Vehicle thermal management control method and device, electronic equipment and vehicle
By using a model predictive controller in the vehicle thermal management system to obtain feedback adjustment quantities and correct the target control quantities, the problem of control target state deviation in the prior art is solved, and the control accuracy and effect of the vehicle thermal management system are improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING CO WHEELS TECH CO LTD
- Filing Date
- 2022-06-13
- Publication Date
- 2026-08-04
AI Technical Summary
Existing vehicle thermal management systems, even under optimal control, are affected by external environment, model accuracy, and device performance errors, leading to deviations between the actual state of the control target and the target state, thus impacting overall vehicle performance.
The target control quantity is determined by using a pre-built model predictive controller, the actual state of the thermal management system is obtained, the feedback adjustment quantity is determined based on the actual state, and the target control quantity of the model predictive controller in the next control time domain is corrected. The control quantity is adjusted through feedback adjustment to reduce the deviation.
The prediction accuracy of the model predictive controller has been improved, the control effect of the thermal management system has been enhanced, and the overall vehicle performance has been guaranteed.
Smart Images
Figure CN117261527B_ABST
Abstract
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) and apply this optimal control quantity to the vehicle, aiming to achieve the target state under its control. However, due to the influence of external environmental factors, model accuracy, and device performance errors, the actual state achieved by the control objective under the optimal control quantity often deviates from the target state. During the system's control process, these errors accumulate, affecting the system's control effectiveness and consequently impacting overall vehicle performance. 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] The target control quantity of the control objective in the current control time domain is determined by using a pre-built model predictive controller;
[0006] After outputting the target control quantity to the thermal management system, the actual state fed back by the thermal management system is obtained; the actual state is the actual state after the thermal management system executes the target control quantity.
[0007] Based on the actual state, determine the feedback adjustment amount;
[0008] Based on the feedback adjustment, the target control quantity of the model predictive controller is corrected in the next control time domain.
[0009] Optionally, the target control quantity of the control target within the current control time domain can be multiple sets;
[0010] Obtaining the actual status feedback from the thermal management system includes:
[0011] Obtain the actual feedback status corresponding to each group of target control variables;
[0012] Based on the actual state, determine the feedback adjustment amount, including:
[0013] Calculate the difference between each actual state and the corresponding target state; wherein, the target state is the state of the thermal management system predicted by the model predictive controller based on the target control quantity;
[0014] The feedback adjustment amount is obtained by integrating each difference within the current control time domain.
[0015] Optionally, based on the feedback adjustment, the target control quantity of the model predictive controller in the next control time domain is corrected, including:
[0016] The target state is adjusted according to the feedback adjustment amount to obtain the adjusted target state;
[0017] Based on the adjusted target state, the model predictive controller is used to determine the target control quantity for the control target in the next control time domain.
[0018] Optionally, based on the actual state, the feedback adjustment amount is determined, including:
[0019] Determine the rate of change of the state of the control target within the current control time domain; wherein, the rate of change of the state is how fast the state of the control target changes within the current time domain;
[0020] Based on the actual state and the rate of change of the state, a preset feedback adjustment table is queried to obtain the feedback adjustment amount corresponding to the actual state and the rate of change of the state.
[0021] Optionally, based on the feedback adjustment, the target control quantity of the model predictive controller in the next control time domain is corrected, including:
[0022] Based on the feedback adjustment amount, the target control amount in the current time domain is adjusted to obtain the adjusted target control amount;
[0023] Based on the adjusted target control quantity, the target control quantity for the next control time domain is determined using the model predictive controller.
[0024] Optionally, determining the rate of change of the state of the control target within the current control time domain includes:
[0025] Within the current control time domain, determine the initial state corresponding to the start time point and the end state corresponding to the end time point;
[0026] Calculate the state change based on the initial and final states;
[0027] The rate of change of state is calculated based on the change in state and the time elapsed from the start time to the end time.
[0028] Optionally, the target control quantity for the control objective within the current control time domain is multiple sets; based on the actual state, the feedback adjustment quantity is determined, including:
[0029] Determine the rate of change of the state of the control target within the current control time domain; wherein, the rate of change of the state is how fast the state of the control target changes within the current time domain;
[0030] In response to the state change rate being greater than or equal to a preset change threshold, a preset feedback adjustment table is queried based on the actual state and the state change rate to obtain the feedback adjustment amount corresponding to the actual state and the state change rate.
[0031] In response to the state change being less than the change threshold, the difference between the actual state of the control target and the corresponding target state for each set of target control quantities is calculated; the difference is integrated within the current control time domain to obtain the feedback adjustment quantity; wherein, the target state is the state of the thermal management system predicted by the model predictive controller based on the target control quantity.
[0032] This application also provides a vehicle thermal management control device, including:
[0033] The control quantity determination module is used to predict the controller using a pre-built model to determine the target control quantity of the control objective in the current control time domain.
[0034] The acquisition module is used to acquire the actual state fed back by the thermal management system after outputting the target control quantity to the thermal management system; the actual state is the actual state after the thermal management system executes the target control quantity.
[0035] The feedback module is used to determine the feedback adjustment amount based on the actual state.
[0036] The correction module is used to correct the target control quantity of the model predictive controller in the next control time domain based on the feedback adjustment amount.
[0037] 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, characterized in that the processor implements the vehicle thermal management control method when executing the program.
[0038] 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.
[0039] This application also provides a vehicle including the aforementioned electronic device.
[0040] As can be seen from the above, the vehicle thermal management control method, device, electronic equipment, and vehicle provided in this application utilize a pre-built model predictive controller to determine the target control quantity of the control objective within the current control time domain; after outputting the target control quantity to the thermal management system, the actual state fed back by the thermal management system is obtained; based on the actual state, a feedback adjustment quantity is determined; and based on the feedback adjustment quantity, the target control quantity of the model predictive controller in the next control time domain is corrected. This application can realize the feedback control adjustment of the model predictive controller to the control objective, improve the prediction accuracy of the model predictive controller through feedback control adjustment, thereby improving the control effect of the vehicle thermal management system and ensuring the overall vehicle performance. Attached Figure Description
[0041] Figure 1 This is a schematic diagram of the method flow of an embodiment of this application;
[0042] Figure 2 This is a schematic diagram of a feedback control process according to an embodiment of this application;
[0043] Figure 3 This is a schematic diagram of the feedback control process according to another embodiment of this application;
[0044] Figure 4 This is a block diagram of the device structure of one or more embodiments of this specification;
[0045] Figure 5 This is a block diagram of an electronic device structure for one or more embodiments of this specification. Detailed Implementation
[0046] 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.
[0047] 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.
[0048] 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.
[0049] like Figure 1 As shown in the figure, this application provides a vehicle thermal management control method, including:
[0050] S101: Use a pre-built model predictive controller to determine the target control quantity of the control objective in the current control time domain;
[0051] In this embodiment, the model predictive controller is based on a predictive model built upon the thermal management system. Under specific operating conditions of the vehicle, it determines the control objectives and target states of the vehicle's thermal management system. The control objectives are the controlled objects within the thermal management system. 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 control objectives differ depending on the vehicle's operating conditions, and the target states for each control objective vary. For instance, under the condition of engine start-up and passenger compartment air conditioning on, the control objectives include battery temperature, energy consumption, and safety thresholds. The target states for each control objective include maintaining the battery temperature within a certain range, ensuring energy consumption does not exceed a certain energy consumption threshold, and requiring heating and actuation components to operate within safety thresholds. Given the complexity and diversity of vehicle operating conditions, this embodiment is only illustrative and does not provide a detailed enumeration or explanation of the principles involved.
[0052] After determining the control objective and its corresponding target state, based on the iteratively selected control variable and the current state of the control objective, a pre-built model predictive controller outputs a predicted state in the control time domain. Under a specific control variable, the predicted state output by the model predictive controller can achieve the target state. This specific control variable is the target control variable determined by the model predictive controller in the current control time domain, and the state predicted by the model predictive controller based on the target control variable is the target state of the control objective.
[0053] S102: After outputting the target control quantity to the thermal management system, obtain the actual state fed back by the thermal management system; the actual state is the actual state after the thermal management system executes the target control quantity.
[0054] In this embodiment, after determining the target control quantity, the target control quantity is output to the thermal management system. The thermal management system controls the various components to work together based on the target control quantity. Under the control of the target control quantity, the actual state of the control target feedback of the thermal management system is obtained. For example, under the control of the target control quantity, the actual temperature value of the battery is obtained from the battery system, and the actual temperature value of the water inlet is obtained from the temperature sensor of the heater.
[0055] S103: Determine the feedback adjustment amount based on the actual situation;
[0056] S104: Based on the feedback adjustment, the correction model predicts the target control quantity of the controller in the next control time domain.
[0057] In this embodiment, ideally, under the control of the target control quantity, the actual state fed back by the thermal management system should reach the target state. However, due to the influence of factors such as the external environment, component performance, and model accuracy, after the thermal management system executes actions based on the target control quantity, the actual state of the control target deviates from the expected target state, i.e., there is an overshoot or misalignment problem. If no adjustment is made, the deviation will continue to accumulate. Therefore, after outputting the target control quantity of the current control time domain to the thermal management system, the actual state of the control target output by the thermal management system is acquired in real time, and the control quantity of the model predictive controller in the next control time domain is corrected based on the actual state. During the prediction process of the model predictive controller, the target control quantity in the next control time domain is continuously corrected based on the actual state, so that the actual state output by the thermal management system continuously approaches the target state. The target control quantity after feedback correction acts on the thermal management system, which can improve the control effect of the thermal management system and meet the thermal management requirements of the vehicle.
[0058] In some embodiments, the target control quantity of the control objective within the current control time domain is multiple sets; obtaining the actual state of the control objective includes:
[0059] Obtain the actual feedback status corresponding to each group of target control variables;
[0060] Based on the actual situation, determine the feedback adjustment amount, including:
[0061] Calculate the difference between each actual state and the corresponding target state; wherein, the target state is the state of the thermal management system predicted by the model predictive controller based on the target control quantity;
[0062] Integrate each difference within the current control time domain to obtain the feedback adjustment.
[0063] In this embodiment, integral regulation is used to determine the feedback adjustment amount. The model predictive controller determines multiple sets of target control quantities within the current control time domain, each set corresponding to a target state. After outputting each set of target control quantities to the thermal management system, the actual state obtained from the thermal management system under that set of target control quantities is acquired. Thus, within the current control time domain, multiple sets of target control quantities and their corresponding target and actual states can be obtained. Then, for each time point within the current control time domain, the difference between the target and actual states is calculated, and each difference is integrated within the current control time domain. The resulting integral value is used as the feedback adjustment amount. In this embodiment, after determining the deviation between the actual and target states, integral regulation is used to determine the feedback adjustment amount based on the deviation, and this feedback adjustment amount is used to correct the control quantity in the next control time domain.
[0064] In some embodiments, based on the feedback adjustment, the model predicts the target control quantity of the controller in the next control time domain, including:
[0065] The target state is adjusted based on the feedback adjustment amount to obtain the adjusted target state;
[0066] Based on the adjusted target state, the target control quantity for the control target in the next control time domain is determined using the model predictive controller.
[0067] In this embodiment, after determining the feedback adjustment amount using integral control, the target state of the control objective is adjusted using the feedback adjustment amount, and the adjusted target state is taken as the state that the control objective wants to achieve. After redetermining the target state, the control amount is re-selected iteratively. The re-selected control amount and the current state of the control objective are used as the inputs to the model predictive controller, which outputs the predicted state. During the iteration process, when the selected control amount enables the predicted state output by the model predictive controller to reach the adjusted target state, this control amount is used as the target control amount for the next control time domain, thus completing the feedback correction of the control amount.
[0068] Combination Figure 2 , 3As shown, after determining the control objective and its target state under a specific operating condition, the current state and other states of the control objective are obtained. The solver uses a predetermined algorithm to iteratively select a control quantity based on the current state and other states. The current state, other states, and the selected control quantity are input into the model predictive control quantity. The model predictive controller outputs the predicted state that the current state can reach under the action of this control quantity. This process is repeated until the predicted state output by the model predictive controller can reach the target state of the control objective. The control quantity in this case is taken as the target control quantity and output to the vehicle thermal management system. The thermal management system controls the actions of various components based on the target control quantity. Under the control of the target control quantity, the actual state of the control objective is obtained from the thermal management system in real time. If there is a deviation between the real-time state and the target state, a feedback adjustment quantity needs to be determined. Based on the feedback adjustment quantity, the target control quantity in the next control time domain is determined. The deviation is reduced and the control accuracy is improved through feedback control adjustment. In some methods, the difference between the actual state and the target state corresponding to the target control quantity is calculated, and the difference is integrated. The integral value is used as the feedback adjustment quantity in the current control time domain. This feedback adjustment quantity is used to correct the target state of the control objective, and the target state is redefined. Then, based on the current state of the control objective and the reselected control quantity, the model predictive controller outputs a predicted state until the predicted state reaches the redefined target state. This predicted state is then used as the target control quantity in the next control time domain after feedback correction. Optionally, the solver can use a differential evolution algorithm or a genetic algorithm to determine the selected control quantity; the methods and principles for selecting the control quantity are not specifically explained.
[0069] In some approaches, the model predictive controller is built based on the thermal management system model of the range-extended vehicle. The thermal management system of a range-extended vehicle mainly includes heating components and execution components, such as motors, batteries, heaters, radiators, fans, water pumps, compressors, cooling circulation pipes, etc. The corresponding thermal management system models include motor thermal models, battery thermal models, radiator models, fan models, water pump models, cooling pipe models, etc. This embodiment does not provide a detailed explanation of the system composition and model principles. The components in the thermal management system have coupling relationships. To achieve state prediction of the control target, the parameters required for the model predictive controller to input include not only the current state of the control target but also the current states of other components and the current state of the external environment. For example, if the target state of the control target is to adjust the battery temperature to 20 degrees Celsius, the parameters required for the model predictive controller to input include the current battery temperature, the ambient temperature, the water pump speed, and the heater inlet temperature, etc.
[0070] In some embodiments, the feedback adjustment amount is determined based on the actual state, including:
[0071] Determine the rate of change of the state of the control objective within the current control time domain; where the rate of change is the speed at which the state of the control objective changes within the current time domain.
[0072] Based on the actual state and the rate of change of state, the preset feedback adjustment table is consulted to obtain the feedback adjustment amount corresponding to the actual state and the rate of change of state.
[0073] In this embodiment, the feedback adjustment amount is determined through a pre-constructed feedback adjustment table. This feedback adjustment table can be determined through experimental calibration, and the table entries include the actual state and the feedback adjustment amount corresponding to the state change rate within a control time domain. Therefore, during feedback correction, the actual state is acquired, and the state change rate within the current control time domain is determined. The feedback adjustment amount is then retrieved based on the actual state and the state change rate to obtain the corresponding feedback adjustment amount. This embodiment determines the feedback adjustment amount based on the state change rate and the actual state, and uses the feedback adjustment amount to perform feedback correction on the control quantity in the next control time domain.
[0074] In some embodiments, determining the rate of change of the control objective within the current control time domain includes:
[0075] Within the current control time domain, determine the initial state corresponding to the start time point and the end state corresponding to the end time point;
[0076] Calculate the state change based on the initial and final states;
[0077] Calculate the rate of change of state based on the change in state and the time elapsed from the start time to the end time.
[0078] This embodiment provides a method for determining the rate of change of state. The model predictive controller can predict the state changes within the current control time domain. Within the current control time domain, each time point corresponds to a state. Based on the initial state at the start time point and the final state at the end time point, the rate of change of state within the current control time domain is calculated. For example, if the control target is battery temperature, the current battery temperature is 10 degrees Celsius, and the current control time domain is 5 seconds, under the action of the target control quantity, in the first second of the current control time domain, the actual battery temperature is 10 degrees Celsius; in the second second, it is 11 degrees Celsius; in the third second, it is 12 degrees Celsius; in the fourth second, it is 13 degrees Celsius; and in the fifth second, it is 14 degrees Celsius. From the first second to the fifth second, the rate of change of battery temperature is 0.8 degrees Celsius per second ((14-10) / 5). After determining the rate of change of temperature, the corresponding feedback adjustment quantity is obtained by querying the feedback adjustment table based on the current actual temperature of 14 degrees Celsius and the rate of change of temperature of 0.8 degrees Celsius per second.
[0079] In some embodiments, based on the feedback adjustment, the model predicts the target control quantity of the controller in the next control time domain, including:
[0080] Based on the feedback adjustment, the target control quantity in the current time domain is adjusted to obtain the adjusted target control quantity;
[0081] Based on the adjusted target control quantity, the target control quantity for the next control time domain is determined using a model predictive controller.
[0082] In this embodiment, after determining the feedback adjustment amount by consulting the feedback adjustment table based on the actual state and the rate of change of state, the target control amount in the current control time domain is adjusted using the feedback adjustment amount to obtain the adjusted target control amount. This adjusted target control amount is then input into the model predictive controller to obtain the target state for the next control time domain. For example, if the control target is the pump speed, and the target control amount in the current control time domain is 1000 revolutions per minute, and the feedback adjustment amount obtained by consulting the table is 5 revolutions per minute, then the adjusted target control amount is 1005 revolutions per minute. Repeating the above feedback correction process makes the prediction results of the model predictive controller more accurate, improving the control effect of the thermal management system.
[0083] In some embodiments, determining the feedback adjustment amount based on the actual state includes:
[0084] Determine the rate of change of the state of the control objective within the current control time domain;
[0085] In response to a state change rate greater than or equal to a preset change threshold, the system queries a preset feedback adjustment table based on the actual state and the state change rate to obtain the feedback adjustment amount corresponding to the actual state and the state change rate.
[0086] In response to a change in state that is less than a threshold, the difference between the actual state of the control target and the corresponding target state for each set of target control variables is calculated; the difference is integrated within the current control time domain to obtain the feedback adjustment.
[0087] In this embodiment, for both integral adjustment and table lookup feedback correction methods, the appropriate feedback correction method can be determined based on the degree of change in the rate of change of the state. When the rate of change of the state is greater than or equal to a certain threshold, the feedback adjustment amount is determined by looking up the feedback adjustment table. When the rate of change of the state is less than the threshold, the feedback adjustment amount is determined by integral adjustment. After determining the feedback adjustment amount, the target state or target control quantity is adjusted according to the corresponding feedback correction method.
[0088] This application provides a vehicle thermal management control method. After determining the target control quantity in the current control time domain, the target control quantity is output to the thermal management system. The actual state of the target object under the target control quantity is acquired in real time. Based on the actual state, a feedback adjustment quantity is determined using an integral adjustment method or a lookup table method. The control quantity in the next control time domain is then corrected based on the feedback adjustment quantity. Through continuous feedback correction during the prediction process of the model predictive controller and the control process of the thermal management system, the prediction accuracy of the model predictive controller can be improved, and the control effect of the thermal management system can be enhanced, thereby meeting the vehicle's thermal management requirements and ensuring the overall vehicle operating efficiency.
[0089] like Figure 4 As shown in the illustration, this application also provides a vehicle thermal management control device, comprising:
[0090] The control quantity determination module 401 is used to determine the target control quantity of the control target in the current control time domain by using a pre-built model predictive controller.
[0091] The acquisition module 402 is used to acquire the actual state fed back by the thermal management system after outputting the target control quantity to the thermal management system; the actual state is the actual state after the thermal management system executes the target control quantity.
[0092] Feedback module 403 is used to determine the feedback adjustment amount based on the actual state;
[0093] The correction module 404 is used to correct the target control quantity of the controller in the next control time domain based on the feedback adjustment.
[0094] 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.
[0095] 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.
[0096] Figure 5 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] 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.).
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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.
[0105] 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.
[0106] 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.
[0107] 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: The pre-built model predicts the controller to determine multiple sets of target control variables for the control objective within the current control time domain; After outputting the target control quantity to the thermal management system, the actual status fed back by the thermal management system is obtained; The actual state is the actual state after the thermal management system executes the target control quantity; Based on the actual state, the feedback adjustment amount is determined, including: determining the rate of change of the state of the control target within the current control time domain; wherein, the rate of change of the state is the speed at which the state of the control target changes within the current control time domain; In response to the state change rate being greater than or equal to a preset change rate threshold, a preset feedback adjustment table is queried based on the actual state and the state change rate to obtain the feedback adjustment amount corresponding to the actual state and the state change rate; In response to the state change rate being less than the change rate threshold, the difference between the actual state and the corresponding target state corresponding to each set of target control quantities is calculated; the difference is integrated within the current control time domain to obtain the feedback adjustment quantity; wherein, the target state is the state of the thermal management system predicted by the model predictive controller based on the target control quantity; Based on the feedback adjustment, the target control quantity of the model predictive controller is corrected in the next control time domain.
2. The method according to claim 1, characterized in that, Obtaining the actual status feedback from the thermal management system includes: Obtain the actual status of the feedback corresponding to each group of target control variables.
3. The method according to claim 1, characterized in that, Based on the feedback adjustment, the target control quantity of the model predictive controller in the next control time domain is corrected, including: The target state is adjusted according to the feedback adjustment amount to obtain the adjusted target state; Based on the adjusted target state, the model predictive controller is used to determine the target control quantity for the control target in the next control time domain.
4. The method according to claim 1, characterized in that, Based on the feedback adjustment, the target control quantity of the model predictive controller in the next control time domain is corrected, including: Based on the feedback adjustment amount, the target control amount in the current control time domain is adjusted to obtain the adjusted target control amount; Based on the adjusted target control quantity, the target control quantity for the next control time domain is determined using the model predictive controller.
5. The method according to claim 1, characterized in that, Determining the rate of change of the state of the control target within the current control time domain includes: Within the current control time domain, determine the initial state corresponding to the start time point and the end state corresponding to the end time point; Calculate the state change based on the initial and final states; The rate of change of state is calculated based on the change in state and the time elapsed from the start time to the end time.
6. A vehicle thermal management control device, characterized in that, include: The control quantity determination module is used to predict the controller using a pre-built model to determine multiple sets of target control quantities for the control objective within the current control time domain; The acquisition module is used to acquire the actual status fed back by the thermal management system after outputting the target control quantity to the thermal management system; The actual state is the actual state after the thermal management system executes the target control quantity; A feedback module is used to determine a feedback adjustment amount based on the actual state, including: determining the rate of change of the state of the control target within the current control time domain; wherein the rate of change is the speed at which the state of the control target changes within the current control time domain; in response to the rate of change being greater than or equal to a preset rate of change threshold, querying a preset feedback adjustment table based on the actual state and the rate of change to obtain the feedback adjustment amount corresponding to the actual state and the rate of change; in response to the rate of change being less than the rate of change threshold, calculating the difference between the actual state and the corresponding target state corresponding to each set of target control quantities; integrating each difference within the current control time domain to obtain the feedback adjustment amount; wherein the target state is the state of the thermal management system predicted by the model predictive controller based on the target control quantity; The correction module is used to correct the target control quantity of the model predictive controller in the next control time domain based on the feedback adjustment amount.
7. 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 5.
8. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions, which are used to cause the computer to perform the method according to any one of claims 1 to 5. 。 9. A vehicle, characterized in that, Including the electronic device as described in claim 7.