Vehicle thermal management method and electronic device

Abstract

The application provides a vehicle thermal management method and electronic equipment, comprising: obtaining predicted trip information, the predicted trip information comprising a predicted trip distance and a predicted trip duration; predicting power predicted energy consumption of the vehicle according to the predicted trip information and environmental information, the environmental information comprising road condition information and climate temperature information associated with the predicted trip information; predicting a first controlled object according to the power predicted energy consumption, the climate temperature information, and the predicted trip information, the first controlled object comprising at least zero item of a first set of a seat, a steering wheel, and a battery operating at a variable temperature, and an air conditioning system operating at a start-up temperature; and operating or not operating the first controlled object.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicles, in particular to a vehicle thermal management method 、 Electronic device and computer readable storage medium. BACKGROUND

[0002] Statistics show that the energy consumption of short-distance travel of a vehicle is higher than that of long-distance travel, and the energy consumption of the air conditioning system and the battery of the vehicle accounts for a high proportion. For example, when traveling a short distance, the thermal management system of the vehicle has not been able to utilize the energy consumption after completing the temperature change of warming up or / and cooling down due to the end of the trip, resulting in high energy consumption for short-distance travel.

[0003] Therefore, how to solve the extra energy consumption loss of vehicle travel becomes a problem to be solved. SUMMARY

[0004] The present application provides a vehicle thermal management method and an electronic device for reducing the energy consumption of vehicle travel.

[0005] The first aspect of the present application provides a vehicle thermal management method, comprising: obtaining predicted travel information, the predicted travel information comprising a predicted travel distance and a predicted travel time; predicting a power prediction energy consumption of the vehicle according to the predicted travel information and environmental information, the environmental information comprising road condition information and climate temperature information associated with the predicted travel information; predicting a first controlled object according to the power prediction energy consumption and the climate temperature information, and the predicted travel information, the first controlled object comprising at least zero items of a first set: a seat, a steering wheel and a battery that can be operated at a variable temperature, and an air conditioning system that can be started; and operating or not operating the first controlled object.

[0006] In some embodiments, predicting the first controlled object according to the power prediction energy consumption and the climate temperature information, and the predicted travel information, the first controlled object comprising one or a combination of the first set: a seat, a steering wheel and a battery that can be operated at a variable temperature, and an air conditioning system that can be started, comprises:

[0007] determining a second controlled object from a second set according to the predicted travel information, and further predicting a vehicle cabin prediction energy consumption of the second controlled object, wherein the prediction of the vehicle cabin prediction energy consumption comprises the predicted travel information and the climate temperature information, the second set comprising: the seat and the steering wheel that can be operated at a variable temperature, and the air conditioning system that can be started, the second controlled object comprising at least zero items of the second set; predicting a third controlled object comprising or not comprising the battery according to the power prediction energy consumption and the vehicle cabin prediction energy consumption, and battery information of the battery; determining the second controlled object and the third controlled object as the first controlled object.

[0008] In some embodiments, the battery information comprises a current power level of the battery, a predicted power level of the battery, and a predicted temperature control energy consumption of the battery.

[0009] In some embodiments, the predicting whether to include the battery as the third controlled object according to the power predicted energy consumption, the cabin predicted energy consumption, and the battery information of the battery comprises: predicting to include the battery as the third controlled object in response to a first ratio being greater than a second ratio; predicting to not include the battery as the third controlled object in response to the first ratio being less than or equal to the second ratio; wherein the first ratio is calculated according to the power predicted energy consumption, the cabin predicted energy consumption, and the current power level, and the second ratio is calculated according to the power predicted energy consumption, the cabin predicted energy consumption, the predicted temperature control energy consumption, and the predicted power level.

[0010] In some embodiments, the specific calculation formula of the first ratio is:

[0011]

[0012] The specific calculation formula of the second ratio is:

[0013]

[0014] wherein Q drive represents the power predicted energy consumption, Q hs represents the cabin predicted energy consumption, Q battery_heat represents the predicted temperature control energy consumption, represents the current power level of the battery, represents the predicted power level of the battery.

[0015] In some embodiments, the calculation method of the cabin predicted energy consumption comprises a first coefficient determined by the predicted travel distance and the predicted travel time length.

[0016] In some embodiments, the predicted temperature control energy consumption comprises a temperature change predicted energy consumption predicted for the battery to change temperature from a current temperature to a target temperature, or a temperature change predicted energy consumption predicted for the battery to change temperature from a current temperature to a target temperature and a temperature maintenance predicted energy consumption predicted for the battery to maintain the target temperature.

[0017] In some embodiments, the calculation formula of the temperature change predicted energy consumption is: Q BH1 = a (T N - T0), and the calculation formula of the temperature maintenance predicted energy consumption is: Q BH2 = h · (T N - T E ) · t, wherein TN represents the target temperature predicted by the battery, T0 represents the current temperature of the battery.

[0018] In some embodiments, the method further comprises a trip category of a short trip, a middle trip and a long trip, and the determining the second controlled object from the second set according to the predicted trip information comprises: in response to the predicted trip information being the short trip, determining the seat and / or the steering wheel as the second controlled object from the second set; in response to the predicted trip information being the middle trip, determining the seat and / or the steering wheel and the air conditioning system as the second controlled object from the second set; and in response to the predicted trip information being the long trip, determining the air conditioning system as the second controlled object from the second set.

[0019] In some embodiments, the obtaining the predicted trip information comprises:

[0020] determining habitual trip data from historical trip data to obtain the predicted trip information; or, in response to input data input by a user, obtaining the predicted trip information.

[0021] In a second aspect, the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the vehicle thermal management method of the first aspect.

[0022] In a third aspect, the present application provides a computer readable storage medium, which internally stores program instructions, and the program instructions are executed by a processor to perform the vehicle thermal management method of the first aspect.

[0023] Compared with the prior art, the technical solution of the present application generates the power prediction energy consumption of the vehicle according to the predicted trip information and the environmental information including road condition information and climate temperature information, then determines the first controlled object according to the power prediction energy consumption and the climate temperature information, and the predicted trip information, and finally runs or does not run the first controlled object. The present application autonomously selects the first controlled object matched with the predicted trip information from the first set comprising the seat, the steering wheel, the battery and the air conditioning system based on the predicted trip information, effectively manages the possible trip energy consumption object in the vehicle trip, and reduces the trip energy consumption of the vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without any creative effort based on these drawings.

[0025] Figure 1 is a schematic diagram of a vehicle thermal management method according to an embodiment of the present application;

[0026] Figure 2 is a schematic diagram of a process of determining a first controlled object in a vehicle thermal management method according to an embodiment of the present application;

[0027] Figure 3 is a schematic diagram of a process of determining whether a third controlled object includes a battery in a vehicle thermal management method according to an embodiment of the present application;

[0028] Figure 4 is a schematic diagram of a first coefficient in a vehicle thermal management method according to an embodiment of the present application;

[0029] Figure 5 is a schematic diagram of a process of determining a specific second controlled object in a vehicle thermal management method according to an embodiment of the present application;

[0030] Figure 6 is a schematic diagram of a vehicle thermal management device according to an embodiment of the present application;

[0031] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0033] In the following description, specific details are set forth in order to provide a thorough understanding of the present application. However, persons having ordinary skill in the art will appreciate that embodiments of the present application can be practiced without incorporating these specific details.

[0034] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort fall within the scope of the present application.

[0035] The terms "first", "second", "third", etc. in the present application are only for descriptive purpose and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second", "third" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative position relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0036] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of the application. The appearances of the phrase that in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is expressly understood that the embodiments described herein are merely examples from a much larger number of embodiments that can be claimed.

[0037] Please refer to Figure 1 , Figure 1 A vehicle thermal management method is shown, which is used for effectively managing possible travel energy consumption objects in vehicle travel to reduce the travel energy consumption of the vehicle.

[0038] 101: Obtain predicted travel information, the predicted travel information including a predicted travel distance and a predicted travel time length;

[0039] The vehicle obtains predicted travel information of the current trip, including a predicted travel distance and a predicted travel time length.

[0040] In some embodiments, a neural network model is trained by collecting the travel time, travel mileage and starting destination location of the user's historical navigation information, and historical traffic condition information, so as to predict the predicted travel distance and the predicted travel time length of the current trip of the user based on the neural network model.

[0041] In some implementations, the vehicle acquires the user's historical travel data to determine habitual travel data in order to obtain predicted trip information. That is, for vehicle users with regular travel patterns, the vehicle acquires the origin and destination of their habitual trips to obtain the predicted trip distance, and then uses the predicted trip distance and road condition information to obtain the predicted trip duration.

[0042] For example, for regularly occurring vehicle users, it could be the residential address and workplace of commuting users, or the origin and destination of transportation users, such as bus scenarios and freight transportation scenarios, without being limited here.

[0043] In some implementations, the vehicle responds to the user's input data of origin and destination, and then obtains the predicted trip distance and the predicted trip duration based on the input data, for example, through navigation software.

[0044] 102: Predict the vehicle's power consumption based on the predicted trip information and environmental information, wherein the environmental information includes road condition information and climate temperature information associated with the predicted trip information;

[0045] The vehicle generates a predicted energy consumption figure based on predicted trip information and environmental information, including road conditions and climate temperature. The predicted energy consumption figure represents the estimated energy consumption for the vehicle during this trip.

[0046] In some implementations, the predicted energy consumption is calculated based on the predicted travel distance and the obtained historical average energy consumption, which can be recorded locally in the vehicle system or obtained via the cloud. Specifically, the calculation formula is as follows:

[0047] Q drive =E ave ·S

[0048] In the formula, E ave S represents the historical average power consumption, and S represents the predicted travel distance.

[0049] In some implementations, to further improve the accuracy of power consumption prediction, the vehicle acquires a predicted travel distance comprising multiple predicted travel intervals, i.e., the sum of multiple predicted travel intervals is the predicted travel distance, and acquires road condition information for each predicted travel interval, including the predicted vehicle speed and the predicted road gradient. The road gradient can be obtained based on navigation software or historical road gradient data. Similarly, the vehicle speed can be obtained based on navigation software combined with the current predicted travel information and corresponding route traffic conditions.

[0050] The segmental power prediction energy consumption is calculated by predicting the segmental distance and road condition information, and all segmental power prediction energy consumptions, i.e., the power prediction energy consumption, are obtained.

[0051] The segmental power prediction energy consumption is calculated by predicting the segmental distance and road condition information, and all segmental power prediction energy consumptions, i.e., the power prediction energy consumption, are obtained.

[0052]

[0053] In the formula, m is the mass of the vehicle, g is the acceleration of gravity, f is the rolling resistance coefficient, C D is the wind resistance coefficient, A is the windward area, u is the speed of the vehicle, β is the road slope, δ is the rotational mass conversion coefficient, S i is the predicted segmental distance, wherein the rolling resistance coefficient, the windward area, the wind resistance coefficient and the rotational mass conversion coefficient are preset values.

[0054] 103: predicting a first controlled object based on the power prediction energy consumption, the climate temperature information and the predicted trip information, wherein the first controlled object comprises at least zero item of a first set including a seat, a steering wheel, a battery and an air conditioning system, and the seat, the steering wheel and the battery can be operated at a variable temperature, and the air conditioning system can be started.

[0055] The vehicle determines the first controlled object based on the power prediction energy consumption, the climate temperature information and the predicted trip information. The first controlled object is determined from the first set including the seat, the steering wheel, the battery and the air conditioning system. The seat, the steering wheel and the battery can be operated at a variable temperature, and the air conditioning system can be started.

[0056] It can be understood that the determined first controlled object can be zero item of the first set, i.e., the first controlled object does not include any object of the first set; or the determined first controlled object can be a combination of the first set.

[0057] 104: operating or not operating the first controlled object.

[0058] After the vehicle determines the first controlled object, the vehicle operates or does not operate the first controlled object based on the specific object of the first controlled object.

[0059] It can be understood that when the first controlled object is zero item, the vehicle does not operate the first controlled object. When the first controlled object is a combination of the first set, the vehicle operates the first controlled object, for example, when the first controlled object is the seat and the air conditioning system, the vehicle operates the seat at a variable temperature and starts the air conditioning system.

[0060] In the present application, the first controlled object is autonomously selected from the first set including the seat, the steering wheel, the battery and the air conditioning system based on the predicted trip information, and the first controlled object matched with the predicted trip information is obtained, so that the possible trip energy consumption object is effectively managed in the vehicle trip, and the trip energy consumption of the vehicle is reduced.

[0061] Referring to Figure 2 , Figure 2 It is shown how to determine the first controlled object, comprising:

[0062] 201: determining a second controlled object from a second set according to the predicted trip information, and predicting a vehicle cabin predicted energy consumption of the second controlled object, wherein the prediction of the vehicle cabin predicted energy consumption comprises the predicted trip information and the climate temperature information, the second set comprises the variable temperature operated seat and steering wheel, and the startable operated air conditioning system, and the second controlled object comprises at least zero items of the second set;

[0063] The vehicle determines the second controlled object based on the predicted trip information, and obtains the vehicle cabin predicted energy consumption of the second controlled object. Wherein the second controlled object is determined from the second set comprising the seat and the steering wheel, and the air conditioning system. The vehicle cabin predicted energy consumption represents the energy consumed by the second controlled object in the current trip of the vehicle.

[0064] It can be understood that the determined second controlled object can be zero items of the second set, i.e. the second controlled object does not include any object of the second set; the determined second controlled object can also be a combination of the second set.

[0065] 202: predicting a third controlled object including or not including the battery according to the power predicted energy consumption and the vehicle cabin predicted energy consumption, and battery information of the battery;

[0066] The vehicle predicts the third controlled object including or not including the battery according to the power predicted energy consumption and the vehicle cabin predicted energy consumption, and the battery information of the battery. Wherein the battery information comprises battery predicted class information and current power of the battery, and the battery predicted class information comprises power predicted margin of the battery and predicted temperature control energy consumption of the battery.

[0067] The current power of the battery represents the current power of the battery in the current trip of the vehicle.

[0068] The predicted temperature control energy consumption of the battery represents the predicted temperature change energy consumption of the battery from the current temperature to the target temperature, or the predicted temperature change energy consumption of the battery from the current temperature to the target temperature and the temperature maintenance predicted energy consumption for maintaining the target temperature.

[0069] The power predicted margin of the battery represents the predicted battery margin based on the current power of the battery and the temperature change from the current temperature to the target temperature.

[0070] 203: determining the second controlled object and the third controlled object as the first controlled object.

[0071] After determining whether the third controlled object includes the battery, the vehicle determines the second controlled object and the third controlled object as the first controlled object.

[0072] It can be understood that, when the second controlled object is the zero item of the second set and the third controlled object does not include the battery, the first controlled object is an empty set, which does not have any controlled object, and thus the first controlled object is not run in the aforementioned step 104; when the second controlled object is the combination of the second set and the third controlled object does not include the battery, the first controlled object includes the combination of the second set; when the second controlled object is the combination of the second set and the third controlled object includes the battery, the first controlled object includes the combination of the second set and the battery, and for the latter two, the first controlled object is run in the aforementioned step 104.

[0073] Referring to Figure 3 , Figure 3 The process of determining whether the third controlled object includes the battery is shown, wherein the vehicle calculates a first ratio based on obtaining the power predicted energy consumption, the cabin predicted energy consumption and the current battery capacity, the vehicle calculates a second ratio based on the power predicted energy consumption, the vehicle predicted energy consumption and the predicted temperature control energy consumption, and the capacity predicted margin, Figure 3 including:

[0074] 301: in response to the first ratio being greater than the second ratio, the third controlled object including the battery is predicted;

[0075] The vehicle predicts the third controlled object including the battery in response to the first ratio being greater than the second ratio.

[0076] 302: in response to the first ratio being less than or equal to the second ratio, the third controlled object not including the battery is predicted.

[0077] The vehicle predicts the third controlled object not including the battery in response to the first ratio being less than or equal to the second ratio.

[0078] In some embodiments, the specific calculation formula of the first ratio is:

[0079]

[0080] The specific calculation formula of the second ratio is:

[0081]

[0082] wherein Q drive represents the power predicted energy consumption, Q hs represents the cabin predicted energy consumption, Q battery_heat represents the predicted temperature control energy consumption, represents the current capacity of the battery, represents the predicted energy consumption of the battery.

[0083] In some embodiments, after the second controlled object is determined, since the predicted energy consumption of each respective second controlled object is known in the art, the present application will not be described in detail, and the predicted energy consumption of each respective second controlled object can be obtained by the existing technology, and then the vehicle cabin predicted energy consumption is obtained by summation, for example, the vehicle cabin predicted energy consumption calculation formula is Q HS = Q H1 + Q H2 + Q H3 , wherein Q H1 represents the predicted energy consumption of starting the air conditioning system, Q H2 represents the predicted energy consumption of the temperature-variable steering wheel, Q H3 represents the predicted energy consumption of the temperature-variable seat.

[0084] In some embodiments, in order to further optimize the energy consumption of the air conditioning system, a first coefficient k is set to adjust the energy consumption of the air conditioning system. Please refer to Figure 4 , Figure 4 different combinations of predicted travel distance and predicted travel time, i.e. Q H1 = kQ h1 , Q h1 represents the predicted energy consumption of the air conditioning system before optimization, which is calculated by the load curve of the air conditioning system working and the predicted travel time, for example, through the ambient temperature and the target control temperature of the air conditioning.

[0085] In some embodiments, the predicted energy consumption of the temperature-variable steering wheel and the predicted energy consumption of the temperature-variable seat can be determined by the corresponding heating power and working time determined by the temperature-variable strategy, wherein the working time can be the predicted travel time, can be the user-set time, or can be the working time determined based on the temperature-variable strategy.

[0086] In some embodiments, since the predicted temperature control energy consumption of the battery represents the predicted energy consumption consumed by the battery for temperature-variable from the current temperature to the target temperature, or the predicted energy consumption consumed by the battery for temperature-variable from the current temperature to the target temperature and the predicted energy consumption consumed by the battery for maintaining the target temperature, the calculation formula of the temperature-variable predicted energy consumption of the predicted temperature control energy consumption of the battery is Q BH1 = a (T N - T0), and the calculation formula of the temperature-maintaining predicted energy consumption of the predicted temperature control energy consumption of the battery is Q BH2 = h · (T N - T E ) · t, wherein T N represents the predicted target temperature of the battery, T0 represents the current temperature of the battery, and T EA climate temperature representing climate temperature information, a battery heating coefficient a, and a battery heat exchange correlation coefficient h, the battery heating coefficient and the battery heat exchange correlation coefficient being preset values, t representing a predicted travel time.

[0087] In some embodiments, the battery power prediction margin can be based on T N and Soc. Further, to improve the accuracy of the battery power prediction margin, the battery power prediction margin of the vehicle battery can be obtained according to the battery type of the vehicle and the corresponding T N and Soc.

[0088] Referring to Figure 5 , Figure 5 Further, based on a preset association relationship between the second controlled object and the plurality of predicted travel information, the specific second controlled object under the corresponding predicted travel information is determined, wherein the association relationship includes a travel category in which the predicted travel information is pre-divided into a short trip, a middle trip or a long trip, and includes:

[0089] 501: In response to the predicted travel information being the short trip, the seat and / or the steering wheel are determined as the second controlled object from the second set;

[0090] In response to the predicted travel information being the short trip, the seat and / or the steering wheel are determined as the second controlled object from the second set.

[0091] 502: In response to the predicted travel information being the middle trip, the seat, the steering wheel and the air conditioning system are determined as the second controlled object from the second set;

[0092] In response to the predicted travel information being the middle trip, the seat, the steering wheel and the air conditioning system are determined as the second controlled object from the second set.

[0093] Preferably, for the middle trip, the air conditioning system adopts limited power operation during operation to reduce energy consumption.

[0094] 503: In response to the predicted travel information being the long trip, the air conditioning system is determined as the second controlled object from the second set.

[0095] In response to the predicted travel information being the long trip, the air conditioning system is determined as the second controlled object from the second set.

[0096] It can be understood that the vehicle of the foregoing embodiments is a vehicle including a battery as a power source, such as an electric vehicle or a hybrid electric vehicle, which is not specifically limited here.

[0097] Referring to Figure 6The application also includes a vehicle thermal management device, comprising:

[0098] The acquisition unit 601 is configured to acquire predicted trip information, the predicted trip information including a predicted trip distance and a predicted trip duration.

[0099] The first prediction unit 602 is configured to predict a power prediction energy consumption of the vehicle according to the predicted trip information and environmental information, the environmental information including road condition information and climate temperature information associated with the predicted trip information.

[0100] The second prediction unit 603 is configured to predict a first controlled object according to the power prediction energy consumption and the climate temperature information, and the predicted trip information, the first controlled object including at least zero items in a first set of a seat, a steering wheel, a battery, and an air conditioning system that can be started.

[0101] The execution unit 604 is configured to run or not run the first controlled object.

[0102] The application also includes an electronic device, please refer to Figure 7 The electronic device includes a memory 701 and a processor 702, wherein the memory is configured to store a program, and the processor is configured to execute the program in the memory, including executing the method of any one of the preceding embodiments.

[0103] Optionally, the electronic device further includes a bus system for connecting the memory and the processor to enable the memory and the processor to communicate.

[0104] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0105] The memory may, in some embodiments, be an internal storage unit of the cloud device, for example. A hard disk or a memory of the cloud device. The memory may, in other embodiments, also be an external storage device of the cloud device, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the cloud device. Further, the memory may also include both the internal storage unit and the external storage device of the cloud device. The memory is used to store an operating system, an application program, a boot loader, data, and other programs, etc., such as program codes of computer programs, etc. The memory may also be used to temporarily store data that has been output or will be output.

[0106] The application also includes a computer-readable storage medium having program instructions stored therein, which are executed by a processor to implement the method of any of the preceding embodiments.

[0107] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, the application can implement all or part of the processes in the above-mentioned embodiments by a computer program to instruct related hardware to complete, and the computer program can be stored in a computer-readable storage medium. The computer program, when executed by a processor, can implement the steps of each method embodiment.

[0108] The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer-readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium may not be an electrical carrier signal and a telecommunications signal.

[0109] The above is only an embodiment of the application, and does not limit the patent protection scope of the application. Any equivalent structure or equivalent process transformation using the contents of the specification and drawings, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the application.

Claims

1. A vehicle thermal management method, characterized by, The method comprises: obtaining predicted trip information, the predicted trip information comprising a predicted trip distance and a predicted trip duration; predicting a power predicted energy consumption of the vehicle according to the predicted trip information and environment information, the environment information comprising road condition information and climate temperature information associated with the predicted trip information, the power predicted energy consumption representing a predicted power energy consumption in the current trip of the vehicle; determining a second controlled object from a second set according to the predicted trip information, and further predicting a vehicle cabin predicted energy consumption of the second controlled object, wherein the prediction of the vehicle cabin predicted energy consumption comprises the predicted trip information and the climate temperature information, the second set comprising a seat and a steering wheel capable of variable temperature operation, and an air conditioning system capable of starting operation, the second controlled object comprising at least one item of the second set, and the vehicle cabin predicted energy consumption representing a predicted energy consumption of the second controlled object in the current trip of the vehicle; in response to a first ratio being greater than a second ratio, predicting a third controlled object comprising a battery; in response to the first ratio being less than or equal to the second ratio, predicting a third controlled object not comprising the battery, wherein the first ratio is calculated according to the power predicted energy consumption, the vehicle cabin predicted energy consumption, and a current battery capacity, the second ratio is calculated according to the power predicted energy consumption, the vehicle cabin predicted energy consumption, a predicted temperature control energy consumption of the battery, and a battery capacity prediction margin, the battery capacity prediction margin representing a predicted battery capacity based on the current battery capacity and a temperature change from a current temperature to a target temperature, and the predicted temperature control energy consumption comprising a temperature change predicted energy consumption predicted for the battery to change temperature from the current temperature to the target temperature and a temperature maintenance predicted energy consumption predicted for maintaining the target temperature; determining the second controlled object and the third controlled object as a first controlled object; operating or not operating the first controlled object.

2. The vehicle thermal management method of claim 1, wherein, The specific calculation formula of the first ratio is: The specific calculation formula of the second ratio is: wherein, represents the power prediction energy consumption, represents the cabin prediction energy consumption, represents the prediction temperature control energy consumption, represents the current charge of the battery, represents the charge prediction margin of the battery.

3. The vehicle thermal management method of claim 2, wherein, The calculation of the vehicle cabin predicted energy consumption comprises a first coefficient determined by the predicted trip distance and the predicted trip duration.

4. The vehicle thermal management method of claim 1, wherein, The predicted temperature control energy consumption comprises a temperature change predicted energy consumption predicted for the battery to change temperature from the current temperature to the target temperature and a temperature maintenance predicted energy consumption predicted for maintaining the target temperature.

5. The vehicle thermal management method of claim 4, wherein, The calculation formula of the variable-temperature predicted energy consumption is: The calculation formula of the constant-temperature predicted energy consumption is: Wherein, represents the target temperature predicted by the battery, represents the current temperature of the battery, represents the climate temperature of the climate temperature information, a represents a battery heating coefficient, h represents a battery heat exchange related coefficient, and t represents a predicted travel duration.

6. The vehicle thermal management method according to any one of claims 1 to 5, characterized in that, The method further comprises trip categories of a short trip, an intermediate trip, and a long trip, and the determining of the second controlled object from the second set according to the predicted trip information comprises: in response to the predicted trip information being the short trip, determining the seat and / or the steering wheel as the second controlled object from the second set; in response to the predicted trip information being the intermediate trip, determining the seat and / or the steering wheel and the air conditioning system as the second controlled object from the second set; in response to the predicted trip information being the long trip, determining the air conditioning system as the second controlled object from the second set.

7. The vehicle thermal management method according to any one of claims 1 to 5, characterized in that, The obtaining of the predicted trip information comprises: determining habitual trip data from historical trip data to obtain the predicted trip information; or In response to input data input by a user, the predicted travel information is acquired.

8. An electronic device comprising a memory and a processor, characterized in that The memory stores a computer program, and the processor is configured to execute the computer program to perform the vehicle thermal management method according to any one of claims 1 to 7.

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