Vehicle control method, device and equipment based on thermal power, medium and product
By generating optimized parameters through a comprehensive vehicle thermodynamic model and optimization functions, the problem of non-optimal energy consumption distribution in the vehicle thermal management system was solved, achieving global optimization of energy consumption and improving the vehicle's range and comfort.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-04
- Publication Date
- 2026-04-03
AI Technical Summary
Existing vehicle thermal management systems lack forward-looking prediction and multi-objective optimization, resulting in high energy consumption, insufficient precision in comfort adjustment, and an inability to achieve globally optimal energy allocation.
Based on the comprehensive thermodynamic model of the whole vehicle, data on vehicle status, environmental status and comfort requirements are obtained. Optimization parameters are generated and the actuators are controlled to achieve energy consumption optimization by using the optimization function and constraints that minimize total heat power consumption.
It reduces the total power consumption of the vehicle's thermal management system, improves the vehicle's range and comfort, and provides a highly efficient and energy-saving control solution.
Smart Images
Figure CN121777640A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle thermal management and energy consumption control, and in particular to a vehicle control method, device, equipment, medium, and product based on thermodynamics. Background Technology
[0002] In the field of vehicle thermal management energy consumption control, how to achieve optimal energy consumption allocation while ensuring passenger comfort is the core link to improving vehicle energy utilization efficiency, which is directly related to the vehicle's operating economy and user experience.
[0003] Existing vehicle thermal management control methods mostly rely on rule-based logical judgment or traditional feedback control, lacking forward prediction and multi-objective optimization of vehicle thermodynamic models. This can easily lead to control response lag, non-global optimal energy consumption allocation, and other issues, resulting in high energy consumption of the thermal management system and insufficient accuracy of comfort adjustment. Consequently, these methods cannot provide efficient and reliable energy-saving control for the vehicle's thermal management system. Summary of the Invention
[0004] This invention provides a vehicle control method, device, equipment, medium, and product based on thermodynamics, to reduce vehicle thermal management energy consumption and improve vehicle energy utilization efficiency.
[0005] According to one aspect of the present invention, a vehicle control method based on thermodynamics is provided, the method comprising:
[0006] Obtain the target vehicle's current vehicle status data, current environmental status data, and comfort requirement data within the current time period;
[0007] Based on current status data, environmental status data, and comfort requirement data, and using a pre-built comprehensive vehicle thermodynamic model, the constraint performance parameter data are determined.
[0008] Based on the constraint performance parameter data and the pre-built constraints, the initial values of the current optimization parameters for the current time period are generated.
[0009] Based on the initial values of the current optimization parameters, the target optimization parameters are determined according to the pre-constructed optimization function for minimizing total heat consumption and the constraints.
[0010] Control the corresponding actuators to execute the target optimization parameters.
[0011] According to another aspect of the present invention, a thermodynamic vehicle control device is provided, the device comprising:
[0012] The vehicle status data acquisition module is used to acquire the target vehicle's current vehicle status data, current environmental status data, and comfort requirement data in the current time period.
[0013] The constraint performance parameter data determination module is used to determine constraint performance parameter data based on the current state data, environmental state data, and comfort requirement data, and on a pre-built comprehensive vehicle thermodynamic model.
[0014] The module for determining the initial value of optimization parameters is used to generate the initial value of the current optimization parameters for the current time period based on the constraint performance parameter data and the pre-built constraint conditions.
[0015] The target optimization parameter determination module is used to determine the target optimization parameters based on the initial values of the current optimization parameters, the pre-built total heat power consumption minimization optimization function, and the constraints.
[0016] The target optimization parameter execution module is used to control the corresponding executor to execute the target optimization parameters.
[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:
[0018] At least one processor; and
[0019] A memory that is communicatively connected to at least one processor; wherein,
[0020] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to execute a thermodynamic vehicle control method according to any embodiment of the present invention.
[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement a thermodynamic vehicle control method according to any embodiment of the present invention.
[0022] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements a thermodynamic vehicle control method according to any embodiment of the present invention.
[0023] In the field of vehicle thermal management and energy consumption control, the technical solution of this invention acquires the current vehicle state data, current environmental state data, and comfort requirement data of the target vehicle in the current time period. Based on the current state data, environmental state data, and comfort requirement data, and using a pre-built comprehensive vehicle thermodynamic model, constraint performance parameter data is determined. Based on the constraint performance parameter data and the pre-built constraints, initial values of the current optimization parameters in the current time period are generated. Based on the initial values of the current optimization parameters, and using a pre-built minimum total heat power consumption optimization function and constraints, the target optimization parameters are determined. The corresponding actuators are then controlled to execute the target optimization parameters. This method, based on the current vehicle state, environmental state, and comfort requirements, utilizes a pre-built comprehensive vehicle thermodynamic model to determine constraint performance parameters and generate initial values of optimization parameters. Then, the target optimization parameters are solved and executed using the minimum total heat power consumption optimization function. This effectively solves the problem of suboptimal energy allocation in traditional control methods, reduces the total power consumption of the vehicle thermal management system, improves the vehicle's range and comfort, and provides reliable technical support for the efficient and energy-saving operation of vehicles.
[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description
[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0026] Figure 1 This is a flowchart of a vehicle control method based on thermodynamics according to Embodiment 1 of the present invention;
[0027] Figure 2 This is a flowchart of a vehicle control method based on thermodynamics according to Embodiment 2 of the present invention;
[0028] Figure 3 This is a flowchart of a vehicle control method based on thermodynamics according to Embodiment 3 of the present invention;
[0029] Figure 4 This is a schematic diagram of a vehicle control device based on thermodynamics according to Embodiment 4 of the present invention;
[0030] Figure 5This is a schematic diagram of the structure of an electronic device that implements a vehicle control method based on thermodynamics according to an embodiment of the present invention. Detailed Implementation
[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0033] Example 1
[0034] Figure 1 This is a flowchart of a vehicle control method based on thermodynamics provided in Embodiment 1 of the present invention. This embodiment is applicable to scenarios involving reducing vehicle thermal management energy consumption in the field of vehicle thermal management energy consumption control. The method can be executed by a vehicle control device based on thermodynamics, which can be implemented in hardware and / or software and can be configured in an electronic device. For example... Figure 1 As shown, the method includes:
[0035] S101. Obtain the target vehicle's current vehicle status data, current environmental status data, and comfort requirement data for the current time period.
[0036] S102. Based on the current state data, environmental state data, and comfort requirement data, determine the constraint performance parameter data based on the pre-built comprehensive vehicle thermodynamic model.
[0037] S103. Based on the constraint performance parameter data and the pre-built constraint conditions, generate the initial values of the current optimization parameters for the current time period.
[0038] S104. Based on the initial values of the current optimization parameters, determine the target optimization parameters according to the pre-constructed optimization function for minimizing total heat consumption and the constraints.
[0039] S105, Control the corresponding actuator to execute the target optimization parameters.
[0040] The target vehicle can be a hybrid vehicle, a pure electric vehicle, or similar type of vehicle. The current time period can be the present time. Current vehicle status data describes the vehicle's status within the current time period. Current environmental status data describes the environmental status within the current time period. Comfort requirement data describes the comfort level within the current time period.
[0041] For example, corresponding sensors can be deployed on the target vehicle to obtain relevant current vehicle status data, current environmental status data, and comfort requirement data.
[0042] Furthermore, in order to accurately describe the current vehicle status data, current environmental status data, and comfort requirement data, and to improve the accuracy of the collected data, in one optional embodiment, the current vehicle status data includes engine speed, engine torque, motor power, remaining battery charge, battery current, cabin temperature, engine temperature, battery temperature, and coolant temperature at key points; the current environmental status data includes ambient temperature and sunlight intensity; and the comfort requirement data includes the target cabin temperature set by the driver.
[0043] The technical solutions described above ensure that the comprehensive thermodynamic model of the vehicle obtains comprehensive and accurate input data, providing reliable support for the determination of constraint parameters and optimization calculations, thereby improving the accuracy of the coordinated control of the thermal management system and the energy efficiency optimization effect, while ensuring passenger comfort and component safety.
[0044] The integrated vehicle thermodynamic model can be used to perform constraint calculations based on current vehicle state data, current environmental state data, and comfort requirement data. The constraint performance parameters can be the parameters indicating the existence of constraints calculated by inputting the current vehicle state data, current environmental state data, and comfort requirement data into the integrated vehicle thermodynamic model.
[0045] For example, the relationship between battery temperature and cabin temperature can be calculated using a model based on battery temperature. If there are constraints on cabin temperature, then battery temperature can be one of the constraint performance parameter data.
[0046] Furthermore, in order to regulate the heat transfer process in the vehicle as a whole and achieve the linkage effect of the vehicle thermal management module, in an optional embodiment, the overall vehicle thermodynamic model includes an integrated cabin thermal dynamic model, a powertrain heat generation and dissipation model, and a dynamic heat flow allocation model; the dynamic heat flow allocation model is constructed by connecting the cabin thermal dynamic model and the powertrain heat generation and dissipation model in series.
[0047] The integrated cockpit thermal dynamics model is as follows:
[0048]
[0049]
[0050]
[0051]
[0052]
[0053]
[0054] in, The equivalent heat capacity of the cabin is obtained from standard vehicle data. Represents the average temperature of the cabin. Represents the rate of change of cabin temperature over time. For HVAC (Heating, Ventilation and Air Conditioning) systems, the heating or cooling capacity of the cabin; The heat dissipation power of the occupants' bodies is calculated based on a pre-set human thermal model and the number of occupants by relevant technical personnel. Thermal power, representing solar radiation, is calculated using vehicle sensors. Represents the total heat loss between the cabin and the environment. Representing the heat transfer area, obtained from standard vehicle data. Represents ambient temperature, obtained through the vehicle's built-in sensors. Represents the thermal sensing coefficient of the entire vehicle. Represents vehicle fan speed, The representative arbitrarily pre-sets a form using relevant technology, according to It can be found Specific value, This represents the heating power of a PTC (Positive Temperature Coefficient) heater. This refers to the electrothermal conversion efficiency of the PTC heater (which is close to 1). Represents the heating power of the electric compressor, (Coefficient of Performance) can be obtained in advance by relevant technical personnel. Represents the power generated by the engine's waste heat. Represents battery heat generation capacity, This represents the heat output power of the motor.
[0055] The powertrain heat generation and dissipation model includes an engine waste heat sub-model, an electric motor heat generation sub-model, and a battery heat generation sub-model, specifically:
[0056] The engine waste heat sub-model includes:
[0057]
[0058]
[0059]
[0060]
[0061] in, Represents the heat power carried away by the coolant from the engine. Represents engine temperature, Represents the rate of change of engine temperature over time. The specific heat capacity of the coolant is obtained in advance by relevant technical personnel. Represents the mass flow rate of coolant flowing through the engine. The coolant temperature at the engine outlet can be obtained from vehicle sensors. This represents the engine inlet coolant temperature, which can be obtained from vehicle sensors. Represents the waste heat generated by the engine, The power representing the heat dissipated by the engine to the outside environment is usually negligible. Represents the mass flow rate of fuel, (Lower Heating Value) is obtained in advance by relevant technical personnel. This represents engine thermal efficiency, which can be pre-calculated by relevant personnel based on the current engine speed and torque corresponding to different engine types. Represents engine speed, Represents engine torque, The power output of the engine shaft can be obtained in advance by relevant personnel based on different engine types.
[0062] The electric motor heating element model includes:
[0063]
[0064] in, This represents the heat generated by the motor. Represents the electrical power input to the motor system, The efficiency of a motor is a property that is related to motor temperature. motor angular velocity and motor torque The relevant functions, such as motor torque and motor angular velocity, can be obtained in advance by relevant technicians based on the motor.
[0065] The battery heating element model includes:
[0066]
[0067] in, Represents the heat output generated by the vehicle battery. Represents the current of the vehicle battery, The internal resistance of a vehicle battery can be obtained in advance by relevant technicians. (State of Charge, remaining battery power) represents the remaining battery power, which can be obtained from vehicle sensors. Represents the temperature of the vehicle battery, This indicates that the vehicle battery reaction is reversible and can be obtained in advance by relevant technicians. The convective transfer coefficient between the vehicle battery and coolant can be obtained in advance by relevant technicians. The heat dissipation area of the vehicle battery Represents the temperature of the vehicle battery coolant. The overall thermal capacity of the vehicle battery can be obtained in advance by relevant technicians. This represents the rate of change of vehicle battery temperature over time.
[0068] The dynamic heat flow distribution model includes:
[0069]
[0070]
[0071]
[0072] in, This represents the heat power transferred to the cabin through the heating heat exchanger. The performance of a heat exchanger can be obtained in advance by relevant technical personnel. The proportional constant representing the vehicle's water pump can be obtained in advance by relevant technical personnel. Represents the vehicle's water pump speed, The specific heat capacity at constant pressure, representing the coolant, can be obtained in advance by relevant technical personnel. The temperature of the coolant flowing into the heat exchanger can be pre-acquired by relevant technicians based on sensors on the vehicle. Represents the valve opening degree, when =1 indicates that engine coolant is introduced into the cockpit; when =0 indicates that coolant from the battery circuit is introduced into the cabin.
[0073] The above solution integrates the cabin thermal dynamics model, the powertrain heat generation and dissipation model, and then connects them with a dynamic heat flow allocation model. This breaks the traditional situation where the cabin and powertrain thermal management systems operate independently, and integrates cabin temperature regulation, component heat generation and dissipation, and heat transfer paths into a unified system, thereby achieving global control over the heat flow of the entire vehicle.
[0074] Among them, the pre-built constraints can include temperature constraints to ensure occupant comfort, temperature constraints to ensure the safe operation of key vehicle components, and physical constraints to ensure the normal operation of actuators. The initial values of the current optimization parameters can be random values of the optimization parameters that satisfy the constraints for all time periods within the prediction time domain, generated based on the pre-built constraints.
[0075] Specifically, the pre-built constraints include:
[0076]
[0077]
[0078]
[0079]
[0080]
[0081]
[0082]
[0083] in, The minimum value representing the cabin temperature can be preset by relevant technical personnel. Represents the cabin temperature under the current time period k. The maximum value representing the cabin temperature can be preset by relevant technical personnel. Represents the engine temperature under the current time period k. The maximum value representing engine temperature can be preset by relevant technicians. The minimum value representing the battery temperature can be preset by relevant technicians. Represents the battery temperature under the current time period k. The maximum value representing the battery temperature can be preset by relevant technicians. The maximum value representing the PTC heater can be preset by relevant technical personnel. The maximum value representing the vehicle's water pump can be preset by relevant technicians. The maximum value representing the vehicle's fan can be preset by relevant technicians. This represents the valve opening degree under the current time period k.
[0084] Specifically, optimize parameters include:
[0085]
[0086] in, Represents the power of the electric compressor over k time periods. The valve opening degree represents the k-th time period. This represents the power of the PTC heater over k time cycles. This represents the power of the vehicle's water pump in the kth time period. This represents the power of the vehicle fan (fan) in the k-th time period.
[0087] For example, if the prediction time domain is 10 seconds and one time period is 5 seconds, the prediction time domain can be divided into two time periods. Initial values for the current optimization parameters are then randomly generated for each of the two time periods. For instance, the initial value for the current optimization parameters corresponding to the first time period could be: , , , , The initial values of the current optimization parameters corresponding to the second time period can be: , , , , In this process, the initial values of the current optimization parameters for any given period should satisfy the constraints.
[0088] The pre-built optimization function for minimizing total heat consumption can be used to calculate the vehicle's total power consumption based on the current optimization parameters. The target optimization parameters can be the target values for PTC heater power, electric compressor power, water pump speed, vehicle fan speed, and valve opening, which are obtained by iteratively updating the initial values of the current optimization parameters while satisfying the constraints.
[0089] The optimization function for minimizing total heat dissipation is expressed as follows:
[0090]
[0091] Where, min( () indicates the minimum value operation, J represents the total heat consumption, N represents the preset prediction time domain based on actual needs, k represents the preset time period, and N represents the prediction time domain, which includes at least one time period. This represents the power of the electric compressor in the k-th time period. This represents the heater power in the k-th time period. This represents the pump power in the k-th time period. This represents the vehicle fan power in the k-th time period. Indicates the duration of the time period. Where:
[0092]
[0093]
[0094] Right now and It is a function relative to the water pump speed and the vehicle fan speed, and can be obtained by relevant technicians according to a pre-set table. For example, if the vehicle sensor detects a fan speed of 1000 rpm, the relevant technicians can find the corresponding power of the fan to be 120W by looking up the table.
[0095] For example, the target optimization parameters can be determined using numerical optimization algorithms such as gradient descent and sequential quadratic programming. Specifically, based on the initial values of the current optimization parameters, the control sequence is iteratively updated to gradually reduce the objective function while satisfying the constraints. Each iteration generates a new set of current optimization parameters until the convergence conditions preset by the technicians are met (e.g., the change in the objective function is very small, or the maximum number of iterations is reached) to obtain the target optimization parameters. For example, taking gradient descent as an example, the algorithm pre-sets a learning rate with a value range between 0 and 1. The value of the optimization variable is adjusted according to the learning rate and the partial derivative of the objective function with respect to the optimization variable. For example, if the learning rate is set to 0.2, i.e., η=0.2, then the new parameter at the next moment = the old parameter at the current moment - the learning rate × the change in the objective function. Taking PTC power in the optimization variable as an example, if a prediction time domain is divided into three periods, then three periods satisfying the constraints are first randomly generated. When k=0, When k=1, When k=2, If the calculated partial derivative of the objective function with respect to PTC power is 1, and the learning rate is 0.2, then when k=0 after optimization, When k=1, Repeat the iterations until the objective function is less than a preset threshold, or the preset maximum number of iterations is reached, to obtain the final target optimization parameters.
[0096] The executor can execute the corresponding target optimization parameters.
[0097] For example, the executor can execute only the target optimization parameters corresponding to the current time period. For instance, if the optimization parameters corresponding to the first time period in the target optimization parameters are: , , , , The optimization parameters for the second time period are: , , , , The third time period and , , , , Since the target optimization parameters for the second and third time periods are all predicted values, the actuator only executes the target optimization parameters for the current time period based on the current vehicle state data, environmental state data, and comfort requirement data. In the next time period, it performs rolling optimization calculations based on the current vehicle state data, environmental state data, and comfort requirement data for the next time period, according to the minimum total heat power consumption optimization parameters.
[0098] In the field of vehicle thermal management and energy consumption control, the technical solution of this invention acquires the current vehicle state data, current environmental state data, and comfort requirement data of the target vehicle in the current time period. Based on the current state data, environmental state data, and comfort requirement data, and using a pre-built comprehensive vehicle thermodynamic model, constraint performance parameter data is determined. Based on the constraint performance parameter data and the pre-built constraints, initial values of the current optimization parameters in the current time period are generated. Based on the initial values of the current optimization parameters, and using a pre-built minimum total heat power consumption optimization function and constraints, the target optimization parameters are determined. The corresponding actuators are then controlled to execute the target optimization parameters. This method, based on the current vehicle state, environmental state, and comfort requirements, utilizes a pre-built comprehensive vehicle thermodynamic model to determine constraint performance parameters and generate initial values of optimization parameters. Then, the target optimization parameters are solved and executed using the minimum total heat power consumption optimization function. This effectively solves the problem of suboptimal energy allocation in traditional control methods, reduces the total power consumption of the vehicle thermal management system, improves the vehicle's range and comfort, and provides reliable technical support for the efficient and energy-saving operation of vehicles.
[0099] Example 2
[0100] Figure 2 This is a flowchart of a vehicle control method based on thermodynamics provided in Embodiment 2 of the present invention. This embodiment optimizes and improves upon the above-mentioned technical solutions. The step "determine the target optimization parameter based on the initial value of the current optimization parameter, and based on the pre-constructed optimization function for minimizing total heat power consumption and constraints" is refined to "determine the prediction time period based on the pre-set prediction time domain; iteratively update the initial value of the current optimization parameter based on the initial value of the current optimization parameter, and based on the pre-constructed optimization function for minimizing total heat power consumption and constraints, until the preset model training termination condition is met, to obtain the target value of the current optimization parameter in each prediction time domain within the prediction time period; and determine the target value of the current optimization parameter in the current prediction time domain within the prediction time period as the target optimization parameter." This improves the specific generation method of the target optimization parameter.
[0101] It should be noted that for parts not described in detail in the embodiments of the present invention, please refer to the descriptions in other embodiments. For example... Figure 2As shown, the method includes the following specific steps:
[0102] S201. Obtain the target vehicle's current vehicle status data, current environmental status data, and comfort requirement data for the current time period.
[0103] S202. Based on the current state data, environmental state data, and comfort requirement data, and using a pre-built comprehensive vehicle thermodynamic model, determine the constraint performance parameter data.
[0104] S203. Based on the constraint performance parameter data and the pre-built constraint conditions, generate the initial values of the current optimization parameters for the current time period.
[0105] S204. Determine the prediction time period based on the preset prediction time domain.
[0106] S205. Based on the initial values of the current optimization parameters, and based on the pre-constructed optimization function for minimizing total heat consumption and constraints, iteratively update the initial values of the current optimization parameters until the preset model training termination condition is met, thereby obtaining the target values of the current optimization parameters in each prediction time domain within the prediction time period.
[0107] S206. Determine the target value of the current optimization parameter in the current prediction time domain within the prediction time period as the target optimization parameter.
[0108] S207. Control the corresponding actuator to execute the target optimization parameters.
[0109] The prediction time domain can be a preset prediction time domain according to the actual needs of relevant technical personnel. The prediction time period can be a preset time period according to the actual needs of relevant technical personnel. A prediction time domain N can be divided into several time periods k.
[0110] For example, the relevant technicians preset the prediction time domain N to 240 seconds. According to the response speed requirements of the vehicle thermal management system, the prediction time domain is divided into 120 time periods k, and the duration of each time period k is 2 seconds, that is, the prediction time period is 2 seconds.
[0111] The preset model training termination conditions can be pre-set by relevant technical personnel. For example, the difference between the optimized parameters obtained from two adjacent iterations is less than a preset threshold, the number of iterations reaches a preset maximum, or the calculation result of the optimization function that minimizes total heat consumption tends to stabilize. The target values of the current optimization parameters can be the target values of PTC heater power, electric compressor power, water pump speed, fan speed, and valve opening degree that satisfy the constraints and minimize total heat consumption for each time period k.
[0112] For example, if the relevant technicians preset the prediction time domain N to be 240 seconds, and according to the response speed requirements of the vehicle thermal management system, the prediction time domain is divided into 120 time periods k, each time period k having a duration of 2 seconds, and the preset model training termination condition is that the difference between the optimized parameters of two adjacent iterations is less than 0.01, and the number of iterations does not exceed 50, based on the current initial value of the optimized parameters, after iterating and updating 15 times through the sequential quadratic programming algorithm, the termination condition is met, and the target values of the optimized parameters under 120 time periods are obtained. The target values for the first time period are: PTC heater power 800W, electric compressor power 100W, water pump speed 1800rpm, fan speed 600rpm, and valve opening 0.8; and the target values for the second time period are: PTC heater power 700W, electric compressor power 80W, water pump speed 1750rpm, fan speed 580rpm, and valve opening 0.85, etc.
[0113] The corresponding actuators can be PTC heaters, electric compressors, water pumps, fans, and three-way valves, which respectively receive and execute the corresponding target optimization parameters.
[0114] For example, if the target values for the first time period are 800W PTC heater power, 100W electric compressor power, 1800rpm water pump speed, 600rpm fan speed, and 0.8 valve opening, and the target values for the second time period are 700W PTC heater power, 80W electric compressor power, 1750rpm water pump speed, 580rpm fan speed, and 0.85 valve opening, etc., and the current time period is the first time period, then the vehicle will determine the target values of the current optimization parameters as the target optimization parameters, that is, execute the current optimization parameters of the first time period, i.e., the vehicle will adjust the PTC heater power to 800W, the electric compressor power to 100W, the water pump speed to 1800rpm, the fan speed to 600rpm, and the valve opening to 0.8.
[0115] The above-described embodiment scheme defines the optimization time dimension by pre-setting the prediction time domain and dividing it into several time periods. Then, based on the optimization function that minimizes total heat consumption and multiple constraints, it iteratively updates the initial values of the optimization parameters to ensure that the optimal solution that meets the requirements of comfort and component safety is obtained. Finally, the target value of the optimization parameters in the first time period within the prediction time domain is used as the target optimization parameter for the current execution. This effectively solves the problems of shallow control coordination level and suboptimal decision-making in traditional systems, realizes deep coordination between the cabin and powertrain thermal management system, and improves the vehicle's energy utilization efficiency.
[0116] Example 3
[0117] Figure 3This is a flowchart of a vehicle control method based on thermodynamics provided in Embodiment 3 of the present invention. This embodiment provides a preferred example based on the above embodiments.
[0118] S301. Obtain the target vehicle's current vehicle status data, current environmental status data, and comfort requirement data for the current time period.
[0119] The current vehicle status data includes engine speed, engine torque, motor power, remaining battery charge, battery current, cabin temperature, engine temperature, battery temperature, and coolant temperature at key points; the current environmental status data includes ambient temperature and sunlight intensity; and the comfort requirement data includes the target cabin temperature set by the driver.
[0120] S302. Based on the current state data, environmental state data, and comfort requirement data, and using a pre-built comprehensive vehicle thermodynamic model, determine the constraint performance parameter data.
[0121] The overall vehicle thermodynamic model includes an integrated cabin thermodynamic model, a powertrain heat generation and dissipation model, and a dynamic heat flow distribution model; the dynamic heat flow distribution model is constructed by connecting the cabin thermodynamic model and the powertrain heat generation and dissipation model.
[0122] S303. Based on the constraint performance parameter data and the pre-built constraint conditions, generate the initial values of the current optimization parameters for the current time period.
[0123] S304. Determine the prediction time period based on the preset prediction time domain.
[0124] S305. Based on the initial values of the current optimization parameters, and based on the pre-constructed optimization function for minimizing total heat consumption and constraints, iteratively update the initial values of the current optimization parameters until the preset model training termination condition is met, thereby obtaining the target values of the current optimization parameters in each prediction time domain within the prediction time period.
[0125] S306. Determine the target value of the current optimization parameter in the current prediction time domain within the prediction time period as the target optimization parameter.
[0126] S307: Controls the PTC heater to execute heater power; controls the electric compressor to execute electric compressor power; controls the electric water pump to execute pump speed; controls the radiator fan to execute fan speed; controls the three-way valve to execute valve opening.
[0127] Example 4
[0128] Figure 4This is a schematic diagram of a thermodynamic-based vehicle control device provided in Embodiment 4 of the present invention. The thermodynamic-based vehicle control device provided in this embodiment is applicable to scenarios involving reducing vehicle thermal management energy consumption in the field of vehicle thermal management energy consumption control. This thermodynamic-based vehicle control device can be implemented in hardware and / or software, and can be applied to a thermodynamic-based vehicle control method. Specifically, it can be configured in a controller, such as... Figure 4 As shown, the device includes: a vehicle status data acquisition module 401, a constraint performance parameter data determination module 402, an initial value determination module for optimization parameters 403, a target optimization parameter determination module 404, and a target optimization parameter execution module 405. Wherein:
[0129] The vehicle status data acquisition module 401 is used to acquire the current vehicle status data, current environmental status data, and comfort requirement data of the target vehicle in the current time period.
[0130] The constraint performance parameter data determination module 402 is used to determine constraint performance parameter data based on the current state data, environmental state data, and comfort requirement data, and on a pre-built comprehensive vehicle thermodynamic model.
[0131] The initial value determination module 403 for optimization parameters is used to generate the initial values of the current optimization parameters for the current time period based on the constraint performance parameter data and the pre-built constraint conditions.
[0132] The target optimization parameter determination module 404 is used to determine the target optimization parameters based on the initial values of the current optimization parameters, the pre-constructed total heat power consumption minimization optimization function, and the constraints.
[0133] The target optimization parameter execution module 405 is used to control the corresponding actuator to execute the target optimization parameters.
[0134] In the field of vehicle thermal management and energy consumption control, the technical solution of this invention acquires the current vehicle state data, current environmental state data, and comfort requirement data of the target vehicle in the current time period. Based on the current state data, environmental state data, and comfort requirement data, and using a pre-built comprehensive vehicle thermodynamic model, constraint performance parameter data is determined. Based on the constraint performance parameter data and the pre-built constraints, initial values of the current optimization parameters in the current time period are generated. Based on the initial values of the current optimization parameters, and using a pre-built minimum total heat power consumption optimization function and constraints, the target optimization parameters are determined. The corresponding actuators are then controlled to execute the target optimization parameters. This method, based on the current vehicle state, environmental state, and comfort requirements, utilizes a pre-built comprehensive vehicle thermodynamic model to determine constraint performance parameters and generate initial values of optimization parameters. Then, the target optimization parameters are solved and executed using the minimum total heat power consumption optimization function. This effectively solves the problem of suboptimal energy allocation in traditional control methods, reduces the total power consumption of the vehicle thermal management system, improves the vehicle's range and comfort, and provides reliable technical support for the efficient and energy-saving operation of vehicles.
[0135] Optionally, the target optimization parameter determination module 404 is specifically used for:
[0136] The prediction time period is determined based on the pre-set prediction time domain.
[0137] Based on the initial values of the current optimization parameters, and using the pre-built optimization function for minimizing total heat consumption and constraints, the initial values of the current optimization parameters are iteratively updated until the preset model training termination condition is met, thus obtaining the target values of the current optimization parameters in each prediction time domain within the prediction time period.
[0138] The target value of the current optimization parameter in the current prediction time domain within the prediction time period is determined as the target optimization parameter.
[0139] The total heat consumption minimization optimization function is expressed as follows:
[0140]
[0141] Where, min( () indicates the minimum value operation, J represents the total heat consumption, N represents the preset prediction time domain based on actual needs, k represents the preset time period, and N represents the prediction time domain, which includes at least one time period. This represents the power of the electric compressor in the k-th time period. This represents the heater power in the k-th time period. This represents the pump power in the k-th time period. This represents the vehicle fan power in the k-th time period. Indicates the duration of a time period.
[0142] Optionally, the target optimization parameter execution module 405 is specifically used for:
[0143] Control the PTC heater to execute heater power.
[0144] Control the electric compressor to execute the electric compressor power.
[0145] Control the speed of the electric water pump.
[0146] Control the radiator fan to set the fan speed.
[0147] Control the three-way valve to determine the valve opening degree.
[0148] The vehicle control device based on thermodynamics provided in this embodiment of the invention can execute a vehicle control method based on thermodynamics provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.
[0149] Example 5
[0150] Figure 5 A schematic diagram of an electronic device 50 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0151] like Figure 5 As shown, the electronic device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52 and a random access memory (RAM) 53, communicatively connected to the at least one processor 51. The memory stores computer programs executable by the at least one processor. The processor 51 can perform various appropriate actions and processes based on the computer program stored in the ROM 52 or loaded from storage unit 58 into the RAM 53. The RAM 53 can also store various programs and data required for the operation of the electronic device 50. The processor 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.
[0152] Multiple components in electronic device 50 are connected to I / O interface 55, including: input unit 56, such as keyboard, mouse, etc.; output unit 57, such as various types of monitors, speakers, etc.; storage unit 58, such as disk, optical disk, etc.; and communication unit 59, such as network card, modem, wireless transceiver, etc. Communication unit 59 allows electronic device 50 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0153] Processor 51 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 51 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 suitable processor, controller, microcontroller, etc. Processor 51 performs the various methods and processes described above, such as a thermodynamic vehicle control method.
[0154] In some embodiments, a thermodynamic vehicle control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 50 via ROM 52 and / or communication unit 59. When the computer program is loaded into RAM 53 and executed by processor 51, one or more steps of the thermodynamic vehicle control method described above may be performed. Alternatively, in other embodiments, processor 51 may be configured as a thermodynamic vehicle control method by any other suitable means (e.g., by means of firmware).
[0155] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0156] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0157] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0158] To provide interaction with a user, the systems and techniques described herein 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 pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; 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 sound input, voice input, or tactile input).
[0159] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0160] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0161] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.
[0162] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. 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 substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A vehicle control method based on thermodynamics, characterized in that, include: Obtain the target vehicle's current vehicle status data, current environmental status data, and comfort requirement data within the current time period; Based on the current state data, the environmental state data, and the comfort requirement data, and using a pre-built comprehensive vehicle thermodynamic model, the constraint performance parameter data are determined. Based on the constraint performance parameter data and pre-built constraints, the initial values of the current optimization parameters for the current time period are generated. Based on the initial values of the current optimization parameters, and using the pre-constructed optimization function for minimizing total heat consumption and the constraints, the target optimization parameters are determined. Control the corresponding actuators to execute the target optimization parameters.
2. The method according to claim 1, characterized in that, Based on the initial values of the current optimization parameters, and using the pre-constructed optimization function for minimizing total heat consumption and the constraints, the target optimization parameters are determined, including: The prediction time period is determined based on the pre-set prediction time domain; Based on the initial value of the current optimization parameter, and based on the pre-constructed optimization function for minimizing total heat consumption and the constraints, the initial value of the current optimization parameter is iteratively updated until the preset model training termination condition is met, thereby obtaining the target value of the current optimization parameter in each prediction time domain within the prediction time period. The target value of the current optimization parameter in the current prediction time domain within the prediction time period is determined as the target optimization parameter.
3. The method according to claim 2, characterized in that, The total heat consumption minimization optimization function is expressed in the following form: Where, min( () indicates the minimum value operation, J represents the total heat consumption, N represents the preset prediction time domain based on actual needs, k represents the preset time period, and N represents the prediction time domain, which includes at least one time period. This represents the power of the electric compressor in the k-th time period. This represents the heater power in the k-th time period. This represents the pump power in the k-th time period. This represents the vehicle fan power in the k-th time period. Indicates the duration of a time period.
4. The method according to claim 1, characterized in that, The target optimization parameters include heater power, electric compressor power, water pump speed, vehicle fan speed, and valve opening. Accordingly, the optimization parameters for the execution target of the corresponding actuator are controlled, including: Control the positive temperature coefficient PTC heater to execute the heater power; Control the electric compressor to execute the power of the electric compressor; Control the water pump of the electric vehicle to execute the water pump speed; Control the vehicle radiator fan to execute the vehicle fan speed; Control the three-way valve to perform the valve opening.
5. The method according to claim 1, characterized in that, The overall vehicle thermodynamic model includes an integrated cabin thermodynamic model, a powertrain heat generation and dissipation model, and a dynamic heat flow distribution model; the dynamic heat flow distribution model is constructed by connecting the integrated cabin thermodynamic model and the powertrain heat generation and dissipation model in series.
6. The method according to claim 1, characterized in that, The current vehicle status data includes engine speed, engine torque, motor power, remaining battery charge, battery current, cabin temperature, engine temperature, battery temperature, and coolant temperature at critical points; the current environmental status data includes ambient temperature and sunlight intensity; and the comfort requirement data includes the target cabin temperature set by the driver.
7. A vehicle control device based on thermodynamics, characterized in that, include: The vehicle status data acquisition module is used to acquire the target vehicle's current vehicle status data, current environmental status data, and comfort requirement data in the current time period. The constraint performance parameter data determination module is used to determine constraint performance parameter data based on the current state data, environmental state data, and comfort requirement data, and on a pre-built comprehensive vehicle thermodynamic model. The module for determining the initial value of optimization parameters is used to generate the initial value of the current optimization parameters for the current time period based on the constraint performance parameter data and the pre-built constraint conditions. The target optimization parameter determination module is used to determine the target optimization parameters based on the initial values of the current optimization parameters, the pre-built total heat power consumption minimization optimization function, and the constraints. The target optimization parameter execution module is used to control the corresponding executor to execute the target optimization parameters.
8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a thermodynamic vehicle control method according to any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute and implement the vehicle control method based on thermodynamics as described in any one of claims 1-6.
10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements a vehicle control method based on thermodynamics according to any one of claims 1-6.