CN117261526A - 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 PDF

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Publication number
CN117261526A
CN117261526A CN202210665632.4A CN202210665632A CN117261526A CN 117261526 A CN117261526 A CN 117261526A CN 202210665632 A CN202210665632 A CN 202210665632A CN 117261526 A CN117261526 A CN 117261526A
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control
state
target
time domain
determining
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刘凯峰
纪铮
薛剑
马春山
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Beijing Co Wheels Technology Co Ltd
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Beijing Co Wheels Technology Co Ltd
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Priority to CN202210665632.4A priority Critical patent/CN117261526A/en
Publication of CN117261526A publication Critical patent/CN117261526A/en
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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
    • 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
    • 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
    • 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
    • B60H1/004—Air-conditioning arrangements specially adapted for particular vehicles for vehicles having an electrical drive, e.g. hybrid or fuel cell for vehicles having a combustion engine and electric drive means, e.g. hybrid electric vehicles
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/61—Types of temperature control
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/62—Heating or cooling; Temperature control specially adapted for specific applications
    • H01M10/625—Vehicles
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/63—Control systems
    • H01M10/633—Control systems characterised by algorithms, flow charts, software details or the like

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Thermal Sciences (AREA)
  • Mechanical Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • Manufacturing & Machinery (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Combustion & Propulsion (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

The application provides a vehicle thermal management control method, a device, electronic equipment and a vehicle, comprising the following steps: acquiring the running condition of a vehicle; determining a control target and a target state and a current state of the control target according to the operation condition; if the difference value between the current state and the target state is larger than the adjustment threshold value, determining a plurality of control time domains and target states of the control time domains required for adjusting the current state to the target state according to the difference value; and determining a state change curve of each control time domain; for each control time domain, determining a predicted state of the control target by using a model prediction controller according to the current state of the control target and the iteratively selected control quantity; when the predicted state matches the state change curve, the corresponding control amount is taken as the target control amount. By adjusting the target state of the control target to a state change curve and determining a proper target control amount based on the state change curve, the thermal management system can be controlled to gradually adjust the optimal state so as to meet the thermal management requirement.

Description

Vehicle thermal management control method and device, electronic equipment and vehicle
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, a model predictive controller is generally utilized to determine an optimal control amount of a specific control target (for example, battery temperature) in a control time domain, and the optimal control amount is applied to a vehicle so that the control target can reach a preset target state under the control of the optimal control amount. However, in some cases, the gap between the current state of the control target and the target state is too large, and it is difficult to adjust to the target state in the control time domain; for example, in a certain environment, the current temperature value of the battery is 5 degrees, and the target temperature value to be reached is 20 degrees, and it is difficult to adjust the battery temperature to the target temperature value in one control time domain due to a large difference between the current temperature value and the target temperature value.
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:
acquiring the running condition of a vehicle;
determining a control target and a target state and a current state of the control target according to the operation condition;
determining a plurality of control time domains required for adjusting the current state to the target state and a target state of each control time domain according to the difference value in response to the difference value between the current state and the target state being greater than a preset adjustment threshold;
determining a state change curve of each control time domain according to the current state and the target state of each control time domain and a preset state change rate; the state change curve is a curve formed by the state of each time point in the control time domain;
iteratively selecting a control quantity according to a preset algorithm for each control time domain according to the target state of each control time domain, and determining the prediction state of the control target by using a pre-constructed model prediction controller according to the current state of the control target and the control quantity selected at the time; when the predicted state matches the state change curve, the corresponding control amount is taken as a target control amount.
Optionally, determining a state change curve of each control time domain according to the current state and the target state of each control time domain and a preset state change rate, including:
starting from the current state to a target state of the control time domain, and determining the state of each time point in the control time domain according to the state change rate;
the state change curve is formed based on the states at all time points.
Optionally, when the predicted state matches the state change curve, the corresponding control amount is taken as the target control amount, including:
calculating state difference values between the predicted states of all time points in the control time domain and the states of the corresponding time points on the state change curve;
and taking the control quantity corresponding to the state difference value sum reaching the minimum value at each time point as the target control quantity.
Optionally, after determining the target state of each control time domain, the method further includes:
and determining the state change rate according to the operation conditions, wherein the state change rate is a preset change rate based on different operation conditions.
Optionally, the control targets are multiple, and the priorities of the control targets are different;
and determining a plurality of control time domains required for adjusting the current state to the target state and a target state of each control time domain according to the difference value, wherein the difference value between the current state and the target state is larger than a preset adjustment threshold value, and the target state of each control time domain comprises:
and in response to the difference between the current state of the high-priority control target and the corresponding target state being greater than the adjustment threshold, determining a plurality of control time domains for adjusting the current state of the high-priority control target to the corresponding target state and a target state of each control time domain according to the difference.
Optionally, the control targets with high priority are multiple, and the weight values of the control targets are different;
when the predicted state matches the state change curve, taking the corresponding control amount as a target control amount, including:
when the predicted state of each high-priority control target is matched with the corresponding state change curve, taking the corresponding control quantity as the reference control quantity of the corresponding control target;
and determining the target control quantity according to the reference control quantity and the corresponding weight value of each high-priority control target.
The application also provides a vehicle thermal management control device, comprising:
the acquisition module is used for acquiring the running condition of the vehicle;
the target determining module is used for determining a control target and a target state and a current state of the control target according to the operation condition;
a control time domain determining module, configured to determine, according to a difference value between the current state and a target state that is greater than a preset adjustment threshold, a plurality of control time domains required for adjusting the current state to the target state, and a target state of each control time domain;
the curve determining module is used for determining a state change curve of each control time domain according to the current state and the target state of each control time domain and a preset state change rate; the state change curve is a curve formed by the state of each time point in the control time domain;
the control quantity determining module is used for iteratively selecting the control quantity according to a preset algorithm for each control time domain according to the target state of each control time domain, and determining the prediction state of the control target by utilizing a pre-constructed model prediction controller according to the current state of the control target and the control quantity selected at the time; when the predicted state matches the state change curve, the corresponding control amount is taken as a target control amount.
The application also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the vehicle thermal management control method when executing the program.
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.
As can be seen from the foregoing, the method, the apparatus, the electronic device, and the vehicle for controlling thermal management of a vehicle provided by the present application determine, after determining a control target under an operating condition and a target state and a current state of the control target, when determining that a difference between the current state and the target state exceeds an adjustment threshold, determine, according to the difference, a plurality of control time domains required for adjusting the current state to the target state and a target state of each control time domain, determine, according to a preset state change rate, a state change curve of each control time domain, for each control time domain, determine, according to the current state of the control target and a iteratively selected control amount, a predicted state of the control target by using a model prediction controller, and when the predicted state matches the state change curve, use a corresponding control amount as a target control amount. According to the method and the device, the actual use condition of the vehicle is combined, one control target in the prior art is divided into a plurality of small control targets, a group of control quantity is generated for each small control target, the thermal management system can be controlled to gradually adjust the optimal state, and the thermal management requirement is met.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present application;
FIG. 2 is a block diagram of an apparatus according to one or more embodiments of the present disclosure;
fig. 3 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: acquiring the running condition of a vehicle;
in this embodiment, the operation condition of the vehicle is first obtained by the vehicle controller or the sensor. For example, the engine starting condition is determined according to the starting state of the engine, the battery charging condition is determined according to the charging state of the battery, the condition that the air conditioner is started in the passenger compartment under the condition that the engine is not started, or the condition that the engine and the air conditioner are simultaneously started, etc., and the vehicle working condition types are various, and the embodiment is not illustrated one by one.
S102: determining a control target and a target state and a current state of the control target according to the operation condition;
in this embodiment, according to the acquired operation condition, a control target to be adjusted and a target state of the control target are determined, and at the same time, a current state of the control target is acquired through a vehicle controller or a specific sensor. Wherein the control target is a controlled object in the thermal management system, and the target state of the control target is a state to be reached by the object; for example, the control target includes a battery temperature, a water inlet temperature and/or a water outlet temperature of the water heating heater, an engine temperature, energy consumption, a safety threshold of the heating component and the executing component, and the like, and the target state of the control target includes that the battery temperature needs to be kept within a certain temperature range, the energy consumption should not exceed a certain energy consumption threshold, the heating component and the executing component need to operate within the safety threshold, and the like.
In some approaches, different conditions correspond to different control targets and target states. For example, under the working condition that the engine is started, the control target comprises a battery temperature, an engine temperature, a water inlet temperature of a heater and the like, and under the working condition that the engine is started and an air conditioner of a passenger cabin is started, the control target comprises the battery temperature, the engine temperature, the water inlet temperature of the heater, energy consumption, a safety threshold value of the heater and the like, and the control target and the target state corresponding to the specific working conditions are not illustrated one by one.
S103: determining a plurality of control time domains required for adjusting the current state to the target state and a target state of each control time domain according to the difference value in response to the difference value between the current state and the target state being greater than a preset adjustment threshold;
the target state of the control target is generally an ideal state that can meet the thermal management requirements under different conditions, however, in some cases, it is difficult to adjust the current state of the control target to the ideal target state within a limited time. For example, when the vehicle is in a low-temperature environment and the battery is in a temperature-raising condition, the target state of the battery is that the temperature value reaches 20 degrees, and the current temperature of the battery is 0 degrees, and due to the large temperature adjustment range, it is difficult to adjust the current state to the target state by selecting the control amount in the control time domain (for example, 120 seconds) of the predictive model controller.
In this embodiment, after determining the current state and the target state of the control target, a difference between the two is calculated, and it is determined whether the difference is greater than an adjustment threshold, that is, whether the current state can be adjusted to the target state in one control time domain. When the difference value of the two is larger than the adjustment threshold value, the current state cannot be adjusted to the target state in one control time domain, and a plurality of control time domains required for adjusting to the target state and the target state to be reached by each control time domain are required to be determined according to the difference value.
S104: determining a state change curve of each control time domain according to the current state and the target state of each control time domain and a preset state change rate; the state change curve is a curve formed by the state of each time point in the control time domain;
in this embodiment, after determining a plurality of control time domains and a target state of each control time domain, a state change curve of each control time domain is determined according to a current state of a control target and a target state of each control time domain, which are acquired in real time. It is understood that, in order to adjust the current state of the control target to the target state, the state needs to be adjusted according to the state change curves of the plurality of control time domains. That is, the target state of the control target is readjusted to the state change curve in consideration of the actual situation of the vehicle, and the model predictive controller selects the target control amount that can be adjusted according to the target change curve.
S105: iteratively selecting a control quantity according to a preset algorithm for each control time domain according to the target state of each control time domain, and determining the prediction state of the control target by using a pre-constructed model prediction controller according to the current state of the control target and the control quantity selected at the time; when the predicted state matches the state change curve, the corresponding control amount is taken as the target control amount.
In this embodiment, after the state change curve of each control time domain is determined, the target control amount of each control time domain may be determined based on the state change curve. And determining the predicted state of the control target by using a model prediction controller according to the current state of the control target and the iteratively selected control quantity, wherein the control quantity is the determined target control quantity when the predicted state is matched with the state change curve under the specific control quantity.
In some embodiments, determining a state change curve of each control time domain according to the current state and the target state of each control time domain and a preset state change rate includes:
starting from the current state to the target state of the control time domain, and determining the state of each time point in the control time domain according to the state change rate;
a state change curve is formed based on the states at all time points.
In this embodiment, the method for determining the state change curve of the control time domain is to determine the state of each time point in the control time domain according to the state change rate from the current state acquired in real time to the target state of the control time domain, and then form the state change curve of the control time domain based on the states of all the time points, and adjust the state change curve from the current state to the target state. For example, the target temperature of the battery temperature is 10 degrees, the collected current temperature is 0 degrees, the state change rate of the battery temperature is 1 degree, one control time domain is 5 seconds, and the control step length is 1 second, then on the state change curve of the first control time domain, the temperature corresponding to the 1 st second is 0 degrees, the temperature corresponding to the 2 nd second is 1 degree, the temperature corresponding to the 3 rd second is 2 degrees, the temperature corresponding to the 4 th second is 3 degrees, and the temperature corresponding to the 5 th second is 4 degrees; in the second control time domain, if the current temperature is 4 degrees, on a state change curve of the second control time domain, the temperature corresponding to the 1 st second is 4 degrees, the temperature corresponding to the 2 nd second is 5 degrees, the temperature corresponding to the 3 rd second is 6 degrees, the temperature corresponding to the 4 th second is 7 degrees, and the temperature corresponding to the 5 th second is 8 degrees; in the third control time domain, if the acquired current temperature is 8 degrees, on a state change curve of the third control time domain, the temperature corresponding to the 1 st second is 8 degrees, the temperature corresponding to the 2 nd second is 9 degrees, the temperature corresponding to the 3 rd second is 10 degrees, the target temperature is reached, and the temperatures corresponding to the 4 th and 5 th seconds are kept at 10 degrees.
In some embodiments, after determining the target state for each control domain, further comprising: according to the operating conditions, a state change rate is determined, which is a preset change rate based on different operating conditions. That is, in determining the state change curve, the degree of slowing of the state change may be determined according to the vehicle operation condition, for example, the state change rate of the battery temperature is a first change rate under the engine start condition, and the state change rate of the battery temperature is a second change rate under the engine non-start condition. In some cases, the state change rate of each control target is different under different working conditions at different external environment temperatures. For different vehicles, the state change rate can be set in an experimental calibration mode according to the hardware configuration and performance of the vehicles and in combination with the conditions of external environment and the like.
In some embodiments, when the predicted state matches the state change curve, the corresponding control amount is taken as the target control amount, including:
calculating state difference values between the predicted states of all time points in the control time domain and the states of corresponding time points on the state change curve;
and taking the control quantity corresponding to the state difference value sum reaching the minimum value at each time point as a target control quantity.
In this embodiment, after determining a state change curve of a control time domain, when determining a target control quantity by using a model prediction controller, for each control quantity selected in an iterative manner, the model prediction controller outputs a corresponding prediction state, compares the prediction state of each time point in the control time domain with the state of the corresponding time point on the state change curve, calculates a state difference value of the prediction state and the state difference value of the state change curve, and then adds the state difference values of the time points to obtain a sum of state difference values corresponding to the control quantity selected at this time; in the iteration process of the control quantity, the control quantity which is selected last time and the control quantity which is selected this time are compared, the control quantity which is smaller in the sum of the state difference values is reserved, when the iteration ending condition is met, iteration is stopped, and the control quantity which is corresponding to the minimum value of the sum of the state difference values is selected as the final target control quantity. The difference between the predicted state and the corresponding state on the state change curve can be evaluated, and the larger the value is, the smaller the value is, and the closer the predicted state is to the corresponding state on the curve.
Optionally, a plurality of groups of control amounts are selected based on a differential evolution algorithm or a genetic algorithm, etc., and the specific algorithm principle of selecting the control amounts is not described in detail.
In some embodiments, there are a plurality of control targets, each control target having a different priority;
and determining a plurality of control time domains required for adjusting the current state to the target state and the target state of each control time domain according to the difference value, wherein the difference value between the current state and the target state is larger than a preset adjustment threshold value, and the target state of each control time domain comprises:
and determining a plurality of control time domains for adjusting the current state of the control target with high priority to the corresponding target state according to the difference value and the target state of each control time domain in response to the difference value between the current state of the control target with high priority and the corresponding target state being greater than the adjustment threshold.
Considering that there may be a plurality of control targets under different operation conditions, it is difficult to determine a target control amount capable of adjusting all the control targets to a target state in some cases. In this embodiment, in order to meet the thermal management requirement, user experience is ensured, and for the case of multiple control targets, the control target with high priority is preferentially adjusted to the corresponding target state, that is, the target control amount of the control target with high priority needs to be determined. Thus, after the current state and the target state of the control target with high priority are obtained, calculating the difference value of the current state and the target state, and if the difference value is larger than the adjustment threshold value, determining a plurality of control time domains and the target state of each control time domain according to the difference value; and then, determining a state change curve of each control time domain, outputting a predicted state by using a model prediction controller according to the current state of the control target with high priority and the iteratively selected control quantity, and determining the target control quantity of the control target with high priority when the predicted state is matched with the state change curve. By gradually adjusting the state of the control target of high priority to the target state, user experience and thermal management requirements can be satisfied.
In some manners, under different working conditions, the priority of each control target may be divided into at least two, for example, a high priority and a low priority, or the first priority and the second priority … … are the nth priority, where the manner of dividing the priority may be determined according to the usage scenario and actual requirements of the thermal management system, and the specific manner of dividing is not limited. The priorities of the control targets may be different or partially the same for a plurality of control targets under a specific operating condition, for example, the control targets include a heater temperature and a cabin temperature under a heater start-up condition, the target state of the heater temperature is to reach an operating temperature while being less than a maximum temperature threshold, the target state of the cabin temperature is to reach an adjusted target temperature, the heater temperature has a high priority in consideration of system safety, and the cabin temperature has a low priority. The control targets and the priorities of the different working conditions can be preset.
In some modes, under a specific operation condition, when only one control target is determined, the control target is a high-priority control target; when the determined control targets have a plurality of control targets and the priorities of the plurality of control targets are the same, the plurality of control targets are all control targets with high priority; when the determined control targets have a plurality of control targets and the priorities of the plurality of control targets are different, the target control quantity is determined by taking the control target with the highest priority as the target reaching state as the target to be reached preferentially, or the control targets which are arranged in the first few control targets are taken as the target determining target control quantity with the target reaching states as the targets to be reached preferentially according to the order of the priorities from high to low; can be determined according to specific working conditions and system requirements, and is not particularly limited.
In some embodiments, there are a plurality of control targets with high priority, and the weight values of the control targets are different;
when the predicted state matches the state change curve, the corresponding control amount is taken as the target control amount, including:
when the predicted state of each high-priority control target is matched with the corresponding state change curve, taking the corresponding control quantity as the reference control quantity of the corresponding control target;
and determining the target control quantity according to the reference control quantity and the corresponding weight value of each high-priority control target.
In this embodiment, if there are a plurality of control targets of high priority and the weight values are different, the state change curves of the plurality of control time domains of each high priority control target are determined respectively. In this case, the predicted state of each control target may be matched with the corresponding state change curve, the target control amount of each control target may be determined, the target control amount of each control target with high priority may be used as the reference control amount, and the final target control amount may be determined according to the reference control amounts and the corresponding weight values of each control target. The determined target control quantity comprehensively considers the importance degree of each high-priority control target, and can achieve a relatively balanced control effect.
In other modes, if there are multiple control targets with high priority and the weight values are the same, the prediction state of each control target can be respectively matched with the corresponding state change curve, the target control quantity of each control target is determined, the control quantity average value is determined according to the target control quantity of each control target, and the control quantity average value is used as the final target control quantity.
In some cases, for the high-priority control target, in the process of iteratively selecting the control quantity, when comparing the sum of the state differences corresponding to the control quantity selected last time and the control quantity selected this time, the sum of the state differences corresponding to the control quantity selected twice is the same. In this case, the control amounts of the control targets of low priority may be further considered, so that the target control amounts of all the control targets are integrated is determined.
Specifically, for a control target with high priority, after determining a state change curve of a control time domain, when determining a target control quantity by using a model prediction controller, outputting a corresponding prediction state by the model prediction controller for each iteratively selected control quantity, comparing the prediction state of each time point in a control time domain with the state of a corresponding time point on the state change curve, calculating the state difference value of the two, and then adding the state difference values of all the time points to obtain the sum of the state difference values corresponding to the selected control quantity; similarly, for the control target with low priority, after determining a state change curve of a control time domain, according to the current state of the control target with low priority and the iteratively selected control quantity, outputting a corresponding prediction state by a model prediction controller, comparing the prediction state of each time point in the control time domain with the state of the corresponding time point on the state change curve, calculating the state difference value of the prediction state and the state change curve, and adding the state difference values of the time points to obtain the sum of the state difference values corresponding to the selected control quantity. When the control quantity is iterated, a control target with high priority is preferentially considered, the sum of the state difference values corresponding to the control quantity selected last time and the control quantity selected this time is compared, and the control quantity with smaller sum of the state difference values is reserved; if the sum of the state differences of the high-priority control targets corresponding to the two selected control amounts is equal, then considering the low-priority control targets, comparing the sum of the state differences of the low-priority control targets corresponding to the two selected control amounts, reserving the control amount with smaller sum of the state differences, and finally determining the target control amount comprehensively considering the high-priority control targets and the low-priority control targets. Therefore, under the control of the determined target control quantity, the control target with high priority can reach the target state, and the control target with low priority can also reach the better state, so that the thermal management system achieves the optimal operation efficiency.
In some embodiments, a model predictive controller is constructed based on a thermal management system model of an extended range vehicle. The thermal management system of the vehicle comprises an engine, a motor, a battery, a passenger cabin, a heater, a radiator, a compressor, a water pump, a fan, a cooling circulation pipeline and the like, and under a specific working condition, part or all of components in the thermal management system are mutually controlled in a cooperative manner, and the thermal management requirement of the range-extended vehicle is realized through a cooperative control process. Correspondingly, the thermal management system model comprises an engine thermal model, a motor thermal model, a battery thermal model, a passenger cabin thermal model, a water pump thermal model, a heater thermal model, a radiator model, a compressor thermal model, a fan thermal model, a cooling pipeline model and the like, and a prediction result of a control target in a certain time can be obtained in the cooperative control process of each model. For example, the current temperature and current torque of the motor, other parameters (such as ambient temperature, cooling water temperature, heater water inlet temperature and the like) and selected control quantity are input into a motor thermal model, so that prediction results of motor prediction temperature, motor prediction water outlet temperature and the like can be obtained.
In this embodiment, in order to solve the control of the thermal management system of the extended-range vehicle, a model predictive controller is constructed in accordance with a model predictive control framework (Model Predictive Control, MPC) based on the thermal management system model of the extended-range vehicle. After the control target of the extended range vehicle is determined, the model prediction controller is utilized to predict the prediction states of the control target under different control amounts, and in the prediction process, the predetermined algorithm is utilized to iteratively select different control amounts until the model prediction controller can obtain the prediction state reaching the state change curve according to the current state of the control target and the selected control amount, and the selected control amount is used as the target control amount to act on the thermal management system of the vehicle, so that the thermal management system can reach the optimal operation efficiency after being adjusted according to the target control amount, and the thermal management requirement of the vehicle under the current working condition is met.
The embodiment of the application provides a vehicle thermal management control method, which is characterized in that under the running condition of a vehicle, a corresponding control target and a target state and a current state of the control target are determined, when the difference between the current state and the target state exceeds an adjustment threshold, a plurality of control time domains and target states of each control time domain required for adjusting the current state to the target state are determined according to the difference, and a state change curve of each control time domain is determined according to the current state and the target state of each control time domain and a preset state change rate; for each control time domain, determining a predicted state of the control target by using a model prediction controller according to the current state of the control target and the iteratively selected control quantity, and taking the corresponding control quantity as a target control quantity when the predicted state is matched with a state change curve. According to the method and the device, the actual use condition of the vehicle is combined, the target state of the control target is adjusted to be the state change curve, the proper target control quantity is determined based on the state change curve, the thermal management system can be controlled to gradually adjust the optimal state, and the thermal management requirements under different working conditions are met.
As shown in fig. 2, an embodiment of the present application further provides a vehicle thermal management control device, including:
an acquisition module 201, configured to acquire an operation condition of a vehicle;
the target determining module 202 is configured to determine a control target and a target state and a current state of the control target according to the operation condition;
a control time domain determining module 203, configured to determine, according to a difference between the current state and a target state that is greater than a preset adjustment threshold, a plurality of control time domains required for adjusting the current state to the target state, and a target state of each control time domain;
the curve determining module 204 is configured to determine a state change curve of each control time domain according to the current state and a target state of each control time domain and a preset state change rate; the state change curve is a curve formed by the state of each time point in the control time domain;
a control amount determining module 205, configured to iteratively select, for each control time domain, a control amount according to a predetermined algorithm according to a target state of each control time domain, and determine a prediction state of the control target by using a model prediction controller constructed in advance according to a current state of the control target and the control amount selected at the time; when the predicted state matches the state change curve, the corresponding control amount is taken as a target control amount.
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. 3 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 (10)

1. A vehicle thermal management control method characterized by comprising:
acquiring the running condition of a vehicle;
determining a control target and a target state and a current state of the control target according to the operation condition;
determining a plurality of control time domains required for adjusting the current state to the target state and a target state of each control time domain according to the difference value in response to the difference value between the current state and the target state being greater than a preset adjustment threshold;
determining a state change curve of each control time domain according to the current state and the target state of each control time domain and a preset state change rate; the state change curve is a curve formed by the state of each time point in the control time domain;
iteratively selecting a control quantity according to a preset algorithm for each control time domain according to the target state of each control time domain, and determining the prediction state of the control target by using a pre-constructed model prediction controller according to the current state of the control target and the control quantity selected at the time; when the predicted state matches the state change curve, the corresponding control amount is taken as a target control amount.
2. The method of claim 1, wherein determining a state change curve for each control time domain according to a preset state change rate based on the current state and a target state for each control time domain, comprises:
starting from the current state to a target state of the control time domain, and determining the state of each time point in the control time domain according to the state change rate;
the state change curve is formed based on the states at all time points.
3. The method according to claim 1, wherein when the predicted state matches a state change curve, taking the corresponding control amount as a target control amount includes:
calculating state difference values between the predicted states of all time points in the control time domain and the states of the corresponding time points on the state change curve;
and taking the control quantity corresponding to the state difference value sum reaching the minimum value at each time point as the target control quantity.
4. The method of claim 1, further comprising, after determining the target state for each control time domain:
and determining the state change rate according to the operation conditions, wherein the state change rate is a preset change rate based on different operation conditions.
5. The method of claim 1, wherein there are a plurality of control targets, each control target having a different priority;
and determining a plurality of control time domains required for adjusting the current state to the target state and a target state of each control time domain according to the difference value, wherein the difference value between the current state and the target state is larger than a preset adjustment threshold value, and the target state of each control time domain comprises:
and in response to the difference between the current state of the high-priority control target and the corresponding target state being greater than the adjustment threshold, determining a plurality of control time domains for adjusting the current state of the high-priority control target to the corresponding target state and a target state of each control time domain according to the difference.
6. The method according to claim 5, wherein the high-priority control targets are plural, and the weight values of the control targets are different;
when the predicted state matches the state change curve, taking the corresponding control amount as a target control amount, including:
when the predicted state of each high-priority control target is matched with the corresponding state change curve, taking the corresponding control quantity as the reference control quantity of the corresponding control target;
and determining the target control quantity according to the reference control quantity and the corresponding weight value of each high-priority control target.
7. A vehicle thermal management control apparatus characterized by comprising:
the acquisition module is used for acquiring the running condition of the vehicle;
the target determining module is used for determining a control target and a target state and a current state of the control target according to the operation condition;
a control time domain determining module, configured to determine, according to a difference value between the current state and a target state that is greater than a preset adjustment threshold, a plurality of control time domains required for adjusting the current state to the target state, and a target state of each control time domain;
the curve determining module is used for determining a state change curve of each control time domain according to the current state and the target state of each control time domain and a preset state change rate; the state change curve is a curve formed by the state of each time point in the control time domain;
the control quantity determining module is used for iteratively selecting the control quantity according to a preset algorithm for each control time domain according to the target state of each control time domain, and determining the prediction state of the control target by utilizing a pre-constructed model prediction controller according to the current state of the control target and the control quantity selected at the time; when the predicted state matches the state change curve, the corresponding control amount is taken as a target control amount.
8. 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 6 when the program is executed by the processor.
9. 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 6.
10. A vehicle comprising the electronic device of claim 8.
CN202210665632.4A 2022-06-13 2022-06-13 Vehicle thermal management control method and device, electronic equipment and vehicle Pending CN117261526A (en)

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Application Number Priority Date Filing Date Title
CN202210665632.4A CN117261526A (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
CN202210665632.4A CN117261526A (en) 2022-06-13 2022-06-13 Vehicle thermal management control method and device, electronic equipment and vehicle

Publications (1)

Publication Number Publication Date
CN117261526A true CN117261526A (en) 2023-12-22

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