Vehicle thermal management control method and device, electronic equipment and vehicle
By prioritizing control objectives and using a model predictive controller in the vehicle thermal management system, and iteratively selecting control variables to achieve the target state, the efficiency and user experience issues of the thermal management system under multiple control objectives are solved, thereby improving the system's operational efficiency and user satisfaction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING CO WHEELS TECH CO LTD
- Filing Date
- 2022-06-13
- Publication Date
- 2026-08-04
AI Technical Summary
Existing vehicle thermal management systems struggle to simultaneously meet the state requirements of various control objectives under multiple control goals, resulting in poor user experience and high energy consumption.
By acquiring the vehicle's operating conditions, multiple control objectives and their priorities are determined. The model predictive controller iteratively selects control variables to ensure that the predicted state of the high-priority control objective reaches the target state, and the corresponding control variables are used as the target control variables.
It enables priority adjustment of the status of important control targets under different operating conditions, thereby improving the operational efficiency and user experience of the thermal management system.
Smart Images
Figure CN117261528B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of new energy vehicle technology, and in particular to a vehicle thermal management control method, device, electronic equipment, and vehicle. Background Technology
[0002] In the field of new energy vehicles, the performance of the vehicle's thermal management system directly affects the vehicle's range and overall operating performance. Under specific operating conditions, the thermal management system may have multiple control objectives. For example, during engine start-up, control objectives include passenger compartment temperature, battery temperature, and motor temperature. Currently, for controlling multiple objectives, model predictive controllers are generally used to determine the control quantity for each objective separately. Then, the average of these control quantities is determined as the final target control quantity, which is applied to the thermal management system to achieve optimal thermal management efficiency. However, the target control quantity obtained by existing control methods may prevent any single objective from reaching its target state, thus failing to meet user experience requirements or achieve low energy consumption. Summary of the Invention
[0003] In view of this, the purpose of this application is to provide a vehicle thermal management control method, device, electronic equipment and vehicle.
[0004] To achieve the above objectives, this application provides a vehicle thermal management control method, comprising:
[0005] Obtain the vehicle's operating status;
[0006] Based on the operating conditions, determine multiple control objectives, the target status of each control objective, and the priority of each control objective;
[0007] Obtain the current state of high-priority control targets;
[0008] The control quantity is selected iteratively according to a predetermined algorithm. Based on the current state of the high-priority control target and the selected control quantity, the predicted state of the high-priority control target is determined by the pre-built model predictive controller.
[0009] When the predicted state reaches the target state, the corresponding control quantity is taken as the target control quantity.
[0010] Optionally, there are multiple high-priority control targets, and each control target has a different weight value;
[0011] Based on the current state of the high-priority control objective and the selected control variable, the predicted state of the high-priority control objective is determined using a pre-built model predictive controller, including:
[0012] Based on the current state of the control objective with the highest priority and largest weight value and the control variable selected this time, the predicted state of the control objective is determined using the model predictive controller.
[0013] Optionally, there are multiple high-priority control targets, and each control target has a different weight value;
[0014] When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity, including:
[0015] When the predicted state of each high-priority control objective reaches the target state, the corresponding control quantity is used as the reference control quantity for the corresponding control objective.
[0016] The target control quantity is determined based on the reference control quantity and corresponding weight value of each high-priority control objective.
[0017] Optionally, there are multiple high-priority control targets, and each control target has the same weight value;
[0018] When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity, including:
[0019] When the predicted state of each high-priority control objective reaches the target state, the corresponding control quantity is used as the reference control quantity for the corresponding control objective.
[0020] Based on the reference control values of each high-priority control objective, calculate the average control value and use this average control value as the target control value.
[0021] Optionally, after obtaining the current state of the high-priority control target, the following steps are also included:
[0022] In response to a situation where the difference between the current state of a high-priority control target and the corresponding target state is greater than a preset adjustment threshold, multiple control time domains required to adjust the current state to the corresponding target state, and the target state of each control time domain, are determined based on the difference.
[0023] Based on the current state and the target state of each control time domain, a state change curve for each control time domain is determined according to a preset state change rate; the state change curve is a curve formed by the state at each time point within the control time domain.
[0024] When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity, including:
[0025] When the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity.
[0026] Optionally, based on the current state and the target state of each control time domain, and according to a preset state change rate, a state change curve for each control time domain is determined, including:
[0027] From the current state to the target state in the control time domain, the state at each time point in the control time domain is determined according to the state change rate.
[0028] The state change curve is formed based on the state at all points in time.
[0029] Optionally, when the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity, including:
[0030] Calculate the state difference between the predicted state at each time point in the control time domain and the state at the corresponding time point on the state change curve;
[0031] The control quantity corresponding to the minimum sum of state differences at each time point is taken as the target control quantity.
[0032] This application also provides a vehicle thermal management control device, including:
[0033] The acquisition module is used to acquire the vehicle's operating conditions; and after determining multiple control targets based on the operating conditions, to acquire the current state of the high-priority control target.
[0034] The target determination module is used to determine multiple control targets, the target status of each control target, and the priority of each control target based on the operating conditions.
[0035] The model prediction module is used to iteratively select control quantities according to a predetermined algorithm. Based on the current state of the high-priority control target and the selected control quantity, the module uses a pre-built model prediction controller to determine the predicted state of the high-priority control target. When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity.
[0036] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle thermal management control method.
[0037] This application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the vehicle thermal management control method.
[0038] This application also provides a vehicle including the aforementioned electronic device.
[0039] As can be seen from the above, the vehicle thermal management control method, device, electronic equipment, and vehicle provided in this application determine multiple control objectives under the vehicle's operating conditions, the target states of each control objective, and the priorities of each control objective. It then identifies the high-priority control objective among these multiple objectives. Based on the current state of the high-priority control objective and the iteratively selected control variable, a pre-built model predictive controller determines the predicted state of the high-priority control objective. When the predicted state reaches the target state, the target control variable is determined. This application combines the importance of different control objectives under different operating conditions to determine the target control variable that can adjust the high-priority control objective to its optimal state. This meets the thermal management needs under different operating conditions, ensures the operational efficiency of the thermal management system, and improves the user experience. Attached Figure Description
[0040] Figure 1 This is a schematic diagram of the method flow of an embodiment of this application;
[0041] Figure 2 This is a block diagram of the device structure of one or more embodiments of this specification;
[0042] Figure 3 This is a block diagram of an electronic device structure for one or more embodiments of this specification. Detailed Implementation
[0043] To better understand the above-mentioned objectives, features, and advantages of this application, the solution of this application will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.
[0044] Many specific details are set forth in the following description in order to provide a full understanding of this application, but this application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of this application, and not all embodiments.
[0045] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0046] like Figure 1 As shown in the figure, this application provides a vehicle thermal management control method, including:
[0047] S101: Obtain the vehicle's operating status;
[0048] In this embodiment, the current operating conditions of the vehicle are first obtained through the vehicle controller or sensors. For example, the engine starting condition is determined based on the engine starting status, the battery charging condition is determined based on the battery charging status, and the operating conditions of the passenger compartment air conditioning being turned on when the engine is not started or the operating conditions of the engine and air conditioning being turned on simultaneously are determined based on the engine starting status and the air conditioning starting status. There are various types of vehicle operating conditions, and this embodiment will not provide examples of each one.
[0049] S102: Based on the operating conditions, determine multiple control objectives, the target status of each control objective, and the priority of each control objective;
[0050] In this embodiment, based on the acquired operating conditions, multiple control objectives requiring adjustment and the target states of each control objective are determined. The control objective is the controlled object within the thermal management system, and the target state of the control objective is the state that the object is to achieve. For example, control objectives include battery temperature, the inlet and / or outlet temperature of the water heater, engine temperature, energy consumption, and safety thresholds for heating and actuation components. Target states of the control objectives include maintaining the battery temperature within a certain temperature range, ensuring energy consumption does not exceed a certain energy consumption threshold, and requiring heating and actuation components to operate within safety thresholds.
[0051] In some methods, different operating conditions correspond to different control objectives and target states. For example, when the engine is running, the control objectives include battery temperature, engine temperature, and heater inlet temperature. When the engine is running and the passenger compartment air conditioning is on, the control objectives include battery temperature, engine temperature, heater inlet temperature, energy consumption, and heater safety threshold. The specific control objectives and target states corresponding to each operating condition will not be listed one by one.
[0052] Under specific operating conditions, there may be multiple control objectives, and in some cases, it may be difficult to adjust the state of all control objectives to the target state. Considering the varying importance of the multiple control objectives, in order to meet thermal management and user needs, the most important control objective can be prioritized, and high-priority control objectives can be adjusted to the target state.
[0053] S103: Obtain the current state of the high-priority control target;
[0054] S104: Select control variables iteratively according to a predetermined algorithm. Based on the current state of the high-priority control target and the selected control variables, use a pre-built model predictive controller to determine the predicted state of the high-priority control target.
[0055] S105: When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity.
[0056] In this embodiment, after determining multiple control objectives and target states under the current operating conditions, the high-priority control objective is identified. A control quantity is selected according to a predetermined algorithm. The current state of the high-priority control objective and the selected control quantity are used as inputs to the model predictive controller (MMC). The MMC outputs the predicted state of the corresponding control objective. If the predicted state does not reach the target state, a new control quantity is selected, and the MMC re-determines the predicted state. This process of selecting the control quantity and making predictions is repeated until the predicted state obtained based on the current state and the selected control quantity reaches the target state of the high-priority control objective. The control quantity that reaches the target state is used as the target control quantity for the thermal management system. This target control quantity is input into the thermal management system, which then controls the actions of each component based on the target control quantity to adjust the current state of the high-priority control objective to the desired target state.
[0057] In some methods, the priority of each control objective can be divided into at least two categories, such as high priority and low priority, or first priority, second priority, and so on up to Nth priority. The method of priority division can be determined according to the usage scenario and actual needs of the thermal management system, and the specific division method is not limited. For multiple control objectives under specific operating conditions, the priorities of each control objective may be different or partially the same. For example, under the condition of heater startup, the control objectives include heater temperature and crew cabin temperature. The target state of heater temperature is to reach the operating temperature while being less than the maximum temperature threshold, and the target state of crew cabin temperature is to reach the regulated target temperature. Considering system safety, heater temperature has high priority, while crew cabin temperature has low priority. The control objectives and their priorities for different operating conditions can be preset.
[0058] In some methods, under specific operating conditions, when there is only one identified control objective, that control objective is the high-priority control objective; when there are multiple identified control objectives with the same priority, all multiple control objectives are high-priority control objectives; when there are multiple identified control objectives with different priorities, the target control quantity is determined by the highest priority control objective reaching the target state, or by the control objectives that reach the target state in descending order of priority. The specific method can be determined based on the specific operating conditions and system requirements, and is not limited in any particular way.
[0059] In some embodiments, there are multiple high-priority control objectives, and each control objective has a different weight value;
[0060] Based on the current state of the high-priority control objective and the selected control variable, the predicted state of the high-priority control objective is determined using a pre-built model predictive controller, including:
[0061] Based on the current state of the control objective with the highest priority and largest weight value and the control variable selected in this study, the predicted state of the control objective is determined using a model predictive controller.
[0062] In this embodiment, when there are multiple high-priority control objectives, each objective has a different weight value, meaning the importance of these high-priority control objectives varies. In this case, the most important control objective, i.e., the one with the largest weight value, can be prioritized. Using this objective as the benchmark, its current state and the iteratively selected control variable are used as inputs to the model predictive controller (MMC). The MMC then predicts the predicted state of this control objective. During the iteration process, when the MMC's predicted state reaches the target state of the control objective under a specific control variable, this control variable is determined as the target control variable for the thermal management system. Based on this target control variable, the MMC can predict the target state of the high-priority control objective with the largest weight value.
[0063] In other embodiments, when the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity, including:
[0064] When the predicted state of each high-priority control objective reaches the target state, the corresponding control quantity is used as the reference control quantity for the corresponding control objective.
[0065] The target control quantity is determined based on the reference control quantity and corresponding weight value of each high-priority control objective.
[0066] In this embodiment, when there are multiple high-priority control objectives, and each control objective has a different weight value, the control quantity of each high-priority control objective can be comprehensively considered to ultimately determine a target control quantity that balances all control objectives. Specifically, for each high-priority control objective, the current state of the control objective and the iteratively selected control quantity are used as inputs to the model predictive controller. The model predictive controller determines the target control quantity that can achieve the target state of the control objective. The determined target control quantity of each high-priority control objective is used as a reference control quantity. Based on the reference control quantity and corresponding weight value of each control objective, the final target control quantity is determined. In this way, the determined target control quantity comprehensively considers the target control quantity and importance of each high-priority control objective. Under the control of the target control quantity, the states of multiple high-priority control objectives can be adjusted to a more balanced optimal state. Optionally, when calculating the final target control quantity, a weighted sum can be performed based on the reference control quantity and corresponding weight value of each control objective, and the final target control quantity is determined based on the weighted sum result.
[0067] In some embodiments, there are multiple high-priority control objectives, and each control objective has the same weight value;
[0068] When the predicted state reaches the target state, the corresponding control variable is used as the target control variable, including:
[0069] When the predicted state of each high-priority control objective reaches the target state, the corresponding control quantity is used as the reference control quantity for the corresponding control objective.
[0070] Based on the reference control values of each high-priority control objective, calculate the average control value and use this average control value as the target control value.
[0071] In this embodiment, when there are multiple high-priority control objectives, and each objective has the same weight value, the target control quantity of each high-priority control objective can be comprehensively considered to ultimately determine a target control quantity that balances all control objectives. Specifically, for each high-priority control objective, the current state of the objective and the iteratively selected control quantity are used as inputs to the model predictive controller. The model predictive controller determines the target control quantity that can achieve the target state of the objective. The determined target control quantity of each high-priority control objective is used as a reference control quantity. The average control quantity is calculated based on the reference control quantity of each objective, and this average control quantity is used as the final target control quantity. The determined target control quantity comprehensively considers the target control quantities of each high-priority control objective. Under the control of the target control quantity, the states of multiple high-priority control objectives can be adjusted to a more balanced optimal state.
[0072] In some embodiments, after obtaining the current state of a high-priority control target, the method further includes:
[0073] In response to a high-priority control target, if the difference between the current state and the corresponding target state is greater than a preset adjustment threshold, multiple control time domains required to adjust the current state to the corresponding target state are determined based on the difference, as well as the target state of each control time domain.
[0074] Based on the current state and the target state of each control time domain, determine the state change curve for each control time domain according to the preset state change rate; wherein, the state change curve is the curve formed by the state at each time point within the control time domain;
[0075] When the predicted state reaches the target state, the corresponding control variable is used as the target control variable, including:
[0076] When the predicted state matches the state change curve, the corresponding control variable is used as the target control variable.
[0077] In this embodiment, under different operating conditions, the target state of the control objective is generally an ideal state that can meet the thermal management requirements under that operating condition. However, in some cases, it is difficult to adjust the current state of the control objective to the ideal target state within a control time domain. For example, when the vehicle is in a low-temperature environment and the battery is heating up, the target state of the battery heating is a temperature of 20 degrees Celsius, while the current temperature of the battery is 0 degrees Celsius. Within the control time domain of the predictive model controller (e.g., 120 seconds), it is difficult to adjust the current state to the target state by selecting a control variable.
[0078] To address the aforementioned issues, after determining the target state of a high-priority control objective and acquiring its current state, the difference between the target state and the current state is calculated. If this difference exceeds an adjustment threshold, it is determined that the current state cannot be adjusted to the target state within a single control time domain. In this case, multiple control time domains required to adjust from the current state to the target state, along with the target state to be achieved in each control time domain, are determined based on this difference. A state change curve for each control time domain is then determined based on the current state and the target state of each control time domain. Subsequently, for each control time domain, a control variable is iteratively selected according to a predetermined algorithm. Based on the real-time acquired current state of the control objective and the selected control variable, a model predictive controller is used to determine the predicted state of the control objective. When the predicted state matches the state change curve, the corresponding control variable is used as the target control variable. In this way, considering the actual vehicle conditions, the target state of the high-priority control objective is readjusted into state change curves across multiple control time domains, ensuring that the state of the high-priority control objective can be adjusted to an optimal state within each control time domain.
[0079] In some embodiments, based on the current state and the target state of each control time domain, a state change curve for each control time domain is determined according to a preset state change rate, including:
[0080] From the current state to the target state of the control objective, determine the state at each point in time within the control time domain according to the rate of change of state;
[0081] A state change curve is generated based on the state at all time points.
[0082] In this embodiment, the method for determining a state change curve in the control time domain is to determine the state at each time point in the control time domain from the current state acquired in real time to the target state in the control time domain according to the state change rate, and then form a state change curve from the current state to the target state in the control time domain based on the states at all time points. For example, if the target battery temperature is 10 degrees Celsius, the current temperature is 0 degrees Celsius, the rate of change of battery temperature is 1 degree Celsius, one control time domain is 5 seconds, and the control step size is 1 second, then on the state change curve of the first control time domain, the temperature corresponding to the 1st second is 0 degrees Celsius, the temperature corresponding to the 2nd second is 1 degree Celsius, the temperature corresponding to the 3rd second is 2 degrees Celsius, the temperature corresponding to the 4th second is 3 degrees Celsius, and the temperature corresponding to the 5th second is 4 degrees Celsius. In the second control time domain, if the current temperature is 4 degrees Celsius, then on the state change curve of the second control time domain, the temperature corresponding to the 1st second is 4 degrees Celsius, the temperature corresponding to the 2nd second is 5 degrees Celsius, the temperature corresponding to the 3rd second is 6 degrees Celsius, the temperature corresponding to the 4th second is 7 degrees Celsius, and the temperature corresponding to the 5th second is 8 degrees Celsius. In the third control time domain, if the current temperature is 8 degrees Celsius, then on the state change curve of the third control time domain, the temperature corresponding to the 1st second is 8 degrees Celsius, the temperature corresponding to the 2nd second is 9 degrees Celsius, the temperature corresponding to the 3rd second is 10 degrees Celsius, the target temperature has been reached, and the temperature corresponding to the 4th and 5th seconds remains at 10 degrees Celsius.
[0083] In some embodiments, when the predicted state matches the state change curve, the corresponding control variable is used as the target control variable, including:
[0084] Calculate the state difference between the predicted state at each time point in the control time domain and the state at the corresponding time point on the state change curve;
[0085] The control variable corresponding to the minimum sum of state differences at each time point is taken as the target control variable.
[0086] In this embodiment, after determining a state change curve in the control time domain, when using a model predictive controller to determine the target control quantity, for each control quantity selected iteratively, the model predictive controller outputs a corresponding predicted state. The predicted state at each time point in the control time domain is compared with the state at the corresponding time point on the state change curve, and the state difference between the two is calculated. Then, the state differences at each time point are summed to obtain the sum of the state differences corresponding to the currently selected control quantity. During the iteration of the control quantity, the sum of the state differences corresponding to the previously selected control quantity and the currently selected control quantity is compared. The control quantity with the smaller sum of state differences is retained. When the iteration termination condition is met, the iteration stops, and the control quantity corresponding to the smallest sum of state differences is selected as the final target control quantity. The magnitude of the sum of state differences can evaluate the difference between the predicted state and the corresponding state on the state change curve. The larger the value, the more the predicted state deviates from the corresponding state on the curve; the smaller the value, the closer the predicted state is to the corresponding state on the curve.
[0087] Optionally, multiple sets of control variables can be selected based on differential evolution algorithms or genetic algorithms. The specific algorithm principles for selecting control variables will not be explained in detail.
[0088] In some embodiments, if there are multiple high-priority control objectives with different weight values, and it is impossible to adjust the current state of each high-priority control objective to the target state within a single control time domain, then a state change curve for each control time domain is determined. In this case, the predicted state of each control objective can be matched with its corresponding state change curve to determine the target control quantity for each control objective. The target control quantities of each control objective are used as reference control quantities, and the final target control quantity is determined based on the reference control quantities and weight values of each control objective. If there are multiple high-priority control objectives with the same weight value, the predicted state of each control objective can be matched with its corresponding state change curve to determine the target control quantity for each control objective. The average control quantity is determined based on the target control quantities of each control objective, and the average control quantity is used as the final target control quantity.
[0089] In some methods, during the iterative selection of control variables, when comparing the sum of the state differences corresponding to the previously selected control variable and the currently selected control variable, the sum of the state differences corresponding to the two selected control variables is the same. In this case, control variables for lower priority control objectives can be further considered to determine the target control variable that integrates all control objectives.
[0090] Specifically, for high-priority control targets, after determining a state change curve in the control time domain, when using a model predictive controller to determine the target control quantity, for each control quantity selected iteratively, the model predictive controller outputs the corresponding predicted state. The predicted state at each time point in the control time domain is compared with the state at the corresponding time point on the state change curve, and the state difference between the two is calculated. Then, the state differences at each time point are added together to obtain the sum of the state differences corresponding to the selected control quantity. Similarly, for low-priority control targets, after determining a state change curve in the control time domain, based on the current state of the low-priority control target and the iteratively selected control quantity, the model predictive controller outputs the corresponding predicted state. The predicted state at each time point in the control time domain is compared with the state at the corresponding time point on the state change curve, and the state difference between the two is calculated. Then, the state differences at each time point are added together to obtain the sum of the state differences corresponding to the selected control quantity. When iterating the control input, high-priority control objectives are prioritized. The sum of the state differences corresponding to the previously selected control input and the currently selected control input is compared, and the control input with the smaller sum of state differences is retained. If the sum of the state differences of the high-priority control objectives corresponding to the two selected control inputs is equal, then low-priority control objectives are considered. The sum of the state differences of the low-priority control objectives corresponding to the two selected control inputs is compared, and the control input with the smaller sum of state differences is retained. Finally, the target control input that comprehensively considers both high-priority and low-priority control objectives is determined. In this way, under the control of the determined target control input, high-priority control objectives can achieve their target states, and low-priority control objectives can also achieve their optimal states, thereby enabling the thermal management system to achieve optimal operating performance.
[0091] In some embodiments, a model predictive controller is constructed based on the thermal management system model of the range-extended vehicle. The vehicle's thermal management system includes an engine, motor, battery, passenger compartment, heater, radiator, compressor, water pump, fan, cooling circulation piping, etc. Under specific operating conditions, some or all components in the thermal management system coordinate with each other to achieve the thermal management requirements of the range-extended vehicle through this coordinated control process. Correspondingly, the thermal management system model includes engine thermal models, motor thermal models, battery thermal models, passenger compartment thermal models, water pump thermal models, heater thermal models, radiator models, compressor thermal models, fan thermal models, cooling piping models, etc. During the coordinated control process of these models, prediction results of the control target can be obtained within a certain timeframe. For example, by inputting the current temperature and torque of the motor, other parameters (e.g., ambient temperature, coolant temperature, heater inlet water temperature, etc.), and selected control variables into the motor thermal model, prediction results such as the predicted motor temperature and the predicted motor outlet water temperature can be obtained.
[0092] In some implementations, to address the control of the thermal management system of range-extended vehicles, a model predictive controller is constructed based on the thermal management system model of the range-extended vehicle, following a Model Predictive Control (MPC) framework. After determining the control objective of the range-extended vehicle, the MPC predicts the predicted state of the control objective under different control variables. During the prediction process, a predetermined algorithm iteratively selects different control variables until the MPC, based on the current state of the control objective and the selected control variables, can obtain the predicted state that achieves the target state. At this point, the selected control variable is used as the target control variable and applied to the vehicle's thermal management system, enabling the thermal management system to achieve optimal operating efficiency after adjustment according to the target control variable, thus meeting the vehicle's thermal management requirements under the current operating conditions.
[0093] This application provides a vehicle thermal management control method. Based on the vehicle's operating conditions, multiple control objectives and their target states are determined, from which high-priority control objectives are identified. Based on the current state of the high-priority control objectives and the iteratively selected control variables, a model predictive controller is used to determine the predicted state of the high-priority control objectives. When the predicted state reaches the target state, the corresponding control variable is used as the target control variable. This application can determine the target control variable that can adjust the high-priority control objectives to their optimal state by combining the importance of different control objectives under different operating conditions. This meets thermal management requirements, ensures the efficiency of the thermal management system, and improves the user experience.
[0094] like Figure 2 As shown in the illustration, this application also provides a vehicle thermal management control device, comprising:
[0095] The acquisition module 201 is used to acquire the operating conditions of the vehicle; and after determining multiple control targets based on the operating conditions, acquire the current state of each control target.
[0096] The target determination module 202 is used to determine multiple control targets, the target status of each control target, and the priority of each control target based on the operating conditions.
[0097] The model prediction module 203 is used to iteratively select control quantities according to a predetermined algorithm. Based on the current state of the high-priority control target and the selected control quantity, the predicted state of the high-priority control target is determined using a pre-built model prediction controller. When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity.
[0098] For ease of description, the above apparatus is described in terms of function, divided into various modules. Of course, when implementing one or more embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware.
[0099] The apparatus described above is used to implement the corresponding methods in the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.
[0100] Figure 3 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.
[0101] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.
[0102] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.
[0103] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.
[0104] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).
[0105] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.
[0106] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.
[0107] The electronic devices described above are used to implement the corresponding methods in the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.
[0108] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.
[0109] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.
[0110] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.
[0111] One or more embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.
Claims
1. A vehicle thermal management control method, characterized in that, include: Obtain the vehicle's operating status; Based on the operating conditions, determine multiple control objectives, the target status of each control objective, and the priority of each control objective; Obtain the current state of high-priority control targets; In response to a situation where the difference between the current state of a high-priority control target and the corresponding target state is greater than a preset adjustment threshold, multiple control time domains required to adjust the current state to the corresponding target state, and the target state of each control time domain, are determined based on the difference. Based on the current state and the target state of each control time domain, a state change curve for each control time domain is determined according to a preset state change rate; the state change curve is a curve formed by the state at each time point within the control time domain. The control quantity is selected iteratively according to a predetermined algorithm. Based on the current state of the high-priority control target and the selected control quantity, the predicted state of the high-priority control target is determined by the pre-built model predictive controller. When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity, including: when the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity.
2. The method according to claim 1, characterized in that, There are multiple high-priority control objectives, and each control objective has a different weight value; Based on the current state of the high-priority control objective and the selected control variable, the predicted state of the high-priority control objective is determined using a pre-built model predictive controller, including: Based on the current state of the control objective with the highest priority and largest weight value and the control variable selected this time, the predicted state of the control objective is determined using the model predictive controller.
3. The method according to claim 1, characterized in that, There are multiple high-priority control objectives, and each control objective has a different weight value; When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity, including: When the predicted state of each high-priority control objective reaches the target state, the corresponding control quantity is used as the reference control quantity for the corresponding control objective. The target control quantity is determined based on the reference control quantity and corresponding weight value of each high-priority control objective.
4. The method according to claim 1, characterized in that, There are multiple high-priority control targets, and each control target has the same weight value; When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity, including: When the predicted state of each high-priority control objective reaches the target state, the corresponding control quantity is used as the reference control quantity for the corresponding control objective. Based on the reference control values of each high-priority control objective, calculate the average control value and use this average control value as the target control value.
5. The method according to claim 1, characterized in that, Based on the current state and the target state of each control time domain, and according to a preset state change rate, determine the state change curve for each control time domain, including: From the current state to the target state in the control time domain, the state at each time point in the control time domain is determined according to the state change rate. The state change curve is formed based on the state at all points in time.
6. The method according to claim 1, characterized in that, When the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity, including: Calculate the state difference between the predicted state at each time point in the control time domain and the state at the corresponding time point on the state change curve; The control quantity corresponding to the minimum sum of state differences at each time point is taken as the target control quantity.
7. A vehicle thermal management control device, characterized in that, include: The acquisition module is used to acquire the vehicle's operating conditions; And after determining multiple control objectives based on the operating conditions, obtain the current state of the high-priority control objective; The target determination module is used to determine multiple control targets, the target status of each control target, and the priority of each control target based on the operating conditions. In response to a situation where the difference between the current state of a high-priority control target and the corresponding target state is greater than a preset adjustment threshold, multiple control time domains required to adjust the current state to the corresponding target state, and the target state of each control time domain, are determined based on the difference. Based on the current state and the target state of each control time domain, a state change curve for each control time domain is determined according to a preset state change rate; the state change curve is a curve formed by the state at each time point within the control time domain. The model prediction module is used to iteratively select control variables according to a predetermined algorithm. Based on the current state of the high-priority control target and the selected control variable, the module uses a pre-built model prediction controller to determine the predicted state of the high-priority control target. When the predicted state reaches the target state, the corresponding control quantity is used as the target control quantity, including: when the predicted state matches the state change curve, the corresponding control quantity is used as the target control quantity.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.
9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions, which are used to cause the computer to perform the method according to any one of claims 1 to 7. 。 10. A vehicle, characterized in that, Including the electronic device as described in claim 9.