Range extender working overheating control method and system based on fuzzy control

Abstract

The invention discloses a range extender working overheating control method and system based on fuzzy control, and relates to the technical field of automobile thermal management. The method comprises the following steps: acquiring heat source equipment parameters and environment parameters related to the range extender, and preprocessing the equipment operation parameters and the environment parameters; establishing a multi-heat-source coupling state space model by taking heat risk minimization and energy consumption minimization as double targets; on the basis of the multi-heat-source coupling state space model, a fuzzy control algorithm is used for predicting the heat state according to heat source equipment parameters and environment parameters, and cooling resource distribution is optimized; and taking a cooling resource allocation result obtained by the fuzzy control algorithm as an initial solution, and further correcting by using rolling optimization to obtain a final cooling resource allocation scheme. According to the invention, accurate prediction and coordinated control of the overheating condition in the working process of the range extender can be realized.

Description

Technical Field

[0001] This invention relates to the field of automotive thermal management technology, and in particular to a method and system for controlling overheating of a range extender based on fuzzy control. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The emergence of range-extended hybrid electric vehicles (REEVs) has solved the major problem of range anxiety associated with pure electric vehicles, promoting the rapid popularization of vehicle electrification. Compared to pure electric vehicles, REEVs add power source components such as a drive motor and a generator; these two components are collectively referred to as the range extender. When the battery is low, the range extender becomes the power source for the REEV. It generates electricity through the engine, part of which is used to directly drive the motor, and part is used to charge the battery. Therefore, ensuring the normal operation of the range extender under different operating conditions is particularly important. From the perspective of automotive thermal management, controlling the cooling fan and electric water pump ensures that the range extender is within the appropriate temperature range to avoid the risk of overheating and runaway, thereby ensuring the overall vehicle's electrical load.

[0004] Regarding thermal management control of range extenders, some existing technologies detect the range extender's power, and the coolant temperatures of the engine, motor, and motor controller. When these temperatures exceed set thresholds, the range extender's speed and torque are adjusted at the source to mitigate overheating. However, this method limits the range extender's operating power, inevitably affecting the vehicle's electrical load under harsh conditions. Furthermore, the optimal operating power range of the range extender is determined jointly by the engine and generator, and cannot be considered from a single perspective; therefore, this control method is not energy-efficient.

[0005] Some technologies utilize the temperature difference between the engine cooling circuit and the generator cooling circuit, exchanging heat through a shared radiator. They also add three control valves and their control strategies to monitor the water temperature in the motor, engine, and heating circuits, thereby enhancing the thermal management system's effectiveness. However, this method of heat dissipation through temperature differences has limitations. In high-temperature environments with large electrical loads, the temperatures of both the engine and generator cooling circuits are extremely high, resulting in almost zero heat exchange between the two cycles. Furthermore, the control strategy is only implemented when the circuit water temperature is detected to be too high, which has a delayed effect and cannot ensure that the range extender's temperature is within its optimal operating range.

[0006] Therefore, the control logic of existing range extender thermal management control technology is passive response-oriented, lacks forward-looking thermal risk prediction, has not established a multi-heat source coupling model, and cannot coordinate the thermal resources between the engine, generator, battery, and cooling system; the control strategy is deterministic threshold judgment, and does not consider uncertain factors such as ambient temperature, load fluctuation, and thermal inertia. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for overheat control of range extenders based on fuzzy control, which can accurately predict and coordinate the overheating situation during the operation of the range extender.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: The first aspect of this invention provides a method for controlling overheating of a range extender based on fuzzy control, comprising the following steps: Acquire parameters of various heat source devices and environmental parameters related to the range extender, and preprocess the device operating parameters and environmental parameters; A multi-heat-source coupled state-space model is established with the dual objectives of minimizing thermal risk and minimizing energy consumption. Based on a multi-heat-source coupled state-space model, a fuzzy control algorithm is used to predict the thermal state based on the parameters of the heat source equipment and environmental parameters, and to optimize the allocation of cooling resources. The cooling resource allocation result obtained by the fuzzy control algorithm is used as the initial solution, and rolling optimization is used to further refine it to obtain the final cooling resource allocation scheme.

[0009] Furthermore, the parameters of the heat source equipment include the operating parameters of the heat source equipment and the parameters of the heat transfer and loss paths, and the environmental parameters include the external ambient temperature, solar radiation intensity, atmospheric pressure, vehicle speed, longitudinal acceleration and altitude.

[0010] Furthermore, the heat source equipment includes engines, generators, batteries, air conditioners, and power electronic units, and the heat transfer and dissipation paths include coolant circuits, airflow paths, and metal structures.

[0011] Furthermore, preprocessing of equipment operating parameters and environmental parameters includes low-pass filtering and outlier removal.

[0012] Furthermore, with the dual objectives of minimizing thermal risk and minimizing energy consumption, the specific steps for establishing a multi-heat-source coupled state-space model include: Quantify the rate of heat generation and the rate of heat dissipation; Key temperature measurement points are selected as state variables, and a state-space model describing the dynamic process of heat generation, transfer and dissipation is constructed as a multi-heat-source coupled state-space model.

[0013] Furthermore, based on the multi-heat-source coupled state-space model, the specific steps of using fuzzy control algorithms to predict the thermal state based on heat source equipment parameters and environmental parameters, and to optimize the allocation of cooling resources are as follows: The parameters of the heat source equipment and environmental parameters are fuzzified. Construct a multidimensional fuzzy rule library and set the activation strength of each rule; Construct the output membership function and assign activation intensity values. Obtain the prediction results of thermal state and cooling resource allocation results through defuzzification operation.

[0014] Furthermore, based on real vehicle operation data, an online gradient descent method is used to adaptively update the fuzzy rule base and scheduling weights.

[0015] A second aspect of the present invention provides a range extender overheat control system based on fuzzy control, comprising: The data acquisition module is configured to acquire parameters of various heat source devices and environmental parameters related to the range extender, and to preprocess the device operating parameters and environmental parameters. The model building module is configured to establish a multi-heat source coupled state-space model with the dual objectives of minimizing thermal risk and minimizing energy consumption. The model solving module is configured to use a multi-heat source coupled state-space model and a fuzzy control algorithm to predict the thermal state based on the parameters of the heat source equipment and environmental parameters, and to optimize the allocation of cooling resources. The parameter optimization module is configured to use the cooling resource allocation result obtained by the fuzzy control algorithm as the initial solution, and then further refine it using rolling optimization to obtain the final cooling resource allocation scheme.

[0016] A third aspect of the present invention provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and to execute steps in the fuzzy control-based overheat control method for range extenders as described in the first aspect of the present invention.

[0017] A fourth aspect of the present invention provides a computer device comprising: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the fuzzy control-based overheat control method for range extenders as described in the first aspect of the present invention.

[0018] The above one or more technical solutions have the following beneficial effects: This invention discloses a method and system for overheat control of a range extender based on fuzzy control. By constructing a multi-heat source coupled state-space model, the heat conduction path is used as the coupling link of independent heat source terms to form a closed-loop dynamic description of heat generation-transfer-dissipation. On this basis, a fuzzy control algorithm is introduced to perform fuzzy inference on multi-dimensional and multi-modal data, and output the initial allocation values ​​of cooling fan duty cycle, electric water pump duty cycle and three-way valve opening. Then, the model predictive control framework is used to iteratively optimize the system in the rolling time domain with dual objective functions of thermal risk and energy consumption to achieve forward-looking thermal regulation.

[0019] The collaborative control strategy of this invention eliminates the inherent detection-response lag defect of traditional threshold-based thermal management and reduces the peak water temperature and temperature fluctuation amplitude. At the same time, by adaptively updating the weights of fuzzy rules and model parameters through online gradient descent, the control accuracy increases with the running mileage, avoiding repeated calibration caused by seasonal, altitude or vehicle type changes.

[0020] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the overheat control method for range extender based on fuzzy control in Embodiment 1 of the present invention; Figure 2 This is a structural diagram of the heat transfer and dissipation path of the range extender in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the range extender control principle in Embodiment 1 of the present invention; Among them, 1. Low temperature radiator, 2. High temperature radiator, 3. Cooling fan, 4. Electric water pump, 5. Range extender, 6. Heat exchanger, 7. Battery, 8. Heater, 9. Three-way valve, 10. Vehicle controller, 11. Ambient temperature sensor, 12. Target water temperature sensor. Detailed Implementation

[0023] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof. The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0025] Example 1: Embodiment 1 of this invention provides a fuzzy control-based overheat control method for range extenders, applicable to hybrid vehicles equipped with range extenders, such as... Figure 1 As shown, it includes the following steps: S1: Obtain parameters of various heat source devices and environmental parameters related to the range extender, and preprocess the device operating parameters and environmental parameters.

[0026] S11: Obtain parameters of various heat source devices and environmental parameters related to the range extender based on the range extender structure.

[0027] Among them, the parameters of the heat source equipment include the operating parameters of the heat source equipment and the parameters of heat transfer and loss paths, and the environmental parameters include the external ambient temperature, solar radiation intensity, atmospheric pressure, vehicle speed, longitudinal acceleration and altitude.

[0028] The heat source equipment includes an engine, generator, battery, air conditioner, and power electronics unit. The heat transfer and dissipation paths include coolant circuits, airflow paths, and metal structures. The heat transfer and dissipation path structure of the range extender in this embodiment is as follows: Figure 2As shown, one end of the range extender 5 is connected to the low-temperature radiator 1 and the high-temperature radiator 2. The low-temperature radiator 1 forms a closed loop with the range extender 5 through the electric water pump 4. The high-temperature radiator 2 is cooled by the cooling fan 3 and is directly connected to the other end of the range extender 5 to form a closed loop. The range extender 5 is also connected to the heat exchanger 6 through the three-way valve 9 to form a closed loop. The heat exchanger 6 is also connected to the battery 7 to form a closed loop. One end of the three-way valve 9 is connected to the heater 8, and the other end of the heater 8 is connected to the range extender 5. The system also includes a vehicle controller 10, an ambient temperature sensor 11, and a target water temperature sensor 12. The vehicle controller 10 is electrically connected to the cooling fan 3, the high-temperature radiator 2, the low-temperature radiator 1, the electric water pump 4, and the range extender 5. The ambient temperature sensor 11 is located on the low-temperature radiator, and the target water temperature sensor 12 is located on the range extender.

[0029] like Figure 3 As shown, the target water temperature sensor and the ambient temperature sensor transmit signals to the vehicle controller. After analysis, the vehicle controller generates control commands and sends them to the electronic water pump controller, the cooling fan controller, and other electrical equipment, thereby realizing power control of the electronic water pump and cooling fan.

[0030] When the range extender is working, it obtains the ambient temperature and the target water temperature at the outlet of the range extender based on the temperature sensor. The measurement point for the ambient temperature includes, but is not limited to, the low-temperature radiator. The target water temperature of the range extender is generally set inside the pipe connected to the range extender. The measured water temperature is the outlet water temperature of the range extender. The collected signal is fed back to the vehicle controller 10. After judgment, the commands are sent to the controllers of the cooling fan 3 and the electric water pump 4 respectively.

[0031] When the range-extended electric vehicle's battery is low, the range extender starts working. To ensure the range extender operates within a suitable temperature range, heat is dissipated through a low-temperature radiator 1 and a high-temperature radiator 2. The power of the radiators is constant, so the heat exchange rate is determined by the operating power (duty cycle) of the cooling fan 3 and the electric water pump 4. In cold winter weather, the heat from the range extender can be dissipated by heating the battery through a heat exchanger or by heating the vehicle's interior heater (PTC). The heat exchange ratio between these two methods is adjusted by a three-way valve 9.

[0032] S12: Preprocess equipment operating parameters and environmental parameters, including low-pass filtering and outlier removal.

[0033] Specifically, the equipment operating parameters and environmental parameters are first subjected to a moving average low-pass filter to filter out high-frequency spikes caused by electrical interference or sensor noise. Then, the "3σ-truncation" method is used to identify and remove abrupt outliers to ensure that the input data is smooth and the physical trend is continuous, providing a clean signal source for subsequent models and algorithms.

[0034] S2: A multi-heat-source coupled state-space model is established with the dual objectives of minimizing thermal risk and minimizing energy consumption.

[0035] S21: Quantify the rate of heat generation and the rate of heat dissipation.

[0036] Specifically, in this embodiment, the heat generation rate originates from the sum of various heat sources, including combustion heat power, copper and iron losses, ohmic heat, switching heat, and condensation heat. Specifically, combustion heat power is obtained from the engine fuel flow rate and universal characteristic diagram; copper and iron losses are deduced from the generator power and efficiency diagram; ohmic heat is calculated from the battery current and internal resistance diagram; switching heat is obtained from the power electronic switch duty cycle and loss curve; and condensation heat is obtained from the air conditioning compressor current diagram.

[0037] On the heat dissipation side, the liquid cooling rate is obtained by estimating the coolant temperature difference and flow rate, the air cooling rate is obtained by estimating the fan speed and airflow velocity, and the heat conduction rate is obtained by estimating the temperature difference and thermal resistance of the metal heat conduction path. The total heat dissipation rate is formed by adding the three together.

[0038] S22: Select key temperature measurement points as state variables and construct a state-space model describing the dynamic process of heat generation, transfer and dissipation as a multi-heat source coupled state-space model.

[0039] Specifically, a representative temperature measurement point is taken at each of the range extender outlet, engine water outlet, generator winding, average cell of battery, and power electronic radiator surface as a state variable. The difference between the total heat generation rate and the total heat dissipation rate is used as the driving term to establish a dynamic description of the heat balance to temperature change, forming a multi-heat source coupled state space model.

[0040] S3: Based on a multi-heat-source coupled state-space model, a fuzzy control algorithm is used to predict the thermal state based on the parameters of the heat source equipment and environmental parameters, and to optimize the allocation of cooling resources.

[0041] S31: Perform fuzzification processing on the parameters of the heat source equipment and environmental parameters.

[0042] Specifically, the four precise quantities—preprocessed ambient temperature, range extender outlet water temperature, thermal inertia time, and load change rate—are mapped to five levels of linguistic values: "low, medium-low, medium, medium-high, and high." The activation level of each linguistic value is then calculated using a triangular membership function to complete the fuzzification.

[0043] S311: Define the domain of discourse based on heat demand.

[0044] The rules for defining the domain of discourse in this embodiment are as follows: ambient temperature scale: -40℃ to 60℃, outlet water temperature scale: 40℃ to 120℃, thermal inertia time scale: 0s to 120s, load change rate scale: -20kW / s to +20kW / s. A 5% extension zone is reserved at both ends of the domain of discourse to ensure that extreme values ​​are still covered by the linguistic values.

[0045] S312: Assign the corresponding linguistic values ​​to each domain.

[0046] In this embodiment, each domain is divided into four segments, forming five regions, with the linguistic values ​​named "low, low-medium, medium, high-medium, and high" from left to right. Adjacent regions have 50% overlap, meaning the right half of "low-medium" and the left half of "medium" share the same physical region, ensuring that there are no jumps when the input signal slides.

[0047] S313: Fix the triangle vertices based on the language value.

[0048] For each linguistic value, a vertex is set on the universe of discourse: the "low" vertex is located at 0%, the "medium-low" vertex at 25%, the "medium" vertex at 50%, the "medium-high" vertex at 75%, and the "high" vertex at 100%. A straight line is drawn from each side of the vertex to the adjacent vertex, forming a continuous triangular wave with no gaps between the waves.

[0049] S314: Extract real-time membership degrees based on triangle vertices.

[0050] The four precise quantities processed during the current sampling period are projected onto the corresponding domain of discourse; the controller immediately uses the "vertical line method" to read the height of the intersection point of the vertical line and the five triangular waves, and the height value is the activation level of the linguistic value. If the vertical line falls exactly on the vertex, the activation level of the corresponding linguistic value is full, and the activation level of its adjacent linguistic values ​​is zero; if the vertical line falls on the overlapping area of ​​two waves, both waves give non-zero heights at the same time, and the sum of the two heights is always equal to the full value, ensuring energy conservation.

[0051] Each precise quantity is then transformed into a five-element vector, with the elements arranged in the order of "low-low-medium-high-high". The size of each element represents the activation level. The four vectors are combined to form a 4×5 activation table, which is used for subsequent rule matching to complete the fuzzification.

[0052] S32: Construct a multidimensional fuzzy rule base and set the activation strength of each rule.

[0053] Among them, based on real vehicle operation data, the online gradient descent method is used to adaptively update the fuzzy rule base and scheduling weights.

[0054] Specifically, using the template "IF ambient temperature AND outlet water temperature AND thermal inertia time AND load change rate THEN fan duty cycle, water pump duty cycle, three-way valve opening," 625 initial rules were summarized from bench and high-altitude / high-temperature tests and stored in the rule base. During online operation, the activation strength of each rule is obtained by taking the smallest membership degree of the four inputs, and then synthesized according to the principle of "taking the larger for the same conclusion" to form the effective rule set for the current cycle.

[0055] S321: Full coverage of the rule space.

[0056] In this embodiment, at the steady-state operating point on the test bench, an orthogonal scan was performed based on five levels of ambient temperature, five levels of outlet water temperature, five levels of thermal inertia, and five levels of load change rate, forming 5×5×5×5=625 fixed operating conditions. Under each operating condition, the fan, water pump, and three-way valve were gradually adjusted to stabilize the outlet water temperature at the target range and minimize the fan current. The duty cycle combination at this point was recorded as the "optimal cooling command" for that operating condition. The 625 commands were directly written into the FLASH memory to form an initial rule base, with each rule corresponding to a unique "IF four-input AND" address.

[0057] S322: Encode the rule address.

[0058] A four-dimensional linear index is established: ambient temperature dimension 0-4, outlet water temperature dimension 0-4, thermal inertia dimension 0-4, and load change rate dimension 0-4. The four five-element membership vectors obtained from online sampling are arranged in the same order, so that the address of the i-th rule corresponds one-to-one with the i-th group of test bench conditions, ensuring seamless integration between hardware table lookup and software inference.

[0059] S323: Minimum membership degree operation.

[0060] In each sampling cycle, a 4×5 real-time membership table is ready. For the k-th rule, its four corresponding linguistic value memberships are retrieved sequentially, and a "compare and retain smaller" operation is performed: first, the memberships of ambient temperature and outlet water temperature are compared, and the smaller value is retained; then, the membership of thermal inertia is compared, and the smaller value is retained again; finally, the membership of load change rate is compared, and the final single value obtained is the activation strength of the rule. The entire process is completed within a fixed clock cycle, without floating-point operations, using only comparisons and shifts.

[0061] S324: Synthesize the same conclusions.

[0062] The 625 rules are grouped according to "same conclusion," forming 7 × 3 × 7 = 147 conclusion buckets (seven languages ​​for fans, seven for water pumps, and seven for three-way valves). After the activation strength of all rules is calculated, the controller puts each strength into the corresponding conclusion bucket. If multiple rules exist in the same bucket, a "keep the largest" operation is performed, retaining only the highest activation strength and discarding the rest, thus completing the "take the largest for the same conclusion" synthesis, resulting in 147 valid conclusions and their activation values. The activation values ​​of the 147 synthesized conclusions are recorded with their language labels, forming the valid rule set for the current period. This set has eliminated conflicts and redundancies, ensuring that subsequent defuzzification operations only need to process 147 records, significantly reducing the computational load and guaranteeing the continuity and consistency of control output.

[0063] S325: The "predicted temperature - measured temperature" deviation sequence recorded during actual vehicle operation is used to correct the weight of the rule conclusions one by one through online gradient descent, so that the rule base is optimized as the mileage increases.

[0064] S33: Construct the output membership function and assign activation intensity values. Obtain the prediction results of thermal state and cooling resource allocation results through defuzzification operation.

[0065] Specifically, seven levels of output language values ​​("zero, small, medium-small, medium, medium-large, large, and maximum") are defined for the fan, water pump, and three-way valve, and triangular membership functions are established. The synthesized activation intensity is assigned to the corresponding triangular membership function, and the aggregated graph is formed after truncation. The area centroid method is used to defuzzify the image, and the precise initial values ​​of the fan duty cycle, water pump duty cycle, and three-way valve opening are obtained and written into the initial solution register of the rolling optimization module.

[0066] The process of defuzzification using the area centroid method is as follows: The controller scans the aggregated graphic from left to right along the horizontal axis with a fixed microstep size, reads the upper envelope height point by point, and simultaneously accumulates "height × horizontal axis" and "height" itself; after the scan is completed, the accumulated result is divided, and the quotient is the horizontal axis of the geometric centroid of the aggregated graphic. This value directly corresponds to the precise percentage of the fan duty cycle, water pump duty cycle, or three-way valve opening.

[0067] S4: Using the cooling resource allocation result obtained by the fuzzy control algorithm as the initial solution, further refine it using rolling optimization to obtain the final cooling resource allocation scheme.

[0068] Specifically, starting from the initial solution, the multi-heat source coupled state-space model is invoked to calculate the temperature trajectory and power consumption trajectory in the time domain over the next 10 seconds. With the dual objectives of reducing temperature overshoot and reducing fan and water pump power consumption, a multi-step search is performed within ±10% of the initial duty cycle. The first step control command that minimizes the objective function is selected as the final cooling resource allocation scheme and sent to the cooling fan controller, electronic water pump controller, and three-way valve driver via PWM or CAN to complete the closed-loop control.

[0069] Example 2: Embodiment 2 of the present invention provides a range extender overheat control system based on fuzzy control, comprising: The data acquisition module is configured to acquire parameters of various heat source devices and environmental parameters related to the range extender, and to preprocess the device operating parameters and environmental parameters. The model building module is configured to establish a multi-heat source coupled state-space model with the dual objectives of minimizing thermal risk and minimizing energy consumption. The model solving module is configured to use a multi-heat source coupled state-space model and a fuzzy control algorithm to predict the thermal state based on the parameters of the heat source equipment and environmental parameters, and to optimize the allocation of cooling resources. The parameter optimization module is configured to use the cooling resource allocation result obtained by the fuzzy control algorithm as the initial solution, and further refine it using rolling optimization to obtain the final cooling resource allocation scheme. Example 3: Embodiment 3 of the present invention provides a computer-readable storage medium storing a computer program adapted for loading by a processor and executing the steps of the range extender overheat control method based on fuzzy control as described in Embodiment 1 of the present invention.

[0070] Example 4: Embodiment 4 of the present invention provides a computer device, the device comprising: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the steps in the fuzzy control-based overheat control method for range extenders as described in Embodiment 1 of the present invention.

[0071] The steps and methods involved in Examples 2, 3 and 4 above correspond to those in Example 1. For specific implementation details, please refer to the relevant description section of Example 1.

[0072] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium, etc. The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling overheating of a range extender based on fuzzy control, characterized in that, Includes the following steps: Acquire parameters of various heat source devices and environmental parameters related to the range extender, and preprocess the device operating parameters and environmental parameters; A multi-heat-source coupled state-space model is established with the dual objectives of minimizing thermal risk and minimizing energy consumption. Based on a multi-heat-source coupled state-space model, a fuzzy control algorithm is used to predict the thermal state based on the parameters of the heat source equipment and environmental parameters, and to optimize the allocation of cooling resources. The cooling resource allocation result obtained by the fuzzy control algorithm is used as the initial solution, and rolling optimization is used to further refine it to obtain the final cooling resource allocation scheme.

2. The overheat control method for range extenders based on fuzzy control as described in claim 1, characterized in that, The parameters of the heat source equipment include the operating parameters of the heat source equipment and the parameters of the heat transfer and loss paths. The environmental parameters include the external ambient temperature, solar radiation intensity, atmospheric pressure, vehicle speed, longitudinal acceleration, and altitude.

3. The overheat control method for range extenders based on fuzzy control as described in claim 2, characterized in that, Heat source equipment includes engines, generators, batteries, air conditioners, and power electronic units. The heat transfer and dissipation paths include coolant circuits, airflow paths, and metal structures.

4. The overheat control method for range extenders based on fuzzy control as described in claim 1, characterized in that, Preprocessing of equipment operating parameters and environmental parameters includes low-pass filtering and outlier removal.

5. The overheat control method for range extenders based on fuzzy control as described in claim 1, characterized in that, The specific steps for establishing a multi-heat-source coupled state-space model with the dual objectives of minimizing thermal risk and minimizing energy consumption include: Quantify the rate of heat generation and the rate of heat dissipation; Key temperature measurement points are selected as state variables, and a state-space model describing the dynamic process of heat generation, transfer and dissipation is constructed as a multi-heat-source coupled state-space model.

6. The overheat control method for range extenders based on fuzzy control as described in claim 1, characterized in that, Based on a multi-heat-source coupled state-space model, the specific steps of using a fuzzy control algorithm to predict the thermal state based on heat source equipment parameters and environmental parameters, and to optimize the allocation of cooling resources are as follows: The parameters of the heat source equipment and environmental parameters are fuzzified. Construct a multidimensional fuzzy rule library and set the activation strength of each rule; Construct the output membership function and assign activation intensity values. Obtain the prediction results of thermal state and cooling resource allocation results through defuzzification operation.

7. The overheat control method for range extenders based on fuzzy control as described in claim 1, characterized in that, Based on real vehicle operation data, an online gradient descent method is used to adaptively update the fuzzy rule base and scheduling weights.

8. A range extender overheat control system based on fuzzy control, characterized in that, include: The data acquisition module is configured to acquire parameters of various heat source devices and environmental parameters related to the range extender, and to preprocess the device operating parameters and environmental parameters. The model building module is configured to establish a multi-heat source coupled state-space model with the dual objectives of minimizing thermal risk and minimizing energy consumption. The model solving module is configured to use a multi-heat source coupled state-space model and a fuzzy control algorithm to predict the thermal state based on the parameters of the heat source equipment and environmental parameters, and to optimize the allocation of cooling resources. The parameter optimization module is configured to use the cooling resource allocation result obtained by the fuzzy control algorithm as the initial solution, and then further refine it using rolling optimization to obtain the final cooling resource allocation scheme.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1-7: a range extender overheat control system based on fuzzy control.

10. A computer device, characterized in that, include: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, implements the range extender overheat control system based on fuzzy control as described in any one of claims 1-7.

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