Vehicle thermal management system control method and device and thermal management system

Abstract

The invention relates to the technical field of thermal management systems, in particular to a vehicle thermal management system control method and device and a thermal management system. The method comprises the following steps: pre-training a neural network model according to bench test data of the electric drive thermal management system; parameters of the electric drive thermal management system are input into the neural network model, and the outlet water temperature of the electric drive thermal management system is obtained through prediction; the parameters of the electric drive thermal management system comprise the rotating speed of a water pump; the neural network model serves as a prediction model of model prediction control, iteration is conducted on the water pump rotating speed, and the optimal water pump rotating speed is obtained; and controlling the water pump by adopting the optimal water pump rotating speed. The rotating speed of the water pump can be finely controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management systems, and in particular to a vehicle thermal management system control method, device and thermal management system. Background Art

[0002] Taking the electric drive thermal management system of new energy vehicles as an example, the system is usually composed of a motor controller, a motor, a radiator, a water pump, and a DC converter and a charger that may be connected in series. The heat generated by the motor controller, the motor and other electrical components such as the DC converter and the charger during operation is driven by the coolant in the thermal management system to flow through the radiator and exchange heat with the outside air to ensure that the electrical components operate at a suitable safe temperature. Among them, the water pump is an energy-consuming component, and its speed control is usually a step-by-step control based on the water temperature of the electric drive circuit or the motor temperature.

[0003] It is difficult to accurately control the speed of the water pump with step control, making it difficult to achieve a balance between the motor cooling effect and the water pump energy consumption.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The object of the present invention is to provide a vehicle thermal management system control method, device and thermal management system to perform fine control on the water pump speed.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a vehicle thermal management system control method, comprising: Pre-train the neural network model based on bench test data of the electric drive thermal management system; Inputting the parameters of the electric drive thermal management system into the neural network model to predict the outlet water temperature of the electric drive thermal management system; the parameters of the electric drive thermal management system include the water pump speed; Using the neural network model as a prediction model for model predictive control, iterating the water pump speed to obtain an optimal water pump speed; The water pump is controlled using the optimal water pump speed.

[0007] In a second aspect, the present invention provides a vehicle thermal management system control device, comprising: A training module, used to pre-train the neural network model based on bench test data of the electric drive thermal management system; A prediction module, used for inputting the parameters of the electric drive thermal management system into the neural network model to predict the outlet water temperature of the electric drive thermal management system; the parameters of the electric drive thermal management system include the water pump speed; An iteration module, used for taking the neural network model as a prediction model of model predictive control, iterating the water pump speed, and obtaining an optimal water pump speed; A control module is used to control the water pump using the optimal water pump speed.

[0008] In a third aspect, the present invention provides a thermal management system, characterized in that it includes: an electronic device, a water pump, a radiator, a motor, a motor controller and a DC converter; Wherein, the electronic device comprises: at least one processor, and a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors. The instructions are executed by at least one of the processors to enable the at least one of the processors to execute a vehicle thermal management system control method.

[0009] Compared with the prior art, the present invention has the following beneficial effects: The present invention innovatively replaces the state space model with a neural network model to achieve physical simulation of the thermal management system. Based on this, the neural network model is used as a prediction model for model predictive control, the water pump speed is iterated, and the optimal water pump speed is obtained, thereby achieving refined control of the water pump speed and achieving a balance between the motor cooling effect and the water pump energy consumption. There is no solution in the prior art to control the water pump speed using this indicator and prediction model algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 is a flow chart of a vehicle thermal management system control method provided by an embodiment of the present invention; Figure 2 is a schematic diagram of the structure of a bench test provided by an embodiment of the present invention; Figure 3 is a schematic diagram of an optimization process provided by an embodiment of the present invention; Figure 4 is a schematic structural diagram of a vehicle thermal management system control device provided by an embodiment of the present invention; Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0012] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0013] Figure 1 is a flow chart of a vehicle thermal management system control method provided by this embodiment. This embodiment is applicable to the case of controlling the speed of a water pump in an electric drive thermal management system in a new energy vehicle. This method can be executed by an electronic device. Figure 1 , the method provided in this embodiment includes the following operations: S110. Pre-training a neural network model based on bench test data of an electric drive thermal management system.

[0014] The neural network model in this embodiment is preferably a Long Short-Term Memory (LSTM) model, which is specially designed to solve the gradient vanishing and gradient exploding problems of traditional recurrent neural networks when processing long sequence data. LSTM can effectively capture long-term dependencies in time series data by introducing memory units and gating mechanisms.

[0015] In this embodiment, bench test data for training the neural network model is first obtained.

[0016] Figure 2 It is a structural schematic diagram of the bench test provided in the embodiment of the present application, including an environmental control system, a radiator, a water pump, a direct current (DC-DC) converter, a motor controller, a motor and a dynamometer. Optionally, based on the actual road condition test, the electric drive thermal management system is carried on the bench for testing, and the bench test data is collected. Specifically, the bench dynamometer (located on both sides of the motor) provides road load simulation for the motor, and the environmental control system of the bench provides air intake and temperature control for the radiator. The bench can collect data such as the speed and torque output by the electric drive system (i.e., the motor in this embodiment), the input voltage and current, the inlet water temperature and outlet water temperature of the electric drive thermal management system, the coolant flow rate, the water pump speed, and the motor body temperature. In this embodiment, the bench test data includes the heat generated by the electric drive system, the water pump speed, the inlet water temperature of the electric drive thermal management system, the motor body temperature, and the outlet water temperature of the electric drive thermal management system. Among them, the heat generated by the electric drive system can be obtained by subtracting the output mechanical power from the input electrical power of the electric drive system.

[0017] During the test process, transient operating condition tests are generally included, because the vehicle speed in these operating conditions is constantly changing with time. Therefore, the input electrical power and output mechanical power of the electric drive system, the water pump speed, the coolant flow rate and water temperature are also constantly changing. These bench test data can be discretized at equal time intervals (such as 1s) and used as learning samples for neural network model training. Specifically, the heat generation of the electric drive system, the inlet water temperature, the motor body temperature and the water pump speed are used as inputs. For example, the input is a time series of T1~T10, and the outlet water temperature is used as the output label to pre-train the neural network model. The outlet water temperature will be used as the reference temperature for the water pump speed control strategy.

[0018] S120, inputting the parameters of the electric drive thermal management system into the neural network model to predict the outlet water temperature of the electric drive thermal management system.

[0019] After the neural network model is trained, the parameters of the actual electric drive thermal management system need to be input into the neural network model to predict the outlet water temperature at the next moment. The parameters of the electric drive thermal management system include the electric drive system heat generation, inlet water temperature, motor body temperature and water pump speed collected at the current moment. The model output is the outlet water temperature at the next moment, that is, the predicted outlet water temperature.

[0020] S130, using the neural network model as a prediction model for model predictive control, iterating the water pump speed to obtain an optimal water pump speed.

[0021] The innovation of the present invention is to use the neural network model as the prediction model of the model predictive control (MPC) of the electronic water pump. In the prior art, the MPC model relies on the prediction model established by the state space model. The implementation of the MPC model predictive control is divided into three main links: the first is to build a prediction model to predict the future behavior of the system; the second is rolling optimization, that is, to optimize the problem in a limited time domain to obtain a control sequence; the last is feedback correction, which collects system information in real time, corrects the prediction model, and continuously optimizes it. The establishment of the state space model often used in the MPC control strategy requires detailed thermal management loop parameters and a complete physical model, and the model is easily affected by the aging of components, changes in the performance of electrical components, etc., which will produce errors and affect the control strategy of the MPC. The motor temperature is difficult to accurately predict using the traditional state space equation, but the neural network has a strong nonlinear fitting ability. Using the LSTM neural network as a prediction model on the basis of the traditional MPC to realize the neural network predictive control can avoid the above problems.

[0022] Optionally, the particle swarm optimization algorithm (PSO) is used to optimize the pump speed to achieve better thermal management effects. The PSO algorithm can search in the entire search space and is not easy to fall into the local optimal solution, which is very important for complex nonlinear and non-convex optimization problems in MPC, and is more likely to find the global optimal control strategy. Compared with some traditional optimization algorithms, the principle and implementation of the PSO algorithm are relatively simple, and do not require complex mathematical derivation and calculation, such as the calculation of the derivative of the objective function, which reduces the difficulty of algorithm development and application. Specifically, the pump speed represented by each particle is iterated multiple times, and the optimized outlet water temperature is obtained through the neural network model after each iteration; the motor cooling range is obtained according to the outlet water temperature and the motor body temperature; the optimal pump speed is selected according to the pump speed represented by each particle and the motor cooling range.

[0023] The following is a specific implementation method, see Figure 3 , detailed explanation of the application of PSO algorithm in the water pump speed control scenario.

[0024] Step 1: Initialize parameters. Prediction time domain and control time domain The maximum number of iterations is consistent with the particle dimension D, which is set to 10 in this example. is 50, the number of particles N is 75, and the position, speed and other parameters in the PSO algorithm.

[0025] The first dimension in the particle is the current motor body temperature. The second dimension in the particle is the current inlet water temperature. The third dimension in the particle is the current electric drive system heat. The fourth dimension in the particle is the current outlet water temperature. The fifth dimension in the particle is the motor body temperature in the previous time step. The sixth dimension in the particle is the inlet water temperature in the previous time step. The seventh dimension in the particle is the electric drive system heat in the previous time step. The eighth dimension in the particle is the outlet water temperature in the previous time step. The ninth dimension in the particle is the current water pump speed. The tenth dimension in the particle is the optimized water pump speed.

[0026] Step 2: Update the position of the particle. First, update the components of the velocity vector of each particle according to the inertia weight obtained, and then update the components of the position vector according to the updated weight.

[0027] Step 3: Pump speed prediction. Each updated particle in Step 3 is regarded as a potential feasible control sequence. The trained LSTM prediction model is used to predict the motor temperature after control and output the speed corresponding to each particle.

[0028] Among them, the updated particles include the heat generation of the electric drive system, the speed of the water pump, the inlet water temperature of the electric drive thermal management system, and the temperature of the motor body. Among them, the speed of the water pump is updated with each iteration, but the heat generation of the electric drive system, the inlet water temperature, and the motor body temperature use the current parameters of the electric drive thermal management system and the historical data of the previous time step in each iteration.

[0029] Step 4: Calculate the fitness. Calculate the fitness value corresponding to each group of particles according to the designed cost function, that is, evaluate the control effect of the control sequence represented by each particle in the prediction time domain according to the cost function.

[0030] Optionally, the following cost function J is used: ; in, and is the weight, which can be set according to actual needs. is the water pump speed, that is, the water pump speed represented by the particle after iteration. In this embodiment, 75 water pump speeds can be obtained by iteration. is the motor body temperature, is the outlet water temperature.

[0031] If the cost function of a particle reaches the minimum after iteration or the number of iterations reaches the threshold, the water pump speed represented by the particle is taken as the optimal water pump speed.

[0032] Step 5: Determine whether the iteration condition is met. If not, return to Step 2 and continue iterating until the maximum number of iterations is reached. , the vector represented by the best historical position of the particle swarm (i.e. the position with the minimum cost function) is recorded as the control vector.

[0033] Step 6: Motor speed control. The last position in the obtained control vector (representing the optimal pump speed) is input into the actual motor control as the control value to complete the control of the current control cycle.

[0034] S140, controlling the water pump using the optimal water pump speed.

[0035] Experiments have proved that the simulation condition is the NEDC (New European Driving Cycle) condition, the ambient temperature is 30°C, and the control target is: the outlet water temperature is 32°C. The results show that the control error of the outlet water temperature can be kept within 2% and achieve a 3% energy saving effect.

[0036] The present invention innovatively replaces the state space model with a neural network model to achieve physical simulation of the thermal management system. Based on this, the neural network model is used as a prediction model for model predictive control, the water pump speed is iterated, and the optimal water pump speed is obtained, thereby achieving refined control of the water pump speed and achieving a balance between the motor cooling effect and the water pump energy consumption. There is no solution in the prior art to control the water pump speed using this indicator and prediction model algorithm.

[0037] The neural network model replaces the state space model as the prediction model of the electronic water pump MPC control strategy. The advantages of this approach include: 1. Real road conditions are the commonly used test conditions in the development and testing process. It is only necessary to organize the test data without adding additional testing steps.

[0038] 2. It eliminates the need for complex mathematical modeling to establish a state space model, and does not require detailed design parameters of the electric drive system. 3. The neural network can be updated and learned in real time. When the radiator state and electrical component performance change, the neural network can be updated to ensure the accuracy of the MPC control strategy prediction model.

[0039] 3. Model predictive control (MPC) can control the water pump speed more precisely, and save energy consumption of the thermal management system while controlling the water temperature of the thermal management loop and the motor temperature not to overheat.

[0040] Figure 4 It is a structural schematic diagram of a vehicle thermal management system control device provided by an embodiment of the present invention, including a training module 201, a prediction module 202, an iteration module 203 and a control module 204.

[0041] A training module 201 is used to pre-train a neural network model based on bench test data of an electric drive thermal management system; A prediction module 202 is used to input the parameters of the electric drive thermal management system into the neural network model to predict the outlet water temperature of the electric drive thermal management system; the parameters of the electric drive thermal management system include the water pump speed; An iteration module 203 is used to use the neural network model as a prediction model of model predictive control to iterate the water pump speed to obtain an optimal water pump speed; The control module 204 is used to control the water pump using the optimal water pump speed.

[0042] Optionally, the training module 201 is used to test the electric drive thermal management system on a test bench based on actual road conditions, and collect test bench test data; wherein the test bench test data includes the heat generation of the electric drive system, the water pump speed, the inlet water temperature of the electric drive thermal management system, the motor body temperature and the outlet water temperature of the electric drive thermal management system; the test bench dynamometer provides road load simulation for the motor, and the test bench environmental control system provides air intake and temperature control for the radiator; the heat generation of the electric drive system, the inlet water temperature, the motor body temperature and the water pump speed are used as inputs, and the outlet water temperature is used as the output label to pre-train the neural network model.

[0043] Optionally, the iteration module 203 is used to use a particle swarm optimization algorithm to iterate the water pump speed represented by each particle for multiple times, and obtain the optimized outlet water temperature through the neural network model after each iteration; obtain the cooling amplitude of the motor according to the outlet water temperature and the motor body temperature; and select the optimal water pump speed according to the water pump speed represented by each particle and the cooling amplitude of the motor.

[0044] Optionally, when the iteration module 203 selects the optimal water pump speed according to the water pump speed represented by each particle and the temperature reduction range of the motor, it is specifically configured to: use the following as the cost function J: ; in, and is the weight, is the pump speed, is the motor body temperature, is the outlet water temperature; if the cost function of a particle reaches the minimum after iteration or the number of iterations reaches the threshold, the water pump speed represented by the particle is taken as the optimal water pump speed.

[0045] This embodiment also provides a thermal management system, including: electronic equipment, a water pump, a radiator, a motor, a motor controller and a DC converter. The electronic equipment executes the vehicle thermal management system control method provided in the above embodiment and is electrically connected to the water pump to control the water pump to reach an optimal speed. Then, the predictive control of the next cycle is performed.

[0046] Among them, see Figure 5 , the electronic device includes at least one processor; and a memory communicatively connected to at least one of the processors.

[0047] The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to perform the above method. At least one processor in the electronic device can perform the above method, and thus has at least the same advantages as the above method.

[0048] Optionally, the electronic device also includes interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on a memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to an interface). In other embodiments, if necessary, multiple processors can be used together with multiple memories, and / or multiple buses can be used together with multiple memories. Similarly, multiple electronic devices can be connected (for example, as a server array, a group of blade servers, or a multi-processor system), and each device provides some necessary operations. Figure 5 A processor 301 is taken as an example.

[0049] The memory 302 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the vehicle thermal management system control method in the embodiment of the present invention. The processor 301 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 302, that is, realizing the above-mentioned vehicle thermal management system control method.

[0050] The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 302 may further include a memory remotely arranged relative to the processor 301, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0051] The electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0052] The input device 303 can receive input digital or character information, and the output device 304 can include a display device, an auxiliary lighting device (e.g., LED), and a tactile feedback device (e.g., a vibration motor), etc. The display device can include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0053] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0054] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle thermal management system control method, characterized in that: include: Pre-train the neural network model based on bench test data of the electric drive thermal management system; Inputting the parameters of the electric drive thermal management system into the neural network model to predict the outlet water temperature of the electric drive thermal management system; the parameters of the electric drive thermal management system include the water pump speed; Using the neural network model as a prediction model for model predictive control, iterating the water pump speed to obtain an optimal water pump speed; The water pump is controlled using the optimal water pump speed.

2. The method according to claim 1, characterized in that Based on the bench test data of the electric drive thermal management system, the neural network model is pre-trained, including: Based on the actual road condition test, the electric drive thermal management system is carried out on the test bench and the test bench data is collected; wherein the test bench data includes the heat generation of the electric drive system, the speed of the water pump, the inlet water temperature of the electric drive thermal management system, the motor body temperature and the outlet water temperature of the electric drive thermal management system; The bench dynamometer provides road load simulation for the motor, and the bench environmental control system provides air intake and temperature control for the radiator; The heat generation of the electric drive system, the inlet water temperature, the motor body temperature and the water pump speed are used as inputs, and the outlet water temperature is used as the output label to pre-train the neural network model.

3. The method according to claim 1, characterized in that: The neural network model is a long short-term memory network LSTM model.

4. The method according to claim 1, characterized in that The neural network model is used as a prediction model of model predictive control, and the water pump speed is iterated to obtain the optimal water pump speed, including: The particle swarm optimization algorithm is used to iterate the water pump speed represented by each particle for multiple times, and after each iteration, the optimized outlet water temperature is obtained through the neural network model; Obtaining a cooling amplitude of the motor according to the outlet water temperature and the motor body temperature; The optimal water pump speed is selected according to the water pump speed represented by each particle and the temperature reduction range of the motor.

5. The method according to claim 4, characterized in that The optimal water pump speed is selected according to the water pump speed represented by each particle and the temperature reduction range of the motor, including: The following cost function J is used: ； in, and is the weight, is the pump speed, is the motor body temperature, is the outlet water temperature; If the cost function of a particle reaches the minimum after iteration or the number of iterations reaches a threshold, the water pump speed represented by the particle is taken as the optimal water pump speed.

6. A vehicle thermal management system control device, characterized in that: include: A training module, used to pre-train the neural network model based on bench test data of the electric drive thermal management system; A prediction module, used for inputting the parameters of the electric drive thermal management system into the neural network model to predict the outlet water temperature of the electric drive thermal management system; the parameters of the electric drive thermal management system include the water pump speed; An iteration module, used for taking the neural network model as a prediction model of model predictive control, iterating the water pump speed, and obtaining an optimal water pump speed; A control module is used to control the water pump using the optimal water pump speed.

7. A thermal management system, characterized in that: include: Electronics, water pumps, radiators, motors, motor controllers and DC converters; Wherein, the electronic device comprises: at least one processor, and a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the vehicle thermal management system control method according to any one of claims 1 to 5.

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