Vehicle thermal management predictive control method, device, equipment, medium and product

Abstract

The invention discloses a vehicle thermal management predictive control method, device and equipment, a medium and a product. The method comprises the following steps: acquiring real-time state monitoring information of a whole vehicle, and determining a predicted long-time-domain energy demand under a driving condition of the whole vehicle according to the real-time state monitoring information of the whole vehicle; the whole vehicle real-time monitoring information comprises first information, second information and third information; according to the energy demand of the long time domain, the segmented control target temperature of the long time domain is determined; and a model prediction control target is determined according to the long-time-domain segmented control target temperature, and short-time-domain optimal control over the whole vehicle thermal management actuator is achieved through control over the model prediction control target. According to the technical scheme, long-time-domain predictive control can be carried out on the thermal management system of the whole vehicle, the real-time performance of control is guaranteed, meanwhile, the energy consumption of the thermal management system of the whole vehicle is minimum, and the endurance mileage of the whole vehicle is increased.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of new energy vehicle thermal management prediction control, and in particular to a vehicle thermal management prediction control method, device, equipment, medium and product. BACKGROUND

[0002] The existing MPC (Model Predictive Control) technology has the following limitations in practical application:

[0003] Insufficient energy consumption optimization: The existing MPC technology mainly focuses on model control itself, and the consideration of energy consumption optimization is relatively less. Although this single control strategy can achieve stable operation of the system to a certain extent, it has limited effect on energy consumption improvement. Especially in complex actual working conditions, relying only on model control cannot fully tap the energy-saving potential of the system, resulting in unsatisfactory energy consumption optimization effect.

[0004] Model accuracy problem: The prediction model relied on by the current MPC technology has insufficient accuracy. These models are often based on theoretical assumptions or simplified conditions, and there is a large difference with the actual running environment. For example, in the field of automobiles, the existing prediction model may not accurately reflect the dynamic characteristics of the vehicle under different road conditions, loads and environmental conditions. This disconnection between the model and the actual situation leads to a large difference between the model prediction result and the actual vehicle running result, reducing the effectiveness and reliability of MPC in practical application.

[0005] Lack of global optimal control: In the application process of the existing MPC technology, the traditional PID (Proportional-Integral-Derivative) control or lookup table control is simply replaced by MPC control, without considering the global optimal control strategy from the perspective of the entire working condition. This local optimization approach, although it can improve control performance in some cases, cannot achieve energy consumption optimization in the entire working condition due to the lack of comprehensive consideration of the global characteristics of the system. Therefore, this simple control strategy replacement cannot bring significant energy consumption improvement effect, limiting the potential of MPC technology in energy consumption optimization.

[0006] Multi-system collaborative prediction of missing: In the prediction process, existing MPC technology often only focuses on the dynamic behavior of a single system, ignoring the interaction between the power system and the passenger cabin system. For example, in an electric vehicle, the energy consumption of the power system is closely related to the energy consumption of the air conditioner, electronic devices, etc. in the passenger cabin. However, the existing MPC technology lacks simultaneous prediction of the behavior of the power system and the passenger cabin system, resulting in an inability to accurately predict the energy consumption demand of the entire vehicle. This single-system prediction method cannot fully reflect the energy consumption characteristics of the entire vehicle, thereby affecting the formulation and implementation of the energy consumption optimization strategy.

[0007] Due to the above-mentioned defects of MPC technology, the current new energy vehicle thermal management prediction effect is poor, the entire control process is relatively lagging, the timeliness of controlling the power assembly to change the current temperature is poor, the temperature change of the power assembly may not meet the expected requirements, and thereby the working efficiency of the power assembly is affected. SUMMARY

[0008] Embodiments of the present application provide a vehicle thermal management prediction control method, device, equipment, medium and product, so as to realize long-time domain predictive control of the entire vehicle thermal management system, ensure the real-time of the control, also minimize the energy consumption of the entire vehicle thermal management system, and improve the cruising range of the entire vehicle.

[0009] According to an aspect of the present application, a vehicle thermal management prediction control method is provided, comprising:

[0010] obtaining real-time state monitoring information of the entire vehicle, and determining long-time domain energy demand predicted under the driving condition of the entire vehicle according to the real-time state monitoring information of the entire vehicle; the real-time monitoring information of the entire vehicle comprises first information, second information and third information;

[0011] determining long-time domain segmented control target temperature according to the long-time domain energy demand;

[0012] determining model predictive control target according to the long-time domain segmented control target temperature, and realizing short-time domain optimal control of the entire vehicle thermal management actuator by controlling the model predictive control target.

[0013] According to another aspect of the present application, a vehicle thermal management prediction control device is provided, comprising:

[0014] a first determining module configured to obtain real-time state monitoring information of the entire vehicle, and determine long-time domain energy demand predicted under the driving condition of the entire vehicle according to the real-time state monitoring information of the entire vehicle; the real-time monitoring information of the entire vehicle comprises first information, second information and third information;

[0015] a second determining module configured to determine long-time domain segmented control target temperature according to the long-time domain energy demand;

[0016] a third determining module configured to determine a model predictive control target according to the segmented control target temperature of the long time domain, and to realize short time domain optimal control of the vehicle thermal management actuator by controlling the model predictive control target.

[0017] According to another aspect of the present application, an electronic device is provided, which comprises:

[0018] at least one processor; and

[0019] a memory connected to the at least one processor in communication; wherein

[0020] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the vehicle thermal management predictive control method according to any one of the embodiments of the present application.

[0021] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the vehicle thermal management predictive control method according to any one of the embodiments of the present application when executed by the processor.

[0022] According to another aspect of the present application, the embodiments of the present application further provide a computer program product, which comprises a computer program, and the computer program, when executed by a processor, implements the vehicle thermal management predictive control method according to any one of the embodiments of the present application.

[0023] The embodiments of the present application acquire real-time state monitoring information of the vehicle, and determine predicted energy demand of a long time domain under a driving condition of the vehicle according to the real-time state monitoring information of the vehicle, wherein the real-time monitoring information of the vehicle comprises first information, second information and third information; determine segmented control target temperature of the long time domain according to the energy demand of the long time domain; determine a model predictive control target according to the segmented control target temperature of the long time domain, and realize short time domain optimal control of the vehicle thermal management actuator by controlling the model predictive control target. Through the technical solution of the present application, the vehicle thermal management system can be controlled in a long time domain, which guarantees real-time control and minimizes energy consumption of the vehicle thermal management system, and improves the cruising range of the vehicle.

[0024] It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0026] Figure 1 is a flow chart of a vehicle thermal management predictive control method in an embodiment of the present application;

[0027] Figure 2 is a structural schematic diagram of a vehicle thermal management predictive control device in an embodiment of the present application;

[0028] Figure 3 is a structural schematic diagram of an electronic device for implementing a vehicle thermal management predictive control method in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the technical personnel in the art better understand the present application scheme, the following will be combined with the drawings in the embodiments of the present application, the technical solutions in the embodiments of the present application are described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and the like are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units need not be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0031] It can be understood that before using the technical solutions disclosed in the embodiments of the present disclosure, the type of personal information involved in the present disclosure, the scope of use, the use scenario and the like should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0032] Embodiment one

[0033] Figure 1is a flow chart of a vehicle thermal management predictive control method in an embodiment of the present application. The embodiment can be applicable to the case of new energy vehicle thermal management predictive control. The method can be executed by a vehicle thermal management predictive control device in an embodiment of the present application. The device can be realized in the form of software and / or hardware, for example, as shown in the figure. The method specifically includes the following steps: Figure 1

[0034] S101, real-time vehicle state monitoring information is acquired, and long-time domain energy demand predicted under vehicle driving conditions is determined according to the real-time vehicle state monitoring information.

[0035] In the embodiment, the real-time vehicle state monitoring information can be information of various states monitored in real time by the vehicle during operation, for example, can include road information, vehicle information, and passenger cabin information, etc.

[0036] The real-time vehicle state monitoring information includes first information, second information, and third information.

[0037] Optionally, the first information includes navigation information, road speed limit information, road curvature information, road slope information, human-machine interface driver cruise control setting information, vehicle speed information, and vehicle acceleration information; the second information includes passenger characteristic information (for example, can include passenger BMI (Body Mass Index), age, gender, clothing, height, etc. It should be noted that the acquisition of passenger characteristic information needs to be performed under the authorization of the passenger), passenger cabin state information, and weather forecast information; the passenger cabin state information includes passenger cabin temperature and passenger cabin humidity; and the third information includes real vehicle speed, real vehicle acceleration, vehicle distance information, and passenger cabin real-time temperature demand information.

[0038] It should be noted that the long-time domain energy demand can be future energy consumption demand of the vehicle.

[0039] Specifically, based on vehicle operating conditions, the future energy demand of the vehicle is globally predicted through real-time traffic flow, vehicle speed, vehicle information, and road information, in combination with the power system and the passenger cabin air conditioning system. The energy consumption of the vehicle system is accurately predicted by establishing an energy consumption model of the vehicle power system, an energy consumption model of the vehicle thermal management system, and an energy demand model of the passenger cabin.

[0040] S102, long-time domain segmented control target temperature is determined according to the long-time domain energy demand.

[0041] The segmented control target temperature can be a target temperature of the vehicle battery in the long-time domain under charging and discharging conditions.

[0042] ​Specifically, full operating condition analysis is performed, a target temperature prediction method under charging and discharging conditions is established, and based on predicted position information, predicted vehicle SOC (State of Charge) information (in actual operation, the long-term SOC value can be calculated by applying the ampere-hour integral method based on the current SOC value and voltage, through the predicted long-term demand current), predicted ambient temperature, motor efficiency and battery efficiency, the predicted target temperature of the optimal efficiency, i.e. the long-term segmented control target temperature, is calculated.

[0043] S103, determine the model predictive control target according to the long-term segmented control target temperature, and realize short-term optimal control of the vehicle thermal management actuator by controlling the model predictive control target.

[0044] For example, the model predictive control target may include any one or any combination of the compressor, water pump, fan, valve, etc. in the thermal management system.

[0045] Specifically, the optimal temperature control target in the full time domain is sent to the bottom layer real-time MPC control layer, and the short-term optimal control of the vehicle thermal management actuator is realized through the real-time vehicle thermal management feedback state.

[0046] The embodiment of the application can obtain real-time state monitoring information of the vehicle, and determine the predicted long-term energy demand of the vehicle driving condition according to the real-time state monitoring information of the vehicle; wherein the real-time monitoring information of the vehicle includes: first information, second information and third information; determine the long-term segmented control target temperature according to the long-term energy demand; determine the model predictive control target according to the long-term segmented control target temperature, and realize short-term optimal control of the vehicle thermal management actuator by controlling the model predictive control target. Through the technical scheme of the application, the long-term predictive control of the vehicle thermal management system can be realized, which ensures the real-time of the control, and also minimizes the energy consumption of the vehicle thermal management system, and improves the endurance mileage of the vehicle.

[0047] Optionally, determining the predicted long-term energy demand of the vehicle driving condition according to the real-time state monitoring information of the vehicle, comprising:

[0048] The first information and the second information are input into the long-term vehicle power prediction state equation, and quadratic programming is solved to obtain the energy consumption of the predicted power system.

[0049] Specifically, a vehicle motion state equation is established based on a vehicle longitudinal motion equation through a vehicle longitudinal dynamics formula. A constraint function is established based on a fuel consumption rate limit on torque, a road curvature limit, a slope limit, a vehicle speed limit in road regulations, a vehicle acceleration limit, and a passenger speed limit on the vehicle. A running distance to a destination is calculated through navigation information, a time interval for a long-time-domain prediction step number is determined, a long-time-domain vehicle power prediction state equation is established, quadratic programming is solved, and energy consumption of a predicted power system is obtained.

[0050] The second information is input into the passenger long-time-domain human comfort model to obtain a predicted passenger cabin long-time-domain temperature demand, and heat exchange conversion is performed on the predicted passenger cabin long-time-domain temperature demand to obtain a predicted passenger cabin long-time-domain energy demand.

[0051] It can be known that the passenger long-time-domain human comfort model refers to a PMV (Predicted Mean Vote) model.

[0052] The passenger cabin long-time-domain temperature demand can be a predicted human comfort temperature demand in a long-time domain, and the passenger cabin long-time-domain energy demand can be a predicted passenger cabin long-time-domain energy demand obtained by performing heat exchange conversion on an optimal human comfort temperature.

[0053] Specifically, a predicted human comfort index in a long-time domain is calculated through a predicted human comfort model based on predicted vehicle position information, a predicted weather environment temperature, and passenger human characteristics, an optimal human comfort temperature is obtained by de-fuzzification, and heat exchange conversion is performed on the optimal human comfort temperature to obtain a predicted passenger cabin long-time-domain energy demand.

[0054] For example, a predicted passenger cabin demand temperature and a blower air volume demand can be calculated through a predicted human comfort value. For the predicted passenger cabin demand temperature, a compressor power demand is calculated based on a demand temperature of an evaporator, and for the blower air volume demand, a blower power demand is calculated. Finally, a predicted passenger cabin long-time-domain energy demand is obtained according to the compressor power demand and the blower power demand.

[0055] According to the energy consumption of the predicted power system and the predicted passenger cabin long-time-domain energy demand, a predicted long-time-domain energy demand under a vehicle driving condition is determined.

[0056] Specifically, a predicted long-time-domain energy demand under a vehicle driving condition is calculated based on vehicle driving energy consumption (i.e., the energy consumption of the predicted power system) and passenger heat demand (i.e., the predicted passenger cabin long-time-domain energy demand).

[0057] Optionally, the segmented control target temperature in the long time domain is determined according to the energy demand in the long time domain, comprising:

[0058] The optimal target temperature in the long time domain is determined according to the second information.

[0059] The optimal target temperature can be a target temperature of optimal efficiency.

[0060] Specifically, the optimal target temperature of efficiency is calculated based on the predicted position (which can be obtained by inputting the first information into a long time domain vehicle motion state equation), the ambient temperature information (which can be obtained according to the second information), and the motor and battery efficiency.

[0061] For example, in the battery discharge state, the ambient temperature at different positions is obtained based on the predicted position information. Based on the efficiency map of the battery and the motor, the optimal target temperature of the motor and the battery at different predicted positions can be calculated.

[0062] For example, in the battery charging state, the ambient temperature at different positions and the predicted SOC value when reaching the charging pile are obtained based on the predicted position information. The optimal target temperature of the battery when charging can be calculated based on the predicted ambient temperature and the predicted SOC value when charging.

[0063] The predicted demand power is determined according to the energy demand in the long time domain, and the vehicle charging judgment result is determined based on the state of charge prediction model of the battery according to the predicted demand power.

[0064] The demand power can be the power required by the vehicle battery, and the state of charge prediction model of the battery can be a prediction model for predicting the SOC of the vehicle, which can be referred to as a SOC prediction model hereinafter.

[0065] For example, the vehicle charging judgment result can be that the vehicle needs to be charged, or the vehicle does not need to be charged.

[0066] Specifically, the future demand current is calculated based on the calculated vehicle energy demand in the long time domain, and the SOC prediction model is used to determine whether the vehicle will be charged.

[0067] If the vehicle charging judgment result is that the vehicle needs to be charged, the optimal battery charging temperature when reaching the charging pile is determined according to the real-time state monitoring information of the vehicle, which is used as the segmented control target temperature in the long time domain.

[0068] Specifically, if the vehicle needs to be charged, the optimal battery charging temperature when the vehicle reaches the charging pile is calculated based on the ambient temperature, the position information and the predicted SOC value, so as to ensure the optimal battery charging efficiency.

[0069] If the vehicle charging determination result is that charging is not needed, it is determined through the heat generation model whether the vehicle battery temperature exceeds the highest efficient operation temperature interval, and if the vehicle battery exceeds the highest efficient operation temperature interval, model prediction control of the long-time-domain heat management heat transfer state equation is performed to solve the long-time-domain segmented control target temperature.

[0070] It can be known that the heat generation model is a mathematical model for describing heat generation of a system or equipment during operation. In the embodiment, the heat generation model can be a mathematical model for describing heat generation of the battery and the motor of the vehicle during operation. Specifically, the battery heat generation model calculates the predicted battery temperature change based on the Bernardi battery heat generation formula, the motor heat generation model calculates the predicted motor temperature change based on motor copper loss and mechanical loss, and then the global heat generation model is obtained.

[0071] The highest efficient operation temperature interval can be a preset battery temperature interval of the vehicle during operation, which is composed of a minimum threshold and a maximum threshold, and the specific threshold is not limited in the embodiment.

[0072] In the embodiment, the heat management heat transfer state equation includes a motor heat transfer state equation and a battery heat transfer state equation.

[0073] Specifically, if charging is not needed, it is determined through the heat generation model whether the battery will exceed the highest efficient operation temperature interval during the entire long-time-domain operation condition. If the battery will exceed the highest efficient operation temperature interval, model prediction control of the long-time-domain heat management heat transfer state equation is performed to solve the long-time-domain segmented control target temperature.

[0074] Optionally, the model prediction control target is determined according to the long-time-domain segmented control target temperature, including:

[0075] According to the third information and the long-time-domain segmented control target temperature, the model prediction control target is determined through quadratic programming solution based on the heat management heat transfer state equation.

[0076] Optionally, the heat management heat transfer state equation is a state equation corrected based on extended Kalman filtering.

[0077] In order to ensure that the long-time-domain state equation is consistent with the actual system state of the vehicle, the state feedback of the lower short-time-domain is received, the state equation of the upper long-time-domain is corrected based on extended Kalman filtering, and the accuracy of the state equation model is ensured.

[0078] In the quadratic programming solution, the actual power limit, the compressor high and low pressure limit, the environment temperature, the compressor speed limit, the fan speed limit and the water pump speed limit are used for quadratic programming solution.

[0079] Specifically, after receiving the segmented target temperature of the upper layer, the short-time-domain thermal management heat transfer state equation of the lower layer is modified based on the extended Kalman filter, and then the optimal model predictive control target is calculated by quadratic programming based on the actual power limit, compressor high and low pressure limit, environmental temperature, compressor speed limit, fan speed limit and water pump speed limit, to control the actuator while feeding back to the long-time-domain thermal management heat transfer state equation of the upper layer.

[0080] In actual operation, after determining the model predictive control target according to the segmented control target temperature of the long-time-domain and controlling the model predictive control target, the long-time-domain thermal management heat transfer state equation is modified based on the short-time-domain predicted thermal management heat transfer state feedback. Since the thermal management heat transfer state equation is a nonlinear state equation, and in order to ensure the accuracy of the thermal management heat transfer state equation, feedback correction and linearization processing of the long-time-domain thermal management heat transfer state equation are required. After receiving the state feedback of the short-time-domain, the transition matrix A and the transition matrix B in the state equation are corrected by the extended Kalman filter, to obtain the modified linear thermal management heat transfer state equation. Finally, based on the optimal predicted battery and motor target temperature and the constrained energy limit condition, the quadratic programming is solved to obtain the segmented control target temperature of the upper layer of the long-time-domain.

[0081] Among them, the heat transfer equation of the motor first calculates the heat generation of the motor, and then obtains the nonlinear motor heat transfer state equation through the heat exchange with the motor coil, the heat exchange with the motor shell, the heat exchange with the motor cooling plate and the heat exchange with the motor cooling liquid. The heat exchange of the non-cooling liquid is calculated through the heat transfer coefficient and the contact area between objects. The temperature change of the motor cooling liquid is calculated by subtracting the heat carried away by the motor cooling liquid due to flow from the heat exchange with the motor cooling plate. Based on the actual motor temperature feedback, the heat transfer and heat exchange coefficients are corrected by the extended Kalman filter to obtain the modified linear motor heat transfer state equation.

[0082] Among them, the heat transfer equation of the battery first calculates the heat generation of the battery, and then obtains the nonlinear battery heat transfer state equation through the heat exchange with the adhesive, the heat exchange with the battery shell, the heat exchange with the gap filler, the heat exchange with the cooling plate and the heat exchange with the battery cooling liquid. The heat exchange of the non-cooling liquid is calculated through the heat transfer coefficient and the contact area between objects. The temperature change of the battery cooling liquid is calculated by subtracting the heat carried away by the motor cooling liquid due to flow from the heat exchange with the motor cooling plate. Based on the actual battery temperature feedback, the heat transfer and heat exchange coefficients are corrected by the extended Kalman filter to obtain the modified linear battery heat transfer state equation.

[0083] In summary, the method of the embodiment is based on the running conditions of the whole vehicle, and the energy demand of the vehicle in the future is globally predicted by combining the power system and the air conditioning system of the passenger cabin through real-time traffic flow, vehicle speed, vehicle information and road information. The energy consumption of the vehicle power system, the energy consumption model of the whole vehicle thermal management system and the energy demand model of the passenger cabin are established to accurately predict the energy consumption of the whole vehicle system. The whole running condition analysis is carried out, and the target temperature prediction method under the charging and discharging conditions is established. Based on the predicted position information, the predicted SOC information, the predicted ambient temperature, the motor efficiency and the battery efficiency, the optimal efficiency of the predicted target temperature is calculated. The method of short-time feedback correction of the long-time thermal management heat transfer state equation based on the extended Kalman filter is established, so that the predicted state of the thermal management system is consistent with the actual whole vehicle performance, and the optimal dynamic target operating temperature of the motor and the battery is calculated based on the predicted weather temperature, which ensures the accuracy of the control target solution of the MPC quadratic programming. Through the hierarchical prediction control method, the MPC control temperature target with optimal energy consumption in the whole time domain is sent to the real-time short-time MPC control layer of the lower layer to control the thermal management components, and finally the energy consumption of the whole vehicle is minimized.

[0084] The technical scheme of the embodiment of the application provides a hierarchical prediction thermal management control algorithm based on whole vehicle driving energy consumption and passenger thermal demand. The thermal management prediction control method obtains road information, vehicle information and passenger cabin information, considers the optimal passenger cabin temperature, the target temperature of the battery and the motor under different ambient temperatures, performs long-time predictive control on the whole vehicle thermal management system for different vehicle running conditions, ensures the real-time performance of the control, minimizes the energy consumption of the whole vehicle thermal management system, improves the cruising range of the whole vehicle, and improves the intelligence and real-time performance of the thermal management control of the new energy vehicle.

[0085] Embodiment two

[0086] Figure 2 Fig. 1 is a structural schematic diagram of a vehicle thermal management prediction control device in the embodiment of the application. The embodiment can be applied to the case of new energy vehicle thermal management prediction control. The device can be realized in the form of software and / or hardware, and can be integrated in any device providing the function of vehicle thermal management prediction control, such as a vehicle thermal management prediction control device. Figure 2 As shown in Fig. 1, the vehicle thermal management prediction control device specifically comprises a first determination module 201, a second determination module 202 and a third determination module 203.

[0087] The first determination module 201 is configured to obtain whole vehicle real-time state monitoring information, and determine the predicted long-time energy demand in the running condition of the whole vehicle according to the whole vehicle real-time state monitoring information. The whole vehicle real-time monitoring information comprises first information, second information and third information.

[0088] The second determining module 202 is configured to determine a segmented control target temperature in a long time domain according to the energy demand in the long time domain.

[0089] The third determining module 203 is configured to determine a model prediction control target according to the segmented control target temperature in the long time domain, so as to realize short time domain optimal control of the vehicle thermal management actuator through control of the model prediction control target.

[0090] Optionally, the first determining module 201 is specifically configured to:

[0091] The first information and the second information are input into a vehicle power prediction state equation in a long time domain, and quadratic programming is solved to obtain energy consumption of a predicted power system.

[0092] The second information is input into a passenger long time domain human comfort model to obtain a predicted passenger cabin long time domain temperature demand, and heat exchange conversion is performed on the predicted passenger cabin long time domain temperature demand to obtain a predicted passenger cabin long time domain energy demand.

[0093] The predicted long time domain energy demand under the vehicle driving condition is determined according to the energy consumption of the predicted power system and the predicted passenger cabin long time domain energy demand.

[0094] Optionally, the second determining module 202 is specifically configured to:

[0095] An optimal target temperature in the long time domain is determined according to the second information.

[0096] A predicted demand power is determined according to the energy demand in the long time domain, and a vehicle charging judgment result is determined according to the predicted demand power and a state of charge prediction model of a battery.

[0097] If the vehicle charging judgment result is that charging is needed, an optimal battery charging temperature when reaching a charging pile is determined according to the real-time state monitoring information of the vehicle, and the optimal battery charging temperature is taken as the segmented control target temperature in the long time domain.

[0098] If the vehicle charging judgment result is that charging is not needed, whether the temperature of the vehicle battery exceeds a highest efficient operation temperature interval is determined through a heat generation model, and if the temperature of the vehicle battery exceeds the highest efficient operation temperature interval, model prediction control of a thermal management heat transfer state equation in the long time domain is performed to solve the segmented control target temperature in the long time domain.

[0099] Optionally, the third determining module 203 is specifically configured to:

[0100] A model prediction control target is determined according to the third information and the segmented control target temperature in the long time domain and through quadratic programming solution based on a thermal management heat transfer state equation.

[0101] Optionally, the heat management heat transfer state equation is a state equation corrected based on an extended Kalman filter.

[0102] In the quadratic programming solving, based on actual power limit, compressor high and low pressure limit, environment temperature, compressor rotating speed limit, fan rotating speed limit and water pump rotating speed limit, the quadratic programming is solved.

[0103] Optionally, the first information includes: navigation information, road speed limit information, road curvature information, road slope information, human-computer interface driver cruise control setting information, vehicle speed information and vehicle acceleration information.

[0104] The second information includes: passenger characteristic information, passenger compartment state information and weather forecast information; wherein the passenger compartment state information includes: passenger compartment temperature and passenger compartment humidity.

[0105] The third information includes: real vehicle speed, real vehicle acceleration, vehicle distance information and passenger compartment real-time temperature demand information.

[0106] The product can execute the vehicle thermal management prediction control method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.

[0107] Embodiment three

[0108] Figure 3 A structural schematic diagram of an electronic device 30 that can be used to implement embodiments of the application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the inventiveness in the way in which the described and / or claimed application is implemented.

[0109] As Figure 3As shown, the electronic device 30 includes at least one processor 31, and a memory, such as a read-only memory (ROM) 32, a random access memory (RAM) 33, etc., connected in communication with the at least one processor 31, wherein the memory stores a computer program executable by the at least one processor. The processor 31 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 32 or loaded from the storage unit 38 into the random access memory (RAM) 33. In the RAM 33, various programs and data required for the operation of the electronic device 30 can also be stored. The processor 31, the ROM 32, and the RAM 33 are connected to each other through a bus 34. An input / output (I / O) interface 35 is also connected to the bus 34.

[0110] A plurality of components in the electronic device 30 are connected to the I / O interface 35, including: an input unit 36, such as a keyboard, a mouse, etc.; an output unit 37, such as various types of displays, speakers, etc.; a storage unit 38, such as a magnetic disk, an optical disk, etc.; and a communication unit 39, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0111] The processor 31 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 31 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 31 performs various methods and processes described above, such as the vehicle thermal management predictive control method:

[0112] Obtaining real-time vehicle state monitoring information, and determining a predicted long-time-domain energy demand under a vehicle driving condition according to the real-time vehicle state monitoring information; the real-time vehicle monitoring information includes: first information, second information, and third information;

[0113] Determining a long-time-domain segmented control target temperature according to the long-time-domain energy demand;

[0114] Determining a model predictive control target according to the long-time-domain segmented control target temperature, and realizing short-time-domain optimal control of the vehicle thermal management actuators through control of the model predictive control target.

[0115] In some embodiments, the vehicle thermal management predictive control method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 38. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 30 via, e.g., ROM 32 and / or communication unit 39. When the computer program is loaded onto RAM 33 and executed by processor 31, one or more steps of the vehicle thermal management predictive control method described above can be performed. Alternatively, in other embodiments, processor 31 can be configured to perform the vehicle thermal management predictive control method by other means, e.g., with the aid of firmware.

[0116] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0117] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0118] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0119] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0120] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain network, and the Internet.

[0121] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are a host product in the cloud computing service system to solve the defects of large management difficulty and weak business scalability in traditional physical hosts and VPS services.

[0122] In an embodiment, the present embodiment further includes a computer program product comprising a computer program which, when executed by a processor, implements the vehicle thermal management predictive control method of any of the embodiments of the present application.

[0123] The computer program product can be written in any form of programming language, including object-oriented programming languages such as Java, Smalltalk, C++, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0124] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present application. For example, the steps recited in the present application can be performed in parallel, in series, or in a different order, without departing from the desired results of the technical solutions of the present application, and this is not limited herein.

[0125] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A vehicle thermal management predictive control method, characterized by, The method comprises the following steps: acquiring real-time vehicle state monitoring information, and determining a predicted long-time-domain energy demand under a vehicle driving condition according to the real-time vehicle state monitoring information; the real-time vehicle monitoring information comprises first information, second information and third information; determining a long-time-domain segmented control target temperature according to the long-time-domain energy demand; determining a model prediction control target according to the long-time-domain segmented control target temperature, and performing short-time-domain optimal control on a vehicle thermal management actuator by controlling the model prediction control target.

2. The method of claim 1, wherein, determining a predicted long-time-domain energy demand under a vehicle driving condition according to the real-time vehicle state monitoring information, comprising: inputting the first information and the second information into a long-time-domain vehicle power prediction state equation, and performing quadratic programming to obtain a predicted power system energy consumption; inputting the second information into a passenger long-time-domain human comfort model to obtain a predicted passenger cabin long-time-domain temperature demand, and performing heat exchange conversion on the predicted passenger cabin long-time-domain temperature demand to obtain a predicted passenger cabin long-time-domain energy demand; determining a predicted long-time-domain energy demand under a vehicle driving condition according to the predicted power system energy consumption and the predicted passenger cabin long-time-domain energy demand.

3. The method of claim 1, wherein, determining a long-time-domain segmented control target temperature according to the long-time-domain energy demand, comprising: determining an optimal target temperature under the long-time-domain according to the second information; determining a predicted demand power according to the long-time-domain energy demand, and determining a vehicle charging judgment result based on a state of charge prediction model of a battery according to the predicted demand power; if the vehicle charging judgment result is that charging is needed, determining an optimal battery charging temperature when reaching a charging pile as the long-time-domain segmented control target temperature according to the real-time vehicle state monitoring information; if the vehicle charging judgment result is that charging is not needed, determining whether the vehicle battery temperature exceeds a highest efficient operation temperature interval by a heat generation model, and if the vehicle battery exceeds the highest efficient operation temperature interval, performing model prediction control on a long-time-domain thermal management heat transfer state equation to solve the long-time-domain segmented control target temperature.

4. The method of claim 1, wherein, determining a model prediction control target according to the long-time-domain segmented control target temperature, comprising: determining the model prediction control target by quadratic programming based on a thermal management heat transfer state equation according to the third information and the long-time-domain segmented control target temperature.

5. The method of claim 4, wherein, the thermal management heat transfer state equation is a state equation corrected based on an extended Kalman filter; in the quadratic programming, actual power limitation, compressor high and low pressure limitation, environmental temperature, compressor speed limitation, fan speed limitation and water pump speed limitation are used for quadratic programming.

6. The method of claim 1, wherein, the first information comprises navigation information, road speed limitation information, road curvature information, road slope information, human-machine interface driver cruise control setting information, vehicle speed information and vehicle acceleration information; the second information comprises passenger characteristic information, passenger cabin state information and weather forecast information; the passenger cabin state information comprises passenger cabin temperature and passenger cabin humidity; The third information includes: real vehicle speed, real vehicle acceleration, vehicle distance information, and real-time temperature demand information of the passenger cabin.

7. A vehicle thermal management predictive control apparatus, characterized by, The method comprises the following steps: The first determining module is configured to acquire real-time vehicle state monitoring information, and determine a long-time domain energy demand predicted under a vehicle driving condition according to the real-time vehicle state monitoring information; The real-time vehicle monitoring information includes: first information, second information, and third information; The second determining module is configured to determine a long-time domain segmented control target temperature according to the long-time domain energy demand; The third determining module is configured to determine a model predictive control target according to the long-time domain segmented control target temperature, and perform short-time domain optimal control on vehicle thermal management actuators by controlling the model predictive control target.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the vehicle thermal management predictive control method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the vehicle thermal management predictive control method in any one of claims 1-6 when executed.

10. A computer program product comprising a computer program which, when executed by a processor, implements the vehicle thermal management predictive control method according to any one of claims 1-6.

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