CN117261527A - Vehicle thermal management control method and device, electronic equipment and vehicle - Google Patents
Vehicle thermal management control method and device, electronic equipment and vehicle Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
- B60H1/00—Heating, cooling or ventilating [HVAC] devices
- B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
- B60H1/0073—Control systems or circuits characterised by particular algorithms or computational models, e.g. fuzzy logic or dynamic models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
- B60H1/00—Heating, cooling or ventilating [HVAC] devices
- B60H1/00271—HVAC devices specially adapted for particular vehicle parts or components and being connected to the vehicle HVAC unit
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
- B60H1/00—Heating, cooling or ventilating [HVAC] devices
- B60H1/00271—HVAC devices specially adapted for particular vehicle parts or components and being connected to the vehicle HVAC unit
- B60H1/00278—HVAC devices specially adapted for particular vehicle parts or components and being connected to the vehicle HVAC unit for the battery
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
- B60H1/00—Heating, cooling or ventilating [HVAC] devices
- B60H1/00357—Air-conditioning arrangements specially adapted for particular vehicles
- B60H1/00385—Air-conditioning arrangements specially adapted for particular vehicles for vehicles having an electrical drive, e.g. hybrid or fuel cell
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Abstract
The application provides a vehicle thermal management control method, a device, electronic equipment and a vehicle, wherein the method comprises the following steps: determining a target control quantity of a control target in a current control time domain by using a pre-constructed model predictive controller; after outputting the target control quantity to a thermal management system, acquiring an actual state fed back by the thermal management system; determining a feedback adjustment amount according to the actual state; and correcting the target control quantity of the model predictive controller in the next control time domain according to the feedback adjustment quantity. The method and the device can realize the feedback control adjustment of the model predictive controller to the control target, and improve the predictive accuracy of the model predictive controller through the feedback control adjustment, so that the control effect of the vehicle thermal management system is improved, and the performance of the whole vehicle is ensured.
Description
Technical Field
The application relates to the technical field of new energy automobiles, in particular to a vehicle thermal management control method and device, electronic equipment and a vehicle.
Background
In the field of new energy automobiles, the performance of a thermal management system of a vehicle is directly related to the cruising ability and the running performance of the whole vehicle. In the existing vehicle thermal management system, an optimal control amount of a specific control target (for example, battery temperature) in the system is determined by using a model predictive controller, and the optimal control amount is applied to a vehicle so that the control target can reach a target state under the control of the optimal control amount. However, the actual state reached by the control target under the control of the optimal control amount is often deviated from the target state due to the influence of error factors such as external environment, model accuracy, device performance and the like. In the control process of the system, the generated deviation is continuously accumulated under the influence of error factors, so that the control effect of the system is influenced, and the performance of the whole vehicle is further influenced.
Disclosure of Invention
In view of the foregoing, an object of the present application is to provide a vehicle thermal management control method, a device, an electronic apparatus, and a vehicle.
Based on the above object, the present application provides a vehicle thermal management control method, including:
determining a target control quantity of a control target in a current control time domain by using a pre-constructed model predictive controller;
after outputting the target control quantity to a thermal management system, acquiring an actual state fed back by the thermal management system; the actual state is an actual state after the thermal management system executes the target control amount;
determining a feedback adjustment amount according to the actual state;
and correcting the target control quantity of the model predictive controller in the next control time domain according to the feedback adjustment quantity.
Optionally, the target control quantity of the control target in the current control time domain is multiple groups;
acquiring the actual state of the thermal management system feedback, including:
respectively acquiring the actual state of feedback corresponding to each group of target control quantity;
determining a feedback adjustment amount according to the actual state, including:
calculating the difference between each actual state and the corresponding target state; wherein the target state is a state of the thermal management system predicted by the model predictive controller based on the target control amount;
and integrating the difference values in the current control time domain to obtain the feedback adjustment quantity.
Optionally, correcting the target control amount of the model predictive controller in the next control time domain according to the feedback adjustment amount includes:
adjusting the target state according to the feedback adjustment quantity to obtain an adjusted target state;
and determining the target control quantity of the control target in the next control time domain by using the model predictive controller according to the adjusted target state.
Optionally, determining the feedback adjustment amount according to the actual state includes:
determining a state change rate of a control target in the current control time domain; wherein the state change rate is the change speed of the state of the control target in the current time domain;
and inquiring a preset feedback regulation table according to the actual state and the state change rate to obtain feedback regulation amounts corresponding to the actual state and the state change rate.
Optionally, correcting the target control amount of the model predictive controller in the next control time domain according to the feedback adjustment amount includes:
according to the feedback adjustment quantity, adjusting the target control quantity in the current time domain to obtain an adjusted target control quantity;
and determining the target control quantity of the next control time domain by using the model predictive controller according to the adjusted target control quantity.
Optionally, determining the state change rate of the control target in the current control time domain includes:
determining a starting state corresponding to a starting time point and an ending state corresponding to an ending time point in the current control time domain;
calculating state change quantity according to the starting state and the ending state;
and calculating the state change rate according to the state change amount and the elapsed time from the starting time point to the ending time point.
Optionally, the target control quantity of the control target in the current control time domain is multiple groups; determining a feedback adjustment amount according to the actual state, including:
determining a state change rate of a control target in the current control time domain; wherein the state change rate is the change speed of the state of the control target in the current time domain;
responding to the state change rate being greater than or equal to a preset change amount threshold, and inquiring a preset feedback regulation table according to the actual state and the state change rate to obtain feedback regulation amounts corresponding to the actual state and the state change rate;
calculating a difference value between an actual state of a control target corresponding to each set of target control amounts and a corresponding target state in response to the state change amount being smaller than the change amount threshold; integrating the difference values in the current control time domain to obtain the feedback adjustment quantity; wherein the target state is a state of the thermal management system predicted by the model predictive controller based on the target control amount.
The application also provides a vehicle thermal management control device, comprising:
the control quantity determining module is used for determining a target control quantity of a control target in the current control time domain by utilizing a pre-constructed model predictive controller;
the acquisition module is used for acquiring 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 an actual state after the thermal management system executes the target control amount;
the feedback module is used for determining a feedback adjustment quantity according to the actual state;
and the correction module is used for correcting the target control quantity of the model predictive controller in the next control time domain according to the feedback adjustment quantity.
The application also provides electronic equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and is characterized in that the processor executes the program to realize the vehicle thermal management control method.
The present 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.
The application also provides a vehicle comprising the electronic equipment.
From the above, it can be seen that the method, the device, the electronic device and the vehicle for controlling thermal management of the vehicle provided by the application determine the target control amount of the control target in the current control time domain by using the pre-constructed model predictive controller; after outputting the target control quantity to the thermal management system, acquiring the actual state fed back by the thermal management system; determining a feedback adjustment amount according to the actual state; and according to the feedback adjustment quantity, correcting the target control quantity of the model predictive controller in the next control time domain. The method and the device can realize the feedback control adjustment of the model predictive controller to the control target, and improve the predictive accuracy of the model predictive controller through the feedback control adjustment, so that the control effect of the vehicle thermal management system is improved, and the performance of the whole vehicle is ensured.
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FIG. 1 is a schematic flow chart of a method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a feedback control process according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a feedback control process according to another embodiment of the present application;
FIG. 4 is a block diagram of an apparatus according to one or more embodiments of the present disclosure;
fig. 5 is a block diagram of an electronic device in accordance with one or more embodiments of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present application may be more clearly understood, a further description of the aspects of the present application will be provided below. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the application.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As shown in fig. 1, an embodiment of the present application provides a vehicle thermal management control method, including:
s101: determining a target control quantity of a control target in a current control time domain by using a pre-constructed model predictive controller;
in this embodiment, the model predictive controller is a predictive model constructed based on the thermal management system, and determines the control target of the thermal management system of the vehicle and the target state of the control target under a specific working condition of the vehicle. Wherein the control target is an object to be controlled in the thermal management system. For example, control targets include battery temperature, water inlet and/or outlet temperatures of the water heater, engine temperature, energy consumption, safety thresholds for heating and implement components, and the like. The vehicle is under different working conditions, the corresponding control targets are different, the target states to be achieved by the control targets are different, for example, under the working conditions of starting and starting an air conditioner of a passenger cabin, the control targets comprise battery temperature, energy consumption, a safety threshold value and the like, the target states of the control targets comprise battery temperature to be kept within a certain temperature range, the energy consumption does not exceed a certain energy consumption threshold value, and the heating component and the executing component are required to operate within the safety threshold value and the like. The working conditions of the vehicle are complex and various, and the embodiment is only exemplary and does not describe in detail and principles.
After determining the control target and the corresponding target state, the controller is predicted by utilizing a pre-constructed model to output a prediction state in a control time domain according to the iteratively selected control quantity and the acquired current state of the control target. Under a specific control quantity, the predicted state output by the model prediction controller can reach a target state, wherein the specific control quantity is the target control quantity determined by the model prediction controller in the current control time domain, and the state predicted by the model prediction controller based on the target control quantity is the target state of the control target.
S102: after outputting the target control quantity to the thermal management system, acquiring 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 amount;
in this embodiment, after the target control amount is determined, the target control amount is output to the thermal management system, and the thermal management system controls each component to work cooperatively based on the target control amount, and under the control of the target control amount, the actual state of the control target feedback of the thermal management system is obtained, for example, the actual temperature value of the battery is obtained from the battery system, the actual temperature value of the water inlet is obtained from the temperature sensor of the heater, and the like.
S103: determining a feedback adjustment amount according to the actual state;
s104: and according to the feedback adjustment quantity, correcting the target control quantity of the model predictive controller in the next control time domain.
In this embodiment, ideally, under the control of the target control amount, the actual state fed back by the thermal management system should reach the target state, however, the actual state of the control target deviates from the target state expected to be reached after the thermal management system performs the action based on the target control amount, i.e. there is an overshoot or an imbalance problem, and if no adjustment is performed, the deviation will be accumulated continuously. Therefore, after the target control quantity of the current control time domain is output 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 subjected to feedback correction according to the actual state. In the prediction process of the model prediction controller, the actual state is combined to continuously perform feedback correction on the target control quantity of the next control time domain, so that the actual state output by the thermal management system is continuously close to the target state, the feedback corrected target control quantity acts on the thermal management system, the control effect of the thermal management system can be improved, and the thermal management requirement of a vehicle is met.
In some embodiments, the target control amounts of the control targets in the current control time domain are multiple groups; acquiring an actual state of a control target, including:
respectively acquiring the actual state of feedback corresponding to each group of target control quantity;
determining a feedback adjustment amount based on the actual state, comprising:
calculating the difference between each actual state and the corresponding target state; wherein the target state is a state of the thermal management system predicted by the model predictive controller based on the target control amount;
and integrating the difference values in the current control time domain to obtain the feedback adjustment quantity.
In this embodiment, the feedback adjustment amount is determined by integrating adjustment. The model predictive controller determines a plurality of groups of target control amounts in the current control time domain, each group of target control amounts corresponds to one target state, and after each group of target control amounts are output to the thermal management system, the actual state obtained through feedback under the group of target control amounts is obtained from the thermal management system. Thus, in the current control time domain, a plurality of sets of target control amounts and target states and actual states corresponding to the respective sets of target control amounts can be obtained. Then, for the target state and the actual state of each time point in the current control time domain, respectively calculating the difference between the target state and the actual state, then integrating each difference in the current control time domain, and taking the obtained integrated value as a feedback adjustment quantity. In this embodiment, after determining the deviation between the actual state and the target state, the feedback adjustment amount is determined according to the deviation by using the integral adjustment method, and the feedback adjustment amount is used to perform feedback correction on the control amount in the next control time domain.
In some embodiments, correcting the target control amount of the model predictive controller in the next control time domain based on the feedback adjustment amount includes:
adjusting the target state according to the feedback adjustment quantity to obtain an adjusted target state;
and determining the target control quantity of the control target in the next control time domain by using the model predictive controller according to the adjusted target state.
In this embodiment, after the feedback adjustment amount is determined by using the integral adjustment method, the target state of the control target is adjusted by using the feedback adjustment amount, and the adjusted target state is used as the state to be reached by the control target. After the target state is redetermined, the control quantity is selected again in an iterating way, the re-selected control quantity and the current state of the control target are taken as the input of the model predictive controller, the model predictive controller outputs the predicted state, and in the iterating process, when the selected control quantity can enable the predicted state output by the model predictive controller to reach the adjusted target state, the control quantity is taken as the target control quantity of the next control time domain, so that the feedback correction of the control quantity is completed.
In combination with the descriptions of fig. 2 and 3, after determining a control target under a specific working condition and a target state of the control target, acquiring a current state and other states of the control target, iteratively selecting control amounts by a solver according to the current state and other states by using a predetermined algorithm, inputting the current state and other states and the currently selected control amounts into a model prediction control amount, outputting a predicted state which can be reached by the current state under the action of the current control amount by the model prediction controller, repeating the above processes until the predicted state output by the model prediction controller can reach the target state of the control target, outputting the target control amount under the condition as the target control amount to a vehicle thermal management system, controlling each component to execute actions by the thermal management system based on the target control amount, and acquiring the actual state of the control target from the thermal management system in real time under the control of the target control amount. If the deviation exists between the real-time state and the target state, the feedback adjustment quantity needs to be determined, the target control quantity in the next control time domain is determined according to the feedback adjustment quantity, the deviation is reduced through feedback control adjustment, and the control precision is improved. In some modes, a difference value between an actual state and a target state corresponding to a target control amount is calculated, the difference value is integrated, the obtained integrated value is used as a feedback adjustment amount of a current control time domain, the feedback adjustment amount is used for correcting the target state of the control target, and the target state is redetermined. And then, according to the current state of the control target and the re-selected control quantity, outputting a predicted state by using the model prediction controller until the predicted state reaches the re-determined target state, wherein the control quantity at the moment is used as the target control quantity of the next control time domain after feedback correction. Alternatively, the solver may determine the selected control amount using a differential evolutionary algorithm or a genetic algorithm, and the method and principle of selecting the control amount are not specifically described.
In some embodiments, the model predictive controller is constructed based on a thermal management system model of an extended range vehicle, where the thermal management system of the extended range vehicle mainly includes heating components and executing components, such as a motor, a battery, a heater, a radiator, a fan, a water pump, a compressor, a cooling circulation line, etc., and the corresponding thermal management system model includes a motor thermal model, a battery thermal model, a radiator model, a fan model, a water pump model, a cooling line model, etc., and the system composition and model are not described in detail in principle in this embodiment. The parameters required to be input by the model pre-side controller comprise parameters such as the current temperature of the battery, the external environment temperature, the water pump rotating speed, the water inlet temperature of the heater and the like.
In some embodiments, determining the feedback adjustment based on the actual state includes:
determining a state change rate of a control target in a current control time domain; wherein, the state change rate is the change speed of the state of the control target in the current time domain;
and inquiring a preset feedback regulation table according to the actual state and the state change rate to obtain a feedback regulation quantity corresponding to the actual state and the state change rate.
In this embodiment, the feedback adjustment amount is determined by a feedback adjustment table constructed in advance. The feedback regulation table can be determined in an experimental calibration mode, and the included table items comprise actual states and feedback regulation amounts corresponding to state change rates in a control time domain. And acquiring an actual state during feedback correction, and determining a state change rate in the current control time domain, so that the feedback adjustment quantity is inquired according to the actual state and the state change rate, and the corresponding feedback adjustment quantity is obtained. The embodiment determines a feedback adjustment amount according to the state change rate and the actual state, and performs feedback correction on the control amount of the next control time domain by using the feedback adjustment amount.
In some embodiments, determining a rate of change of state of a control target in a current control time domain includes:
determining a starting state corresponding to a starting time point and an ending state corresponding to an ending time point in a current control time domain;
calculating a state change amount according to the starting state and the ending state;
the state change rate is calculated from the state change amount and the elapsed time from the start time point to the end time point.
The embodiment provides a method for determining a state change rate, and a model prediction controller can predict a state change condition in a current control time domain, wherein each time point corresponds to one state in the current control time domain, and the state change rate in the current control time domain is calculated according to a starting state of a starting time point and an ending state of an ending time point. For example, the control target is the battery temperature, the current temperature of the battery is 10 degrees, the current control time domain is 5 seconds, the actual temperature of the battery is 10 degrees, 2 seconds, the actual temperature of the battery is 11 degrees, 3 seconds, the actual temperature of the battery is 12 degrees, 4 seconds, the actual temperature of the battery is 13 degrees, 5 seconds, the actual temperature of the battery is 14 degrees, and the temperature change rate of the battery temperature is 0.8 degrees per second ((14-10)/5) from 1 st to 5 seconds in the current control time domain under the effect of the target control amount. After the temperature change rate is determined, a feedback adjustment table is queried every second according to the current actual temperature of 14 degrees and the temperature change rate of 0.8 degrees, and the corresponding feedback adjustment quantity is obtained.
In some embodiments, correcting the target control amount of the model predictive controller in the next control time domain based on the feedback adjustment amount includes:
according to the feedback adjustment quantity, adjusting the target control quantity in the current time domain to obtain an adjusted target control quantity;
and determining the target control quantity of the next control time domain by using the model predictive controller according to the adjusted target control quantity.
In this embodiment, after determining the feedback adjustment amount by querying the feedback adjustment table according to the actual state and the state change rate, the target control amount of the current control time domain is adjusted by using the feedback adjustment amount to obtain an adjusted target control amount, and the adjusted target control amount is input into the model predictive controller to obtain the target state of the next control time domain. For example, the control target is the rotation speed of the water pump, the target control amount of the current control time domain is 1000 revolutions per minute, the feedback adjustment amount obtained by looking up the table is 5 revolutions per minute, and then the adjusted target control amount is 1005 revolutions per minute. The feedback correction process is repeated, so that the prediction result of the model prediction controller is more accurate, and the control effect of the thermal management system is improved.
In some embodiments, determining the feedback adjustment based on the actual state includes:
determining a state change rate of a control target in a current control time domain;
responding to the state change rate being greater than or equal to a preset change amount threshold value, and inquiring a preset feedback regulation table according to the actual state and the state change rate to obtain a feedback regulation amount corresponding to the actual state and the state change rate;
calculating a difference value between an actual state of a control target corresponding to each set of target control amounts and a corresponding target state in response to the state change amount being smaller than the change amount threshold; and integrating the difference values in the current control time domain to obtain the feedback adjustment quantity.
In this embodiment, for two feedback correction modes, i.e., integral adjustment and table lookup, an appropriate feedback correction mode may be determined according to the degree of change of the state change rate. When the state change rate is greater than or equal to a certain change amount threshold, determining a feedback adjustment amount by inquiring a feedback adjustment table, when the state change rate is smaller than the change amount threshold, determining the feedback adjustment amount by an integral mode, and adjusting the target state or the target control amount according to a corresponding feedback correction mode after determining the feedback adjustment amount.
The embodiment of the application provides a vehicle thermal management control method, after determining a target control amount of a current control time domain, the target control amount is output to a thermal management system, an actual state of a target object under the target control amount is obtained in real time, a feedback adjustment amount is determined by utilizing an integral adjustment mode or a table look-up mode according to the actual state, and feedback correction is performed on the control amount of the next control time domain according to the feedback adjustment amount. In the prediction process of the model predictive controller and the control process of the thermal management system, the prediction precision of the model predictive controller can be improved and the control effect of the thermal management system can be improved through continuous feedback correction, so that the thermal management requirement of a vehicle is met, and the running efficiency of the whole vehicle is ensured.
As shown in fig. 4, an embodiment of the present application further provides a vehicle thermal management control device, including:
a control amount determining module 401 for determining a target control amount of a control target in a current control time domain by using a model predictive controller constructed in advance;
an obtaining module 402, configured to obtain an actual state fed back by the thermal management system after outputting the target control amount to the thermal management system; the actual state is the actual state after the thermal management system executes the target control amount;
a feedback module 403, configured to determine a feedback adjustment amount according to the actual state;
and the correction module 404 is used for correcting the target control quantity of the model predictive controller in the next control time domain according to the feedback adjustment quantity.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in one or more pieces of software and/or hardware when implementing one or more embodiments of the present description.
The device of the foregoing embodiment is configured to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Fig. 5 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The electronic device of the foregoing embodiment is configured to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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 technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the application (including the claims) is limited to these examples; combinations of features of the above embodiments or in different embodiments are also possible within the spirit of the application, steps may be implemented in any order, and there are many other variations of the different aspects of one or more embodiments of the application as described above, which are not provided in detail for the sake of brevity.
While the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present application is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalents, improvements and/or the like which are within the spirit and principles of the embodiments are intended to be included within the scope of the present application.
Claims (11)
1. A vehicle thermal management control method characterized by comprising:
determining a target control quantity of a control target in a current control time domain by using a pre-constructed model predictive controller;
after outputting the target control quantity to a thermal management system, acquiring an actual state fed back by the thermal management system; the actual state is an actual state after the thermal management system executes the target control amount;
determining a feedback adjustment amount according to the actual state;
and correcting the target control quantity of the model predictive controller in the next control time domain according to the feedback adjustment quantity.
2. The method according to claim 1, wherein the target control amounts of the control targets in the current control time domain are plural sets;
acquiring the actual state of the thermal management system feedback, including:
respectively acquiring the actual state of feedback corresponding to each group of target control quantity;
determining a feedback adjustment amount according to the actual state, including:
calculating the difference between each actual state and the corresponding target state; wherein the target state is a state of the thermal management system predicted by the model predictive controller based on the target control amount;
and integrating the difference values in the current control time domain to obtain the feedback adjustment quantity.
3. The method according to claim 2, wherein correcting the target control amount of the model predictive controller in the next control time domain based on the feedback adjustment amount includes:
adjusting the target state according to the feedback adjustment quantity to obtain an adjusted target state;
and determining the target control quantity of the control target in the next control time domain by using the model predictive controller according to the adjusted target state.
4. The method of claim 1, wherein determining a feedback adjustment based on the actual state comprises:
determining a state change rate of a control target in the current control time domain; wherein the state change rate is the change speed of the state of the control target in the current time domain;
and inquiring a preset feedback regulation table according to the actual state and the state change rate to obtain feedback regulation amounts corresponding to the actual state and the state change rate.
5. The method according to claim 4, wherein correcting the target control amount of the model predictive controller in the next control time domain based on the feedback adjustment amount includes:
according to the feedback adjustment quantity, adjusting the target control quantity in the current time domain to obtain an adjusted target control quantity;
and determining the target control quantity of the next control time domain by using the model predictive controller according to the adjusted target control quantity.
6. The method of claim 4, wherein determining a rate of change of state of a control target in the current control time domain comprises:
determining a starting state corresponding to a starting time point and an ending state corresponding to an ending time point in the current control time domain;
calculating state change quantity according to the starting state and the ending state;
and calculating the state change rate according to the state change amount and the elapsed time from the starting time point to the ending time point.
7. The method according to claim 1, wherein the target control amounts of the control targets in the current control time domain are plural sets; determining a feedback adjustment amount according to the actual state, including:
determining a state change rate of a control target in the current control time domain; wherein the state change rate is the change speed of the state of the control target in the current time domain;
responding to the state change rate being greater than or equal to a preset change amount threshold, and inquiring a preset feedback regulation table according to the actual state and the state change rate to obtain feedback regulation amounts corresponding to the actual state and the state change rate;
calculating a difference value between the actual state fed back by each group of target control amounts and the corresponding target state in response to the state change amount being smaller than the change amount threshold; integrating the difference values in the current control time domain to obtain the feedback adjustment quantity; wherein the target state is a state of the thermal management system predicted by the model predictive controller based on the target control amount.
8. A vehicle thermal management control apparatus characterized by comprising:
the control quantity determining module is used for determining a target control quantity of a control target in the current control time domain by utilizing a pre-constructed model predictive controller;
the acquisition module is used for acquiring 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 an actual state after the thermal management system executes the target control amount;
the feedback module is used for determining a feedback adjustment quantity according to the actual state;
and the correction module is used for correcting the target control quantity of the model predictive controller in the next control time domain according to the feedback adjustment quantity.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 7 when the program is executed by the processor.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1 to 7.
11. A vehicle comprising the electronic device of claim 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210667285.9A CN117261527A (en) | 2022-06-13 | 2022-06-13 | Vehicle thermal management control method and device, electronic equipment and vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210667285.9A CN117261527A (en) | 2022-06-13 | 2022-06-13 | Vehicle thermal management control method and device, electronic equipment and vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117261527A true CN117261527A (en) | 2023-12-22 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210667285.9A Pending CN117261527A (en) | 2022-06-13 | 2022-06-13 | Vehicle thermal management control method and device, electronic equipment and vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117261527A (en) |
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2022
- 2022-06-13 CN CN202210667285.9A patent/CN117261527A/en active Pending
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