New energy vehicle thermal management system test method

Abstract

The present invention discloses a new energy vehicle thermal management system testing method, comprising: simulating operating condition data corresponding to the current operating condition, and inputting the operating condition data into the new energy vehicle thermal management system; collecting the coolant inlet and outlet temperatures corresponding to each of M water pumps in the new energy vehicle thermal management system; inputting the operating condition data and the coolant inlet and outlet temperatures corresponding to each of the M water pumps into a trained thermal management model in sequence to obtain the optimal duty cycle corresponding to each of the M water pumps under the current operating condition; collecting the actual duty cycle corresponding to each of the M water pumps in the new energy vehicle thermal management system in real time, and comparing the actual duty cycle corresponding to each of the M water pumps with the optimal duty cycle to determine the water pump whose duty cycle needs to be adjusted and optimized. The new energy vehicle thermal management system testing method of the present application facilitates optimizing the performance of the thermal management system and improves the energy efficiency and reliability of new energy vehicles.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy vehicles, in particular to a new energy vehicle thermal management system testing method. BACKGROUND

[0002] In the development process of new energy vehicles, the thermal management system plays a crucial role in ensuring the performance, safety and service life of core components such as batteries and motors. Due to the significant differences in heat generation of batteries and motors under different working conditions of new energy vehicles, such as idling, low-speed driving, high-speed driving, sudden acceleration and sudden braking, and the significant influence of external environment temperature on thermal management requirements, traditional thermal management systems often fail to accurately match the heat dissipation requirements under various working conditions, resulting in excessive energy consumption of water pumps or poor heat dissipation effect, thereby affecting the overall performance and range of new energy vehicles. Therefore, it is urgent to develop an efficient and intelligent thermal management system testing method. SUMMARY

[0003] To solve the technical problems in the background art, the present application proposes a new energy vehicle thermal management system testing method.

[0004] The new energy vehicle thermal management system testing method proposed by the present application comprises:

[0005] Simulate the working condition data corresponding to the current working condition and input the working condition data into the new energy vehicle thermal management system, wherein the new energy vehicle thermal management system comprises M water pumps, a cooling liquid circulation system and a plurality of temperature sensors;

[0006] Collect the cooling liquid inlet and outlet temperatures corresponding to each of the M water pumps in the new energy vehicle thermal management system;

[0007] Input the working condition data and the cooling liquid inlet and outlet temperatures corresponding to each of the M water pumps into the trained thermal management model in turn to obtain the optimal duty cycle corresponding to each of the M water pumps under the current working condition;

[0008] Real-time collect the actual duty cycle corresponding to each of the M water pumps in the new energy vehicle thermal management system, and compare the actual duty cycle corresponding to each of the M water pumps with the optimal duty cycle to determine the water pump whose duty cycle needs to be adjusted and optimized.

[0009] Preferably, the current working condition includes but is not limited to idling, low-speed driving, high-speed driving, sudden acceleration and sudden braking; and each working condition corresponds to a set of working condition data.

[0010] Preferably, the working condition data includes but is not limited to vehicle speed , acceleration , ambient temperature , average battery temperature motor winding temperature The training process of the thermal management model is as follows:

[0011] Obtain test sample data, which includes multiple working conditions, multiple working condition data corresponding to the multiple working conditions, and duty cycle data;

[0012] The test sample data is divided according to a preset strategy to form a training set, a test set, and a validation data set;

[0013] The vehicle speed , acceleration , ambient temperature , average battery temperature , motor winding temperature , and the coolant inlet and outlet temperatures corresponding to each of the M water pumps are used as input features to input a preset thermal management network for model training to obtain a thermal management model;

[0014] The network parameters of the preset thermal management network are adjusted according to the training results until the error between the duty cycle obtained by the thermal management network and the actual duty cycle is within a preset range, to obtain a trained thermal management model.

[0015] Preferably, the preset strategy is that the proportion of the training set, the validation set, and the test set is 8:1:1.

[0016] Preferably, the comparison of the actual duty cycle corresponding to each of the M water pumps with the optimal duty cycle to determine the water pump that needs to adjust and optimize the duty cycle specifically includes:

[0017] Taking the optimal duty cycle as the reference, when the actual duty cycle corresponding to each of the M water pumps is not equal to the optimal duty cycle, the corresponding water pump is determined as the water pump that needs to adjust and optimize the duty cycle.

[0018] Preferably, in the thermal management model, the optimal duty cycle calculation process of the water pump is as follows: ;

[0019] Where D is the optimal duty cycle of the water pump; k0-k7 are regression coefficients, which are calculated by least squares method or gradient descent method to fit a large amount of test data; is a random error term used to compensate for complex real factors that the model cannot accurately cover; Tin is the coolant inlet temperature; and Tout is the coolant outlet temperature.

[0020] The new energy vehicle thermal management system test system provided by the application comprises:

[0021] The first simulation module is used for simulating working condition data corresponding to a current working condition and inputting the working condition data into the new energy vehicle thermal management system.

[0022] The data acquisition module is used for acquiring cooling liquid inlet and outlet temperatures corresponding to the M water pumps in the new energy vehicle thermal management system.

[0023] The first processing module is used for inputting the working condition data and the cooling liquid inlet and outlet temperatures corresponding to the M water pumps into the trained thermal management model in sequence to obtain optimal duty cycles corresponding to the M water pumps under the current working condition.

[0024] The second processing module is used for acquiring actual duty cycles corresponding to the M water pumps in the new energy vehicle thermal management system in real time, comparing the actual duty cycles corresponding to the M water pumps with the optimal duty cycles, and determining water pumps whose duty cycles need to be adjusted and optimized.

[0025] Correspondingly, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes steps of the new energy vehicle thermal management system test method.

[0026] Correspondingly, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement steps of the new energy vehicle thermal management system test method.

[0027] In the present application, the new energy vehicle thermal management system test method can effectively calculate optimal duty cycles of water pumps in the new energy vehicle thermal management system by simulating working condition data and collecting temperature data in real time, combining a thermal management model, optimizing thermal management system performance, and improving energy efficiency and reliability of the new energy vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The figure is a working flow structure diagram of the new energy vehicle thermal management system test method.

[0029] Figure 2 The figure is a system architecture diagram of the new energy vehicle thermal management system test method. DETAILED DESCRIPTION

[0030] Reference Figure 1 and Figure 2 The new energy vehicle thermal management system test method comprises the following steps:

[0031] S1, simulate the working condition data corresponding to the current working condition, and input the working condition data into the new energy vehicle thermal management system, the new energy vehicle thermal management system comprising M water pumps, a cooling liquid circulation system and a plurality of temperature sensors.

[0032] In this embodiment, the current working condition includes but is not limited to idle speed, low speed driving, high speed driving, sudden acceleration and sudden braking; any one working condition corresponds to a set of working condition data.

[0033] In this embodiment, the new energy vehicle thermal management system is composed of the following core components:

[0034] M water pumps for driving the cooling liquid circulation, each water pump being independently controlled and capable of adjusting the flow according to the duty ratio.

[0035] The cooling liquid circulation system comprises a radiator, a cooling pipeline, a battery cooling plate, a motor cooling jacket and the like, forming a closed loop.

[0036] Temperature sensors are deployed at key positions such as cooling liquid inlet and outlet, battery module and motor winding to collect temperature data in real time.

[0037] In this embodiment, it also includes a control unit integrating a thermal management model algorithm, receiving sensor data and outputting optimal duty ratio instructions, and a working condition simulation module simulating vehicle driving state (such as vehicle speed, acceleration, ambient temperature, etc.) to generate dynamic working condition data.

[0038] S2, collect the cooling liquid inlet and outlet temperatures corresponding to each of the M water pumps in the new energy vehicle thermal management system.

[0039] S3, input the working condition data and the cooling liquid inlet and outlet temperatures corresponding to each of the M water pumps into the trained thermal management model in turn to obtain the optimal duty ratio corresponding to each of the M water pumps under the current working condition.

[0040] In this embodiment, the working condition data includes but is not limited to vehicle speed , acceleration , ambient temperature , average battery temperature , motor winding temperature ; the training process of the thermal management model is as follows: obtaining test sample data, the test sample data including a plurality of working conditions, a plurality of working condition data corresponding to the plurality of working conditions and duty ratio data; dividing the test sample data according to a preset strategy to form a training set, a test set and a validation data set; vehicle speed , acceleration , ambient temperature , average battery temperature , motor winding temperature and the inlet and outlet temperature of the cooling liquid corresponding to each of the M water pumps as input features, input a preset thermal management network for model training to obtain a thermal management model; adjust the network parameters of the preset thermal management network according to the training result until the error between the duty cycle obtained through the thermal management network and the actual duty cycle is within a preset range, to obtain a trained thermal management model.

[0041] In this embodiment, the preset strategy is specifically that the duty cycles corresponding to the training set, the verification set and the test set are 8:1:1.

[0042] In this embodiment, in the thermal management model, the optimal duty cycle of the water pump is calculated as follows: ;

[0043] wherein D is the optimal duty cycle of the water pump; k0-k7 are regression coefficients, which are obtained by fitting calculation on a large amount of test data through the least square method or the gradient descent method; is a random error term, which is used to compensate for the influence of complex real factors that cannot be accurately covered by the model; Tin is the inlet temperature of the cooling liquid; and Tout is the outlet temperature of the cooling liquid.

[0044] S4, real-time collection of the actual duty cycle of each of the M water pumps in the thermal management system of the new energy vehicle, and comparison of the actual duty cycle of each of the M water pumps with the optimal duty cycle to determine the water pump whose duty cycle needs to be adjusted and optimized.

[0045] In this embodiment, the actual duty cycle of each of the M water pumps is compared with the optimal duty cycle to determine the water pump whose duty cycle needs to be adjusted and optimized, which specifically includes:

[0046] Taking the optimal duty cycle as the benchmark, when the actual duty cycle of each of the M water pumps is not equal to the optimal duty cycle, the corresponding water pump is determined as the water pump whose duty cycle needs to be adjusted and optimized.

[0047] In this embodiment, the workflow of the present application is as follows:

[0048] Data input: the current working condition data (such as vehicle speed , acceleration , ambient temperature ) generated by the working condition simulation module are input to the thermal management system of the new energy vehicle.

[0049] Temperature collection: the temperature sensor collects the inlet and outlet temperature of the cooling liquid corresponding to the M water pumps (Tin, Tout) in real time.

[0050] Model calculation: the control unit inputs the working condition data and the temperature data into the trained thermal management model to calculate the optimal duty cycle of each water pump .

[0051] Duty cycle adjustment: compare with actual duty cycle and ,like , the duty cycle of the corresponding water pump is dynamically adjusted until the error is less than the preset threshold (such as ±2%).

[0052] In this embodiment, through closed-loop control, the system can dynamically optimize the energy consumption of the water pump according to real-time operating conditions and temperature changes, ensuring that the battery and motor operate in the optimal temperature range, while extending the life of key components.

[0053] Specifically, the vehicle is in high-speed driving condition (vehicle speed , ambient temperature ), the average battery temperature When the temperature rises to 45℃, the motor winding temperature The implementation steps of this application are as follows:

[0054] Data input: The working condition simulation module generates high-speed working condition data and inputs it into the control unit.

[0055] Temperature collection: coolant inlet temperature and outlet temperature of water pump 1.

[0056] Model calculation: The thermal management model outputs the optimal duty cycle of water pump 1 .

[0057] Duty cycle adjustment: Actual duty cycle detected , the control unit gradually reduces the duty cycle of water pump 1 to 75%.

[0058] Effect verification: After adjustment, the power consumption of water pump 1 decreased by 18%, the battery temperature stabilized at 42°C, the motor temperature dropped to 55°C, and the overall energy efficiency of the system increased by 12%.

[0059] Reference Figure 1 and Figure 2 The new energy vehicle thermal management system test system proposed by the present invention includes:

[0060] The first simulation module is used to simulate the operating condition data corresponding to the current operating condition and input the operating condition data into the new energy vehicle thermal management system, which includes M water pumps, a coolant circulation system and multiple temperature sensors;

[0061] The data acquisition module is used to collect the coolant inlet and outlet temperatures corresponding to each of the M water pumps in the thermal management system of the new energy vehicle;

[0062] The first processing module is used to input the operating condition data and the coolant inlet and outlet temperatures corresponding to the M water pumps into the trained thermal management model in sequence to obtain the optimal duty cycle corresponding to each of the M water pumps under the current operating conditions;

[0063] The second processing module is configured to collect actual duty cycles of M water pumps in the new energy vehicle thermal management system in real time, compare the actual duty cycles of the M water pumps with the optimal duty cycles, and determine water pumps that need to adjust and optimize the duty cycles.

[0064] Correspondingly, the present application further provides a computer device, which comprises a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the above-mentioned thermal management system test method when executing the computer program. Meanwhile, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the above-mentioned thermal management system test method when being executed by the processor.

[0065] The above is only the preferred embodiment of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can make equivalent replacements or changes according to the technical scheme and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered by the protection scope of the present application.

Claims

1. Testing method for thermal management system of new energy vehicle, characterized in that: include: Simulating operating condition data corresponding to the current operating condition and inputting the operating condition data into the new energy vehicle thermal management system, wherein the new energy vehicle thermal management system includes M water pumps, a coolant circulation system, and multiple temperature sensors; Collecting the coolant inlet and outlet temperatures corresponding to each of the M water pumps in the thermal management system of the new energy vehicle; The operating condition data and the coolant inlet and outlet temperatures corresponding to the M water pumps are sequentially input into the trained thermal management model to obtain the optimal duty cycle corresponding to each of the M water pumps under the current operating conditions; Real-time collection of the actual duty cycle corresponding to each of the M water pumps in the thermal management system of the new energy vehicle, and comparison of the actual duty cycle corresponding to each of the M water pumps with the optimal duty cycle to determine the water pump whose duty cycle needs to be adjusted and optimized; The current operating conditions include but are not limited to idling, low-speed driving, high-speed driving, sudden acceleration, and sudden braking; any operating condition corresponds to a set of operating condition data; The operating condition data includes but is not limited to the vehicle speed , acceleration , ambient temperature , average battery temperature , motor winding temperature The training process of the thermal management model is as follows: Acquire test sample data, where the test sample data includes multiple operating conditions, multiple operating condition data corresponding to the multiple operating conditions, and duty cycle data; Split the test sample data according to the preset strategy to form training set, test set, and validation data set; The vehicle speed , acceleration , ambient temperature , average battery temperature , motor winding temperature The coolant inlet and outlet temperatures corresponding to each of the M water pumps are used as input features and input into the preset thermal management network for model training to obtain a thermal management model; The network parameters of the preset thermal management network are adjusted according to the training results until the error between the duty cycle obtained by the thermal management network and the actual duty cycle is within a preset range, so as to obtain a trained thermal management model.

2. The new energy vehicle thermal management system testing method according to claim 1, characterized in that: The preset strategy is specifically: the corresponding ratios of the training set, validation set and test set are 8:1:

1.

3. The new energy vehicle thermal management system testing method according to claim 1, characterized in that: The comparing the actual duty cycle corresponding to each of the M water pumps with the optimal duty cycle to determine the water pump whose duty cycle needs to be adjusted specifically includes: Taking the optimal duty cycle as a benchmark, when the actual duty cycle corresponding to each of the M water pumps is not equal to the optimal duty cycle, the corresponding water pump is regarded as a water pump that needs to adjust the optimized duty cycle.

4. The new energy vehicle thermal management system testing method according to claim 1, characterized in that: In the thermal management model, the optimal duty cycle of the water pump is calculated as follows: ; Where D is the optimal duty cycle of the water pump; k0-k7 are regression coefficients, which are calculated by fitting massive test data through the least squares method or gradient descent method; ε is the random error term, which is used to compensate for the influence of complex real-world factors that cannot be accurately covered by the model; Tin is the coolant inlet temperature; and Tout is the coolant outlet temperature.

5. New energy vehicle thermal management system test system, characterized by: include: A first simulation module is used to simulate operating condition data corresponding to the current operating condition and input the operating condition data into the new energy vehicle thermal management system, which includes M water pumps, a coolant circulation system and multiple temperature sensors; A data acquisition module is used to collect the coolant inlet and outlet temperatures corresponding to each of the M water pumps in the thermal management system of the new energy vehicle; The first processing module is used to input the operating condition data and the coolant inlet and outlet temperatures corresponding to the M water pumps into the trained thermal management model in sequence to obtain the optimal duty cycle corresponding to each of the M water pumps under the current operating conditions; The second processing module is used to collect the actual duty cycle corresponding to each of the M water pumps in the thermal management system of the new energy vehicle in real time, and compare the actual duty cycle corresponding to each of the M water pumps with the optimal duty cycle to determine the water pump whose duty cycle needs to be adjusted and optimized; The current operating conditions include but are not limited to idling, low-speed driving, high-speed driving, sudden acceleration, and sudden braking; any operating condition corresponds to a set of operating condition data; The operating condition data includes but is not limited to the vehicle speed , acceleration , ambient temperature , average battery temperature , motor winding temperature The training process of the thermal management model is as follows: Acquire test sample data, where the test sample data includes multiple operating conditions, multiple operating condition data corresponding to the multiple operating conditions, and duty cycle data; Split the test sample data according to the preset strategy to form training set, test set, and validation data set; The vehicle speed , acceleration , ambient temperature , average battery temperature , motor winding temperature The coolant inlet and outlet temperatures corresponding to each of the M water pumps are used as input features and input into the preset thermal management network for model training to obtain a thermal management model; The network parameters of the preset thermal management network are adjusted according to the training results until the error between the duty cycle obtained by the thermal management network and the actual duty cycle is within a preset range, so as to obtain a trained thermal management model.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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