, when the vehicle receives the initial value calculation result sent by the cloud within the first time period, the embodiment of the present disclosure can further determine whether the initial value calculation result meets the preset condition. If it is determined that the initial value calculation result meets the preset condition, the initial value calculation result can be used as the first remaining charging time. Conversely, if it is determined that the initial value calculation result does not meet the preset condition, the vehicle can determine the first remaining charging time based on the regression model parameters, the first data, and the second data.

[0074] Here, determining whether the initial value calculation result meets the preset conditions may be determining whether the initial value calculation result is reasonable. As an example, determining whether the initial value calculation result is reasonable may be whether the initial value calculation result is less than a specified value. If it is determined that the initial value calculation result is less than the specified value, then it is determined that the initial value calculation result meets the preset conditions. For example, the initial value calculation result received by the vehicle end is 20h, and the specified value is 15h. It can be seen that the initial value calculation result is greater than the specified value at this time, so the initial value calculation result at this time does not meet the preset conditions. The above data is only an example and is not actually limited. The specific data is subject to the actual situation.

[0075] In the embodiment of the present disclosure, the specified value can be fixed or obtained based on the first data and the second data, that is, there can be a preset relationship between the first data and the second data and the specified value. The corresponding specified value can be obtained by looking up the table in the embodiment of the present disclosure, and the table data here can be obtained through multiple experiments.

[0076] In order to better illustrate the process of obtaining the first remaining charging time, the present disclosure embodiment provides the following Figure 2 The example diagram shown is Figure 2 It is known that after the battery management system is started, the battery management system on the vehicle side and the cloud can perform regression model parameter verification and record. On this basis, the disclosed embodiment can determine whether the battery of the vehicle has entered charging. If it is determined that the battery of the vehicle has entered charging, the battery management system can upload the vehicle-side data (first data) and the pile-side data (second data) of this charging. On the contrary, if it is determined that the battery of the vehicle has not entered charging, the regression model parameter verification and recording operations are re-executed.

[0077] In addition, after uploading the vehicle-side data (first data) and the charging pile-side data (second data) for this charging, the battery management system can receive the initial value sent by the cloud, that is, the cloud can send the initial value to the vehicle-side. Here, it can be determined whether the cloud sends the initial value to the vehicle-side. If it is determined that the cloud has sent the initial value to the vehicle-side, the battery management system on the vehicle-side can receive the calculation result of the initial value of the remaining time sent by the cloud, and finally the vehicle-side can display the initial value of the charging time.

[0078] On the contrary, if it is determined that the cloud has not sent the initial value to the vehicle, the battery management system on the vehicle can calculate the initial value of the remaining time based on the regression model parameters it has received in advance, and display the initial value of the remaining time. For example, if the vehicle has not received the calculated value sent by the cloud for a long time, the battery management system can calculate the initial value of the remaining charging time based on the regression model parameter values ​​obtained in the early stage.

[0079] In summary, the disclosed embodiments can select a calculation method for the initial value of the remaining time to be output based on the transmission timeliness of the cloud data and the verification result of the cloud data, wherein the first calculation method can be the cloud calculation result of the current charging, that is, the first remaining charging time is obtained by cloud calculation; the second calculation method can be a BMS calculation value obtained based on the saved cloud regression model parameters, that is, the first remaining charging time is obtained by vehicle-side calculation.

[0080] In step S130 , a first remaining charging time is displayed.

[0081] As an optional method, after obtaining the first remaining charging time, the embodiment of the present disclosure may display the first remaining charging time. The specific display interface may be as follows: Figure 3 As shown. Figure 3 It is known that when displaying the first remaining charging time, the charging current and the battery power level can also be displayed.

[0082] In the disclosed embodiment, the first remaining charging time can be displayed on the central control display screen of the vehicle end, or on an electronic device bound to the vehicle end, so that the user can check the charging status in time. In addition, when the remaining charging time meets the specified conditions, the vehicle end can also directly send a notification message to the electronic device.

[0083] For example, when the remaining charging time is 1 hour, the vehicle end may send a first notification message to the electronic device, and when the remaining charging time is 10 minutes, the vehicle end may send a second notification message to the electronic device. The notification methods of the first notification message and the second notification message may be different, such as the first notification message may vibrate once when notifying the electronic device, and the second notification message may vibrate twice when notifying the electronic device. In other words, as the remaining charging time decreases, the notification intensity can be increased so that the user can know in time that the charging is about to end.

[0084] In addition, when charging is about to end, the embodiment of the present disclosure can also inform the user of the charging status by changing the prompt mode. For example, when the remaining charging time is 10 minutes, the vehicle end can send a vibration prompt indication to the electronic device. For another example, when the remaining charging time is 1 minute, the vehicle end can send a vibration plus voice prompt indication to the electronic device. How to output the first remaining charging time is not explicitly limited here and can be selected according to actual conditions.

[0085] When the embodiment of the present disclosure detects that a connection is established between a vehicle and a charging pile, first data corresponding to the vehicle and second data corresponding to the charging pile are obtained, wherein the first data is used to characterize the battery information of the vehicle, and the second data is used to characterize the upper limit capacity of the charging pile. On this basis, a first remaining charging time is obtained according to the first data, the second data and the cloud data, and the first remaining charging time is displayed. Here, the first remaining charging time is an initial value of the remaining time required for the vehicle's battery to complete charging. The cloud data may include regression model parameters. By combining the first data, the second data and the cloud data, the embodiment of the present disclosure can simply and effectively obtain the first remaining charging time, and can shorten the calculation time of the initial value of the remaining charging time.

[0086] Figure 4 is a flow chart showing another method for predicting the remaining charging time of a vehicle according to an exemplary embodiment. Figure 4 As shown, the method may include the following steps.

[0087] In step S410, when a connection is established between the vehicle and the charging pile, first data corresponding to the vehicle and second data corresponding to the charging pile are acquired.

[0088] In step S420, a first remaining charging time is obtained according to the first data, the second data and the cloud data.

[0089] In step S430, a first remaining charging time is displayed.

[0090] The specific implementation of step S410 to step S430 has been described in detail in the above embodiment and will not be repeated here.

[0091] In step S440, the starting SOC and the ending SOC of the battery are acquired, and the charging process of the battery is segmented based on the starting SOC and the ending SOC to obtain at least one SOC segment.

[0092] As an optional method, after obtaining the first remaining charging time, the embodiment of the present disclosure can obtain the starting SOC and the end SOC of the battery, where the starting SOC can be the charging starting SOC. On this basis, the charging process of the battery is divided based on the starting SOC and the end SOC to obtain at least one SOC segment. Among them, the number of SOC segments is mainly determined by the starting SOC. Different starting SOCs correspond to different numbers of SOC segments, and the larger the starting SOC, the smaller the number of corresponding SOC segments can be.

[0093] As an example, the minimum SOC can be 0, and the maximum SOC can be 100%, so the corresponding charging process can be divided into 5 segments. The number of corresponding SOC segments is different for different starting SOCs. For example, when the starting SOC is 20%, the number of corresponding SOC segments can be 4, and the SOC segments at this time can include the first segment map, the second segment map, the third segment map, and the fourth segment map. For another example, when the starting SOC is 60%, the number of corresponding SOC segments can be 2, and the SOC segments at this time can include the first segment map and the second segment map.

[0094] In summary, the disclosed embodiment can determine all map segments of this charging according to the charging start SOC and the end SOC. In other words, the vehicle can divide the predicted charging process into n segments based on the battery SOC on the basis of the battery charging map.

[0095] In step S450, the charging time of each SOC segment is obtained according to the starting SOC, the ending SOC and the charging current corresponding to the SOC segment.

[0096] As an optional method, the embodiment of the present disclosure can obtain the charging time of the corresponding SOC segment according to the starting SOC, the end SOC and the charging current corresponding to each SOC segment. Specifically, the charging time required for the first segment and the charging time required for the second segment are obtained in sequence, and the charging time acquisition operation is terminated until the charging time required for all segments is obtained.

[0097] Optionally, during the charging time of the SOC segment, the embodiment of the present disclosure may first obtain the difference between the end SOC and the start SOC, and then obtain the charging time of the SOC segment based on the difference and the charging current.

[0098] Optionally, in the process of obtaining the charging time of each segment, the embodiment of the present disclosure may also obtain the rated capacity of the battery, and on this basis, obtain the charging time of the SOC segment according to the rated capacity, the difference between the end SOC and the start SOC, and the charging current. The specific calculation formula may be: Among them, t i is the predicted charging time for the ith segment, ΔSOC i is the difference between the end SOC and the starting SOC of the i-th segment, C R is the rated capacity of the battery, which may decrease as the battery health (SOH) decreases. i is the current value of the i-th segment.

[0099] In some embodiments, the rated capacity of the battery can be obtained based on the health of the battery and the preset battery capacity. The charging current can be determined based on the current request value of the vehicle, the capacity value of the charging pile, and the actual capacity value of the charging pile, that is, the embodiment of the present disclosure can select one from the current request value, the capacity value of the charging pile, and the actual capacity value of the charging pile as the charging current corresponding to the SOC segment.

[0100] Here, the current request value can be obtained by searching for the current request value of the vehicle corresponding to the battery starting temperature. The specific acquisition formula can be: i =Min(f(SOC i ,T i ),I charger ,I Judge ). Among them, f(SOC i ,T i ) is the current request value, SOC i is the battery state of charge in the ith segment, T i is the starting temperature of the i-th segment; I charger is the pile end capacity value (capacity value of the charging pile); I Judge It is the actual pile end capacity judgment value, which can be obtained by comparing the current request value and the incoming current value. Exemplarily, when the current request value and the incoming current value are different, and the current request value is greater than the incoming current value, the incoming current value is mainly used, that is, the actual pile end capacity judgment value can be the incoming current value.

[0101] In the embodiment of the present disclosure, the battery starting temperature can be obtained according to the temperature model. Specifically, the charging time and the battery starting temperature corresponding to the current SOC segment are input into the temperature model to obtain the battery ending temperature corresponding to the current SOC segment, and the battery ending temperature is used as the battery starting temperature of the next SOC segment. Based on the temperature model, the embodiment of the present disclosure can fully estimate the temperature change during the charging process, so as to accurately estimate the corresponding SOC segment charging current and improve the estimation accuracy.

[0102] Among them, the formula for obtaining the battery end temperature can be Among them, T i-1 is the battery starting temperature of the previous SOC segment, T i is the battery starting temperature of the current SOC segment, a and b are temperature rise coefficients, which can be obtained by fitting according to actual tests. Here, a and b can be model parameters of the temperature model.

[0103] As a specific implementation, the embodiment of the present disclosure can obtain the first segment charging current according to the charging start temperature, wherein the charging start temperature can be the current battery temperature. On this basis, the time required for charging the first segment is estimated according to the starting SOC, the first segment end SOC and the first segment charging current.

[0104] Optionally, when there are multiple segments, the embodiment of the present disclosure can obtain the starting temperature of the next segment (the second segment) based on the temperature model and the charging time of the current segment (the first segment). Specifically, the temperature model is used to obtain the battery temperature at the end of the first segment according to the starting temperature of the first segment and the charging time of the first segment, that is, the starting temperature of the second segment is obtained.

[0105] Based on this, the above steps are repeated, and the embodiment of the present disclosure can obtain the second segment charging current according to the second segment starting temperature, and then estimate the time required for the second segment charging according to the second segment starting SOC, the second segment ending SOC and the second segment charging current. In this way, the charging time and segment starting temperature of all segments can be obtained.

[0106] In summary, the disclosed embodiment can use the current battery temperature as the battery charging start temperature, and can estimate the charging time of the current segment Map based on the start temperature, and can estimate the battery charging start temperature of the next segment Map based on the temperature model. On this basis, the charging time of the next segment Map can be calculated based on the battery charging start temperature of the next segment Map. Similarly, the battery charging start temperature of the next segment Map can be estimated based on the temperature model, and the cycle repeats until all segment charging times are calculated.

[0107] In step S460 , the total charging time is acquired based on the charging time of the plurality of SOC segments.

[0108] As an optional method, after obtaining the charging time of each SOC segment, the embodiment of the present disclosure can obtain the total charging time. Specifically, the remaining charging time of these segments is added to obtain the total time for charging to the end SOC. The calculation formula of the total time can be The total charging time can be obtained by summing up the charging time corresponding to all segments.

[0109] In step S470, a second remaining charging time is determined according to the total charging time and the first remaining charging time, and the second remaining charging time is displayed.

[0110] As an optional manner, after the total charging time is acquired, the embodiment of the present disclosure may determine a second remaining charging time based on the total charging time and the first remaining charging time, and may display the second remaining charging time.

[0111] Specifically, the embodiment of the present disclosure can follow the remaining charging time based on the initial value of the remaining charging time (the first remaining charging time) and the calculated value of the remaining charging time (the total charging time) to obtain the final remaining time display value. For detailed process, please refer to Figure 5 Among them, the embodiment of the present disclosure can follow the remaining time of charging display based on the following formula.

[0112] t i =f(t Cal ,t i-1 ),

[0113] When i=1, t1=f(t Cal , t0), t0 is the initial time display value, which can be the above-mentioned initial value of the remaining charging time (the first remaining charging time), t cal The remaining charging time calculated from the above calculation (total charging time), t i is the displayed value at the i-th moment, that is, it can be the second remaining charging time.

[0114] In summary, combined with Figure 5It is known that the embodiment of the present disclosure can estimate the initial value of the remaining charging time (the first remaining charging time) according to the big data statistical results (combined with the cloud, vehicle and pile end data). On this basis, the segmented Map time, segmented Map temperature rise and total time are calculated. Specifically, the starting temperature of the segmented Map is obtained according to the battery model, and the starting temperature can be used to obtain the requested current, and then the actual maximum output capacity of the pile end is obtained by comparing the requested current with the pile end input current. Finally, the segmented remaining time can be obtained according to the segmented Map starting temperature and the maximum capacity of the pile end, and the cumulative charging time to the upper limit can be obtained by accumulating these segmented remaining times. It can be seen that the embodiment of the present disclosure can obtain the charging remaining time display result relatively quickly, and can estimate the charging remaining time more accurately, thereby greatly improving the user experience.

[0115] When the embodiment of the present disclosure detects that a connection is established between the vehicle and the charging pile, the first data corresponding to the vehicle and the second data corresponding to the charging pile are obtained, wherein the first data is used to characterize the battery information of the vehicle, and the second data is used to characterize the upper limit capacity of the charging pile. On this basis, the first remaining charging time is obtained according to the first data, the second data and the cloud data, and the first remaining charging time is displayed. Here, the first remaining charging time is the initial value of the remaining time required for the battery of the vehicle to complete charging. The cloud data may include regression model parameters. By combining the first data, the second data and the cloud data, the embodiment of the present disclosure can simply and effectively obtain the first remaining charging time. In addition, since the embodiment of the present disclosure fully considers the starting temperature of each segment, it can improve the accuracy of estimating the remaining charging time.

[0116] Figure 6 FIG. 6 is a block diagram of a remaining charging time prediction device 600 according to an exemplary embodiment. Figure 6 The remaining charging time prediction device 600 includes a first acquisition module 610 , a second acquisition module 620 and a display module 630 .

[0117] The first acquisition module 610 is configured to acquire first data corresponding to the vehicle and second data corresponding to the charging pile when a connection is established between the vehicle and the charging pile, wherein the first data is used to characterize battery information of the vehicle, and the second data is used to characterize the upper limit capacity of the charging pile;

[0118] The second acquisition module 620 is configured to acquire a first remaining charging time according to the first data, the second data and cloud data, wherein the first remaining charging time is an initial value of the remaining time required for the battery to complete charging, and the cloud data includes regression model parameters;

[0119] The display module 630 is configured to display the first remaining charging time.

[0120] In some implementations, the second acquisition module 620 may include:

[0121] A sending submodule, configured to send the first data and the second data to a cloud;

[0122] A determination submodule is configured to determine an initial value calculation result sent by the cloud within a first time period, and use the initial value calculation result as the first remaining charging time. The start time of the first time period is the time when the first data and the second data are sent. The initial value calculation result is obtained by the cloud based on the first data, the second data and the regression model parameters.

[0123] In some embodiments, the determination submodule can also be configured to determine that the initial value calculation result sent by the cloud is not received within the first time period, obtain the regression model parameters transmitted by the cloud; and determine the first remaining charging time based on the regression model parameters, the first data and the second data.

[0124] In some embodiments, the determination submodule can also be configured to determine that an initial value calculation result sent by the cloud is received within a first time period, and determine that the initial value calculation result meets a preset condition, and use the initial value calculation result as the first remaining charging time.

[0125] In some implementations, the remaining charging time prediction device 600 may further include:

[0126] A segmentation module is configured to obtain a starting SOC and an ending SOC of the battery, and segment the charging process of the battery based on the starting SOC and the ending SOC to obtain at least one SOC segment;

[0127] A segmented current acquisition module is configured to acquire the charging time of each SOC segment according to the starting SOC, the ending SOC and the charging current corresponding to each SOC segment;

[0128] A total time acquisition module is configured to acquire a total charging time based on the charging time of the plurality of SOC segments;

[0129] The second remaining time determination module is configured to determine a second remaining charging time according to the total charging time and the first remaining charging time, and display the second remaining charging time.

[0130] In some implementations, the remaining charging time prediction device 600 may further include:

[0131] A starting temperature acquisition module is configured to acquire a starting temperature of the battery corresponding to each of the SOC segments;

[0132] a search module configured to search for a current request value of the vehicle corresponding to the battery starting temperature;

[0133] The selection module is configured to select one from the current request value, the capacity value of the charging pile and the actual capacity value of the charging pile as the charging current corresponding to the SOC segment.

[0134] In some implementations, the remaining charging time prediction device 600 may further include:

[0135] The end temperature acquisition module is configured to input the charging time and the battery starting temperature corresponding to the current SOC segment into the temperature model, obtain the battery end temperature corresponding to the current SOC segment, and use the battery end temperature as the battery starting temperature of the next SOC segment.

[0136] In some embodiments, the first data includes at least one of a battery temperature, a battery SOC, and a charging upper limit SOC, and the upper limit of the charging pile includes at least one of a maximum power, a maximum current, and a maximum voltage.

[0137] When the embodiment of the present disclosure detects that a connection is established between a vehicle and a charging pile, first data corresponding to the vehicle and second data corresponding to the charging pile are obtained, wherein the first data is used to characterize the battery information of the vehicle, and the second data is used to characterize the upper limit capacity of the charging pile. On this basis, a first remaining charging time is obtained according to the first data, the second data and the cloud data, and the first remaining charging time is displayed. Here, the first remaining charging time is the initial value of the remaining time required for the vehicle's battery to complete charging. The cloud data may include regression model parameters. By combining the first data, the second data and the cloud data, the embodiment of the present disclosure can simply and effectively obtain the first remaining charging time.

[0138] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0139] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the method for predicting the remaining charging time of a vehicle provided by the present disclosure.

[0140] Figure 7 is a block diagram of a vehicle 700 according to an exemplary embodiment. For example, the vehicle 700 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. The vehicle 700 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0141] Reference Figure 7 , the vehicle 700 may include various subsystems, for example, an infotainment system 710, a perception system 720, a decision control system 730, a drive system 740, and a computing platform 750. The vehicle 700 may also include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and each component of the vehicle 700 may be interconnected by wire or wireless means.

[0142] In some embodiments, the infotainment system 710 may include a communication system, an entertainment system, and a navigation system, etc.

[0143] The perception system 720 may include several sensors for sensing information about the environment around the vehicle 700. For example, the perception system 720 may include a global positioning system (the global positioning system may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0144] The decision control system 730 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0145] The drive system 740 may include components that provide power movement for the vehicle 700. In one embodiment, the drive system 740 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of an internal combustion engine, an electric motor, and an air compression engine. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0146] Some or all functions of the vehicle 700 are controlled by a computing platform 750. The computing platform 750 may include at least one processor 751 and a memory 752, and the processor 751 may execute instructions 753 stored in the memory 752.

[0147] The processor 751 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0148] Memory 752 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0149] In addition to instructions 753 , memory 752 may also store data, such as road maps, route information, vehicle location, direction, speed, etc. The data stored in memory 752 may be used by computing platform 750 .

[0150] In the embodiment of the present disclosure, the processor 751 may execute the instruction 753 to complete all or part of the steps of the above-mentioned method for predicting the remaining charging time of a vehicle.

[0151] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device. The computer program has a code portion for executing the above-mentioned remaining charging time prediction method for a vehicle when executed by the programmable device.

[0152] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0153] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for predicting the remaining charging time of a vehicle, characterized in that: The method comprises: When a connection is established between the vehicle and the charging pile, first data corresponding to the vehicle and second data corresponding to the charging pile are acquired, wherein the first data is used to characterize battery information of the vehicle, and the second data is used to characterize the upper limit capacity of the charging pile; Acquire a first remaining charging time according to the first data, the second data and cloud data, where the first remaining charging time is an initial value of the remaining time required for the battery to complete charging, and the cloud data includes regression model parameters; The first remaining charging time is displayed.

2. The method according to claim 1, characterized in that: The obtaining the first remaining charging time according to the first data, the second data and cloud data includes: Sending the first data and the second data to the cloud; Determine that an initial value calculation result sent by the cloud is received within a first time period, and use the initial value calculation result as the first remaining charging time, the start time of the first time period is the time when the first data and the second data are sent, and the initial value calculation result is comprehensively obtained by the cloud based on the first data, the second data and the regression model parameters.

3. The method according to claim 2, characterized in that The method further comprises: Determine that no initial value calculation result sent by the cloud is received within the first time period, and obtain the regression model parameter transmitted by the cloud; The first remaining charging time is determined based on the regression model parameters, the first data, and the second data.

4. The method according to claim 2, characterized in that: The determining that the initial value calculation result sent by the cloud is received within the first time period, and using the initial value calculation result as the first remaining charging time, includes: Determine that an initial value calculation result sent by the cloud is received within a first time period, and determine that the initial value calculation result meets a preset condition, and use the initial value calculation result as the first remaining charging time.

5. The method according to claim 1, characterized in that After obtaining the first remaining charging time according to the first data, the second data and the cloud data, the method includes: Acquiring a starting SOC and an ending SOC of the battery, and segmenting the charging process of the battery based on the starting SOC and the ending SOC to obtain at least one SOC segment; Obtaining the charging time of each SOC segment according to the starting SOC, the ending SOC and the charging current corresponding to each SOC segment; Acquire a total charging time based on the charging time of the plurality of SOC segments; A second remaining charging time is determined according to the total charging time and the first remaining charging time, and the second remaining charging time is displayed.

6. The method according to claim 5, characterized in that The method further comprises: Obtaining a battery starting temperature corresponding to each of the SOC segments; finding a current request value of the vehicle corresponding to the battery starting temperature; One is selected from the current request value, the capacity value of the charging pile and the actual capacity value of the charging pile as the charging current corresponding to the SOC segment.

7. The method according to claim 6, characterized in that The method further comprises: The charging time and the battery starting temperature corresponding to the current SOC segment are input into the temperature model to obtain the battery ending temperature corresponding to the current SOC segment, and the battery ending temperature is used as the battery starting temperature of the next SOC segment.

8. The method according to any one of claims 1 to 7, characterized in that: The first data includes at least one of a battery temperature, a battery SOC and a charging upper limit SOC, and the upper limit of the charging pile includes at least one of a maximum power, a maximum current and a maximum voltage.

9. A vehicle remaining charging time prediction device, characterized in that: include: A first acquisition module is configured to acquire first data corresponding to the vehicle and second data corresponding to the charging pile when a connection is established between the vehicle and the charging pile, wherein the first data is used to characterize battery information of the vehicle, and the second data is used to characterize an upper limit capacity of the charging pile; a second acquisition module, configured to acquire a first remaining charging time according to the first data, the second data and cloud data, wherein the first remaining charging time is an initial value of the remaining time required for the battery to complete charging, and the cloud data includes regression model parameters; The display module is configured to display the first remaining charging time.

10. A vehicle, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: When a connection is established between the vehicle and the charging pile, first data corresponding to the vehicle and second data corresponding to the charging pile are acquired, wherein the first data is used to characterize battery information of the vehicle, and the second data is used to characterize the upper limit capacity of the charging pile; Acquire a first remaining charging time according to the first data, the second data and cloud data, where the first remaining charging time is an initial value of the remaining time required for the battery to complete charging, and the cloud data includes regression model parameters; The first remaining charging time is displayed.

11. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the steps of the method described in any one of claims 1 to 8 are implemented.

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