Electric drive thermal management system fault warning method, device, equipment and storage medium

Abstract

A method, device, equipment, and storage medium for warning faults in an electric drive thermal management system are disclosed. The method includes: importing acquired front and rear motor power, front and rear motor mechanical efficiency, onboard charging system power, and thermal management actuator data into a trained deep network learning model to output a predicted radiator outlet temperature; determining the difference between the predicted radiator outlet temperature and the onboard charging system inlet temperature; and reporting a fault code if the difference exceeds a range threshold. Outputting the predicted radiator outlet temperature eliminates the need for a radiator outlet temperature sensor in traditional solutions, reducing hardware costs; and determining the difference between the predicted radiator outlet temperature and the onboard charging system inlet temperature. If the difference exceeds a range threshold, a fault code is reported, notifying the user to visit a repair shop for prompt repair and troubleshooting.

Description

Technical Field

[0001] The present application relates to the field of electric drive thermal management systems, and specifically to a fault warning method, device, equipment and storage medium for an electric drive thermal management system. Background Art

[0002] Vehicle thermal management systems (TMSs) coordinate heat exchange throughout the vehicle's driving process and have long been considered a crucial component for safe and energy-efficient driving. Electric drive circuits have numerous heat sources and heat-interacting components. The electric drive cooling circuit, through air and water cooling, effectively meets the daily heat dissipation needs of components such as the motor, controller, and DC / DC inverter (DC-DC converter), ensuring safe operating temperatures and minimizing heat loss.

[0003] In related technologies, 2 to 3 water temperature sensors are usually arranged in the electric drive cooling circuit, such as the radiator water inlet temperature sensor, the radiator water outlet temperature sensor and the motor drive system water inlet temperature sensor; 2 water temperature sensors are arranged in the power battery cooling circuit, such as the power battery water inlet temperature sensor and the power battery water outlet temperature sensor.

[0004] Among them, once a non-electrical fault occurs in the electric drive cooling circuit, such as coolant leakage, pipe blockage, or thermal management actuator jam (the position of the actuator itself feedback is correct), the control unit cannot effectively identify it and can only light up the user based on the real-time temperature of the electric drive circuit coolant exceeding the limit or the maximum temperature of the cooling component exceeding the limit. If the prompt is too late, it will cause power loss halfway, and sensors need to be installed, which is relatively costly. Summary of the Invention

[0005] The present application provides a fault warning method, device, equipment and storage medium for an electric drive thermal management system, which can solve the technical problems in the related art that a light can only be turned on to remind the user when the real-time temperature of the electric drive circuit coolant exceeds the limit or the maximum temperature of the cooling component exceeds the limit. A late prompt may cause power loss halfway, and a sensor needs to be installed, which is relatively costly.

[0006] In a first aspect, an embodiment of the present application provides a method for early warning of a fault in an electric drive thermal management system, the method comprising:

[0007] The acquired front and rear motor power, front and rear motor mechanical efficiency, onboard charging system power, and thermal management actuator data are fed into the trained deep learning model to output the predicted radiator outlet temperature.

[0008] Determine the difference between the predicted radiator outlet temperature and the vehicle charging system inlet temperature. If the difference exceeds the range threshold, a fault code is broadcast.

[0009] In conjunction with the first aspect, in one embodiment, the step of importing the acquired front and rear motor electric power, front and rear motor mechanical efficiency, onboard charging system electric power, and thermal management actuator data into a trained deep network learning model to output a predicted radiator outlet temperature includes:

[0010] The obtained front and rear motor electric power, front and rear motor mechanical efficiency, on-board charging system electric power, thermal management actuator data information, DC-DC converter power, external ambient temperature and vehicle speed are imported into the trained deep network learning model to output the predicted radiator outlet temperature.

[0011] In conjunction with the first aspect, in one embodiment, the step of importing the acquired front and rear motor electric power, front and rear motor mechanical efficiency, onboard charging system electric power, and thermal management actuator data into a trained deep network learning model to output a predicted radiator outlet temperature includes:

[0012] Upload the acquired front and rear motor power, front and rear motor mechanical efficiency, on-board charging system power, and thermal management actuator data to the first cloud platform;

[0013] Based on the deep network learning model in cloud platform 1, the predicted radiator outlet temperature is calculated and the predicted radiator outlet temperature information is transmitted to the vehicle end.

[0014] In conjunction with the first aspect, in one embodiment, uploading the acquired front and rear motor electric power, front and rear motor mechanical efficiency, onboard charging system electric power, and thermal management actuator data information to the first cloud platform includes:

[0015] If the network signal is abnormal and causes communication interruption, the electric drive cooling circuit and the power battery cooling circuit are controlled to operate in series to make the radiator outlet temperature equal to the power battery inlet temperature;

[0016] Determine the difference between the water inlet temperature of the power battery and the water inlet temperature of the vehicle charging system. If the difference exceeds the range threshold, a fault code will be broadcast.

[0017] In conjunction with the first aspect, in one embodiment, the step of importing the acquired front and rear motor electric power, front and rear motor mechanical efficiency, onboard charging system electric power, and thermal management actuator data into a trained deep network learning model to output a predicted radiator outlet temperature includes:

[0018] Obtaining the DC-DC converter power, front and rear motor electric power, and front and rear motor mechanical efficiency over a period of time in the future;

[0019] The vehicle charging system power, thermal management actuator data information, DC-DC converter power, front and rear motor power, and front and rear motor mechanical efficiency in the future are imported into the trained deep network learning model to output the predicted radiator outlet temperature in the future.

[0020] In conjunction with the first aspect, in one embodiment, obtaining the DC-DC converter power, the front and rear motor electric power, and the front and rear motor mechanical efficiency within a future period of time includes:

[0021] Based on the vehicle speed, slope, and ambient temperature of the road ahead, the DC-DC converter power, front and rear motor power, and front and rear motor mechanical efficiency are predicted for a period of time in the future.

[0022] In conjunction with the first aspect, in one embodiment, the vehicle speed, slope, and ambient temperature of the road ahead are based on the vehicle speed, slope, and ambient temperature of the road ahead, including:

[0023] Based on the cloud platform 2, the vehicle speed, slope and external ambient temperature of the road ahead are obtained.

[0024] In a second aspect, an embodiment of the present application provides a fault warning device for an electric drive thermal management system, the electric drive thermal management system fault warning device comprising:

[0025] A radiator outlet temperature prediction module, which is used to input the acquired front and rear motor power, front and rear motor mechanical efficiency, onboard charging system power, and thermal management actuator data into the trained deep network learning model and output the predicted radiator outlet temperature;

[0026] The fault warning module is used to determine the difference between the predicted radiator outlet temperature and the vehicle charging system inlet temperature. If the difference exceeds the range threshold, a fault code is broadcast.

[0027] In a third aspect, an embodiment of the present application provides an electric drive thermal management system fault warning device, which includes a processor, a memory, and an electric drive thermal management system fault warning program stored in the memory and executable by the processor. When the electric drive thermal management system fault warning program is executed by the processor, the steps of the electric drive thermal management system fault warning method described in some of the above embodiments are implemented.

[0028] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which is stored an electric drive thermal management system fault warning program. When the electric drive thermal management system fault warning program is executed by a processor, the steps of the electric drive thermal management system fault warning method described in some of the above embodiments are implemented.

[0029] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0030] By importing the acquired front and rear motor electric power, front and rear motor mechanical efficiency, vehicle charging system electric power, and thermal management actuator data into the trained deep network learning model, the predicted radiator outlet temperature can be output, thereby eliminating the radiator outlet temperature sensor in the traditional solution and reducing hardware costs; and by judging the difference between the predicted radiator outlet temperature and the vehicle charging system inlet temperature, if the difference exceeds the range threshold, the fault code is broadcast to notify the user to go to the store for repair and investigation in time, which solves the problem that once a non-electrical fault occurs in the electric drive cooling circuit, such as coolant leakage, or pipe blockage, or the thermal management actuator is stuck (the feedback position of the actuator itself is correct), the control unit cannot effectively identify it, and can only light up to remind the user based on the real-time coolant temperature in the electric drive cooling circuit exceeding the limit or the maximum temperature of the cooling component exceeding the limit. If the prompt timing is too late, it will cause power loss halfway. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of an embodiment of a fault warning method for an electric drive thermal management system of the present application;

[0032] Figure 2 This is a schematic diagram of the structure of the electric drive thermal management system of this application;

[0033] Figure 3 This is a schematic diagram of the hardware structure of the electric drive thermal management system fault warning device involved in the embodiment of the present application.

[0034] In the figure: 1. Radiator; 2. Four-way valve; 3. Cooling water tank; 4. Electric drive circuit water pump; 5. On-board charging system water inlet temperature sensor; 6. On-board charging system; 7. Rear electric drive components; 8. Front electric drive components; 9. Three-way valve; 10. Power battery water inlet temperature sensor; 11. Power battery; 12. Power battery water outlet temperature sensor; 13. Battery circuit water pump. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] What you need to know is that Figure 2As shown, the electric drive thermal management system includes an electric drive cooling circuit and a power battery cooling circuit. The electric drive cooling circuit heating device includes a front electric drive component 8 and a rear electric drive component 7 (i.e., a front motor and a rear motor), an on-board charging system 6 (DCDC / OBC / PDU charger), there will also be a smart cockpit or smart driving high-performance chip control system, the heat dissipation device includes a radiator 1 and a cooling water tank 3, the thermal management control actuator includes an electric drive circuit water pump 4, a battery circuit water pump 13, a three-way valve 9, a four-way valve 2, an electronically controlled fan and a variable air intake grille (not shown in the figure), the sensors include an on-board charging system water inlet temperature sensor 5, a power battery water inlet temperature sensor 10 and a power battery outlet temperature sensor 12, a radiator water inlet temperature sensor, a radiator water outlet temperature sensor and a motor drive system water inlet temperature sensor (not shown in the figure); when the water temperature of the electric drive cooling circuit is high, the coolant flow from the three-way valve 9 to the radiator 1 is increased; the electronically controlled fan and the variable air intake grille can control the convection heat transfer effect of the radiator 1, and the four-way valve 2 controls the series connection (circuit fusion) and parallel connection (circuit independence) of the electric drive cooling circuit and the power battery cooling circuit of the power battery 11.

[0037] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0038] In a first aspect, an embodiment of the present application provides a fault warning method for an electric drive thermal management system.

[0039] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the electric drive thermal management system fault warning method of this application. Figure 1 As shown, the electric drive thermal management system fault warning method includes:

[0040] S100: Importing the acquired front and rear motor electric power, front and rear motor mechanical efficiency, onboard charging system electric power, and thermal management actuator data into the trained deep network learning model to output the predicted radiator outlet temperature;

[0041] Among them, the trained deep network learning model can be a neural network model or a Transformer network (transformer model), which is a neural network structure based on self-attention mechanism and position encoding. It mines the characteristics of data through multi-layer self-attention mechanism and position encoding. It is currently widely used in large models, natural language processing, time series prediction and other fields and has shown excellent performance. Since the self-attention module of the Transformer network cannot consider the temporal relationship of data like the recurrent neural network, the temporal information of the data is introduced through the position encoding module before the data is input to the self-attention module. After the input data is combined with the position encoding, the self-attention mechanism first encodes the input information through the key vector (Key), value vector (Value) and query vector (Query), and performs weighted calculation after matching the key vector and query vector. It is normalized with the softmax function, and the obtained attention weight distribution is as shown in formula (1):

[0042]

[0043] Among them, Q, K, and V are query vectors, key vectors, and value vectors respectively, d k is the dimension of the key vector.

[0044] The water temperature in the electric drive cooling circuit has a certain amount of thermal inertia, and calculating the delay constant of the inertia link is very complex (it is difficult to obtain by relying on an accurate thermal management physical model). Because the Transformer network can handle long dependencies in sequential data and can also perform parallel calculations, it improves training efficiency. Therefore, the Transformer network is used to learn the causal relationship between the above variables (front and rear motor electric power, front and rear motor mechanical efficiency, on-board charging system electric power, and thermal management actuator data) and the coolant temperature in the electric drive cooling circuit.

[0045] Among them, the thermal management actuator data information may include the electric drive circuit water pump flow in the electric drive cooling circuit, the fan speed of the radiator, the three-way valve opening, the four-way valve opening, and the variable air intake grille opening.

[0046] S200: Determine the difference between the predicted radiator outlet temperature and the vehicle charging system inlet temperature. If the difference exceeds a range threshold, report a fault code.

[0047] In this embodiment, by importing the acquired front and rear motor electric power, front and rear motor mechanical efficiency, vehicle charging system electric power, and thermal management actuator data information into the trained deep network learning model, the predicted radiator outlet temperature can be output, thereby eliminating the radiator outlet temperature sensor in the traditional solution and reducing hardware costs; and by judging the difference between the predicted radiator outlet temperature and the vehicle charging system inlet temperature, if the difference exceeds the range threshold, a fault code is broadcast to notify the user to go to the store for repair and investigation in time, which solves the problem that once a non-electrical fault occurs in the electric drive cooling circuit, such as coolant leakage, or pipe blockage, or the thermal management actuator is stuck (the position of the actuator itself is correct), the control unit cannot effectively identify it, and can only light up to remind the user based on the real-time coolant temperature in the electric drive cooling circuit exceeding the limit or the maximum temperature of the cooling component exceeding the limit. If the prompt timing is too late, it will cause power loss on the way.

[0048] Furthermore, in one embodiment, in S100, the following steps are included:

[0049] S101: The obtained front and rear motor electric power, front and rear motor mechanical efficiency, on-board charging system electric power, thermal management actuator data information, DC-DC converter power, external ambient temperature and vehicle speed are imported into the trained deep network learning model to output the predicted radiator outlet temperature.

[0050] In this embodiment, new variables (DC-DC converter power, external ambient temperature, and vehicle speed) are added to the original variables (front and rear motor electric power, front and rear motor mechanical efficiency, on-board charging system electric power, and thermal management actuator data information). By using variables that affect the predicted radiator outlet temperature in the actual environment, the accuracy of the predicted radiator outlet temperature can be further improved, thereby improving the reliability of the electric drive thermal management system fault warning.

[0051] Furthermore, in one embodiment, in S100, the following steps are included:

[0052] S101: Uploading the acquired front and rear motor electric power, front and rear motor mechanical efficiency, on-board charging system electric power, and thermal management actuator data to the first cloud platform;

[0053] S102: Based on the deep network learning model in the cloud platform 1, the predicted radiator outlet temperature is calculated, and the predicted radiator outlet temperature information is transmitted to the vehicle end.

[0054] In this embodiment, due to the limited storage and computing power of the vehicle-side controller, the vehicle-cloud collaborative control function can be used. The vehicle-side uploads the front and rear motor electric power, front and rear motor mechanical efficiency, vehicle charging system electric power and thermal management actuator data information. The Transformer model is arranged in the cloud platform 1, and the cloud platform 1 synchronizes the algorithm results to the vehicle-side in real time. The control system responsible for the electric drive cooling circuit is the power domain system control unit. The power domain system control unit realizes the control of the thermal management actuator based on the predicted radiator outlet temperature sent by the cloud platform.

[0055] Furthermore, in one embodiment, in S101, the following steps are included:

[0056] S101-1: If the network signal is abnormal and causes communication interruption, control the electric drive cooling circuit and the power battery cooling circuit to operate in series so that the radiator outlet temperature is equal to the power battery inlet temperature;

[0057] S101-2: Determine the difference between the water inlet temperature of the power battery and the water inlet temperature of the vehicle charging system. If the difference exceeds the range threshold, a fault code is broadcast.

[0058] In this embodiment, if a network signal anomaly causes communication interruption during vehicle-cloud collaborative control, or if the temperature of the electric drive cooling circuit components is abnormal, the power domain system needs to calibrate or back up the actual temperature of the electric drive cooling circuit. This is done by the power domain system control unit sending instructions to the four-way valve and battery circuit water pump, connecting the electric drive cooling circuit and the power battery cooling circuit in series. This will ensure that the radiator outlet temperature and battery inlet temperature signal values ​​are equal, completing a single initial temperature calibration. This prevents the inability to perform fault early warning diagnosis due to the inability to know the actual radiator outlet temperature.

[0059] Furthermore, in one embodiment, in S100, the following steps are included:

[0060] S101: Obtaining the DC-DC converter power, the front and rear motor electric power, and the front and rear motor mechanical efficiency within a future period of time;

[0061] S102: The vehicle charging system power, thermal management actuator data information, DC-DC converter power, front and rear motor power, and front and rear motor mechanical efficiency in the future are imported into the trained deep network learning model to output the predicted radiator outlet temperature in the future.

[0062] In this embodiment, the DC-DC converter power, front and rear motor electric power, and front and rear motor mechanical efficiency in the future period can be obtained, and the on-board charging system electric power, thermal management actuator data information, and the DC-DC converter power, front and rear motor electric power, and front and rear motor mechanical efficiency in the future period are imported into a trained deep network learning model to output the predicted radiator outlet temperature in the future period. This can predict the radiator outlet temperature in the future period, provide an early fault warning for the electric drive thermal management system in the future period, and inform the driver of the risks in advance so that the driver can choose to drive to a nearby repair shop for inspection or reduce the degree of aggressive driving of the vehicle to smoothly reach the destination.

[0063] Furthermore, in one embodiment, in S101, the following steps are included:

[0064] S101-1: Based on the vehicle speed, slope, and ambient temperature of the road ahead, predict the DC-DC converter power, front and rear motor power, and front and rear motor mechanical efficiency over a period of time in the future.

[0065] In this embodiment, the vehicle's driving information for a period of time in the future can be predicted based on the vehicle speed, slope, and external ambient temperature of the road ahead. That is, the DC-DC converter power, front and rear motor electric power, and front and rear motor mechanical efficiency for a period of time in the future can be predicted. This facilitates the prediction of the radiator outlet temperature for a period of time in the future, and provides an early warning of faults in the electric drive thermal management system for a period of time in the future, so that the driver is informed of the risks in advance.

[0066] Furthermore, in one embodiment, in S101-1, the following steps are included:

[0067] S101-1-1: Obtain the vehicle speed, slope and external ambient temperature of the road ahead based on cloud platform 2.

[0068] In this embodiment, cloud platform 2 mainly sends map navigation (such as Baidu Maps or AutoNavi Maps) data to the vehicle-side vehicle system, and calculates the DC-DC converter power of the electric drive circuit, the front and rear motor power and the front and rear motor mechanical efficiency based on the predicted vehicle speed, slope and weather temperature of the road ahead. After uploading the above information to cloud platform 1, the cloud platform immediately sends a predicted radiator outlet temperature for a period of time in the future.

[0069] In the second aspect, an embodiment of the present application also provides an electric drive thermal management system fault warning device, which includes: a radiator outlet temperature prediction module, which is used to import the acquired front and rear motor electric power, front and rear motor mechanical efficiency, on-board charging system electric power and thermal management actuator data information into a trained deep network learning model, and output the predicted radiator outlet temperature; a fault warning module, which is used to judge the difference between the predicted radiator outlet temperature and the on-board charging system inlet temperature, and if the difference exceeds the range threshold, a fault code is broadcast.

[0070] The front and rear motor electric power, front and rear motor mechanical efficiency, on-board charging system electric power, and thermal management actuator data obtained by the radiator outlet temperature prediction module are imported into the trained deep network learning model, which can output the predicted radiator outlet temperature. This can eliminate the radiator outlet temperature sensor in the traditional solution and reduce hardware costs; and the fault warning module determines the difference between the predicted radiator outlet temperature and the on-board charging system inlet temperature. If the difference exceeds the range threshold, the fault code is broadcast to notify the user to go to the store for repair and investigation in time. This solves the problem that once a non-electrical fault occurs in the electric drive cooling circuit, such as coolant leakage, pipe blockage, or thermal management actuator jamming (the position of the actuator itself is correct), the control unit cannot effectively identify it and can only light up to remind the user based on the real-time coolant temperature in the electric drive cooling circuit exceeding the limit or the maximum temperature of the cooling component exceeding the limit. If the prompt is too late, it will cause power loss halfway.

[0071] In a third aspect, an embodiment of the present application provides an electric drive thermal management system fault warning device, which can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0072] Reference Figure 3 , Figure 3 Schematic diagram of the hardware structure of the electric drive thermal management system fault warning device involved in the embodiment of the present application. In the embodiment of the present application, the electric drive thermal management system fault warning device may include a processor, a memory, a communication interface and a communication bus.

[0073] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0074] Communication interfaces include input / output (I / O), physical, and logical interfaces, which interconnect components within the electric drive thermal management system fault warning device and connect it to other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet, fiber optic, and ATM interfaces; user devices can include displays and keyboards.

[0075] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0076] The processor may be a general-purpose processor that can call an electric drive thermal management system fault warning program stored in a memory and execute the electric drive thermal management system fault warning method provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the electric drive thermal management system fault warning program is called can be referenced to the various embodiments of the electric drive thermal management system fault warning method of the present application and will not be further described here.

[0077] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0078] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0079] The readable storage medium of the present application stores an electric drive thermal management system fault warning program, wherein when the electric drive thermal management system fault warning program is executed by the processor, the steps of the electric drive thermal management system fault warning method as described above are implemented.

[0080] Among them, the method implemented when the electric drive thermal management system fault warning program is executed can refer to the various embodiments of the electric drive thermal management system fault warning method of the present application, and will not be repeated here.

[0081] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0082] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0083] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0084] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0085] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0086] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0087] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A fault warning method for an electric drive thermal management system, characterized in that: The electric drive thermal management system fault warning method includes: The acquired front and rear motor power, front and rear motor mechanical efficiency, onboard charging system power, and thermal management actuator data are fed into the trained deep learning model to output the predicted radiator outlet temperature. Determine the difference between the predicted radiator outlet temperature and the vehicle charging system inlet temperature. If the difference exceeds the range threshold, a fault code is broadcast; Among them, the electric drive thermal management system includes an electric drive cooling circuit and a power battery cooling circuit, and its electric drive cooling circuit heating device includes the front motor and the rear motor; the electric drive thermal management system also includes an on-board charging system, and the on-board charging system includes a DC-DC converter; the electric drive thermal management system also includes a heat dissipation device, and its heat dissipation device includes a radiator and a cooling water tank; the electric drive thermal management system also includes a thermal management actuator, and its thermal management actuator includes an electric drive circuit water pump, a battery circuit water pump, a three-way valve, a four-way valve, an electronically controlled fan and a variable air intake grille, and the electric drive thermal management system also includes a sensor assembly, and its sensor assembly includes an on-board charging system water inlet temperature sensor, a power battery water inlet temperature sensor, a power battery water outlet temperature sensor, a radiator water inlet temperature sensor, a radiator water outlet temperature sensor and a motor drive system water inlet temperature sensor; along the cooling direction of the electric drive cooling circuit, the coolant flows through the radiator, the four-way valve, the cooling water tank, the electric drive circuit water pump and the on-board charging system in turn.

2. The electric drive thermal management system fault warning method according to claim 1, characterized in that: The obtained front and rear motor electric power, front and rear motor mechanical efficiency, onboard charging system electric power, and thermal management actuator data are imported into the trained deep network learning model to output the predicted radiator outlet temperature, including: The obtained front and rear motor electric power, front and rear motor mechanical efficiency, on-board charging system electric power, thermal management actuator data information, DC-DC converter power, external ambient temperature and vehicle speed are imported into the trained deep network learning model to output the predicted radiator outlet temperature.

3. The electric drive thermal management system fault warning method according to claim 1, characterized in that: The obtained front and rear motor electric power, front and rear motor mechanical efficiency, onboard charging system electric power, and thermal management actuator data are imported into the trained deep network learning model to output the predicted radiator outlet temperature, including: Upload the acquired front and rear motor power, front and rear motor mechanical efficiency, on-board charging system power, and thermal management actuator data to the first cloud platform; Based on the deep network learning model in cloud platform 1, the predicted radiator outlet temperature is calculated and the predicted radiator outlet temperature information is transmitted to the vehicle end.

4. The electric drive thermal management system fault warning method according to claim 3, characterized in that: The method of uploading the obtained front and rear motor electric power, front and rear motor mechanical efficiency, on-board charging system electric power, and thermal management actuator data information to the cloud platform includes: If the network signal is abnormal and causes communication interruption, the electric drive cooling circuit and the power battery cooling circuit are controlled to operate in series to make the radiator outlet temperature equal to the power battery inlet temperature; Determine the difference between the water inlet temperature of the power battery and the water inlet temperature of the vehicle charging system. If the difference exceeds the range threshold, a fault code will be broadcast.

5. The electric drive thermal management system fault warning method according to claim 1, characterized in that: The obtained front and rear motor electric power, front and rear motor mechanical efficiency, vehicle charging system electric power, and thermal management actuator data are imported into the trained deep network learning model to output the predicted radiator outlet temperature, including: Obtaining the DC-DC converter power, front and rear motor electric power, and front and rear motor mechanical efficiency over a period of time in the future; The vehicle charging system power, thermal management actuator data information, DC-DC converter power, front and rear motor power, and front and rear motor mechanical efficiency in the future are imported into the trained deep network learning model to output the predicted radiator outlet temperature in the future.

6. The electric drive thermal management system fault warning method according to claim 5, characterized in that: The obtaining of the DC-DC converter power, the front and rear motor power, and the front and rear motor mechanical efficiency within a future period of time includes: Based on the vehicle speed, slope, and ambient temperature of the road ahead, the DC-DC converter power, front and rear motor power, and front and rear motor mechanical efficiency are predicted for a period of time in the future.

7. The electric drive thermal management system fault warning method according to claim 6, characterized in that: The vehicle speed, slope and ambient temperature based on the road ahead include: Based on the cloud platform 2, the vehicle speed, slope and external ambient temperature of the road ahead are obtained.

8. An electric drive thermal management system fault warning device applicable to the electric drive thermal management system fault warning method according to any one of claims 1 to 7, characterized in that: The electric drive thermal management system fault warning device includes: The radiator outlet temperature prediction module is used to input the acquired front and rear motor power, front and rear motor mechanical efficiency, onboard charging system power, and thermal management actuator data into the trained deep network learning model and output the predicted radiator outlet temperature; The fault warning module is used to determine the difference between the predicted radiator outlet temperature and the vehicle charging system inlet temperature. If the difference exceeds the range threshold, a fault code is broadcast.

9. A fault warning device for an electric drive thermal management system, characterized in that: The electric drive thermal management system fault warning device includes a processor, a memory, and an electric drive thermal management system fault warning program stored in the memory and executable by the processor. When the electric drive thermal management system fault warning program is executed by the processor, the steps of the electric drive thermal management system fault warning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores an electric drive thermal management system fault warning program, wherein when the electric drive thermal management system fault warning program is executed by the processor, the steps of the electric drive thermal management system fault warning method according to any one of claims 1 to 7 are implemented.

Images