Thermal management control method and system for extended-range electric vehicle and electronic equipment

Abstract

The invention discloses a thermal management control method for an extended-range electric vehicle, and relates to the field of thermal management of electric vehicles, and the method comprises the steps: obtaining vehicle development stage data, and generating a development stage data training database; obtaining driving data of a target user; according to the basic training parameters, training a long-short-term memory neural network in combination with the target user driving data; based on the trained neural network, judging whether the vehicle travel has a destination or not to predict the future average power; presetting a plurality of groups of two-dimensional mapping relation tables; the current cooling system cooling liquid temperature is obtained; the working state of the corresponding cooling system component is inquired in the two-dimensional mapping relation table in combination with prediction of future average power; and if the current cooling liquid temperature and the predicted future average power are not completely matched with the preset value in the two-dimensional mapping relation table, linear interpolation is carried out in a neighborhood of the value to obtain an adjustment value of the working state of the cooling system component, and the working state of the cooling system component is adjusted according to the adjustment value.

Description

Technical Field

[0001] This relates to the field of electric vehicle thermal management, specifically a thermal management control method for range-extended electric vehicles, a thermal management control system for range-extended electric vehicles, electronic equipment, and a storage medium. Background Technology

[0002] Electric vehicles (BEVs) are vehicles powered by onboard electricity, driven by electric motors, and that meet various road traffic and safety regulations. Existing electric vehicle thermal management systems primarily control the temperature of the passenger compartment, battery, and motor. The thermal management system includes battery temperature control circuits, passenger compartment temperature control circuits, and motor temperature control circuits. Adjusting the passenger compartment temperature control circuit ensures a suitable temperature for the driver and passengers, providing a comfortable driving experience. Furthermore, the battery temperature control circuit regulates the battery temperature, and the motor temperature control circuit controls the motor temperature, ensuring that both the motor and battery operate at appropriate temperatures, thus guaranteeing overall vehicle performance.

[0003] In existing technologies, the control of thermal management systems is based on the existing temperature measured by sensors. However, thermal management systems have a large inertia. When a component in the system alarms at a certain temperature, the system control is then initiated, which has a significant lag and is not conducive to the regulation of the system. Summary of the Invention

[0004] The purpose of this invention is to provide a thermal management control method for range-extended electric vehicles, a thermal management control system for range-extended electric vehicles, electronic equipment, and storage medium. The aim is to use historical data and neural networks to anticipate the future thermal management needs of the vehicle, thereby achieving advance control and effectively improving the heat dissipation capacity of the thermal management system, the adaptability of the vehicle, and the user experience of the driver and passengers.

[0005] This invention provides the following solution:

[0006] According to one aspect of the present invention, a thermal management control method for a range-extended electric vehicle is provided, comprising the following steps:

[0007] Data acquisition and neural network training steps:

[0008] Acquire vehicle development phase data, and generate a development phase data training database based on the vehicle development phase data;

[0009] The development phase data training database includes basic training parameters;

[0010] Obtain target user driving data;

[0011] The long short-term memory neural network is trained based on the aforementioned basic training parameters and the target user's driving data.

[0012] Future power prediction steps: Based on the trained long short-term memory neural network, determine whether the vehicle trip has a destination and predict the future average power.

[0013] If there is a destination, the average power of the target journey is predicted, and the vehicle updates the prediction of the remaining mileage in real time.

[0014] If there is no destination, predict the average power within the target time period in the future, and update the prediction results within the target time period in real time as the vehicle moves.

[0015] Cooling system component status adjustment steps: Preset multiple sets of two-dimensional mapping relationship tables;

[0016] Get the current coolant temperature of the cooling system;

[0017] The cooling system components include a water pump, a fan, and a three-way proportional valve. Based on the prediction of future average power, the operating status of the corresponding cooling system components is queried in the two-dimensional mapping table.

[0018] If the current coolant temperature and the predicted future average power do not perfectly match the preset value in the two-dimensional mapping table, then linear interpolation is performed in the neighborhood of that value to obtain the adjustment value of the working state of the cooling system components, and the working state of the cooling system components is adjusted according to the adjustment value.

[0019] Furthermore, including:

[0020] The expression for the average power P(t) is:

[0021] ;

[0022] in The power of electric drive system 1, For the power of electric drive system 2, For the power battery power, For the range extender power, Power required for the crew cabin Power required for the attachments For time, each power is... The average power over a given time period.

[0023] Furthermore, including:

[0024] In step 3, adjusting the working state of the cooling system components according to the adjustment value specifically includes: comparing the predicted average power and the current coolant temperature with a preset adjustment threshold.

[0025] Different adjustment strategies are implemented based on the comparison results;

[0026] The adjustment thresholds include a first adjustment threshold, a second adjustment threshold, and a third adjustment threshold, and the average power values ​​corresponding to the three decrease sequentially.

[0027] When the average power is within the first adjustment threshold range, the operating state of the component is not adjusted;

[0028] When the average power is within the second adjustment threshold range, the operating status of the component is adjusted according to the table lookup result;

[0029] When the average power is within the third adjustment threshold range, control one or more components of the cooling system to stop working.

[0030] Furthermore, including:

[0031] It also includes steps for limiting the overall vehicle output power:

[0032] Real-time monitoring of the operating status of the thermal management system;

[0033] When the thermal management system is operating at its maximum cooling capacity, it executes different power limiting strategies based on the predicted future average power trend.

[0034] When the thermal management system operates at its maximum cooling capacity for the specified time When the predicted power shows a decreasing trend, the output power of the whole vehicle will not be limited;

[0035] When the thermal management system operates at its maximum cooling capacity for the specified time If the predicted power does not show a decreasing trend, the output power of the whole vehicle will be limited;

[0036] The limiting factor is After limiting the actual output power of the whole vehicle ,in To predict power;

[0037] When the thermal management system operates at its maximum cooling capacity for the specified time And the vehicle's actual output power is to If there is no decreasing trend over a period of time, the output power of the entire vehicle will be limited;

[0038] The limiting factor is After limiting the actual output power of the whole vehicle ,in > .

[0039] Furthermore, including:

[0040] When the actual cooling demand of the vehicle exceeds the cooling capacity limit of the thermal management system, the system controls whether to respond to the thermal management demand according to the preset thermal management demand priority.

[0041] The thermal management requirements are prioritized from highest to lowest as follows: thermal management requirements of the power battery and accessories, thermal management requirements of the electric drive system, thermal management requirements of the range extender, and thermal management requirements of the passenger compartment.

[0042] Furthermore, including:

[0043] When multiple cooling systems have control requirements for the same cooling component, a comprehensive control method is used to adjust the state of the cooling component.

[0044] Furthermore, it also includes: fault detection and handling steps: monitoring and recording the number of adjustment failures of each control component of the vehicle's thermal management system under different conditions;

[0045] The number of adjustment failures is compared with a preset failure threshold.

[0046] When the number of adjustment failures of the target component exceeds the failure number threshold, the state of the component is adjusted to a state where adjustment is prohibited.

[0047] When the target component is in a state where adjustment is prohibited, the coolant temperature and the temperature of each component in the thermal management circuit where the component is located are detected.

[0048] If the temperatures are all within the preset range, the component will remain in the non-adjustable state until the vehicle is powered on again and it is adjusted to the adjustable state.

[0049] If the temperature exceeds the preset value, the state of the component will be adjusted to an adjustable state. If the adjustment still fails, the machine must be stopped for maintenance.

[0050] According to a second aspect of the present invention, a thermal management control system for a range-extended electric vehicle is provided, comprising:

[0051] Data acquisition and neural network training module, future power prediction module, and cooling system component status adjustment module;

[0052] Data acquisition and neural network training module: used to acquire data during the vehicle development phase, and generate a development phase data training database based on the vehicle development phase data, the development phase data training database including basic training parameters;

[0053] Used to obtain driving data of target users;

[0054] Used to train a long short-term memory neural network based on the basic training parameters and the target user's driving data;

[0055] Future power prediction module: used to predict the future average power based on the trained long short-term memory neural network to determine whether the vehicle's trip has a destination;

[0056] If there is a destination, the average power of the target journey is predicted, and the vehicle updates the prediction of the remaining mileage in real time.

[0057] If there is no destination, predict the average power within the target time period in the future, and update the prediction results within the target time period in real time as the vehicle moves.

[0058] Cooling system component status adjustment module: used to adjust the status according to multiple preset two-dimensional mapping relationship tables;

[0059] Obtain the current coolant temperature of the cooling system; the cooling system components include a water pump, a fan, and a three-way proportional valve. Based on the prediction of future average power, query the working status of the corresponding cooling system components in the two-dimensional mapping table.

[0060] If the current coolant temperature and the predicted future average power do not perfectly match the preset value in the two-dimensional mapping table, then linear interpolation is performed in the neighborhood of that value to obtain the adjustment value of the working state of the cooling system components, and the working state of the cooling system components is adjusted according to the adjustment value.

[0061] According to three aspects of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0062] The memory stores a computer program, which, when executed by a processor, causes the processor to perform the steps of a thermal management control method for a range-extended electric vehicle.

[0063] According to four aspects of the present invention, a computer-readable storage medium is provided that stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a thermal management control method for a range-extended electric vehicle.

[0064] Compared with the prior art, the present invention has the following advantages:

[0065] By utilizing the predicted power of each vehicle component, this application enables the method to adjust the operating status of each component of the thermal management system in real time based on the predicted power of each component, thereby ensuring that the temperature of each component of the vehicle is within a reasonable range.

[0066] This application utilizes the predicted power of various vehicle components to make the operation of each component of the thermal management system more closely match the actual driving needs, thereby optimizing thermal management and ensuring the continuity and stability of vehicle operation under various conditions.

[0067] This application employs a fault diagnosis and handling process, enabling the system to respond promptly upon detecting consecutive adjustment failures. By preventing further adjustments to the faulty component, it avoids potential mechanical damage or other problems caused by malfunctions. This not only improves vehicle reliability but also ensures driving safety, especially during critical moments when the thermal management system needs to operate under heavy load.

[0068] This application analyzes the sensitivity of the thermal management system to different prediction ranges and vehicle energy consumption based on measured information, determines the length of the prediction range required to maintain optimal performance, and designs a multi-source power prediction based on a long short-term memory neural network based on future traffic information obtained from the navigation system to achieve accurate prediction of future power and obtain the future vehicle heat power distribution, thereby improving the vehicle's adaptability to various environments. Attached Figure Description

[0069] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0070] Figure 1 This is a flowchart of a thermal management control method for a range-extended electric vehicle provided by one or more embodiments of the present invention.

[0071] Figure 2 This is a structural diagram of a thermal management control system for a range-extended electric vehicle provided in one or more embodiments of the present invention.

[0072] Figure 3 This is a schematic diagram of a thermal management control system for a range-extended electric vehicle according to a specific embodiment of the present invention.

[0073] Figure 4 This is a schematic diagram of the principle of energy flow in a vehicle according to a specific embodiment of the present invention.

[0074] Figure 5 This is a flowchart of a specific embodiment of the present invention based on neural network prediction.

[0075] Figure 6 This is a block diagram of an electronic device structure for a thermal management control method for a range-extended electric vehicle provided by one or more embodiments of the present invention.

[0076] Figure 3 In the air conditioning temperature control circuit: 11. Temperature and pressure sensor; 12. Condenser; 13. Compressor; 14. Temperature and pressure sensor; 15. Temperature and pressure sensor; 16. Electronic expansion valve; 17. Cooler; 18. Electronic expansion valve; 19. Evaporator.

[0077] Electric drive cooling circuit: 21. Medium temperature radiator; 22. Electric drive cooling circuit expansion tank; 23. Electric drive cooling circuit water pump; 24. Temperature sensor T1; 25. Second three-way proportional valve; 26. First three-way proportional valve; 27. Generator; 28. Accessories; 29. ​​Electric drive system 1; 210. Electric drive system 2; 211. Temperature sensor T2; 212. Temperature sensor T3; 213. Temperature sensor T4.

[0078] Engine cooling circuit: 31. High-temperature radiator; 32. Engine cooling circuit expansion tank; 33. Engine cooling circuit water pump; 34. Solenoid valve; 35. Heater core; 36. Engine; 37. Thermal regulating valve.

[0079] Battery temperature control circuit: 41. Temperature sensor T5; 42. Power battery; 43. Battery water cooling plate; 44. Temperature sensor T6; 45. PTC heater; 46. Battery cooling circuit water pump; 47. Battery cooling circuit expansion tank; 5. Others: 51. Passenger compartment PTC heater; 52. Passenger compartment fan; 53. Radiator fan. Detailed Implementation

[0080] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0081] Figure 1 This is a flowchart of a thermal management control method for a range-extended electric vehicle provided by one or more embodiments of the present invention.

[0082] like Figure 1 As shown, it includes the following steps:

[0083] Step S1: Obtain vehicle development stage data and generate a development stage data training database based on the vehicle development stage data.

[0084] The development phase data training database includes basic training parameters;

[0085] Step S2: Obtain the target user's driving data;

[0086] The long short-term memory neural network is trained based on the aforementioned basic training parameters and the target user's driving data.

[0087] Step S3: Based on the trained long short-term memory neural network, determine whether the vehicle's trip has a destination and predict the future average power.

[0088] If there is a destination, the average power of the target journey is predicted, and the vehicle updates the prediction of the remaining mileage in real time.

[0089] If there is no destination, predict the average power within the target time period in the future, and update the prediction results within the target time period in real time as the vehicle moves.

[0090] Step S4: Preset multiple sets of two-dimensional mapping relationship tables;

[0091] Get the current coolant temperature of the cooling system;

[0092] The cooling system components include a water pump, a fan, and a three-way proportional valve. Based on the prediction of future average power, the operating status of the corresponding cooling system components is queried in the two-dimensional mapping table.

[0093] If the current coolant temperature and the predicted future average power do not perfectly match the preset value in the two-dimensional mapping table, then linear interpolation is performed in the neighborhood of that value to obtain the adjustment value of the working state of the cooling system components, and the working state of the cooling system components is adjusted according to the adjustment value.

[0094] Specifically, traditional cooling systems are mostly passively adjusted based on real-time operating conditions (such as current power and coolant temperature), without considering future changes in power demand, resulting in control lag (such as insufficient cooling when suddenly outputting high power, or excessive cooling when operating at low power).

[0095] Insufficient personalized adaptation to user driving habits: Different users have significantly different driving styles (such as rapid acceleration, smooth driving, etc.), and it is difficult to accurately predict actual power requirements based solely on general data from the vehicle development stage, resulting in a low degree of matching between the cooling system and actual operating conditions.

[0096] The lack of scenario-based prediction: Whether a vehicle trip has a destination (such as commuting with a known route vs. random driving) will significantly affect the future power change pattern (when the destination is clear, power can be predicted by combining route features; when there is no destination, it needs to be inferred based on historical driving patterns). Traditional methods do not distinguish this scenario, thus limiting the prediction accuracy.

[0097] Insufficient flexibility in matching operating conditions: If the operating status of cooling system components (water pump, fan, three-way valve) relies solely on a preset two-dimensional mapping table, precise adjustment cannot be achieved when the actual operating conditions (current temperature + future power) do not perfectly match the preset values, which can easily lead to control deviations.

[0098] By combining basic data from the vehicle development phase with actual driving data from target users to train an LSTM neural network, a balance is struck between versatility and personalization, making power prediction more aligned with user driving habits.

[0099] By differentiating between destination-oriented and destination-free scenarios, the prediction logic can be adjusted accordingly (e.g., combining trip characteristics when the destination is clear, and basing predictions on short-term driving patterns when the destination is not clear) to further improve prediction accuracy.

[0100] Achieve proactive control of the cooling system: Adjust the operating status of cooling system components in advance based on the prediction of future average power, avoid the lag problem of traditional passive control, and ensure that the cooling capacity is accurately matched with future power demand (such as enhancing cooling in advance before high power output is predicted to avoid overheating of components).

[0101] Enhance the flexibility of operating condition adaptation; when the actual operating conditions (current coolant temperature + predicted power) do not perfectly match the preset mapping table, the adjustment value is calculated by neighborhood linear interpolation, so that the working state of the cooling system components can be smoothly adapted to complex operating conditions and reduce control deviation.

[0102] Optimize vehicle energy efficiency and reliability; precisely control the working status of components such as water pumps and fans to avoid overcooling (reducing energy consumption) or undercooling (preventing component damage), thereby reducing cooling system energy consumption and improving overall vehicle energy efficiency while ensuring vehicle operation stability.

[0103] Furthermore, including:

[0104] The expression for the average power P(t) is:

[0105] ;

[0106] in The power of electric drive system 1, For the power of electric drive system 2, For the power battery power, For the range extender power, Power required for the crew cabin Power required for the attachments For time, each power is... The average power over a given time period.

[0107] Furthermore, including:

[0108] The specific steps of adjusting the operating state of the cooling system components based on the adjustment value include: comparing the predicted average power and the current coolant temperature with a preset adjustment threshold.

[0109] Different adjustment strategies are implemented based on the comparison results;

[0110] The adjustment thresholds include a first adjustment threshold, a second adjustment threshold, and a third adjustment threshold, and the average power values ​​corresponding to the three decrease sequentially.

[0111] When the average power is within the first adjustment threshold range, the operating state of the component is not adjusted;

[0112] When the average power is within the second adjustment threshold range, the operating status of the component is adjusted according to the table lookup result;

[0113] When the average power is within the third adjustment threshold range, control one or more components of the cooling system to stop working.

[0114] Furthermore, including:

[0115] It also includes steps for limiting the overall vehicle output power:

[0116] Real-time monitoring of the operating status of the thermal management system;

[0117] When the thermal management system is operating at its maximum cooling capacity, it executes different power limiting strategies based on the predicted future average power trend.

[0118] When the thermal management system operates at its maximum cooling capacity for the specified time When the predicted power shows a decreasing trend, the output power of the whole vehicle will not be limited;

[0119] When the thermal management system operates at its maximum cooling capacity for the specified time If the predicted power does not show a decreasing trend, the output power of the whole vehicle will be limited;

[0120] The limiting factor is After limiting the actual output power of the whole vehicle ,in To predict power;

[0121] When the thermal management system operates at its maximum cooling capacity for the specified time And the vehicle's actual output power is to If there is no decreasing trend over a period of time, the output power of the entire vehicle will be limited;

[0122] The limiting factor is k2, and the actual output power of the vehicle after the limitation is... ,in > .

[0123] Furthermore, including:

[0124] When the actual cooling demand of the vehicle exceeds the cooling capacity limit of the thermal management system, the system controls whether to respond to the thermal management demand according to the preset thermal management demand priority.

[0125] The thermal management requirements are prioritized from highest to lowest as follows: thermal management requirements of the power battery and accessories, thermal management requirements of the electric drive system, thermal management requirements of the range extender, and thermal management requirements of the passenger compartment.

[0126] Furthermore, including:

[0127] When multiple cooling systems have control requirements for the same cooling component, a comprehensive control method is used to adjust the state of the cooling component.

[0128] Furthermore, it also includes: fault detection and handling steps: monitoring and recording the number of adjustment failures of each control component of the vehicle's thermal management system under different conditions;

[0129] The number of adjustment failures is compared with a preset failure threshold.

[0130] When the number of adjustment failures of the target component exceeds the failure number threshold, the state of the component is adjusted to a state where adjustment is prohibited.

[0131] When the target component is in a state where adjustment is prohibited, the coolant temperature and the temperature of each component in the thermal management circuit where the component is located are detected.

[0132] If the temperatures are all within the preset range, the component will remain in the non-adjustable state until the vehicle is powered on again and it is adjusted to the adjustable state.

[0133] If the temperature exceeds the preset value, the state of the component will be adjusted to an adjustable state. If the adjustment still fails, the machine must be stopped for maintenance.

[0134] Figure 2 This is a structural diagram of a thermal management control system for a range-extended electric vehicle provided in one or more embodiments of the present invention.

[0135] like Figure 2 As shown, it includes:

[0136] Data acquisition and neural network training module, future power prediction module, and cooling system component status adjustment module;

[0137] Data acquisition and neural network training module: used to acquire data during the vehicle development phase, and generate a development phase data training database based on the vehicle development phase data, the development phase data training database including basic training parameters;

[0138] Used to obtain driving data of target users;

[0139] Used to train a long short-term memory neural network based on the basic training parameters and the target user's driving data;

[0140] Future power prediction module: used to predict the future average power based on the trained long short-term memory neural network to determine whether the vehicle's trip has a destination;

[0141] If there is a destination, the average power of the target journey is predicted, and the vehicle updates the prediction of the remaining mileage in real time.

[0142] If there is no destination, predict the average power within the target time period in the future, and update the prediction results within the target time period in real time as the vehicle moves.

[0143] Cooling system component status adjustment module: used to adjust the status according to multiple preset two-dimensional mapping relationship tables;

[0144] Obtain the current coolant temperature of the cooling system; the cooling system components include a water pump, a fan, and a three-way proportional valve. Based on the prediction of future average power, query the working status of the corresponding cooling system components in the two-dimensional mapping table.

[0145] If the current coolant temperature and the predicted future average power do not perfectly match the preset value in the two-dimensional mapping table, then linear interpolation is performed in the neighborhood of that value to obtain the adjustment value of the working state of the cooling system components, and the working state of the cooling system components is adjusted according to the adjustment value.

[0146] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0147] Figure 3 This is a schematic diagram of a thermal management control system for a range-extended electric vehicle according to a specific embodiment of the present invention.

[0148] like Figure 3 As shown, the thermal management system includes: an air conditioning temperature control circuit, an electric drive cooling circuit, an engine cooling circuit, and a battery temperature control circuit.

[0149] Figure 4 This is a schematic diagram of the principle of energy flow in a vehicle according to a specific embodiment of the present invention.

[0150] like Figure 4As shown, in a specific embodiment, a block diagram of a vehicle is provided to illustrate the vehicle's energy flow process. The system includes: a range extender, a power battery, electric drive system 1, electric drive system 2, a passenger compartment, and accessories; the range extender generates electricity to charge the power battery, and the electric battery transfers electrical energy to electric drive system 1, electric drive system 2, the passenger compartment, and accessories. Electric drive system 1 and electric drive system 2 are used to drive the vehicle; accessories include an air compressor, an oil pump, a DC-DC converter, etc.

[0151] above Figure 3 Thermal management system loop and Figure 4 The vehicle energy flow diagram is only used to illustrate the control method of this thermal management system and should not be construed as a limitation of the method.

[0152] Considering the large inertia of the vehicle thermal management system and the impact of the power consumption of various components on vehicle heat generation and thermal management system performance, a neural network-based optimization method for electric vehicle thermal management systems is proposed. Based on measured information, the sensitivity of the thermal management system to different prediction ranges and vehicle energy consumption is studied, and the required prediction range length to maintain optimal performance is determined. Secondly, to address the uncertainty of long-term predicted power, a multi-source power prediction method based on a long short-term memory neural network is designed using future traffic information obtained from the navigation system to achieve accurate prediction of future power and obtain the future vehicle heat generation power distribution. Finally, an adaptive optimization time-domain model predictive control method with multiple time scales is designed to improve the vehicle's adaptability to various environments.

[0153] Data is collected during the vehicle development phase, including but not limited to historical operating condition data (accelerator pedal opening, brake pedal opening, coolant outlet temperature of each cooling component, and real-time power of each energy-consuming component), current ambient temperature, driver driving habits, navigation information, and driving modes. Before the vehicle leaves the factory, this data is used to train the database, obtaining a basic set of training parameters. After the vehicle reaches the user, user driving data is used to further personalize the neural network.

[0154] In this embodiment, the preferred neural network model is a Long Short-Term Memory (LSTM) model, which is specifically designed to address the vanishing and exploding gradient problems that occur in traditional recurrent neural networks when processing long-sequence data. By introducing memory units and gating mechanisms, LSTM can effectively capture long-term dependencies in time-series data.

[0155] Prediction process:

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] ;

[0162] ;

[0163] in, Input the vehicle data at time t; as well as Here is the weight matrix for the forget gate; For the bias term of the forget gate; It is a sigmoid activation function; The output of the forget gate; , and The weights / biases of the input gate; The output of the input gate; , and Weights / biases for candidate memories; It is the hyperbolic tangent activation function; Candidate cell state; , and The weights / biases of the output gate; The content to be memorized at time t-1 The content of memory at time t; for The status of the system at all times. for The status of the system at all times. yes The content of the time forecast; This represents a nonlinear transformation of the cell state. as well as The weights / biases of the output layer.

[0164] ;

[0165] ;

[0166] In the formula: The power of electric drive system 1; The power of electric drive system 2; Power of the battery; For range extender power; Power required for the crew cabin; Power required for the attachment; For time. All power values ​​above are expressed in terms of 0- The average power over a period of time can also be called the average power, and will be referred to as "average power" from now on.

[0167] Each trip a vehicle takes can be categorized as either destination-oriented or destinationless. For destination-oriented trips, the prediction of the entire journey is made, and the remaining distance is updated in real-time as the vehicle travels. For destinationless trips, the prediction is made for the future... The forecast for the specified time period is updated in real time as the vehicle moves. Predictions within a given timeframe.

[0168] The heat generation of each component in an electric vehicle is directly related to its output power; higher output power generates more heat, and lower output power generates less heat. Through vehicle testing and bench calibration, the relationship between output power and heat generation can be accurately determined. This work requires a significant amount of time.

[0169] This method of controlling the vehicle's thermal management system by predicting future power output effectively ensures that all vehicle components operate within appropriate ranges, eliminating the risk of overheating. When a sudden increase in the power demand of a component is predicted in the near future, the cooling intensity of the component's cooling system is increased in advance to dissipate some heat. Simultaneously, the cooling system's power is increased in advance, extending the relative cooling time to remove more heat. Current technology typically controls the cooling intensity by measuring the coolant outlet temperature of a component. However, due to the large inertia of the cooling system, sensor measurement delays, and water flow time issues, by the time the sensor detects that the coolant temperature exceeds the preset threshold, the internal temperature of the component is already very high. Adjusting the cooling intensity at this point is inefficient and directly impacts the driving experience, potentially even causing vehicle damage.

[0170] In addition, if it is detected that the output power of a certain component will decrease in the near future, the cooling power of the corresponding cooling circuit can be reduced in advance to save energy, provided that the component does not overheat.

[0171] The above functions will be explained below through specific embodiments:

[0172] The strategy for adjusting the operating status of cooling system components relies on a pre-set two-dimensional data table, which records the variable values ​​of the component operating status at different power levels and over different time periods.

[0173] By using the predicted power output of a component over a future period, a pre-defined mapping relationship between the component's operating state (fan speed, three-way proportional valve opening, water pump speed, air conditioning compressor speed, etc.) and power can be queried to obtain the component's operating state at that power and temperature. The specific application process is as follows: When a power change over a future period is predicted, the control system refers to the future power output and the current coolant temperature, and searches for the corresponding component's operating state (fan, water pump, three-way proportional valve, etc.) in the relevant control mapping relationship.

[0174] In navigation mode (i.e., the vehicle's current trip has a specific destination), taking the electric drive system's thermal management loop as an example, this control strategy is explained. The table below shows the mapping relationship between different components of the electric drive cooling system and future power and current coolant temperature:

[0175] Water pump speed mapping table

[0176] Electric water pump speed P(Time1) P(Time2) P(Time3) P (Time4) … Coolant temperature Temp1 DQSBspeed11 DQSBspeed12 DQSBspeed13 DQSBspeed14 … Coolant temperature Temp2 DQSBspeed21 DQSBspeed22 DQSBspeed23 DQSBspeed24 … Coolant temperature Temp3 DQSBspeed31 DQSBspeed32 DQSBspeed33 DQSBspeed34 … Coolant temperature Temp4 DQSBspeed41 DQSBspeed42 DQSBspeed43 DQSBspeed44 … … … … … … …

[0177] Fan speed mapping table

[0178] Fan speed P(Time1) P(Time2) P(Time3) P (Time4) … Coolant temperature Temp1 DQFSspeed11 DQFSspeed12 DQFSspeed13 DQFSspeed14 … Coolant temperature Temp2 DQFSspeed21 DQFSspeed22 DQFSspeed23 DQFSspeed24 … Coolant temperature Temp3 DQFSspeed31 DQFSspeed32 DQFSspeed33 DQFSspeed34 … Coolant temperature Temp4 DQFSspeed41 DQFSspeed42 DQFSspeed43 DQFSspeed44 … … … … … … …

[0179] Three-way proportional valve 1

[0180] Fan speed DP(Time1) DP (Time2) DP (Time3) DP (Time4) … Coolant temperature DTemp1 BLFO11 BLFO 12 BLFO 13 BLFO 14 … Coolant temperature DTemp2 BLFO 21 BLFO 22 BLFO 23 BLFO 24 … Coolant temperature DTemp3 BLFO 31 BLFO 32 BLFO 33 BLFO 34 … Coolant temperature DTemp4 BLFO 41 BLFO 42 BLFO 43 BLFO 44 … … … … … … …

[0181] Three-way proportional valve 2

[0182] Fan speed DP(Time1) DP (Time2) DP (Time3) DP (Time4) … Coolant temperature DDTemp1 BLFT11 BLFT 12 BLFT 13 BLFT 14 … Coolant temperature DDTemp2 BLFT 21 BLFT 22 BLFT23 BLFT24 … Coolant temperature DDTemp3 BLFT 31 BLFT 32 BLFT 33 BLFT 34 … Coolant temperature DDTemp4 BLFT 41 BLFT 42 BLFT 43 BLFT 44 … … … … … … …

[0183] In the table:

[0184] ;

[0185] ;

[0186] The coolant temperature is the value measured by temperature sensor T1, where T2 represents the value measured by temperature sensor T2, T3 represents the value measured by temperature sensor T3, and T4 represents the value measured by temperature sensor T4.

[0187] The range of adjustment for each component must be guaranteed not to exceed its physical limits.

[0188] ;

[0189] ;

[0190] ;

[0191] ;

[0192] During actual vehicle operation, the control system adjusts the operating status of cooling system components in real time based on the predicted average power output. This is done by reading the current coolant temperature and the predicted average power output, and then looking up the corresponding operating status in a pre-defined thermal management system component operating status control mapping table. For example, if the coolant temperature of the electric drive system is... The average power is At this point, the fan speed is determined by referring to the table. The speed of the water pump in the electric drive cooling system is Therefore, according to The values ​​adjust the operating status of the components to ensure sufficient cooling capacity to handle potentially high energy consumption.

[0193] In real-world conditions, coolant temperature and average power It is impossible for it to be exactly equal to the preset value in the table. In this case, linear interpolation needs to be performed in the neighborhood of the value to obtain the adjustment value of the working state of the cooling component.

[0194] Through the methods described in the above embodiments, this embodiment effectively improves the thermal management capability of automobiles under different driving conditions, ensures the stability of vehicle driving, and optimizes the thermal management system of electric vehicles.

[0195] In some embodiments, an adjustment strategy for the corresponding component states is determined based on the relationship between the coolant temperature and the predicted average power of each component in the electric drive system and the operating states of each component. Adjusting the operating states of the corresponding components accordingly also includes:

[0196] The predicted average power of each component of the electric drive system and the current coolant temperature are compared with adjustment thresholds. When the average power is within the first adjustment threshold range, the operating status of the components is not adjusted. When the average power is within the second adjustment threshold range, the operating status of the components is adjusted according to the table lookup results. When the average power is within the third adjustment threshold range, one or more components of the cooling system are controlled to stop working.

[0197] The average power values ​​corresponding to the first adjustment threshold, the second adjustment threshold, and the third adjustment threshold decrease sequentially.

[0198] The control system first compares the detected coolant temperature of the electric drive system and the predicted average power of the components with preset thresholds to determine the appropriate component adjustment strategy. For different temperature and average power ranges, the system adopts different adjustment measures:

[0199] First adjustment threshold: When the predicted average power of the vehicle is slightly reduced (e.g., within 5kW), the operating status of the electric drive thermal management system components is not adjusted.

[0200] Second adjustment threshold: When the predicted average power of the vehicle decreases significantly (e.g., from 20kW to 5kW), the operating status of the components is adjusted based on the table lookup results;

[0201] The third adjustment threshold: When the predicted average power of the vehicle decreases significantly (e.g., by more than 20 kW), the components of the thermal management system are controlled to stop working and natural wind is used to cool the components.

[0202] Component operating status adjustment execution phase: When the vehicle's predicted average power decreases, the control system will calculate appropriate operating status adjustment values ​​in real time. The specific adjustment method depends on which threshold range the predicted power falls within. For each threshold range, the system will dynamically adjust the operating status of each component according to the set strategy to ensure improved vehicle thermal management capabilities while avoiding energy waste.

[0203] Through the method described in the above embodiments, this embodiment can intelligently adjust the working status of each component of the thermal management system based on different coolant temperatures and predicted average power, thereby significantly improving the vehicle's thermal management capabilities, reducing energy consumption, and ensuring the stability of vehicle operation, thus enhancing the overall energy efficiency and performance of the vehicle.

[0204] In some embodiments, an adjustment strategy for the corresponding component states is determined based on the relationship between the coolant temperature and the predicted average power of each component in the electric drive system and the operating states of each component. Adjusting the operating states of the corresponding components accordingly also includes:

[0205] The current thermal management system components have reached their operating limits, but the predicted average power is still trending upwards. If the overall vehicle average power continues to rise, it will cause the vehicle's heat generation to exceed the cooling limit of the thermal management system. To ensure that the temperature of each vehicle component does not exceed the preset value, the actual output power of the vehicle is controlled, limiting the overall vehicle output power as follows:

[0206] When the thermal management system operates at its maximum cooling capacity for time t1, and the predicted power of the vehicle shows a decreasing trend, the output power of the entire vehicle is not limited.

[0207] When the thermal management system operates at its maximum cooling capacity for time t1, and the predicted power of the vehicle does not show a decreasing trend, the output power of the whole vehicle is limited, with a limitation factor of k1.

[0208] ;

[0209] When the thermal management system operates at its maximum cooling capacity for a time t2, and the actual output power of the vehicle does not show a decreasing trend during the time period from t1 to t2, the output power of the entire vehicle is limited, with a limitation coefficient of k2.

[0210] ;

[0211] In the formula: k1>k2; p0(t) is the actual power response of the vehicle.

[0212] When the actual cooling demand of a vehicle exceeds the cooling capacity limit of the thermal management system (i.e., the operating temperature of a component of the thermal management system has exceeded its safe operating temperature), the vehicle's thermal management system will control whether to respond to the thermal management demand based on the priority of each cooling system's demand.

[0213] The thermal management requirements of each thermal management system are prioritized as follows: the thermal management requirements of the power battery and accessories that ensure the normal operation of the vehicle have the highest priority, followed by the thermal management requirements of the electric drive system, the thermal management requirements of the range extender have the third priority, and the thermal management requirements of the passenger compartment have the lowest priority.

[0214] The system only controls the vehicle to supply power to the thermal management system and ensures its power output, while stopping power supply to other electrical systems (such as drive motors) until the temperature of each component of the thermal management system returns to normal.

[0215] This ensures that when the vehicle's thermal management system is operating at its maximum capacity, the temperature of all vehicle components remains within a reasonable range.

[0216] In some embodiments, when multiple cooling systems control the same cooling component, the state of the component needs to be adjusted through integrated control. Taking a power battery cooling system as an example, the control method of a cooling component when it simultaneously responds to the power demands of two different cooling circuits is explained.

[0217] The thermal management loop of the power battery cooling system requires control of the electric fan. The table below shows the mapping relationship between the fan's power output and the current coolant temperature over a future time period:

[0218] Fan speed mapping table

[0219] Fan speed PB (Time1) PB (Time2) PB (Time3) PB (Time4) … Coolant temperature Temp1 DLFSspeed11 DLFSspeed12 DLFSspeed13 DLFSspeed14 … Coolant temperature Temp2 DLFSspeed21 DLFSspeed22 DLFSspeed23 DLFSspeed24 … Coolant temperature Temp3 DLFSspeed31 DLFSspeed32 DLFSspeed33 DLFSspeed34 … Coolant temperature Temp4 DLFSspeed41 DLFSspeed42 DLFSspeed43 DLFSspeed44 … … … … … … …

[0220] In the formula, PB(t) is the power of the battery in the predicted future period; the coolant temperature refers to the temperature of the battery coolant; and DLFSspeed is the rotational speed calculated based on the power of the battery and its coolant temperature.

[0221]

[0222] ;

[0223] Ultimately, the final rotational speed of the electric fan is ;

[0224] In the non-navigation state (i.e., the vehicle's current trip has no destination), the control strategy will be explained using the electric drive system's thermal management circuit as an example. Not all components of the electric drive system's thermal management circuit are listed below; only the water pump speed mapping table is used as an example to illustrate the different control strategies in the non-navigation and navigation states.

[0225] Water pump speed mapping table

[0226] Electric water pump speed PT (TTime1) PT (TTime2) PT (TTime3) PT (TTime4) … Coolant temperature TTemp1 DQSBTspeed11 DQSBTspeed12 DQSBTspeed13 DQSBTspeed14 … Coolant temperature TTemp2 DQSBTspeed21 DQSBTspeed22 DQSBTspeed23 DQSBTspeed24 … Coolant temperature TTemp3 DQSBTspeed31 DQSBTspeed32 DQSBTspeed33 DQSBTspeed34 … Coolant temperature TTemp4 DQSBTspeed41 DQSBTspeed42 DQSBTspeed43 DQSBTspeed44 … … … … … … …

[0227] Table Less than The meanings of the remaining symbols and the limit positions of component adjustments are the same as those in the table under navigation status.

[0228] The component adjustment strategies when the vehicle power fluctuates, when the vehicle power decreases, and when the vehicle power reaches the working limit of the thermal management system are the same as those in the navigation state. However, the adjustment thresholds are different from those in the navigation state, and will not be elaborated here.

[0229] In some embodiments, the method further includes (fault detection):

[0230] The number of adjustment failures of each control component of the thermal management system under different conditions is monitored and recorded respectively. The number of adjustment failures of each control component of the thermal management system is compared with the preset failure number threshold. When the number of failures of a certain component exceeds the threshold, the adjustment of that component is prohibited.

[0231] When a component is in the "disable adjustment" state, the coolant temperature and the temperature of each component in the thermal management circuit are monitored. If these temperatures are all within the preset range, the component is kept in the "disable adjustment" state until a temperature in the thermal management circuit exceeds the preset value. Then, the component is adjusted to the "adjustable" state. If the component still cannot be adjusted, the vehicle must be stopped for maintenance. When these temperatures are all within the preset range, the component is kept in the "disable adjustment" state until the vehicle is powered on again, at which point the component is adjusted to the "adjustable" state.

[0232] This fault diagnosis is a mechanism designed to improve the operational reliability of a thermal management system. It determines the existence of potential faults by monitoring the success or failure of adjustments made to various components of the thermal management system under different demands. The implementation process of the fault diagnosis method is described in detail below with reference to specific embodiments, and may include the following:

[0233] Record the number of times the working status adjustment of each component of the thermal management system fails: The system will monitor and record the number of times the working status adjustment of each component of the thermal management system fails, denoted as N.

[0234] Fault diagnosis logic is set: For a component of a thermal management system, if N (the number of times the working state adjustment of this component of the thermal management system has failed) is greater than or equal to N0, the system will determine that the component has a potential fault and prohibit the adjustment of the working state of this component of the thermal management system.

[0235] Fault handling and recovery logic: Once the operating status adjustment of a component in the thermal management system is prohibited, the component will continue to operate in its current state. This state will be lifted when the temperature of a component in the thermal management circuit containing that component becomes too high, or when the vehicle is turned off. For example, if the temperatures of all components in the thermal management circuit containing that component are within a reasonable range, the system will maintain the component in a prohibited adjustment state. If the temperature of a component in the thermal management circuit becomes abnormal, the system will lift the prohibited adjustment state and make the component adjustable. If the temperatures of all components in the thermal management circuit containing that component are within a reasonable range, the system will wait until the vehicle is powered on again before allowing the component to adjust its operating status. If the component's state still cannot be adjusted after the vehicle is restarted, it needs to be stopped for inspection and repair.

[0236] Through this fault diagnosis and handling process, the system can respond promptly when it detects consecutive adjustment failures, preventing potential mechanical damage or other problems caused by malfunctions by halting further adjustments to the faulty component. This not only improves vehicle reliability but also ensures driving safety, especially during critical moments when the thermal management system needs to operate under heavy load.

[0237] According to the technical solutions provided in the embodiments of this application, the embodiments of this application have at least the following advantages:

[0238] By utilizing the predicted power of each vehicle component, this method can adjust the operating status of each component of the thermal management system in real time according to the predicted power of each component, thereby ensuring that the temperature of each component of the vehicle is within a reasonable range.

[0239] Through the implementation of this application, the operation of each component of the thermal management system is more closely aligned with actual driving needs, achieving optimized thermal management and ensuring the continuity and stability of vehicle operation under various conditions. Furthermore, this solution incorporates fault prevention and diagnostic functions, effectively preventing potential faults and improving the overall reliability and safety of the system through continuous monitoring and intelligent analysis of the operating status of each thermal management component.

[0240] Figure 6 This is a block diagram of an electronic device structure for a thermal management control method for a range-extended electric vehicle provided by one or more embodiments of the present invention.

[0241] like Figure 6 As shown, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0242] The memory stores a computer program that, when executed by a processor, causes the processor to perform the steps of a thermal management control method for a range-extended electric vehicle.

[0243] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a thermal management control method for a range-extended electric vehicle.

[0244] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0245] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0246] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A thermal management control method for a range-extended electric vehicle, characterized in that, Includes the following steps: Acquire vehicle development phase data, and generate a development phase data training database based on the vehicle development phase data; The development phase data training database includes basic training parameters; Obtain target user driving data; The long short-term memory neural network is trained based on the aforementioned basic training parameters and the target user's driving data. Based on the trained long short-term memory neural network, the system determines whether a vehicle's journey has a destination and predicts its future average power. If there is a destination, the average power of the target journey is predicted, and the vehicle updates the prediction of the remaining mileage in real time. If there is no destination, predict the average power within the target time period in the future, and update the prediction results within the target time period in real time as the vehicle moves. Multiple pre-set two-dimensional mapping relationship tables; Get the current coolant temperature of the cooling system; The cooling system components include a water pump, a fan, and a three-way proportional valve. Based on the prediction of future average power, the operating status of the corresponding cooling system components is queried in the two-dimensional mapping table. If the current coolant temperature and the predicted future average power do not perfectly match the preset value in the two-dimensional mapping table, then linear interpolation is performed in the neighborhood of that value to obtain the adjustment value of the working state of the cooling system components, and the working state of the cooling system components is adjusted according to the adjustment value.

2. The thermal management control method for a range-extended electric vehicle according to claim 1, characterized in that, The average power The expression is: ； in The power of electric drive system 1, For the power of electric drive system 2, For the power battery power, For the range extender power, Power required for the crew cabin Power required for the attachments For time, each power is... The average power over a period of time.

3. The thermal management control method for a range-extended electric vehicle according to claim 1, characterized in that, In step 3, adjusting the working state of the cooling system components according to the adjustment value specifically includes: comparing the predicted average power and the current coolant temperature with a preset adjustment threshold. Different adjustment strategies are implemented based on the comparison results; The adjustment thresholds include a first adjustment threshold, a second adjustment threshold, and a third adjustment threshold, and the average power values ​​corresponding to the three decrease sequentially. When the average power is within the first adjustment threshold range, the operating state of the component is not adjusted; When the average power is within the second adjustment threshold range, the operating status of the component is adjusted according to the table lookup result; When the average power is within the third adjustment threshold range, control one or more components of the cooling system to stop working.

4. The thermal management control method for a range-extended electric vehicle according to claim 3, characterized in that, The method also includes a step for limiting the overall vehicle output power: Real-time monitoring of the operating status of the thermal management system; When operating at the maximum cooling capacity of the thermal management system, different power limiting strategies are implemented based on the predicted future average power trend; When the thermal management system operates at its maximum cooling capacity for the specified time When the predicted power shows a decreasing trend, the output power of the whole vehicle will not be limited; When the thermal management system operates at its maximum cooling capacity for the specified time If the predicted power does not show a decreasing trend, the output power of the whole vehicle will be limited; The limiting factor is After limiting the actual output power of the whole vehicle ,in To predict power; When the thermal management system operates at its maximum cooling capacity for the specified time And the vehicle's actual output power is to If there is no decreasing trend over a period of time, the output power of the entire vehicle will be limited; The limiting factor is After limiting the actual output power of the whole vehicle ,in > .

5. The thermal management control method for a range-extended electric vehicle according to claim 4, characterized in that, When the actual cooling demand of the vehicle exceeds the cooling capacity limit of the thermal management system, the system controls whether to respond to the thermal management demand according to the preset thermal management demand priority. The thermal management requirements are prioritized from highest to lowest as follows: thermal management requirements of the power battery and accessories, thermal management requirements of the electric drive system, thermal management requirements of the range extender, and thermal management requirements of the passenger compartment.

6. The thermal management control method for a range-extended electric vehicle according to claim 5, characterized in that, When multiple cooling systems have control requirements for the same cooling component, a comprehensive control method is used to adjust the state of the cooling component.

7. The thermal management control method for a range-extended electric vehicle according to claim 1, characterized in that, It also includes fault detection and handling steps: monitoring and recording the number of adjustment failures of each control component of the vehicle's thermal management system under different conditions; The number of adjustment failures is compared with a preset failure threshold. When the number of adjustment failures of the target component exceeds the failure number threshold, the state of the component is adjusted to a state where adjustment is prohibited. When the target component is in a state where adjustment is prohibited, the coolant temperature and the temperature of each component in the thermal management circuit where the component is located are detected. If the temperatures are all within the preset range, the component will remain in the non-adjustable state until the vehicle is powered on again and it is adjusted to the adjustable state. If the temperature exceeds the preset value, the state of the component will be adjusted to an adjustable state. If the adjustment still fails, the machine must be stopped for maintenance.

8. A thermal management control system for a range-extended electric vehicle, characterized in that, include: Data acquisition and neural network training module, future power prediction module, and cooling system component status adjustment module; Data acquisition and neural network training module: used to acquire data during the vehicle development phase, and generate a development phase data training database based on the vehicle development phase data, the development phase data training database including basic training parameters; Used to obtain driving data of target users; Used to train a long short-term memory neural network based on the basic training parameters and the target user's driving data; Future power prediction module: used to predict the future average power based on the trained long short-term memory neural network to determine whether the vehicle's trip has a destination; If there is a destination, the average power of the target journey is predicted, and the vehicle updates the prediction of the remaining mileage in real time. If there is no destination, predict the average power within the target time period in the future, and update the prediction results within the target time period in real time as the vehicle moves. Cooling system component status adjustment module: used to adjust the status according to multiple preset two-dimensional mapping relationship tables; Obtain the current coolant temperature of the cooling system; the cooling system components include a water pump, a fan, and a three-way proportional valve. Based on the prediction of future average power, query the working status of the corresponding cooling system components in the two-dimensional mapping table. If the current coolant temperature and the predicted future average power do not perfectly match the preset value in the two-dimensional mapping table, then linear interpolation is performed in the neighborhood of that value to obtain the adjustment value of the working state of the cooling system components, and the working state of the cooling system components is adjusted according to the adjustment value.

9. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the thermal management control method for a range-extended electric vehicle as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a thermal management control method for a range-extended electric vehicle as described in any one of claims 1-7.

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