Vehicle thermal management control method and device, electronic equipment, storage medium and vehicle

Abstract

The application discloses a vehicle thermal management control method and device, electronic equipment, storage medium and vehicle. The method comprises the following steps: acquiring a current working condition state of the vehicle, acquiring state data of the current working condition state and one or more control targets; inputting the state data and multiple sets of control quantity parameters into a physical prediction model respectively; determining an output control quantity sequence based on comparison between a prediction target output by the physical prediction model and the control target; and performing thermal management control on the vehicle by using the output control quantity sequence. The application can accurately control the vehicle to achieve various control targets, realize the function of automatic thermal management control, avoid a large number of calibration experiments in advance, and reduce the calibration workload.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle, and particularly relates to a vehicle thermal management control method and device, an electronic device, a storage medium and a vehicle. BACKGROUND

[0002] In the existing thermal management control strategy, for the thermal management of a vehicle, a control method based on rule and table (MAP) calibration is adopted. Through a large number of experiments, the specific values of the control quantities of various control targets are calibrated, and then in the real vehicle, according to the control target, based on the rule, the specific control quantity parameters are obtained by looking up the table of the control quantities to control the vehicle. For example, for the fusion control of the engine, the water positive temperature coefficient (WPTC) and the power battery thermal process in a hybrid vehicle, the current strategy is a control method based on rule and table calibration.

[0003] However, the control method based on rule and table calibration has poor control precision, consumes too much energy on the thermal management components, has a slow cooling rate, and may even have an overshoot problem. At the same time, this control method requires a large amount of calibration work, which increases the workload of automobile development. SUMMARY

[0004] Therefore, it is necessary to provide a vehicle thermal management control method, device, electronic device, storage medium and vehicle in view of the technical problems of poor control precision and a large amount of calibration work in the existing thermal management control method.

[0005] The present application provides a vehicle thermal management control method, comprising:

[0006] obtaining a current working condition state of a vehicle;

[0007] obtaining state data of the current working condition state and one or more control targets, the control target being a target expected to be reached by a controlled thermal management parameter under the current working condition;

[0008] inputting the state data and a plurality of sets of control quantity parameters into a physical prediction model respectively, determining an output control quantity sequence based on a comparison between a prediction target output by the physical prediction model and the control target;

[0009] controlling the thermal management of the vehicle by using the output control quantity sequence.

[0010] Further, the step of inputting the state data and a plurality of sets of control quantity parameters into a physical prediction model respectively, and determining an output control quantity sequence based on a comparison between a prediction target output by the physical prediction model and the control target, specifically comprises:

[0011] solving iteratively an output control parameter of each of the controlled thermal management parameters in each period, taking one or more of the output control parameters as an output control sequence, in each iteration process:

[0012] generating a plurality of control parameter sets corresponding to the controlled thermal management parameters, each of the control parameter sets including a control parameter of one control or a plurality of control parameters of a plurality of controls;

[0013] inputting the state data and the plurality of control parameter sets of the current iteration process into a physical prediction model to obtain a plurality of prediction targets, each of the prediction targets being obtained based on the state data and a control parameter set by the physical prediction model, selecting a prediction target closest to the control target as an output prediction target of the controlled thermal management parameter in the current iteration process, and taking a control parameter set corresponding to the output prediction target as an output control parameter of the controlled thermal management parameter in the current iteration process.

[0014] Further, the generating a plurality of control parameter sets corresponding to the controlled thermal management parameters specifically includes:

[0015] in the initial iteration process, for each control corresponding to the controlled thermal management parameter, obtaining a plurality of control parameters of the control by scattering, and generating a plurality of control parameter sets;

[0016] in the subsequent iteration process after the initial iteration process, for each control corresponding to the controlled thermal management parameter, obtaining a plurality of control parameters of the control based on a preset sequence, and generating a plurality of control parameter sets.

[0017] Further, the obtaining a plurality of control parameters of each control by scattering specifically includes:

[0018] for each control, obtaining a maximum granularity, an upper limit of scattering and a lower limit of scattering of the control;

[0019] in a range defined by the lower limit of scattering and the upper limit of scattering, increasing the maximum granularity by the lower limit of scattering step by step to obtain a plurality of control parameters.

[0020] Further, the obtaining a plurality of control parameters of each control based on a preset sequence specifically includes:

[0021] for each control, taking a control parameter of the control in an output control parameter of a previous iteration process as a basic control parameter, and obtaining a plurality of control parameters of the control based on the basic control parameter and a preset sequence.

[0022] Further, the obtaining the plurality of control quantity parameters based on the base control quantity and the preset number series of scatter points specifically comprises:

[0023] obtaining a minimum granularity of the control quantity, an upper limit of the scatter points and a lower limit of the scatter points;

[0024] in the range defined by the lower limit of the scatter points and the upper limit of the scatter points, calculating the base control quantity increased or decreased by the product of the minimum granularity and the preset number series to obtain the plurality of control quantity parameters.

[0025] Further, in the case that the control quantity corresponding to the controlled thermal management parameter is multiple, the generating the plurality of groups of control quantity parameters specifically comprises:

[0026] combining the plurality of control quantity parameters of the plurality of control quantities to obtain the plurality of groups of control quantity parameters, and each group of the control quantity parameters comprises the control quantity parameters of the plurality of control quantities.

[0027] The present application provides a kind of vehicle thermal management control device, comprising:

[0028] working condition state acquisition module, for obtaining the current working condition state of vehicle;

[0029] state target acquisition module, for obtaining the state data of the current working condition state and one or more control targets, the control target is the target that controlled thermal management parameter expects to reach under current working condition;

[0030] prediction module, for inputting the state data and the plurality of groups of control quantity parameters into physical prediction model respectively, based on the comparison between the predicted target output by the physical prediction model and the control target, determine output control quantity sequence;

[0031] control module, for using the output control quantity sequence to carry out thermal management control to vehicle.

[0032] The present application provides a kind of electronic equipment, comprising:

[0033] at least one processor;And,

[0034] memory connected in communication with at least one of the processors;Wherein,

[0035] the memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the vehicle thermal management control method as described above.

[0036] The application provides a storage medium storing computer instructions for executing all steps of the vehicle thermal management control method as described above when the computer executes the computer instructions.

[0037] The application provides a vehicle comprising the vehicle thermal management control device as described above or the electronic device as described above.

[0038] In some embodiments of the present disclosure / application, the comparison between the predicted target of the output and the control target is determined by a physical prediction model, the output control amount parameter of each controlled thermal management parameter is determined, and then the output control amount parameters are combined into an output control amount sequence, and the vehicle is controlled based on the output control amount sequence, so that the vehicle can be accurately controlled to achieve each control target, the function of automatic thermal management control is realized, a large amount of calibration experiments in advance is avoided, and the calibration workload is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 A work flow chart of a vehicle thermal management control method according to an embodiment of the application;

[0040] Figure 2 A work flow chart of a vehicle thermal management control method according to another embodiment of the application;

[0041] Figure 3 A work flow chart of a vehicle thermal management control method according to the best embodiment of the application;

[0042] Figure 4 An iterative calculation schematic diagram of each controlled thermal management parameter according to the best embodiment of the application;

[0043] Figure 5 A schematic diagram of a vehicle thermal management control device according to an embodiment of the application;

[0044] Figure 6 A hardware structure schematic diagram of an electronic device according to the application. DETAILED DESCRIPTION

[0045] The specific embodiments of the application are further described below with reference to the accompanying drawings. Identical parts are denoted by identical reference numerals in the drawings. It should be noted that the words "front", "back", "left", "right", "up" and "down" used in the following description refer to the directions in the drawings, and the words "inner" and "outer" refer to the directions towards or away from the geometric center of a specific part.

[0046] In the existing thermal management control strategy, for the thermal management of the vehicle, a control method based on rules and table (MAP) calibration is adopted. Through a large number of experiments, the specific values of the control quantities of various control targets are calibrated, and then in the real vehicle, according to the control target, based on the rules, the specific control quantity parameters are obtained by looking up the table of the control quantity, and the vehicle is controlled. For example, for the fusion control of the engine, the water heater (Water Positive Temperature Coefficient, WPTC) and the power battery thermal process in the hybrid vehicle, the current strategy is the control method based on rules and table calibration.

[0047] However, the control method based on rules and table calibration has poor control precision, consumes too much energy on the thermal management components, has slow cooling rate, and may even have overshoot problem, and the control method needs a large amount of calibration work, which increases the workload of the automobile development work.

[0048] In order to solve the technical problems existing in the prior art, the present application provides a vehicle thermal management control method, device, electronic equipment, storage medium and vehicle.

[0049] As Figure 1 The working flow chart of the vehicle thermal management control method according to an embodiment of the present application is shown in the figure, which comprises:

[0050] Step S101, acquiring the current working condition state of the vehicle;

[0051] Step S102, acquiring the state data of the current working condition state and one or more control targets, the control target being the target expected to be reached by the controlled thermal management parameter under the current working condition;

[0052] Step S103, inputting the state data and a plurality of sets of control quantity parameters into a physical prediction model respectively, determining an output control quantity sequence based on the comparison between the prediction target output by the physical prediction model and the control target;

[0053] Step S104, using the output control quantity sequence to control the thermal management of the vehicle.

[0054] Specifically, the present application can be applied to an electronic equipment with processing capability of the vehicle. For example, it can be applied to an electronic control unit (Electronic Control Unit, ECU) or an extended domain control unit (Extended Domain Control Unit, XCU).

[0055] First, the electronic equipment executes step S101 to acquire the current working condition state of the vehicle. The current working condition state is determined according to the actual vehicle condition.

[0056] Then, for the operating condition, step S102 is executed to obtain the data and parameters required by the physical prediction model as state data. Simultaneously, based on the actual vehicle conditions, one or more control objectives are determined. These control objectives are the desired targets that the controlled thermal management parameters should achieve under the current operating conditions.

[0057] For example, for vehicle parameters related to battery target temperature, if the battery target temperature is set to 26 degrees, then 26 degrees is the control target for this controlled thermal management parameter.

[0058] Then, step S103 is executed, in which the state data and multiple sets of control parameters are input into the physical prediction model, and the output control sequence is determined based on the comparison between the prediction target output by the physical prediction model and the control target.

[0059] Specifically, the physical prediction model is consistent with the actual vehicle structure. Predictions from the physical prediction model can be achieved through a solver. A sub-thread can be created that repeatedly calls the solver. The solver calls the physical prediction model, inputting state data and multiple sets of control parameters to obtain multiple prediction targets.

[0060] Each predicted target is obtained from state data and a set of control parameters through a physical prediction model. Each set of control parameters includes one or more control variables, which are the values ​​of the control variables corresponding to the controlled thermal management parameters. The control variables are used to adjust the controlled thermal management parameters.

[0061] For example, using the target battery temperature as the controlled thermal management parameter, the compressor power can be selected as the control variable. A controlled thermal management parameter can have multiple control variables.

[0062] The physical prediction model outputs a predicted target for the controlled thermal management parameter based on the input state data and one or more control parameters. By inputting multiple sets of control parameters into the physical prediction model, multiple predicted targets for the controlled thermal management parameter can be obtained.

[0063] Then, the predicted target that is closest to the control objective corresponding to the controlled thermal management parameter is selected as the output predicted target. The set of control parameters that yield the output predicted target is then used as the output control parameters.

[0064] In some embodiments, the step of inputting the state data and multiple sets of control parameters into a physical prediction model, and determining the output control sequence based on a comparison between the predicted target output by the physical prediction model and the control target, specifically includes:

[0065] The state data and multiple sets of control quantity parameters are input into the physical prediction model respectively, to obtain multiple prediction targets for each of the controlled thermal management parameters, for each of the controlled thermal management parameters, the prediction target closest to the control target corresponding to the controlled thermal management parameter is selected as the output prediction target of the controlled thermal management parameter, the set of control quantity parameters obtaining the output prediction target is taken as the output control quantity parameter of the controlled thermal management parameter, and one or more sets of the output control quantity parameters are taken as the output control quantity sequence.

[0066] When there is only one control target, the output control quantity sequence includes one set of output control quantity parameters, and when there are multiple control targets, the output control quantity sequence includes multiple sets of the output control quantity parameters, that is, the output control quantity sequence includes the output control quantity parameters of the controlled thermal management parameter corresponding to each control target.

[0067] For the case where the control quantity corresponding to the controlled thermal management parameter is one, each set of output control quantity parameters includes one output control quantity parameter.

[0068] For the case where the control quantity corresponding to the controlled thermal management parameter is multiple, each set of output control quantity parameters includes multiple output control quantity parameters of different control quantities.

[0069] Then, step S104 is performed, and the vehicle is controlled for thermal management by using the output control quantity sequence.

[0070] In some embodiments, in one cycle, the sub-thread outputs multiple intermediate control quantity sequences in a preset time and the output control quantity sequence reaching the preset time, and the main thread obtains the intermediate control quantity sequence or the output control quantity sequence each time, and controls the vehicle by using the intermediate control quantity sequence or the output control quantity sequence.

[0071] The intermediate control quantity sequence includes one or more sets of intermediate control quantity parameters. When there is only one control target, the intermediate control quantity sequence includes one set of intermediate control quantity parameters, and when there are multiple control targets, the intermediate control quantity sequence includes multiple sets of the intermediate control quantity parameters, that is, the output control quantity sequence includes the intermediate control quantity parameters of the controlled thermal management parameter corresponding to each control target.

[0072] For the case where the control quantity corresponding to the controlled thermal management parameter is one, each set of intermediate control quantity parameters includes one intermediate control quantity parameter.

[0073] For the case where the control quantity corresponding to the controlled thermal management parameter is multiple, each set of intermediate control quantity parameters includes multiple intermediate control quantity parameters of different control quantities.

[0074] The control quantity parameter is a value of the corresponding control quantity, and the control quantity is changed to reach the corresponding control quantity parameter, so as to control the controlled thermal management parameter to reach the control target.

[0075] Specifically, the physical prediction model outputs a predicted target of the controlled thermal management parameter at a preset time according to the input state data and one or more control quantity parameters, and outputs a plurality of intermediate control quantity parameters within the preset time. The control quantity parameter input into the physical prediction model is a value of the control quantity when the physical prediction model reaches the predicted target after the preset time, and the intermediate control quantity parameter is a plurality of values of the control quantity before reaching the control quantity parameter. When the output control quantity parameter is selected, the physical prediction model outputs a plurality of intermediate control quantity parameters of the control quantity reaching the output control quantity parameter.

[0076] For example, the controlled thermal management parameter is the battery temperature, and the target temperature, i.e., the control target, is 26 degrees. The control quantity is the compressor power, and the control quantity parameter is assumed to be 1000w. After prediction by the physical prediction model, the battery temperature is 34 degrees after 30 seconds when the compressor power is 1000w. The control quantity parameter input into the physical prediction model is 1000w, and the corresponding predicted target is 34 degrees. At the same time, the physical prediction model also outputs the control quantity parameter of each second within 30 seconds as the intermediate control quantity parameter.

[0077] By inputting a plurality of control quantity parameters into the physical prediction model, a plurality of predicted targets are obtained. By comparing the predicted targets, the predicted target closest to the control target corresponding to the controlled thermal management parameter is selected as the output predicted target of the controlled thermal management parameter. A set of control quantity parameters obtaining the output predicted target is obtained as the output control quantity parameter of the controlled thermal management parameter. At the same time, the intermediate control quantity parameter of the output control quantity parameter output by the physical prediction model is also obtained.

[0078] The output control quantity parameter and the intermediate control quantity parameter are output to the main thread. As in the foregoing example, the main thread obtains the control quantity parameter of each second within 30 seconds, and the control quantity parameter at 30 seconds, i.e., the control quantity parameter input into the physical prediction model, is 1000w. The main thread controls the controlled thermal management parameter corresponding to the control quantity parameter according to the control quantity parameter of each second, i.e., controls the component corresponding to the controlled thermal management parameter. In this example, the main thread controls the compressor power to be the obtained control quantity parameter every second, and controls the compressor power to be 1000w at 30 seconds, so that the battery temperature reaches 34 degrees after 30 seconds.

[0079] In some embodiments of the present disclosure / application, the output control quantity parameters of each of the controlled thermal management parameters are determined by comparing the predicted target output predicted by the physical prediction model with the control target, and then the output control quantity parameters are combined into an output control quantity sequence, and the vehicle is controlled based on the output control quantity sequence, so that the vehicle can be accurately controlled to achieve the control target, the function of automatic thermal management control is realized, and a large number of calibration experiments are avoided in advance, and the calibration workload is reduced.

[0080] As Figure 2 Fig. 6 shows a workflow diagram of a vehicle thermal management control method according to another embodiment of the present application, which comprises the following steps:

[0081] In step S210, the current working condition state of the vehicle is obtained.

[0082] In step S220, the state data of the current working condition state and one or more control targets are obtained, the control target being a target expected to be achieved by the controlled thermal management parameter under the current working condition.

[0083] In step S230, the output control quantity parameters of each of the controlled thermal management parameters are iteratively solved in each period with a preset time as a period, and one or more groups of the output control quantity parameters are taken as an output control quantity sequence, and in each iteration process, steps S231 to S233 are performed.

[0084] In step S231, a plurality of groups of control quantity parameters corresponding to the controlled thermal management parameters are generated, and each group of the control quantity parameters comprises a control quantity parameter of one control quantity or control quantity parameters of a plurality of control quantities.

[0085] In one embodiment, the generation of the plurality of groups of control quantity parameters corresponding to the controlled thermal management parameters specifically comprises:

[0086] In the initial iteration process, a plurality of control quantity parameters of each control quantity corresponding to the controlled thermal management parameters are obtained by scattering, and a plurality of groups of control quantity parameters are generated.

[0087] In the subsequent iteration process after the initial iteration process, a plurality of control quantity parameters of each control quantity corresponding to the controlled thermal management parameters are obtained by scattering based on a preset sequence, and a plurality of groups of control quantity parameters are generated.

[0088] In one embodiment, the scattering of the plurality of control quantity parameters of each control quantity specifically comprises:

[0089] For each control quantity, the maximum granularity, the upper limit of scattering and the lower limit of scattering of the control quantity are obtained.

[0090] The maximum granularity is increased from the lower limit of the scattering point to obtain a plurality of control quantity parameters within the range defined by the lower limit of the scattering point and the upper limit of the scattering point.

[0091] In one embodiment, the plurality of control quantity parameters of each control quantity based on the preset series of scattering points specifically includes:

[0092] For each control quantity, the control quantity parameter of the control quantity in the output control quantity parameter of the last iteration process is used as a basic control quantity parameter, and the plurality of control quantity parameters of the control quantity are obtained based on the basic control quantity and the preset series of scattering points.

[0093] In one embodiment, the plurality of control quantity parameters of each control quantity based on the preset series of scattering points specifically includes:

[0094] The minimum granularity, the upper limit of the scattering point, and the lower limit of the scattering point of the control quantity are obtained.

[0095] The basic control quantity is increased or decreased by the product of the minimum granularity and the preset series within the range defined by the lower limit of the scattering point and the upper limit of the scattering point to obtain the plurality of control quantity parameters.

[0096] In one embodiment, when the control quantity corresponding to the controlled thermal management parameter is multiple, the plurality of sets of control quantity parameters are generated, specifically including:

[0097] The plurality of control quantity parameters of the plurality of control quantities are combined to obtain a plurality of sets of control quantity parameters, and each set of control quantity parameters includes the control quantity parameters of the plurality of control quantities.

[0098] In step S232, the state data and the plurality of sets of control quantity parameters of the current iteration process are input into a physical prediction model to obtain a plurality of prediction targets, each of which is obtained by a set of control quantity parameters and the state data based on the physical prediction model.

[0099] In step S233, the prediction target closest to the control target is selected as the output prediction target of the controlled thermal management parameter in the current iteration process, and the set of control quantity parameters obtained by the output prediction target is used as the output control quantity parameter of the controlled thermal management parameter in the current iteration process.

[0100] In step S240, the vehicle is controlled by using the output control quantity sequence.

[0101] Specifically, in step S210, the current working condition of the vehicle is first acquired. Then, in step S220, state data about the current working condition and one or more control targets are determined according to the current working condition, the control targets being targets expected to be reached by the controlled thermal management parameters in the current working condition.

[0102] The electronic device can create a main thread, and a working condition determination module of the main thread can update the current working condition once every preset time interval according to actual vehicle conditions.

[0103] Then, every preset time interval, the output prediction target is iteratively solved for each of the controlled thermal management parameters in each time interval.

[0104] In some embodiments, every preset time interval, the sub-thread acquires the state data and the control targets from the main thread, and in the time interval, the solver iteratively solves the output prediction target and the corresponding output control parameter for each of the control targets.

[0105] Specifically, at the beginning of the time interval, the sub-thread acquires the state data and the control targets from the main thread. Then, in the sub-thread, the solver calls the physical prediction model, inputs the state data and multiple sets of control parameters into the physical prediction model, obtains multiple prediction targets, and then selects the prediction target closest to the control target as the output prediction target. The set of control parameters corresponding to the output prediction target is taken as the output control parameter. At the same time, the solver obtains the intermediate control parameter corresponding to the output control parameter from the physical prediction model. Then, every preset time interval, for example, 1 second, the intermediate control parameter or the output control parameter is output to the main thread. Then, at the beginning of the next time interval, the sub-thread again acquires the state data and the control targets from the main thread. That is, every time interval, the sub-thread only acquires the state data and the control targets from the main thread once.

[0106] The solver iteratively solves the output prediction target of each controlled thermal management parameter. The end condition of the iterative solution is that the difference between the output prediction target and the control target is within a preset difference range, or the number of iterations reaches a number threshold, or the output prediction targets obtained in multiple iterations are consistent. When the end condition of the iterative solution is met, the iteration is ended. The output prediction target at the end of the iteration is the output prediction target of the iterative solution, and the corresponding control parameter is the output control parameter of the iterative solution.

[0107] In each iteration process, steps S231 to S233 are performed:

[0108] In step S231, multiple sets of control parameters corresponding to the controlled thermal management parameters are generated, each set of control parameters including a control parameter of one control or control parameters of multiple controls.

[0109] In one embodiment, the generating the plurality of sets of the control quantity parameters corresponding to the controlled thermal management parameters comprises:

[0110] In the initial iteration process, for each control quantity corresponding to the controlled thermal management parameters, a plurality of control quantity parameters of each control quantity is obtained by point scattering, and the plurality of sets of control quantity parameters is generated;

[0111] In the subsequent iteration process after the initial iteration process, for each control quantity corresponding to the controlled thermal management parameters, a plurality of control quantity parameters of each control quantity is obtained by point scattering based on a preset sequence, and the plurality of sets of control quantity parameters is generated.

[0112] Specifically, the point scattering is an operation of selecting a plurality of points in a preset range. In the same period, one or more control quantity parameters are generated by point scattering in each iteration process. In the initial iteration process, a plurality of control quantity parameters of the control quantity is obtained by point scattering, for example, a plurality of control quantity parameters of the control quantity is obtained by average point scattering. In the subsequent iteration process in the same period, a plurality of control quantity parameters of each control quantity is obtained by point scattering based on a preset sequence.

[0113] In some embodiments, the obtaining the plurality of control quantity parameters of each control quantity by point scattering based on the preset sequence comprises: obtaining the plurality of control quantity parameters of each control quantity by point scattering based on a Fibonacci sequence.

[0114] The Fibonacci sequence refers to a sequence: 1, 2, 3, 5, 8, 13, … The Fibonacci sequence is defined by a recursive method as follows: F(0) = 0, F(1) = 1, F(n) = F(n-1) + F(n-2) (n≥2, n∈N*), and F(0) and F(1) are ignored. That is, the sequence starting from the second term F(2) is 1, 2, 3, 5, 8, …, ignoring the 0th term F(0) = 0 and the first term F(1) = 1 in the recursive definition.

[0115] The embodiment obtains a plurality of control quantity parameters by point scattering in the initial iteration, and then obtains a plurality of control quantity parameters by point scattering based on a preset sequence, in particular, a Fibonacci sequence, in the subsequent iteration process, which quickly approaches the output solution of the control quantity parameter and does not miss the output solution.

[0116] In one embodiment, the obtaining the plurality of control quantity parameters of each control quantity by point scattering comprises:

[0117] For each control quantity, the maximum granularity of the control quantity, the upper limit of point scattering, and the lower limit of point scattering are obtained;

[0118] Within the range defined by the lower limit and the upper limit of the spraying point, the maximum particle size is increased successively starting from the lower limit of the spraying point to obtain multiple control parameters.

[0119] Specifically, the maximum granularity, upper limit, and lower limit of each control variable can be preset according to the processor's capabilities. Then, the maximum granularity is increased sequentially starting from the lower limit. The calculated control variable parameter cannot exceed the upper limit. If the calculated control variable parameter exceeds the upper limit, the control variable parameter is discarded, and the calculation stops.

[0120] Preferably, the k-th control parameter ctrl_val is calculated during the initial iteration. k =ctrl_min + wmax * (k - 1), where k is a natural number greater than 0, and ctrl_val k is the k-th control parameter in the first iteration, ctrl_min is the lower limit of the control point, and wmax is the maximum granularity.

[0121] This embodiment achieves the application of control quantities to obtain multiple uniform application points within the range defined by the upper and lower limits of the application points.

[0122] In one embodiment, obtaining multiple control quantity parameters for each control quantity based on a preset sequence of points specifically includes:

[0123] For each control quantity, the control quantity parameter of the control quantity in the output control quantity parameter of the previous iteration process is used as the base control quantity parameter, and multiple control quantity parameters of the control quantity are obtained based on the base control quantity and the preset sequence of points.

[0124] Specifically, each iteration yields an output control parameter, which may include the values ​​of one or more control variables. For each control variable, the control variable parameter from the previous iteration's output control parameter serves as the base control parameter. Therefore, in this iteration, the control variable parameter used in the previous iteration to obtain the output prediction target is used as the base control parameter. Then, multiple control parameters for this control variable are obtained by combining the preset data points.

[0125] In this embodiment, the control parameters of the control quantity described in the output control parameters of the previous iteration are used as the base control parameters. While quickly approximating the output solution of the control parameters, the output solution will not be missed.

[0126] In one embodiment, the step of obtaining multiple control quantity parameters of the control quantity based on the basic control quantity and the preset sequence of points specifically includes:

[0127] obtaining a minimum granularity, an upper limit of a spread point and a lower limit of a spread point of the control quantity;

[0128] calculating the basic control quantity increased or decreased by a product of the minimum granularity and a preset sequence to obtain the plurality of control quantity parameters within a range defined by the lower limit of the spread point and the upper limit of the spread point.

[0129] Specifically, the minimum granularity, the upper limit of the spread point and the lower limit of the spread point of each control quantity can be preset according to the processor capability, and then the basic control quantity increased or decreased by a product of the minimum granularity and a selected preset sequence is calculated to obtain a sequence, and values outside the range defined by the lower limit of the spread point and the upper limit of the spread point in the sequence are deleted, and the values after deletion are the control quantity parameters.

[0130] In some embodiments, in the calculation iteration process, the control quantity parameter sequence of a control quantity is ctrl_val=ctrl_last±wmin*fib_seq, where ctrl_val is the control quantity parameter sequence of the current iteration process, ctrl_last is the control quantity parameter of the control quantity in the output control quantity parameter of the last iteration process, wmin is the minimum granularity, and fib_seq is the spread point of the Fibonacci sequence. Then, values greater than the upper limit of the spread point or less than the lower limit of the spread point in the control quantity sequence are deleted, and the values in the sequence after deletion are the control quantity parameters of the control quantity.

[0131] This embodiment realizes the spread point of the control quantity based on the basic control quantity and the preset sequence.

[0132] For the case where the control quantity corresponding to the controlled thermal management parameter is one, each group of control quantity parameters includes one control quantity parameter, that is, in each iteration process, a plurality of control quantity parameters are generated, and each control quantity parameter serves as a group of control quantity parameters.

[0133] For the case where the control quantity corresponding to the controlled thermal management parameter is multiple, each group of control quantity parameters includes control quantity parameters of multiple different control quantities.

[0134] In one of the embodiments, in the case where the control quantity corresponding to the controlled thermal management parameter is multiple, the generating a plurality of groups of control quantity parameters specifically includes:

[0135] combining a plurality of control quantity parameters of a plurality of control quantities to obtain a plurality of groups of control quantity parameters, and each group of control quantity parameters includes the control quantity parameters of the plurality of control quantities.

[0136] Specifically, each control quantity adopts the aforementioned method to generate multiple control quantity parameters. That is, in the initial iteration, the multiple control quantity parameters of each control quantity are obtained by the method of scattering points, and in the subsequent iteration process after the initial iteration process, the multiple control quantity parameters of each control quantity are obtained by the method of scattering points based on the Fibonacci sequence.

[0137] Then, the multiple control quantity parameters of the multiple control quantities of the same controlled thermal management parameter are arranged and combined, each combination being a combination between the control quantity parameters of different control quantities, and each combination being a set of control quantity parameters. In a set of control quantity parameters, the control quantity parameters of the same control quantity are one.

[0138] The embodiment arranges and combines the multiple control quantity parameters to cover all value conditions of all control quantities of the same controlled thermal management parameter, so as to avoid missing the output solution.

[0139] In each solving process, the output prediction target of each controlled thermal management parameter is iteratively solved, and in each iteration process, when the multiple sets of control quantity parameters corresponding to the controlled thermal management parameter are generated, step S232 is performed, the state data and the multiple sets of control quantity parameters in the current iteration process are input into the physical prediction model, and multiple prediction targets are predicted. Each prediction target is obtained by a set of control quantity parameters and the state data based on the physical prediction model.

[0140] Among them, the state data is obtained at the beginning of the period, and then in each iteration process, the new multiple sets of control quantity parameters are input into the physical prediction model to predict multiple prediction targets in the current iteration process. For each controlled thermal management parameter, multiple prediction targets in the current iteration process are generated.

[0141] Then, step S233 is performed, the prediction target closest to the control target is selected as the output prediction target of the controlled thermal management parameter in the current iteration process, and the set of control quantity parameters obtaining the output prediction target is selected as the output control quantity parameter of the controlled thermal management parameter in the current iteration process.

[0142] Among them, each controlled thermal management parameter compares all prediction targets in the current iteration process with the corresponding control target, selects the prediction target closest to the control target as the output prediction target of the controlled thermal management parameter in the current iteration process, and selects the set of control quantity parameters obtaining the output prediction target as the output control quantity parameter of the controlled thermal management parameter in the current iteration process. Then, if the iteration end condition is reached, the iteration is ended, and the output control quantity parameter of each controlled thermal management parameter is output. If the iteration end condition is not reached, steps S231 to S233 are continuously performed.

[0143] Finally, after the iteration is ended, the output control quantity parameter of each said controlled thermal management parameter is obtained, and one or more groups of said output control quantity parameters are taken as an output control quantity sequence. Then, step S240 is performed to control the thermal management of the vehicle by using said output control quantity sequence.

[0144] The embodiment can make the control target quickly approach the target temperature by iteratively solving the output control quantity parameter of each controlled thermal management parameter when the target temperature is not reached, and can continuously accurately approach the output solution by the minimum granularity when the target temperature is reached, so that the target temperature is maintained and overshoot or undershoot problems do not occur.

[0145] As shown in Figure 3 Fig. 1 shows a working flowchart of an application vehicle thermal management control method according to the embodiment, which includes a main thread and a sub-thread of a loop calling a solver, wherein:

[0146] In the main thread:

[0147] Step S301, a working condition determination module updates the current working condition state every second according to the actual vehicle condition;

[0148] Step S302, an initialization module updates the data and parameters required by the solver every second according to the current working condition state;

[0149] Step S303, an output module outputs a control quantity sequence given by the solver every second in order;

[0150] Step S304, the control quantity sequence is outputted to control the actual vehicle.

[0151] In the sub-thread:

[0152] Step S305, the solver obtains the latest data from the initialization module of the main thread every time it is called, calculates the control quantity sequence every second, obtains 30 groups of control quantity sequences, and provides the 30 groups of control quantity sequences to the output module of the main thread.

[0153] The above steps are cyclically executed until the algorithm is exited.

[0154] The function of the solver is to predict the optimal control parameter of each controlled thermal management parameter after a preset time by using a physical prediction model of a thermal management model predictive control (MPC), wherein the optimal control parameter is the output control parameter. The solver also outputs a plurality of intermediate control parameters before the optimal control value. In the 30 control parameter sequences in step S305, the first 29 control parameter sequences are intermediate control parameter sequences including the intermediate control parameters, and the 30th control parameter sequence is an optimal control parameter sequence including the optimal control parameter, i.e., the output control parameter sequence. When there is only one control target, the control parameter sequence includes one control parameter, and when there are multiple control targets, the control parameter sequence includes the control parameters of the controlled thermal management parameters corresponding to each control target.

[0155] In the main thread, each output module outputs a control parameter sequence every second. Since it takes 3 seconds to calculate, the output module does not output a control parameter sequence in the first 3 seconds of the entire program, and after 3 seconds, the output module of the main thread will continue to output a control parameter sequence.

[0156] The optimal control parameter is calculated by iteration. In each iteration, the results generated by different control parameters are predicted, the prediction target is compared with the control target, and finally the control parameter closest to the prediction target and the control target is selected as the optimal control parameter of the controlled thermal management parameter in this second. The iteration calculation of each controlled thermal management parameter is as shown in Figure 4

[0157] In step S401, the maximum granularity (wmax) and the minimum granularity (wmin) of the control parameter of the controlled thermal management parameter are set in each iteration. In the first iteration, the maximum granularity is used for uniform point scattering. In the second iteration and subsequent iterations, the optimal control parameter (ctrl_last) of the last iteration is added or subtracted by the minimum granularity multiplied by the Fibonacci sequence (fib_seq). The specific formula is ctrl_val=ctrl_last±wmin*fib_seq.

[0158] In step S402, the control parameter obtained by the point scattering is input into the physical prediction model for prediction calculation to obtain the prediction target corresponding to the control parameter.

[0159] In step S403, the prediction target corresponding to all control parameters in the current iteration is compared with the control target of the controlled thermal management parameter.

[0160] In step S404, the control parameter closest to the prediction target and the control target is recorded.​

[0161] After the iteration, the optimal control quantity parameter of the controlled thermal management parameter is obtained, the control quantity of all controlled thermal management parameters is calculated in a loop, and finally the optimal control quantity parameters of all controlled thermal management parameters are combined into an optimal control quantity sequence.

[0162] For example, it is illustrated as:

[0163] Suppose the controlled thermal management parameter is the battery temperature, and the target temperature, i.e., the control target, is 26 degrees. The control quantity is the compressor power, the maximum granularity is 1000w, and the minimum granularity is 50w. The current battery temperature is 35 degrees. The lower limit of the control quantity scattering point is 0w, and the upper limit is 8000w.

[0164] 1. The scattering point of the first iteration process is 0, 1000w, 2000w,..., 8000w.

[0165] 2. All control quantity parameters are input into the physical prediction model for prediction. Suppose the control quantity parameter is 0w, then after the physical prediction model is used for prediction, the battery temperature is 36 degrees after 30s when the compressor power is 0w; the temperature is 34 degrees after 30s when the control quantity parameter is 1000w, and the temperature after 30s is closer to the target temperature 26 degrees at this time, so the control quantity 1000w is recorded; …; suppose the final control quantity parameter is 6000w, the predicted battery temperature is closest to the target temperature, so 6000w is the optimal control quantity parameter of the iteration process.

[0166] 3. In the second iteration process and the subsequent iteration process, the solver performs calculation, and the scattering point strategy is changed to ctrl_val1=6000+50*1; ctrl_val2=6000 -50*1; ctrl_val3=6000+50*2; ctrl_val4=6000-50*2; ctrl_val5=6000+50*3; ctrl_val6=6000 -50*3…;

[0167] 4. Then the control quantity parameters are predicted according to step 2 to obtain the optimal control quantity parameter of each iteration process.

[0168] Steps 3-4 are cycled until the iteration end condition is met, and the iteration ends. The solver can obtain the optimal control quantity parameter of the battery temperature after 30s.

[0169] As shown in the schematic diagram of a vehicle thermal management control device according to an embodiment of the present application, comprising: Figure 5 The working condition state acquisition module 501 is used to acquire the current working condition state of the vehicle.

[0170]

[0171] ​The state target acquisition module 502 is configured to acquire state data of the current working condition and one or more control targets, the control target being a target expected to be reached by a controlled thermal management parameter in the current working condition.

[0172] The prediction module 503 is configured to input the state data and multiple sets of control quantity parameters into a physical prediction model respectively, determine an output control quantity sequence based on a comparison between predicted targets output by the physical prediction model and the control target, and output the output control quantity sequence.

[0173] The control module 504 is configured to combine output control quantity parameters of all the controlled thermal management parameters into the output control quantity sequence, and perform thermal management control on the vehicle by using the output control quantity sequence.

[0174] In some embodiments of the present disclosure / application, the output predicted targets are predicted by the physical prediction model, the output control quantity parameters of each of the controlled thermal management parameters are determined based on a comparison between the predicted targets and the control target, the output control quantity parameters are combined into the output control quantity sequence, and the vehicle is controlled based on the output control quantity sequence, so that the vehicle can be accurately controlled to reach each control target, the function of automatic thermal management control is realized, a large amount of calibration experiments in advance is avoided, and the workload of calibration is reduced.

[0175] In one of the embodiments, the inputting of the state data and the multiple sets of control quantity parameters into the physical prediction model, the determination of the output control quantity sequence based on the comparison between the predicted targets output by the physical prediction model and the control target, and the outputting of the output control quantity sequence specifically include:

[0176] In each period, for each of the controlled thermal management parameters, the output control quantity parameters of each of the controlled thermal management parameters are iteratively solved, one or more sets of the output control quantity parameters are taken as the output control quantity sequence, and in each iteration process:

[0177] A plurality of sets of control quantity parameters corresponding to the controlled thermal management parameters are generated, each set of the control quantity parameters including control quantity parameters of one control quantity or control quantity parameters of multiple control quantities.

[0178] The state data and the multiple sets of control quantity parameters in the current iteration process are input into the physical prediction model respectively, a plurality of predicted targets are predicted, each of the predicted targets being predicted based on the state data and one set of control quantity parameters by using the physical prediction model, the predicted target closest to the control target being selected as an output predicted target of the controlled thermal management parameter in the current iteration process, and the set of control quantity parameters from which the output predicted target is obtained being taken as an output control quantity parameter of the controlled thermal management parameter in the current iteration process.

[0179] In one of the embodiments, the generating the multiple groups of the control quantity parameters corresponding to the controlled thermal management parameters comprises:

[0180] In the initial iteration process, for each control quantity corresponding to the controlled thermal management parameters, multiple control quantity parameters of each control quantity are obtained by point scattering, and multiple groups of control quantity parameters are generated;

[0181] In the subsequent iteration process after the initial iteration process, for each control quantity corresponding to the controlled thermal management parameters, multiple control quantity parameters of each control quantity are obtained by point scattering based on a preset sequence, and multiple groups of control quantity parameters are generated.

[0182] In one of the embodiments, the obtaining multiple control quantity parameters of each control quantity by point scattering comprises:

[0183] For each control quantity, a maximum granularity, an upper limit of point scattering and a lower limit of point scattering of the control quantity are obtained;

[0184] Within a range defined by the lower limit of point scattering and the upper limit of point scattering, the maximum granularity is increased successively from the lower limit of point scattering to obtain multiple control quantity parameters.

[0185] In one of the embodiments, the obtaining multiple control quantity parameters of each control quantity by point scattering based on a preset sequence comprises:

[0186] For each control quantity, a control quantity parameter of the control quantity in the output control quantity parameter of the last iteration process is taken as a basic control quantity parameter, and multiple control quantity parameters of the control quantity are obtained by point scattering based on the basic control quantity and a preset sequence.

[0187] In one of the embodiments, the obtaining multiple control quantity parameters of the control quantity by point scattering based on the basic control quantity and a preset sequence comprises:

[0188] A minimum granularity, an upper limit of point scattering and a lower limit of point scattering of the control quantity are obtained;

[0189] Within a range defined by the lower limit of point scattering and the upper limit of point scattering, multiple control quantity parameters are calculated by adding or subtracting a product of the minimum granularity and a preset sequence to the basic control quantity.

[0190] In one of the embodiments, in a case where the control quantity corresponding to the controlled thermal management parameters is multiple, the generating the multiple groups of control quantity parameters comprises:

[0191] Multiple control quantity parameters of multiple control quantities are combined to obtain multiple groups of control quantity parameters, and each group of the control quantity parameters comprises the control quantity parameters of the multiple control quantities.

[0192] AsFigure 6 The diagram shown is a hardware structure schematic of an electronic device according to the present invention, comprising:

[0193] At least one processor 601; and,

[0194] A memory 602 is communicatively connected to at least one of the processors 601; wherein,

[0195] The memory 602 stores instructions that can be executed by at least one of the processors to enable the at least one of the processors to perform the vehicle thermal management control method as described above.

[0196] Figure 6 Take the 601 processor as an example.

[0197] The electronic device may also include an input device 603 and a display device 604.

[0198] The processor 601, memory 602, input device 603 and display device 604 can be connected by a bus or other means. The figure shows an example of connection by a bus.

[0199] The memory 602, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the vehicle thermal management control method in the embodiments of this application, for example, Figure 1 , Figure 2 The method flow is shown. The processor 601 executes various functional applications and data processing by running non-volatile software programs, instructions, and modules stored in the memory 602, thereby realizing the vehicle thermal management control method in the above embodiments.

[0200] The memory 602 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the vehicle thermal management control method, etc. Furthermore, the memory 602 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 602 may optionally include memory remotely located relative to the processor 601, and these remote memories may be connected via a network to the apparatus performing the vehicle thermal management control method. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0201] The input device 603 can receive input user clicks and generate signal inputs related to user settings and function control of the vehicle thermal management control method. The display device 604 can include a display screen or the like display device.

[0202] When the one or more modules are stored in the memory 602, when executed by the one or more processors 601, the vehicle thermal management control method in any of the above method embodiments is performed.

[0203] In some embodiments of the present disclosure / application, the comparison of the predicted target output predicted by the physical prediction model and the control target determines the output control amount parameter of each of the controlled thermal management parameters, and then the output control amount parameters are combined into an output control amount sequence, and the vehicle is controlled based on the output control amount sequence, so that the vehicle can be accurately controlled to achieve each control target, the function of automatic thermal management control is realized, at the same time, a large number of calibration experiments are avoided in advance, and the calibration workload is reduced.

[0204] An embodiment of the present application provides a storage medium storing computer instructions, when the computer executes the computer instructions, all steps of the vehicle thermal management control method as described above are executed.

[0205] An embodiment of the present application provides a vehicle comprising the vehicle thermal management control device as described above, or the electronic device as described above.

[0206] The above-described embodiments only express several embodiments of the present application, which are described in a more specific and detailed manner, but should not be understood as limiting the scope of the present patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of protection of the present application. Therefore, the scope of protection of the present patent should be subject to the appended claims.

Claims

1. A vehicle thermal management control method, characterized in that, include: Obtain the current operating status of the vehicle; Acquire the status data of the current operating condition and one or more control objectives, wherein the control objectives are the expected targets of the controlled thermal management parameters under the current operating condition; The state data and multiple sets of control parameters are input into the physical prediction model. Based on the comparison between the predicted target output by the physical prediction model and the control target, the output control sequence is determined. The output control sequence is used to perform thermal management control on the vehicle; The step of inputting the state data and multiple sets of control parameters into the physical prediction model, and determining the output control sequence based on the comparison between the predicted target output by the physical prediction model and the control target, specifically includes: Using a preset time period as the cycle, within each cycle, for each controlled thermal management parameter, the output control quantity parameter of each controlled thermal management parameter is iteratively solved, and one or more sets of the output control quantity parameters are used as the output control quantity sequence. In each iteration: Generate multiple sets of control parameters corresponding to the controlled thermal management parameters, each set of control parameters including one control parameter or multiple control parameters; The state data and multiple sets of control parameters in this iteration process are input into the physical prediction model to predict multiple prediction targets. Each prediction target is predicted by a set of control parameters and the state data based on the physical prediction model. The prediction target that is closest to the control target is selected as the output prediction target of the controlled thermal management parameter in this iteration process. The set of control parameters that yields the output prediction target is used as the output control parameters of the controlled thermal management parameter in this iteration process. The generation of multiple sets of control parameters corresponding to the controlled thermal management parameters specifically includes: In the initial iteration, for each control quantity corresponding to the controlled thermal management parameter, multiple control quantity parameters for each control quantity are obtained by scattering points, and multiple sets of control quantity parameters are generated. In subsequent iterations following the initial iteration, for each control quantity corresponding to the controlled thermal management parameter, multiple control quantity parameters for each control quantity are obtained based on a preset sequence of points, generating multiple sets of control quantity parameters.

2. The vehicle thermal management control method according to claim 1, characterized in that, The point-spreading process obtains multiple control quantity parameters for each control quantity, specifically including: For each control variable, obtain the maximum granularity, upper limit of the application point, and lower limit of the application point for the control variable; Within the range defined by the lower limit and the upper limit of the spraying point, the maximum particle size is increased successively starting from the lower limit of the spraying point to obtain multiple control parameters.

3. The vehicle thermal management control method according to claim 1, characterized in that, The multiple control quantity parameters for each control quantity obtained based on a preset sequence of points specifically include: For each control quantity, the control quantity parameter of the control quantity in the output control quantity parameter of the previous iteration process is used as the base control quantity parameter, and multiple control quantity parameters of the control quantity are obtained based on the base control quantity and the preset sequence of points.

4. The vehicle thermal management control method according to claim 3, characterized in that, The multiple control quantity parameters obtained based on the basic control quantity and the preset sequence of points specifically include: Obtain the minimum particle size, upper limit of the spraying point, and lower limit of the spraying point for the control quantity; Within the range defined by the lower limit and the upper limit of the spray point, the product of the minimum granularity and the preset sequence is calculated to obtain the multiple control quantity parameters.

5. The vehicle thermal management control method according to claim 1, characterized in that, When there are multiple control quantities corresponding to the controlled thermal management parameters, the generation of multiple sets of control quantity parameters specifically includes: Multiple control parameters of multiple control quantities are combined to obtain multiple sets of control parameters, and each set of control parameters includes the control parameters of multiple control quantities.

6. A vehicle thermal management control device, characterized in that, include: The operating condition acquisition module is used to acquire the current operating condition status of the vehicle. The status target acquisition module is used to acquire the status data of the current operating condition and one or more control targets, wherein the control targets are the expected targets of the controlled thermal management parameters under the current operating condition. The prediction module is used to input the state data and multiple sets of control parameters into the physical prediction model, and determine the output control sequence based on the comparison between the prediction target output by the physical prediction model and the control target. The control module is used to perform thermal management control on the vehicle using the output control quantity sequence; The step of inputting the state data and multiple sets of control parameters into the physical prediction model, and determining the output control sequence based on the comparison between the predicted target output by the physical prediction model and the control target, specifically includes: Using a preset time period as the cycle, within each cycle, for each controlled thermal management parameter, the output control quantity parameter of each controlled thermal management parameter is iteratively solved, and one or more sets of the output control quantity parameters are used as the output control quantity sequence. In each iteration: Generate multiple sets of control parameters corresponding to the controlled thermal management parameters, each set of control parameters including one control parameter or multiple control parameters; The state data and multiple sets of control parameters in this iteration process are input into the physical prediction model to predict multiple prediction targets. Each prediction target is predicted by a set of control parameters and the state data based on the physical prediction model. The prediction target that is closest to the control target is selected as the output prediction target of the controlled thermal management parameter in this iteration process. The set of control parameters that yields the output prediction target is used as the output control parameters of the controlled thermal management parameter in this iteration process. The generation of multiple sets of control parameters corresponding to the controlled thermal management parameters specifically includes: In the initial iteration, for each control quantity corresponding to the controlled thermal management parameter, multiple control quantity parameters for each control quantity are obtained by scattering points, and multiple sets of control quantity parameters are generated. In subsequent iterations following the initial iteration, for each control quantity corresponding to the controlled thermal management parameter, multiple control quantity parameters for each control quantity are obtained based on a preset sequence of points, generating multiple sets of control quantity parameters.

7. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by at least one of the processors, which, when executed by at least one of the processors, enable the at least one of the processors to perform the vehicle thermal management control method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores computer instructions, which, when executed by the computer, are used to perform all the steps of the vehicle thermal management control method as described in any one of claims 1 to 5.

9. A vehicle, characterized in that, This includes the vehicle thermal management control device as described in claim 6, or the electronic device as described in claim 7.

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