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

Abstract

The application provides a vehicle thermal management control method and device, electronic equipment and vehicle, comprising: obtaining the running condition of the vehicle; determining the control target, the target state and the current state of the control target according to the running condition; if the difference between the current state and the target state is greater than the adjustment threshold, determining the control time domain required for adjusting the current state to the target state and the target state of each control time domain according to the difference; and determining the state change curve of each control time domain; for each control time domain, determining the predicted state of the control target by using the model predictive controller according to the current state of the control target and the iteratively selected control amount; when the predicted state matches the state change curve, the corresponding control amount is taken as the target control amount. By adjusting the target state of the control target to the state change curve and determining the appropriate target control amount based on the state change curve, the thermal management system can be gradually adjusted to the optimal state to meet the thermal management requirements.

Description

Technical Field

[0001] This application relates to the field of new energy vehicle technology, and in particular to a vehicle thermal management control method, device, electronic equipment, and vehicle. Background Technology

[0002] In the field of new energy vehicles, the performance of the vehicle's thermal management system directly affects the vehicle's range and overall operating performance. Existing vehicle thermal management systems generally utilize model predictive controllers to determine the optimal control quantity for a specific control objective (e.g., battery temperature) within the control time domain. This optimal control quantity is then applied to the vehicle, aiming to achieve the preset target state under its control. However, in some cases, the difference between the current state and the target state is too large, making it difficult to adjust the battery temperature to the target state within the control time domain. For example, in a certain environment, the current battery temperature might be 5 degrees Celsius, while the target temperature is 20 degrees Celsius. Due to the significant difference between the current and target temperatures, it is difficult to adjust the battery temperature to the target temperature within a single control time domain. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a vehicle thermal management control method, device, electronic equipment and vehicle.

[0004] To achieve the above objectives, this application provides a vehicle thermal management control method, comprising:

[0005] Obtain the vehicle's operating status;

[0006] Based on the operating conditions, determine the control target, the target state of the control target, and the current state of the control target;

[0007] In response to a difference between the current state and the target state being greater than a preset adjustment threshold, multiple control time domains required to adjust the current state to the target state, and the target state of each control time domain, are determined based on the difference.

[0008] Based on the current state and the target state of each control time domain, a state change curve for each control time domain is determined according to a preset state change rate; the state change curve is a curve formed by the state at each time point within the control time domain.

[0009] Based on the target state of each control time domain, for each control time domain, a control quantity is iteratively selected according to a predetermined algorithm. Based on the current state of the control target and the selected control quantity, the predicted state of the control target is determined using a pre-built model predictive controller. When the predicted state matches the state change curve, the corresponding control quantity is taken as the target control quantity.

[0010] Optionally, based on the current state and the target state of each control time domain, and according to a preset state change rate, a state change curve for each control time domain is determined, including:

[0011] From the current state to the target state in the control time domain, the state at each time point in the control time domain is determined according to the state change rate.

[0012] The state change curve is formed based on the state at all points in time.

[0013] Optionally, when the predicted state matches the state change curve, the corresponding control variable is used as the target control variable, including:

[0014] Calculate the state difference between the predicted state at each time point in the control time domain and the state at the corresponding time point on the state change curve;

[0015] The control quantity corresponding to the minimum sum of state differences at each time point is taken as the target control quantity.

[0016] Optionally, after determining the target state for each control time domain, the following steps are also included:

[0017] The state change rate is determined based on the operating conditions, and the state change rate is a preset change rate based on different operating conditions.

[0018] Optionally, there may be multiple control objectives, each with a different priority.

[0019] In response to a difference between the current state and the target state exceeding a preset adjustment threshold, multiple control time domains required to adjust the current state to the target state are determined based on the difference, and a target state for each control time domain, including:

[0020] In response to a difference between the current state of a high-priority control target and its corresponding target state being greater than the adjustment threshold, multiple control time domains for adjusting the current state of the high-priority control target to the corresponding target state are determined based on the difference, and the target state of each control time domain is determined.

[0021] Optionally, there may be multiple high-priority control targets, and each control target may have a different weight value.

[0022] When the predicted state matches the state change curve, the corresponding control variable is used as the target control variable, including:

[0023] When the predicted state of each high-priority control objective matches the corresponding state change curve, the corresponding control quantity is used as the reference control quantity for the corresponding control objective.

[0024] The target control quantity is determined based on the reference control quantity and corresponding weight value of each high-priority control objective.

[0025] This application also provides a vehicle thermal management control device, including:

[0026] The acquisition module is used to acquire the vehicle's operating conditions;

[0027] The target determination module is used to determine the control target, the target state, and the current state of the control target based on the operating conditions.

[0028] The control time domain determination module is used to determine, in response to the difference between the current state and the target state being greater than a preset adjustment threshold, multiple control time domains required to adjust the current state to the target state, and the target state of each control time domain, based on the difference.

[0029] The curve determination module is used to determine the state change curve for each control time domain according to the current state and the target state for each control time domain, and according to a preset state change rate; the state change curve is the curve formed by the state at each time point in the control time domain.

[0030] The control quantity determination module is used to iteratively select a control quantity according to a predetermined algorithm for each control time domain based on the target state of each control time domain. Based on the current state of the control target and the selected control quantity, the module uses a pre-built model predictive controller to determine the predicted state of the control target. When the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity.

[0031] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle thermal management control method described above.

[0032] This application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the vehicle thermal management control method.

[0033] This application also provides a vehicle including the aforementioned electronic device.

[0034] As can be seen from the above, the vehicle thermal management control method, device, electronic equipment, and vehicle provided in this application, after determining the control target and the target state and current state of the control target under the operating conditions, when it is determined that the difference between the current state and the target state exceeds the adjustment threshold, determines multiple control time domains required to adjust the current state to the target state and the target state of each control time domain based on the difference. Based on the current state and the target state of each control time domain, a state change curve for each control time domain is determined according to a preset state change rate. For each control time domain, based on the current state of the control target and the iteratively selected control quantity, a model predictive controller is used to determine the predicted state of the control target. When the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity. This application, combined with the actual use of vehicles, divides a control target in the prior art into multiple smaller control targets and generates a set of control quantities for each smaller control target, which can control the thermal management system to gradually adjust to the optimal state to meet thermal management requirements. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the method flow of an embodiment of this application;

[0036] Figure 2 This is a block diagram of the device structure of one or more embodiments of this specification;

[0037] Figure 3 This is a block diagram of an electronic device structure for one or more embodiments of this specification. Detailed Implementation

[0038] To better understand the above-mentioned objectives, features, and advantages of this application, the solution of this application will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0039] Many specific details are set forth in the following description in order to provide a full understanding of this application, but this application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of this application, and not all embodiments.

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0041] like Figure 1 As shown in the figure, this application provides a vehicle thermal management control method, including:

[0042] S101: Obtain the vehicle's operating status;

[0043] In this embodiment, the vehicle's operating conditions are first obtained through the vehicle controller or sensors. For example, the engine starting condition is determined based on the engine's starting status, the battery charging condition is determined based on the battery's charging status, and the operating conditions of the passenger compartment air conditioning being on when the engine is not running, or the operating conditions of the engine and air conditioning being on simultaneously, are determined based on the engine starting status and the air conditioning starting status, etc. There are various types of vehicle operating conditions, and this embodiment will not provide examples for each one.

[0044] S102: Determine the control objective, the target state of the control objective, and the current state of the control objective based on the operating conditions;

[0045] In this embodiment, based on the acquired operating conditions, the control objectives to be adjusted and the target states of the control objectives are determined. Simultaneously, the current state of the control objectives is acquired through the vehicle controller or specific sensors. The control objective is the object being controlled within the thermal management system, and the target state of the control objective is the state that the object is to achieve. For example, control objectives include battery temperature, the inlet and / or outlet temperatures of the water heater, engine temperature, energy consumption, and safety thresholds for heating and actuation components. The target states of the control objectives include maintaining the battery temperature within a certain temperature range, ensuring energy consumption does not exceed a certain energy consumption threshold, and requiring the heating and actuation components to operate within safety thresholds.

[0046] In some methods, different operating conditions correspond to different control objectives and target states. For example, when the engine is running, the control objectives include battery temperature, engine temperature, and heater inlet temperature. When the engine is running and the passenger compartment air conditioning is on, the control objectives include battery temperature, engine temperature, heater inlet temperature, energy consumption, and heater safety threshold. The specific control objectives and target states corresponding to each operating condition will not be listed one by one.

[0047] S103: In response to the difference between the current state and the target state being greater than a preset adjustment threshold, determine multiple control time domains required to adjust the current state to the target state, and the target state of each control time domain, based on the difference.

[0048] Under different operating conditions, the target state of the control objective is generally an ideal state that meets the thermal management requirements of that condition. However, in some cases, it is difficult to adjust the current state of the control objective to the ideal target state within a limited time. For example, when the vehicle is in a low-temperature environment and the battery is warming up, the target state for the battery to warm up is a temperature of 20 degrees Celsius, while the current temperature of the battery is 0 degrees Celsius. Due to the large temperature adjustment range, it is difficult to adjust the current state to the target state by selecting control variables within the control time domain of the predictive model controller (e.g., 120 seconds).

[0049] In this embodiment, after determining the current state and the target state of the control target, the difference between the two is calculated, and it is determined whether the difference is greater than an adjustment threshold. That is, it is determined whether the current state can be adjusted to the target state within a control time domain. When it is determined that the difference is greater than the adjustment threshold, the current state cannot be adjusted to the target state within a control time domain. It is necessary to determine multiple control time domains required to adjust to the target state, as well as the target state to be achieved in each control time domain, based on the difference.

[0050] S104: Based on the current state and the target state of each control time domain, determine the state change curve for each control time domain according to the preset state change rate; the state change curve is the curve formed by the state at each time point within the control time domain.

[0051] In this embodiment, after determining multiple control time domains and the target state of each control time domain, a state change curve for each control time domain is determined based on the real-time acquired current state of the control target and the target state of each control time domain. This can be understood as follows: to adjust the current state of the control target to the target state, the state needs to be adjusted according to the state change curves of multiple control time domains. That is, considering the actual situation of the vehicle, the target state of the control target is readjusted into a state change curve, and the model predictive controller selects the target control quantity that can be adjusted according to the target change curve.

[0052] S105: Based on the target state of each control time domain, for each control time domain, the control quantity is iteratively selected according to a predetermined algorithm. Based on the current state of the control target and the selected control quantity, the predicted state of the control target is determined using a pre-built model predictive controller. When the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity.

[0053] In this embodiment, after determining the state change curve for each control time domain, the target control quantity for each control time domain can be determined based on the state change curve. According to the current state of the control target and the iteratively selected control quantity, the model predictive controller is used to determine the predicted state of the control target. When the predicted state matches the state change curve under a specific control quantity, that control quantity is the determined target control quantity.

[0054] In some embodiments, based on the current state and the target state of each control time domain, a state change curve for each control time domain is determined according to a preset state change rate, including:

[0055] Starting from the current state and moving towards the target state in this control time domain, determine the state at each point in time within this control time domain according to the rate of change of state.

[0056] A state change curve is generated based on the state at all time points.

[0057] In this embodiment, the method for determining a state change curve in the control time domain is to determine the state at each time point in the control time domain from the current state acquired in real time to the target state in the control time domain according to the state change rate, and then form a state change curve from the current state to the target state in the control time domain based on the states at all time points. For example, if the target battery temperature is 10 degrees Celsius, the current temperature is 0 degrees Celsius, the rate of change of battery temperature is 1 degree Celsius, one control time domain is 5 seconds, and the control step size is 1 second, then on the state change curve of the first control time domain, the temperature corresponding to the 1st second is 0 degrees Celsius, the temperature corresponding to the 2nd second is 1 degree Celsius, the temperature corresponding to the 3rd second is 2 degrees Celsius, the temperature corresponding to the 4th second is 3 degrees Celsius, and the temperature corresponding to the 5th second is 4 degrees Celsius. In the second control time domain, if the current temperature is 4 degrees Celsius, then on the state change curve of the second control time domain, the temperature corresponding to the 1st second is 4 degrees Celsius, the temperature corresponding to the 2nd second is 5 degrees Celsius, the temperature corresponding to the 3rd second is 6 degrees Celsius, the temperature corresponding to the 4th second is 7 degrees Celsius, and the temperature corresponding to the 5th second is 8 degrees Celsius. In the third control time domain, if the current temperature is 8 degrees Celsius, then on the state change curve of the third control time domain, the temperature corresponding to the 1st second is 8 degrees Celsius, the temperature corresponding to the 2nd second is 9 degrees Celsius, the temperature corresponding to the 3rd second is 10 degrees Celsius, the target temperature has been reached, and the temperature corresponding to the 4th and 5th seconds remains at 10 degrees Celsius.

[0058] In some embodiments, after determining the target state for each control time domain, the method further includes: determining the state change rate based on the operating conditions. This state change rate is a preset change rate based on different operating conditions. That is, when determining the state change curve, the degree of slowing down of the state change can be determined according to the vehicle's operating conditions. For example, under engine start-up conditions, the state change rate of battery temperature is the first change rate, and under engine stop-up conditions, the state change rate of battery temperature is the second change rate. In some cases, the state change rate of each control target differs under different ambient temperatures and operating conditions. For different vehicles, the state change rate can be set through experimental calibration based on the vehicle's hardware configuration and performance, combined with external environmental conditions.

[0059] In some embodiments, when the predicted state matches the state change curve, the corresponding control variable is used as the target control variable, including:

[0060] Calculate the state difference between the predicted state at each time point in the control time domain and the state at the corresponding time point on the state change curve;

[0061] The control variable corresponding to the minimum sum of state differences at each time point is taken as the target control variable.

[0062] In this embodiment, after determining a state change curve in the control time domain, when using a model predictive controller to determine the target control quantity, for each control quantity selected iteratively, the model predictive controller outputs a corresponding predicted state. The predicted state at each time point in the control time domain is compared with the state at the corresponding time point on the state change curve, and the state difference between the two is calculated. Then, the state differences at each time point are summed to obtain the sum of the state differences corresponding to the currently selected control quantity. During the iteration of the control quantity, the sum of the state differences corresponding to the previously selected control quantity and the currently selected control quantity is compared. The control quantity with the smaller sum of state differences is retained. When the iteration termination condition is met, the iteration stops, and the control quantity corresponding to the minimum sum of state differences is selected as the final target control quantity. The magnitude of the sum of state differences can evaluate the difference between the predicted state and the corresponding state on the state change curve. The larger the value, the more the predicted state deviates from the corresponding state on the curve; the smaller the value, the closer the predicted state is to the corresponding state on the curve.

[0063] Optionally, multiple sets of control variables can be selected based on differential evolution algorithms or genetic algorithms. The specific algorithm principles for selecting control variables will not be explained in detail.

[0064] In some embodiments, there are multiple control objectives, each with a different priority;

[0065] In response to a difference between the current state and the target state exceeding a preset adjustment threshold, multiple control time domains required to adjust the current state to the target state are determined based on the difference, along with the target state for each control time domain, including:

[0066] In response to a difference between the current state of a high-priority control objective and its corresponding target state that is greater than an adjustment threshold, multiple control time domains are determined based on the difference to adjust the current state of the high-priority control objective to the corresponding target state, and the target state of each control time domain.

[0067] Considering that there may be multiple control objectives under different operating conditions, it can be difficult to determine the target control quantity that can adjust all control objectives to the target state in some cases. In this embodiment, to meet thermal management requirements and ensure user experience, for cases with multiple control objectives, the higher-priority control objectives are prioritized for adjustment to their corresponding target states; that is, the target control quantity for the higher-priority control objectives needs to be determined. Therefore, after obtaining the current state and target state of the higher-priority control objectives, the difference between them is calculated. If the difference is greater than an adjustment threshold, multiple control time domains and the target state for each control time domain are determined based on this difference. Then, the state change curve for each control time domain is determined. The model predictive controller outputs a predicted state based on the current state of the higher-priority control objectives and the iteratively selected control quantity. When the predicted state matches the state change curve, the target control quantity for the higher-priority control objective is determined. By gradually adjusting the state of the higher-priority control objectives to the target state, both user experience and thermal management requirements can be met.

[0068] In some methods, under different operating conditions, the priority of each control objective can be divided into at least two categories, such as high priority and low priority, or first priority, second priority, and so on up to the Nth priority. The method of priority division can be determined according to the usage scenario and actual needs of the thermal management system, and the specific division method is not limited. For multiple control objectives under a specific operating condition, the priorities of each control objective may be different or partially the same. For example, under the heater start-up condition, the control objectives include heater temperature and passenger compartment temperature. The target state for heater temperature is to reach the operating temperature while being less than the maximum temperature threshold, and the target state for passenger compartment temperature is to reach the regulated target temperature. Considering system safety, heater temperature has high priority, while passenger compartment temperature has low priority. The control objectives and their priorities for different operating conditions can be preset.

[0069] In some methods, under specific operating conditions, when there is only one identified control objective, that control objective is the high-priority control objective; when there are multiple identified control objectives with the same priority, all multiple control objectives are high-priority control objectives; when there are multiple identified control objectives with different priorities, the target control quantity is determined by the highest priority control objective reaching the target state, or by the control objectives that reach the target state in descending order of priority. The specific method can be determined based on the specific operating conditions and system requirements, and is not limited in any particular way.

[0070] In some embodiments, there are multiple high-priority control objectives, and each control objective has a different weight value;

[0071] When the predicted state matches the state change curve, the corresponding control variable is used as the target control variable, including:

[0072] When the predicted state of each high-priority control objective matches the corresponding state change curve, the corresponding control quantity is used as the reference control quantity for the corresponding control objective.

[0073] The target control quantity is determined based on the reference control quantity and corresponding weight value of each high-priority control objective.

[0074] In this embodiment, if there are multiple high-priority control objectives with different weight values, multiple control time-domain state change curves are determined for each high-priority control objective. In this case, the predicted state of each control objective can be matched with its corresponding state change curve to determine the target control quantity for each objective. The target control quantity of each high-priority control objective is used as a reference control quantity, and the final target control quantity is determined based on the reference control quantity and corresponding weight value of each objective. The determined target control quantity comprehensively considers the importance of each high-priority control objective, achieving a relatively balanced control effect.

[0075] In other methods, if there are multiple high-priority control objectives with the same weight value, the predicted state of each control objective can be matched with the corresponding state change curve to determine the target control quantity for each control objective. The average control quantity is then determined based on the target control quantities of each control objective, and the average control quantity is used as the final target control quantity.

[0076] In some cases, for high-priority control objectives, during the iterative selection of control variables, when comparing the sum of the state differences corresponding to the previously selected control variable and the currently selected control variable, the sum of the state differences is the same for the two selected control variables. In this case, the control variables for lower-priority control objectives can be further considered to determine the target control variable that integrates all control objectives.

[0077] Specifically, for high-priority control targets, after determining a state change curve in the control time domain, when using a model predictive controller to determine the target control quantity, for each control quantity selected iteratively, the model predictive controller outputs the corresponding predicted state. The predicted state at each time point in the control time domain is compared with the state at the corresponding time point on the state change curve, and the state difference between the two is calculated. Then, the state differences at each time point are added together to obtain the sum of the state differences corresponding to the selected control quantity. Similarly, for low-priority control targets, after determining a state change curve in the control time domain, based on the current state of the low-priority control target and the iteratively selected control quantity, the model predictive controller outputs the corresponding predicted state. The predicted state at each time point in the control time domain is compared with the state at the corresponding time point on the state change curve, and the state difference between the two is calculated. Then, the state differences at each time point are added together to obtain the sum of the state differences corresponding to the selected control quantity. When iterating the control input, high-priority control objectives are prioritized. The sum of the state differences corresponding to the previously selected control input and the currently selected control input is compared, and the control input with the smaller sum of state differences is retained. If the sum of the state differences of the high-priority control objectives corresponding to the two selected control inputs is equal, then low-priority control objectives are considered. The sum of the state differences of the low-priority control objectives corresponding to the two selected control inputs is compared, and the control input with the smaller sum of state differences is retained. Finally, the target control input that comprehensively considers both high-priority and low-priority control objectives is determined. In this way, under the control of the determined target control input, high-priority control objectives can achieve their target states, and low-priority control objectives can also achieve their optimal states, thereby enabling the thermal management system to achieve optimal operating performance.

[0078] In some embodiments, a model predictive controller is constructed based on the thermal management system model of the range-extended vehicle. The vehicle's thermal management system includes an engine, motor, battery, passenger compartment, heater, radiator, compressor, water pump, fan, cooling circulation piping, etc. Under specific operating conditions, some or all components in the thermal management system coordinate with each other to achieve the thermal management requirements of the range-extended vehicle through this coordinated control process. Correspondingly, the thermal management system model includes engine thermal models, motor thermal models, battery thermal models, passenger compartment thermal models, water pump thermal models, heater thermal models, radiator models, compressor thermal models, fan thermal models, cooling piping models, etc. During the coordinated control process of these models, prediction results of the control target can be obtained within a certain timeframe. For example, by inputting the current temperature and torque of the motor, other parameters (e.g., ambient temperature, coolant temperature, heater inlet water temperature, etc.), and selected control variables into the motor thermal model, prediction results such as the predicted motor temperature and the predicted motor outlet water temperature can be obtained.

[0079] In this embodiment, to address the control of the thermal management system of a range-extended vehicle, a model predictive controller is constructed based on the thermal management system model of the range-extended vehicle, following a Model Predictive Control (MPC) framework. After determining the control objective of the range-extended vehicle, the MPC predicts the predicted state of the control objective under different control variables. During the prediction process, a predetermined algorithm iteratively selects different control variables until the MPC, based on the current state of the control objective and the selected control variables, can obtain the predicted state of the state change curve. At this point, the selected control variable is used as the target control variable and applied to the vehicle's thermal management system, enabling the thermal management system to achieve optimal operating efficiency after adjustment according to the target control variable, thus meeting the vehicle's thermal management requirements under the current operating conditions.

[0080] This application provides a vehicle thermal management control method. Under vehicle operating conditions, a corresponding control target, its target state, and its current state are determined. When the difference between the current state and the target state exceeds an adjustment threshold, multiple control time domains required to adjust the current state to the target state, and the target state of each control time domain, are determined based on this difference. Based on the current state and the target state of each control time domain, a state change curve for each control time domain is determined according to a preset state change rate. For each control time domain, a model predictive controller is used to determine the predicted state of the control target based on the current state of the control target and the iteratively selected control quantity. When the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity. This application, combined with actual vehicle usage, adjusts the target state of the control target to a state change curve and determines a suitable target control quantity based on the state change curve. This enables the thermal management system to gradually adjust to its optimal state, meeting the thermal management requirements under different operating conditions.

[0081] like Figure 2 As shown in the illustration, this application also provides a vehicle thermal management control device, comprising:

[0082] The acquisition module 201 is used to acquire the vehicle's operating conditions;

[0083] The target determination module 202 is used to determine the control target and the target state and current state of the control target based on the operating conditions.

[0084] The control time domain determination module 203 is used to determine, in response to the difference between the current state and the target state being greater than a preset adjustment threshold, multiple control time domains required to adjust the current state to the target state, and the target state of each control time domain, based on the difference.

[0085] The curve determination module 204 is used to determine the state change curve of each control time domain according to the current state and the target state of each control time domain, and according to a preset state change rate; the state change curve is the curve formed by the state at each time point in the control time domain.

[0086] The control quantity determination module 205 is used to select a control quantity iteratively according to a predetermined algorithm for each control time domain based on the target state of each control time domain, and to determine the predicted state of the control target using a pre-built model predictive controller based on the current state of the control target and the selected control quantity; when the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity.

[0087] For ease of description, the above apparatus is described in terms of function, divided into various modules. Of course, when implementing one or more embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware.

[0088] The apparatus described above is used to implement the corresponding methods in the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0089] Figure 3 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0090] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0091] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0092] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0093] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0094] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0095] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0096] The electronic devices described above are used to implement the corresponding methods in the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0097] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0098] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0099] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0100] One or more embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A vehicle thermal management control method, characterized in that, include: Obtain the vehicle's operating status; Based on the operating conditions, determine the control target, the target state of the control target, and the current state of the control target; In response to a difference between the current state and the target state being greater than a preset adjustment threshold, multiple control time domains required to adjust the current state to the target state, and the target state of each control time domain, are determined based on the difference. Based on the current state and the target state of each control time domain, a state change curve for each control time domain is determined according to a preset state change rate; the state change curve is a curve formed by the state at each time point within the control time domain. Based on the target state of each control time domain, for each control time domain, a control quantity is iteratively selected according to a predetermined algorithm. Based on the current state of the control target and the selected control quantity, the predicted state of the control target is determined using a pre-built model predictive controller. When the predicted state matches the state change curve, the corresponding control quantity is taken as the target control quantity.

2. The method according to claim 1, characterized in that, Based on the current state and the target state of each control time domain, and according to a preset state change rate, determine the state change curve for each control time domain, including: From the current state to the target state in the control time domain, the state at each time point in the control time domain is determined according to the state change rate. The state change curve is formed based on the state at all points in time.

3. The method according to claim 1, characterized in that, When the predicted state matches the state change curve, the corresponding control variable is used as the target control variable, including: Calculate the state difference between the predicted state at each time point in the control time domain and the state at the corresponding time point on the state change curve; The control quantity corresponding to the minimum sum of state differences at each time point is taken as the target control quantity.

4. The method according to claim 1, characterized in that, After determining the target state for each control time domain, the following is also included: The state change rate is determined based on the operating conditions, and the state change rate is a preset change rate based on different operating conditions.

5. The method according to claim 1, characterized in that, There are multiple control objectives, each with a different priority; In response to a difference between the current state and the target state exceeding a preset adjustment threshold, multiple control time domains required to adjust the current state to the target state are determined based on the difference, and a target state for each control time domain, including: In response to a difference between the current state of a high-priority control target and its corresponding target state being greater than the adjustment threshold, multiple control time domains for adjusting the current state of the high-priority control target to the corresponding target state are determined based on the difference, and the target state of each control time domain is determined.

6. The method according to claim 5, characterized in that, There are multiple high-priority control objectives, and each control objective has a different weight value; When the predicted state matches the state change curve, the corresponding control variable is used as the target control variable, including: When the predicted state of each high-priority control objective matches the corresponding state change curve, the corresponding control quantity is used as the reference control quantity for the corresponding control objective. The target control quantity is determined based on the reference control quantity and corresponding weight value of each high-priority control objective.

7. A vehicle thermal management control device, characterized in that, include: The acquisition module is used to acquire the vehicle's operating conditions; The target determination module is used to determine the control target, the target state, and the current state of the control target based on the operating conditions. The control time domain determination module is used to determine, in response to the difference between the current state and the target state being greater than a preset adjustment threshold, multiple control time domains required to adjust the current state to the target state, and the target state of each control time domain, based on the difference. The curve determination module is used to determine the state change curve for each control time domain according to the current state and the target state for each control time domain, and according to a preset state change rate; the state change curve is the curve formed by the state at each time point in the control time domain. The control quantity determination module is used to iteratively select a control quantity according to a predetermined algorithm for each control time domain based on the target state of each control time domain. Based on the current state of the control target and the selected control quantity, the module uses a pre-built model predictive controller to determine the predicted state of the control target. When the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1 to 6.

10. A vehicle, characterized in that, Including the electronic device as described in claim 8.

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