Method, device and system for predicting remaining charging duration of vehicle

Abstract

The invention discloses a method, a device and a system for predicting residual charging time of a vehicle, and belongs to the technical field of vehicle charging. A specific embodiment of the method comprises the following steps: searching a target prediction model matched with the battery capacity, the current charging current and the current environment information from a plurality of prediction models according to the battery capacity of a vehicle, the current charging current and the current environment information of the vehicle; and by using the target prediction model, based on the battery capacity, the current charging current and the current environment information, combined with the charged duration, predicting the remaining charging duration of the vehicle. According to the embodiment of the invention, the charging residual time length is estimated according to various factors such as different environment information, charging current and battery capacity, the accuracy and the refinement degree of predicting the residual charging time length are improved, and the vehicle use experience of passengers and the management efficiency of the vehicle battery are improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle charging technology, and in particular to a method, device and system for predicting the remaining time of vehicle charging. Background Art

[0002] With the popularization of new energy vehicles, the charging efficiency and charging management of new energy vehicles are becoming more and more important, especially for the application scenarios of DC fast charging, which usually requires accurate estimation of the remaining charging time for vehicle occupants at multiple time points.

[0003] In the prior art, a fixed battery model is usually used to estimate the charging time. However, the existing method does not take into account the changes in the charging rate during the charging process, the battery's own state, the vehicle's environment (such as temperature, humidity, etc.), and other factors that affect charging. As a result, the accuracy of predicting the remaining charging time is low, which affects the passengers' vehicle experience and the management efficiency of the vehicle battery. Summary of the invention

[0004] In view of this, the present invention provides a method, device and system for predicting the remaining charging time of a vehicle, which can search for a target prediction model that matches the battery capacity, the current charging current and the current environmental information of the vehicle from multiple prediction models according to the battery capacity of the vehicle, the current charging current and the current environmental information of the vehicle; and use the target prediction model to predict the remaining charging time of the vehicle based on the battery capacity, the current charging current and the current environmental information combined with the charging time. The embodiment of the present invention estimates the remaining charging time based on various factors such as different environmental information, charging current and battery capacity, improves the accuracy and refinement of predicting the remaining charging time, and enhances the vehicle experience of the passengers and the management efficiency of the vehicle battery.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for predicting the remaining charging time of a vehicle, comprising: in response to a prediction request, obtaining a battery capacity, a current charging current, and current environmental information of the vehicle, wherein the prediction request indicates a prediction of the remaining charging time of the vehicle; searching for a target prediction model that matches the battery capacity, the current charging current, and the current environmental information from multiple prediction models; and using the target prediction model to predict the remaining charging time of the vehicle based on the battery capacity, the current charging current, and the current environmental information in combination with the charging time.

[0007] Optionally, before searching for a target prediction model that matches the battery capacity, the current charging current and the current environmental information from multiple prediction models, it also includes: collecting the full charging time of the vehicle battery for a fully charged and discharged vehicle battery under various environmental information and various charging currents; and constructing a prediction model using the full charging time, the environmental information corresponding to the full charging time, and the charging current.

[0008] Optionally, constructing the prediction model includes: performing the following operations for different charging currents and full-charge charging times collected under each type of environmental information: combining the battery capacity of the same vehicle battery, different charging currents for the vehicle battery, and the full-charge charging time corresponding to the charging current, to fit the functional relationship between the charging current and the full-charge charging time; using the functional relationship, to calculate the prediction constant corresponding to the charging current; using the different charging currents under each type of environmental information, the prediction constants corresponding to the different charging currents, and the battery capacity of the vehicle battery, to respectively construct corresponding prediction models for different environmental information and different charging currents.

[0009] Optionally, the full charging time of the vehicle battery is collected for a fully charged and discharged vehicle battery under various environmental information and various charging currents, including: collecting, from a variety of actual vehicles, the full charging time corresponding to the batteries of a variety of actual vehicles under a variety of environmental information and a variety of charging currents; or, constructing a variety of test environments; in each test environment, for each of the battery samples of a variety of battery capacities: charging the battery sample with a power of 0 using a variety of test charging currents respectively until the battery sample is fully charged, and collecting the full charging time for the battery sample.

[0010] Optionally, the calculation of the prediction constant corresponding to the charging current includes: for the case where the functional relationship indicates that the charging current is linearly correlated with the full-charge charging time, taking the absolute value of the slope corresponding to the linear correlation as the prediction constant corresponding to the charging current; or, for the case where the functional relationship is non-linearly correlated, constructing a relationship curve between the charging current and the full-charge charging time; and determining the absolute value of the slope of the tangent at each point on the relationship curve as the prediction constant of the charging current value corresponding to the point.

[0011] Optionally, the collecting of the full charging time of the vehicle battery for a fully charged and discharged vehicle battery under various environmental information and various charging currents further includes: selecting a plurality of charging devices for the battery sample, wherein the plurality of charging devices have different maximum output powers; controlling each of the charging devices to charge the battery sample with a power of 0 using a plurality of test charging currents respectively until the battery sample is fully charged, and collecting the full charging time for the battery sample.

[0012] Optionally, after using the target prediction model to predict the remaining charging time of the vehicle based on the battery capacity, the current charging current and the current environmental information combined with the charging time, it further includes: recording the actual charging time required to charge the battery of the vehicle to a full charge state; when the difference between the actual charging time and the predicted remaining charging time exceeds a set difference threshold, updating the target prediction model.

[0013] In a second aspect, an embodiment of the present invention provides a device for predicting the remaining charging time of a vehicle, comprising:

[0014] an information acquisition module, configured to acquire, in response to a prediction request, a battery capacity of the vehicle, a current charging current, and current environmental information of the vehicle, wherein the prediction request indicates a predicted remaining charging time of the vehicle;

[0015] A determination model module, which is used to search for a target prediction model that matches the battery capacity, the current charging current and the current environmental information from a plurality of prediction models;

[0016] A prediction module is used to use the target prediction model, the battery capacity, the current charging current and the current environmental information in combination with the charging time to predict the remaining charging time of the vehicle.

[0017] Optionally, the device for predicting the remaining charging time of a vehicle, before searching for a target prediction model that matches the battery capacity, the current charging current and the current environmental information from multiple prediction models, further includes: collecting the full charging time of the vehicle battery for a fully charged and discharged vehicle battery under various environmental information and various charging currents; and constructing a prediction model using the full charging time, the environmental information corresponding to the full charging time, and the charging current.

[0018] Optionally, the device for predicting the remaining charging time of a vehicle is used to construct a prediction model, including: performing the following operations for different charging currents and full-charge charging times collected under each type of environmental information: combining the battery capacity of the same type of vehicle battery, different charging currents for the vehicle battery, and the full-charge charging time corresponding to the charging current, to fit the functional relationship between the charging current and the full-charge charging time; using the functional relationship, to calculate the prediction constant corresponding to the charging current; using the different charging currents under each type of environmental information, the prediction constants corresponding to the different charging currents, and the battery capacity of the vehicle battery, to respectively construct corresponding prediction models for different environmental information and different charging currents.

[0019] Optionally, the device for predicting the remaining charging time of a vehicle is used to collect the full charging time of a vehicle battery under various environmental information and various charging currents for a fully charged and discharged vehicle battery, including: collecting, from a variety of actual vehicles, the full charging time corresponding to the batteries of a variety of actual vehicles under a variety of environmental information and a variety of charging currents; or, constructing a variety of test environments; in each test environment, for each of the battery samples of a variety of battery capacities, executing: charging a battery sample with a power of 0 using a variety of test charging currents respectively until the battery sample is fully charged, and collecting the full charging time for the battery sample.

[0020] Optionally, the device for predicting the remaining charging time of the vehicle is used to calculate the prediction constant corresponding to the charging current, including: for the case where the functional relationship indicates that the charging current is linearly correlated with the full-charge charging time, taking the absolute value of the slope corresponding to the linear correlation as the prediction constant corresponding to the charging current; or, for the case where the functional relationship is non-linearly correlated, constructing a relationship curve between the charging current and the full-charge charging time; determining the absolute value of the slope of the tangent at each point on the relationship curve as the prediction constant corresponding to the charging current value at that point.

[0021] Optionally, the device for predicting the remaining charging time of a vehicle is used to collect the full charging time of the vehicle battery for a fully charged and discharged vehicle battery under various environmental information and various charging currents, and further includes: selecting a plurality of charging devices for the battery samples, wherein the plurality of charging devices have different maximum output powers; controlling each of the charging devices to charge the battery samples with a power of 0 using a plurality of test charging currents respectively until the battery samples are fully charged, and collecting the full charging time for the battery samples.

[0022] Optionally, the device for predicting the remaining charging time of a vehicle is used to, after using the target prediction model to predict the remaining charging time of the vehicle based on the battery capacity, the current charging current, and the current environmental information in combination with the charging time, further include: recording the actual charging time required for the battery of the vehicle to be fully charged; and updating the target prediction model when the difference between the actual charging time and the predicted remaining charging time exceeds a set difference threshold.

[0023] In a third aspect, an embodiment of the present invention provides a system for predicting the remaining charging time of a vehicle, comprising: a charging device, a vehicle, and the apparatus described in the second aspect; wherein the charging device is used to charge the vehicle; the vehicle is used to obtain the battery capacity of the vehicle, the current charging current, and the current environmental information of the vehicle; the apparatus is used to search for a target prediction model that matches the battery capacity, the current charging current, and the current environmental information from multiple prediction models; and predict the remaining charging time of the vehicle by using the target prediction model, the battery capacity, the current charging current, and the current environmental information in combination with the charging time.

[0024] In a fourth aspect, an embodiment of the present invention provides an electronic device, including:

[0025] one or more processors;

[0026] a storage device for storing one or more programs,

[0027] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting the remaining charging time of a vehicle as described in the above-mentioned embodiment of the present invention.

[0028] In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium on which is stored a computer program for implementing a method for predicting the remaining time of vehicle charging. When the computer program is executed by an on-board processor, the method for predicting the remaining time of vehicle charging according to an embodiment of the present invention is implemented.

[0029] The technical solution of the above invention has the following advantages or beneficial effects: it can search for a target prediction model that matches the battery capacity, the current charging current and the current environmental information of the vehicle from multiple prediction models according to the battery capacity of the vehicle, the current charging current and the current environmental information of the vehicle; and use the target prediction model to predict the remaining charging time of the vehicle based on the battery capacity, the current charging current and the current environmental information combined with the charging time. The embodiment of the present invention estimates the remaining charging time based on various factors such as different environmental information, charging current and battery capacity, improves the accuracy and refinement of predicting the remaining charging time, and improves the vehicle experience of the passengers and the management efficiency of the vehicle battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a schematic diagram of a process for predicting the remaining charging time of a vehicle provided according to an embodiment of the present invention;

[0031] Figure 2 is a schematic diagram of a process for constructing a prediction model according to an embodiment of the present invention;

[0032] Figure 3 is a schematic diagram of the structure of a device for predicting the remaining charging time of a vehicle provided according to an embodiment of the present invention;

[0033] Figure 4 is a schematic diagram of the structure of a system for predicting the remaining charging time of a vehicle provided according to an embodiment of the present invention;

[0034] Figure 5 It is a schematic diagram of the structure of a computer system suitable for implementing the embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0036] It should be pointed out that the embodiments of the present invention and the technical features therein may be combined with each other without conflict.

[0037] In addition, the terms "first", "second", "third", etc. included in the embodiments of the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific number or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances. This is just a way of distinguishing objects with the same attributes when describing them in the embodiments of the present invention.

[0038] Furthermore, the vehicle involved in the embodiment of the present invention may be an internal combustion engine vehicle using an engine as a power source, a hybrid vehicle using an engine and an electric motor as a power source, an electric vehicle using an electric motor as a power source, or the like.

[0039] With the rapid development of new energy vehicles, the charging efficiency and management of new energy vehicles are becoming increasingly important. Especially when using DC fast charging, being able to accurately estimate the remaining charging time is crucial to improving user experience and battery management efficiency. In the prior art, simplified battery models are usually used to estimate charging time. These models often ignore the dynamic changes of factors such as battery status, ambient temperature and current, resulting in inaccurate charging time prediction.

[0040] The existing methods for estimating the remaining charging time mainly have the following problems:

[0041] 1. Nonlinear charging curve: The charging rate of the battery is not constant, especially when the battery is close to full charge, the charging rate will drop significantly. Existing estimation methods often do not simulate this nonlinear behavior well, resulting in inaccurate estimation results when the battery is close to full charge.

[0042] 2. Battery aging and health status: Battery performance will degrade over time and usage cycles, including the original capacity and rechargeable capacity of the battery. Existing estimation methods fail to adjust the remaining charging time estimate in real time to reflect the current state of the battery, resulting in inaccurate predictions.

[0043] 3. Neglect of environmental factors: The charging environment temperature (or humidity) has a significant impact on the battery charging performance. High and low temperatures may cause changes in charging time. The existing estimation method does not take this influencing factor into account.

[0044] In view of this, Figure 1 A schematic diagram showing the main steps of the method for predicting the remaining charging time of a vehicle provided by an embodiment of the present invention is shown:

[0045] Step S101: In response to a prediction request, obtaining a battery capacity of a vehicle, a current charging current, and current environmental information of the vehicle, wherein the prediction request indicates a prediction of a remaining charging time of the vehicle.

[0046] Specifically, an embodiment of the present invention combines the vehicle's battery capacity (battery capacity that can receive charging), the current charging current, and the vehicle's current environmental information (such as the temperature and humidity of the vehicle's environment), when predicting the remaining charging time of the vehicle. The prediction request can be sent by the vehicle occupants via the user interaction interface (or a vehicle application bound to the vehicle) at the start of charging or during the charging process.

[0047] Furthermore, the vehicle's battery capacity and the current charging current used during charging can be obtained from the battery management system through the vehicle system; and environmental information (temperature, humidity) can be obtained from a variety of sensors (temperature sensors, humidity sensors) or other platforms through the vehicle system.

[0048] Step S102: searching for a target prediction model that matches the battery capacity, the current charging current, and the current environmental information from a plurality of prediction models.

[0049] Specifically, the embodiment of the present invention constructs different prediction models for the battery capacity, the current charging current and the current environmental information; for example: the prediction model is expressed as t=C / I k , where t represents the full charge time, C represents the battery capacity, I represents the charging current, and k represents the prediction constant; further, the prediction constants of different prediction models are different; when the prediction constants are determined, they reflect the influence of different environmental information (temperature and humidity, etc.), battery capacity, charging current, and multiple items of the maximum output power of the charging device on the estimated charging time. The embodiment of the present invention determines the corresponding prediction constants by testing the charging time under various environmental information and various charging currents, thereby determining different prediction models. That is, under various environmental information and various charging currents, for a fully charged and discharged vehicle battery, the full charge time of the vehicle battery is collected; the prediction model is constructed using the full charge time, the environmental information corresponding to the full charge time, and the charging current. And, using different charging currents under each of the environmental information, the prediction constants corresponding to the different charging currents, and the battery capacity of the vehicle battery, corresponding prediction models are constructed for different environmental information and different charging currents.

[0050] Furthermore, the following steps are included for building a test environment:

[0051] Test preparation: (1) Select the battery sample for test: select an electric vehicle power battery system; (2) Prepare the test equipment: prepare a battery test bench, including adjustable charging and discharging equipment, high-precision current (or voltage) measurement equipment, and temperature (humidity) control equipment.

[0052] Test parameter setting: (1) Test charging current selection: Select a series of different charging currents for testing, for example: 5A, 15A, 20A, 25A, 30A, 35A, 40A, 45A, 50A, 55A, 60A, 65A, 70A, 75A, 80A, 85A, 90A, 100A, 110A, 120A, 130A, 140A, 150A, 160A, 170A, 180A, 190A, 200A, 250A, 300A, 350A, 400A, etc., to cover the current range in which the battery may operate; (2) Test environmental conditions: Ensure that the test is carried out under controlled environmental conditions. For example: temperature: -30℃ / -20℃ / -10℃ / 0℃ / 5℃ / 10℃ / 15℃ / 20℃ / 25℃ / 30℃ / 35℃ / 40℃ / 45℃ / 50℃ / 55℃, etc.; humidity: 40%-60%, etc.

[0053] Test steps: (1) In an environment with a temperature of -30°C and a humidity of 40%-60%, charge the battery from a completely empty state. The test charging currents used are 5A, 15A, 20A, 25A, 30A, 35A, 40A, 45A, 50A, 55A, 60A, 65A, 70A, 75A, 80A, 85A, 90A, 100A, 110A, 120A, 130A, 140A, 150A, 160A, 170A, 180A, 190A, 200A, 250A, 300A, 350A, 400A, etc., until the battery is fully charged; (2) Record the time from the time the battery is charged to the time the battery is fully charged. The full charging time from the beginning to the full charge, the corresponding battery current and the temperature (humidity, etc.), wherein the full charging time can also include the charging time from different battery states to full charge during the charging process, for example: the charging time from 10% charge to full charge, the charging time from 30% charge to full charge, etc.; (3) Repeat steps (1) and (2) in an environment with a temperature of -20℃ / -10℃ / 0℃ / 5℃ / 10℃ / 15℃ / 20℃ / 25℃ / 30℃ / 35℃ / 40℃ / 45℃ / 50℃ / 55℃ and a humidity of 40%-60%, respectively, until the full charging time from the beginning to the full charge of different test charging currents at each temperature is obtained. That is, multiple test environments are constructed; in each test environment, for each of the battery samples of multiple battery capacities, the following is performed: respectively using multiple test charging currents to charge the battery sample with a charge of 0 until the battery sample is fully charged, and collecting the full charging time for the battery sample.

[0054] Optionally, in an embodiment of the present invention, the full charging time corresponding to different environmental information, different charging currents, different battery capacities and other factors can be obtained from the actually running vehicles (or charging equipment) as the data required to build the prediction model; it is understandable that since different vehicles travel in different geographical ranges, data at different temperatures (or humidity) can be obtained according to different temperatures in the geographical range (for example, different seasons), and since the charging currents and battery capacities between different vehicles are different, the full charging time corresponding to different charging currents can be obtained. It is understandable that this data acquisition method needs to rely on the data acquisition of the vehicle itself. That is, for a vehicle battery that is fully charged and discharged under various environmental information and various charging currents, the full charging time of the vehicle battery is collected, including: from a variety of actual vehicles, collecting the corresponding full charging time of the batteries of a variety of actual vehicles under a variety of environmental information and a variety of charging currents, or, constructing a variety of test environments; in each test environment, for each of the battery samples of a variety of battery capacities, executing: respectively charging the battery sample with a power of 0 using a variety of test charging currents until the battery sample is fully charged, and collecting the full charging time for the battery sample.

[0055] Furthermore, the data obtained from the test are analyzed and the predicted constants obtained by calculation are fitted. (1) Data processing: The relationship between charging time and charging current is extracted from the test data, and the charging time from 0 or other power states (such as 5%, 15%, etc.) to full charge is recorded; (2) Fitting process: The formula t = C / I k Converted into a linear form for easy fitting, the converted formula (i.e., the functional relationship between the charging current and the full charge time) is: logt=logC-klogI. Further, the linear regression method is used to fit the logt and logI obtained from different currents under different temperature environments to determine the slope -k. Through test data, the prediction constant k under different temperatures and different currents is fitted. That is, the construction of the prediction model includes: for different charging currents and full charge times collected under each type of environmental information, the following operations are performed: combining the battery capacity of the same vehicle battery, different charging currents for the vehicle battery, and the full charge time corresponding to the charging current, fitting the functional relationship between the charging current and the full charge time; using the functional relationship, calculating the prediction constant corresponding to the charging current; using the different charging currents under each type of environmental information, the prediction constants corresponding to the different charging currents, and the battery capacity of the vehicle battery, respectively, corresponding prediction models are constructed for different environmental information and different charging currents.

[0056] Furthermore, the calculation of the prediction constant corresponding to the charging current includes: for the case where the functional relationship indicates that the charging current is linearly correlated with the full-charge charging time, taking the absolute value of the slope corresponding to the linear correlation as the prediction constant corresponding to the charging current; or, for the case where the functional relationship is non-linearly correlated, constructing a relationship curve between the charging current and the full-charge charging time; and determining the absolute value of the slope of the tangent at each point on the relationship curve as the prediction constant of the charging current value corresponding to the point.

[0057] Specifically, for the case where the functional relationship indicates that the charging current is linearly correlated with the full-charge charging time, the absolute value k of the slope can be directly used as the prediction constant corresponding to the charging current; for the case where the functional relationship is nonlinearly correlated, a relationship curve is constructed, and the absolute value of the slope of the tangent at each point on the relationship curve is determined as the prediction constant corresponding to the charging current value at the point, wherein the mapping relationship between the charging current, environmental information, battery capacity and prediction constant corresponding to the relationship curve can be stored in the data source to provide query.

[0058] Further, in an embodiment of the present invention, the maximum output power of the DC charging pile is selected to be 120kw / 150kw / 180kw / 200kw / 250kw / 300kw / 350kw / 400kw / 450kw / 500kw respectively, and the current output current is monitored in real time through the communication interface between the vehicle and the charging pile, so as to respectively use a plurality of test charging currents to charge the battery sample with a power of 0 until the battery sample is fully charged, and collect the test steps of the full charging time for the battery sample, which further improves the refinement of the predetermined prediction model. That is, under various environmental information and various charging currents, for a fully charged and discharged vehicle battery, the full charging time of the vehicle battery is collected, further comprising: selecting a plurality of charging devices for the battery sample, wherein the plurality of charging devices have different maximum output powers; controlling each of the charging devices, respectively using a plurality of test charging currents to charge the battery sample with a power of 0 until the battery sample is fully charged, and collecting the full charging time for the battery sample.

[0059] Step S103: using the target prediction model, based on the battery capacity, the current charging current and the current environmental information, combined with the charging time, predict the remaining charging time of the vehicle.

[0060] Specifically, for a request to predict the remaining charging time of a vehicle that is being charged at a certain moment, the battery capacity, the current charging current, and the current environmental information can be input into a target prediction model that matches the battery capacity, the current charging current, and the current environmental information, thereby predicting the remaining charging time of the vehicle based on the full charging time output by the target prediction model and the charging time.

[0061] Further preferably, the embodiment of the present invention provides a verification method for the prediction model, specifically, recording the actual charging time required for the battery of the vehicle to be fully charged; when the difference between the actual charging time and the predicted remaining charging time exceeds a set difference threshold, updating the target prediction model. By recording the actual charging time and comparing it with the predicted remaining charging time, the prediction constant in the target prediction model can be adjusted, thereby updating the target prediction model and improving the accuracy of the prediction model.

[0062] In an embodiment of the present invention, a method for predicting the remaining charging time of a vehicle can be performed using a server, where the server receives the battery capacity of the vehicle, the current charging current, and the current environmental information of the vehicle, and searches for a target prediction model that matches the battery capacity, the current charging current, and the current environmental information from multiple prediction models. The target prediction model is used to predict the remaining charging time of the vehicle based on the battery capacity, the current charging current, and the current environmental information, combined with the charging time; wherein the server can predict the remaining charging time of the vehicle for vehicles of the same brand that are charged using charging equipment managed by the server.

[0063] In another embodiment of the present invention, a method for predicting the remaining charging time of a vehicle may be executed by using a plug-in disposed inside a vehicle computer system.

[0064] Figure 2 A schematic diagram showing the main steps in building a prediction model:

[0065] Step S201: Collecting the full-charge charging time corresponding to the batteries of various actual vehicles under various environmental information and various charging currents from various actual vehicles.

[0066] Step S202: construct multiple test environments; in each test environment, for each of the battery samples of multiple battery capacities, perform: respectively charge the battery sample with zero power using multiple test charging currents until the battery sample is fully charged, and collect the full charging time for the battery sample.

[0067] Step S203: combining the battery capacity of the same type of vehicle battery, different charging currents for the vehicle battery, and the full-charge charging time corresponding to the charging current, to fit the functional relationship between the charging current and the full-charge charging time.

[0068] Step S204: using the functional relationship, calculating a prediction constant corresponding to the charging current.

[0069] Step S205: using different charging currents under each type of environmental information, prediction constants corresponding to different charging currents, and battery capacity of the vehicle battery, corresponding prediction models are constructed for different environmental information and different charging currents.

[0070] Specifically, the method for testing the charging time and the specific content of the functional relationship between the charging current and the full-charge charging time described in steps S201 to S205 are consistent with the description of step S102 and will not be repeated here.

[0071] Figure 3 The device 300 for predicting the remaining time of vehicle charging according to an embodiment of the present invention is shown, comprising:

[0072] An information acquisition module 301 is used to acquire the battery capacity, current charging current and current environment information of the vehicle in response to a prediction request, wherein the prediction request indicates a predicted remaining charging time of the vehicle;

[0073] A determination model module 302 is used to search a target prediction model that matches the battery capacity, the current charging current and the current environmental information from a plurality of prediction models;

[0074] The prediction module 303 is used to use the target prediction model, the battery capacity, the current charging current and the current environmental information in combination with the charging time to predict the remaining charging time of the vehicle.

[0075] Figure 4 A schematic diagram of the structure of a system for predicting the remaining time of vehicle charging provided by an embodiment of the present invention is shown; the system comprises: a charging device 403, a vehicle 402, and an apparatus 401 for predicting the remaining time of vehicle charging;

[0076] The charging device 403 is used to charge the vehicle 402;

[0077] The vehicle 402 is used to obtain the battery capacity, current charging current and current environment information of the vehicle;

[0078] The device 401 is used to search for a target prediction model that matches the battery capacity, the current charging current and the current environmental information from multiple prediction models; and use the target prediction model, the battery capacity, the current charging current and the current environmental information, combined with the charging time, to predict the remaining charging time of the vehicle.

[0079] in, Figure 4 The device 401 for predicting the remaining time of vehicle charging is shown to be arranged at the server end; in another embodiment of the present invention, the device for predicting the remaining time of vehicle charging can also be arranged in the vehicle's on-board system in the form of a plug-in.

[0080] Reference below Figure 5 , which shows a schematic diagram of the structure of a computer system 500 suitable for implementing an embodiment of the present invention. Figure 5 The computer system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0081] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0082] The following components are connected to the I / O interface 505: an input section 506; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0083] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present invention are executed.

[0084] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0085] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0086] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: in response to a prediction request, obtaining the battery capacity, current charging current and current environmental information of the vehicle, wherein the prediction request indicates a prediction of the remaining charging time of the vehicle; searching for a target prediction model that matches the battery capacity, the current charging current and the current environmental information from multiple prediction models; using the target prediction model, based on the battery capacity, the current charging current and the current environmental information, combined with the charging time, predicting the remaining charging time of the vehicle.

[0087] The embodiment of the present invention can search for a target prediction model that matches the battery capacity, the current charging current and the current environmental information of the vehicle from multiple prediction models based on the battery capacity, the current charging current and the current environmental information of the vehicle; and use the target prediction model to predict the remaining charging time of the vehicle based on the battery capacity, the current charging current and the current environmental information combined with the charging time. The embodiment of the present invention estimates the remaining charging time based on various factors such as different environmental information, charging current and battery capacity, improves the accuracy and refinement of predicting the remaining charging time, and enhances the vehicle experience of the passengers and the management efficiency of the vehicle battery.

[0088] The above specific implementations do not constitute limitations on the protection scope of the present invention.

[0089] It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting the remaining time of vehicle charging, characterized in that: include: In response to a prediction request, obtaining a battery capacity of a vehicle, a current charging current, and current environment information of the vehicle, wherein the prediction request indicates a prediction of a remaining charging time of the vehicle; Searching for a target prediction model that matches the battery capacity, the current charging current, and the current environmental information from a plurality of prediction models; The target prediction model is used to predict the remaining charging time of the vehicle based on the battery capacity, the current charging current and the current environmental information in combination with the charging time.

2. The vehicle charging method according to claim 1, characterized in that: Before searching for a target prediction model that matches the battery capacity, the current charging current, and the current environmental information from a plurality of prediction models, the method further includes: Under various environmental information and various charging currents, for a fully charged and discharged vehicle battery, collecting the full charging time of the vehicle battery; A prediction model is constructed using the full-charge charging time, environmental information corresponding to the full-charge charging time, and a charging current.

3. The vehicle charging method according to claim 2, characterized in that: The constructing of the prediction model comprises: For the different charging currents and full-charge charging times collected under each type of environmental information, perform the following operations: Combining the battery capacity of the same vehicle battery, different charging currents for the vehicle battery, and the full-charge charging time corresponding to the charging current, fitting a functional relationship between the charging current and the full-charge charging time; Utilizing the functional relationship, calculating a prediction constant corresponding to the charging current; By using different charging currents under each type of environmental information, prediction constants corresponding to different charging currents and the battery capacity of the vehicle battery, corresponding prediction models are constructed for different environmental information and different charging currents.

4. The vehicle charging method according to claim 2, characterized in that: The collecting of the full charging time of the vehicle battery for a fully charged and discharged vehicle battery under various environmental information and various charging currents includes: Collecting, from a variety of actual vehicles, the full-charge charging time corresponding to the batteries of the various actual vehicles under various environmental information and various charging currents; or, Build multiple test environments; Under each test environment, for each of the battery samples of various battery capacities, the following were performed: A battery sample with zero power is charged using a variety of test charging currents until the battery sample is fully charged, and the full-charge charging time for the battery sample is collected.

5. The vehicle charging method according to claim 3, characterized in that: The calculating a predicted constant corresponding to the charging current includes: In case that the functional relationship indicates that the charging current is linearly correlated with the full-charge charging time, taking the absolute value of the slope corresponding to the linear correlation as a prediction constant corresponding to the charging current; or, In case that the functional relationship is nonlinear, constructing a relationship curve between the charging current and the full-charge charging time; The absolute value of the slope of the tangent line at each point on the relationship curve is determined as a prediction constant for the charging current value corresponding to the point.

6. The vehicle charging method according to claim 4, characterized in that: The collecting of the full charging time of the vehicle battery for a fully charged and discharged vehicle battery under various environmental information and various charging currents further includes: A plurality of charging devices selected for the battery sample, wherein the plurality of charging devices have different maximum output powers; Each of the charging devices is controlled to charge a battery sample with a power of 0 using a plurality of test charging currents respectively until the battery sample is fully charged, and the full charging time for the battery sample is collected.

7. The vehicle charging method according to claim 1, characterized in that: After predicting the remaining charging time of the vehicle by using the target prediction model based on the battery capacity, the current charging current and the current environmental information in combination with the charging time, the method further includes: Record the actual charging time required for charging the battery of the vehicle to a fully charged state; if the difference between the actual charging time and the predicted remaining charging time exceeds a set difference threshold, update the target prediction model.

8. A device for predicting the remaining time of vehicle charging, characterized in that: include: an information acquisition module, configured to acquire, in response to a prediction request, a battery capacity of the vehicle, a current charging current, and current environmental information of the vehicle, wherein the prediction request indicates a predicted remaining charging time of the vehicle; A determination model module, which is used to search for a target prediction model that matches the battery capacity, the current charging current and the current environmental information from a plurality of prediction models; A prediction module is used to use the target prediction model, the battery capacity, the current charging current and the current environmental information in combination with the charging time to predict the remaining charging time of the vehicle.

9. A system for predicting the remaining time of vehicle charging, characterized in that: include: Charging equipment, vehicle and device as claimed in claim 8; The charging device is used to charge the vehicle; The vehicle is used to obtain the battery capacity, current charging current and current environment information of the vehicle; The device is used to search for a target prediction model that matches the battery capacity, the current charging current and the current environmental information from multiple prediction models; and use the target prediction model, the battery capacity, the current charging current and the current environmental information in combination with the charging time to predict the remaining charging time of the vehicle.

10. An electronic device for predicting the remaining time of vehicle charging, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

11. A computer-readable storage medium having a computer program for predicting the remaining time of vehicle charging stored thereon, characterized in that: include: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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