Intelligent thermal management prediction driven water pump multi-mode control method and system

Abstract

The invention discloses a water pump multi-mode control method and system driven by intelligent thermal management prediction, and relates to the technical field of automotive electronics, and the method comprises the steps: collecting engine operation data and water pump state information in real time, constructing an intelligent predictive thermal management algorithm through the collected data and information, and predicting the thermal demand of an engine. Activating a multi-mode water pump control strategy according to a heat demand result, matching a water pump working mode, collecting impurity content data of a sensor in the electronic water pump in real time, automatically cleaning internal impurities, and transmitting the collected operation state of the electronic water pump to a water pump health management platform based on a block chain in real time for fault prediction. And upgrading and maintaining the electronic water pump according to the prediction result. By predicting the heat demand of the engine and accurately matching the working mode of the water pump, the performance and efficiency of the engine are improved, the energy consumption and emission are reduced, the service life of the electronic water pump is prolonged, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of automotive electronics technology, and in particular to a multi-modal control method and system for a water pump driven by intelligent thermal management prediction. Background Art

[0002] With the rapid development of the automotive industry, hybrid vehicles have attracted much attention due to their energy-saving and emission-reduction features. As the core component of a vehicle, the performance of the engine directly affects the performance and efficiency of the entire vehicle. In order to improve engine performance, reduce energy consumption and emissions, electronic water pumps, as an efficient cooling system component, have been widely used in hybrid vehicles. Electronic water pumps can adjust the coolant flow according to the real-time needs of the engine, thereby improving the thermal management efficiency of the engine.

[0003] The existing electronic water pump control system mainly relies on preset parameters and fixed control strategies. This method has some shortcomings in practical applications. First, the traditional control strategy cannot be adaptively adjusted according to the real-time operating status and environmental conditions of the engine, resulting in poor cooling effect. Secondly, the accumulation of impurities inside the electronic water pump will affect its performance and life, but the existing control system lacks real-time monitoring and automatic cleaning functions of impurity content. Finally, the existing fault prediction and maintenance system has problems with inaccurate data and low maintenance efficiency. Summary of the invention

[0004] In view of the above existing problems, the present invention provides an intelligent thermal management prediction-driven water pump multi-modal control method and system to solve the problems of inaccurate data and low maintenance efficiency in existing fault prediction and maintenance systems.

[0005] In order to solve the above technical problems, a multi-mode control method of water pump driven by intelligent thermal management prediction is proposed, including:

[0006] Collect engine operation data and water pump status information in real time, use the collected data and information to build an intelligent predictive thermal management algorithm to predict the engine's thermal demand; activate the multimodal water pump control strategy based on the predicted engine thermal demand results, match the water pump operating mode, collect the impurity content data of the sensor in the electronic water pump in real time, and automatically clean up internal impurities during the operation of the electronic water pump; transmit the collected operating status of the automobile engine's electronic water pump in real time to the blockchain-based water pump health management platform for fault prediction, and upgrade and maintain the water pump based on the prediction results.

[0007] As a preferred solution of the water pump multi-modal control method driven by intelligent thermal management prediction described in the present invention, wherein: the engine operation data and water pump status information include engine temperature data, engine load data, ambient temperature data, water pump operating parameters, engine running time, engine cooling system status, battery status data, vehicle driving mode, vehicle maintenance history and driver behavior data;

[0008] The engine temperature data includes coolant temperature, oil temperature, and exhaust temperature; the water pump operating parameters include water pump speed, flow, and pressure; the engine cooling system status includes coolant flow and cooling system pressure; the driver's behavior data includes the driver's sudden acceleration frequency and sudden braking frequency.

[0009] As a preferred solution of the multi-modal control method of a water pump driven by intelligent thermal management prediction described in the present invention, wherein: the construction of an intelligent predictive thermal management algorithm includes using real-time collection of ambient temperature, engine temperature, engine speed, engine load and coolant temperature data and information to construct an intelligent predictive thermal management algorithm to predict the thermal demand of the engine;

[0010] The formula for predicting the engine heat demand is:

[0011] Q(t)=λ·f env (T env (t))+μ·f eng (n,L(t))+ν·f c (T c (t))

[0012] Where Q(t) is the predicted engine heat demand, λ is the influence coefficient of ambient temperature on heat demand prediction, and f env (T env (t)) is the ambient temperature influence function, T env (t) is the ambient temperature at time t, μ is the influence coefficient of engine speed and load on heat demand prediction, f eng (n,L(t)) is the engine speed and load influence function, n is the engine speed at time t, L(t) is the engine load at time t, ν is the influence coefficient of coolant temperature on heat demand prediction, f c (T c (t)) is the coolant temperature influence function, T c (t) is the coolant temperature at time t;

[0013] The f env (T env (t)) The ambient temperature influence function is expressed as:

[0014]

[0015] Among them, γ is the ambient temperature influence coefficient, T env (t) is the ambient temperature at time t, T ref is the reference temperature;

[0016] The f eng (n,L(t)) The engine speed and load influence function is expressed as:

[0017]

[0018] Where n is the engine speed at time t, φ(n) is the influence function of the engine speed, ψ(L(t)) is the influence function of the engine load, and L(t) is the engine load at time t;

[0019] The f c (T c (t)) The coolant temperature influence function is expressed as:

[0020]

[0021] Among them, T c (t) is the coolant temperature at time t, T ref is the reference temperature, T max is the maximum temperature reached by the coolant;

[0022] The influence function of the φ(n) engine speed is expressed as:

[0023]

[0024] Where n is the engine speed at time t, n min is the minimum speed of the engine, n max is the maximum speed of the engine, n opt The optimal speed of the engine, k is the power exponent of the speed effect, δ is the logarithmic coefficient of the speed effect;

[0025] The influence function of the ψ(L(t)) engine load is expressed as:

[0026]

[0027] Where ρ is the coefficient of load influence, L(t) is the engine load at time t, and L min is the minimum load of the engine, L max is the maximum load of the engine, and m is the power exponent of the load influence;

[0028] Wherein, the reference temperature is 90°C, and the optimal speed of the engine is the speed with the highest thermal efficiency of the engine;

[0029] Among them, the value range of Q(t) is [0, ∞). When Q(t) = 0, it means that the engine does not require additional heat management. When Q(t) is a positive remaining value, it means that the engine requires positive heat management.

[0030] As a preferred solution of the intelligent heat management prediction-driven multi-modal control method for water pumps according to the present invention, wherein: the activation of the multi-modal water pump control strategy includes, according to the predicted engine heat demand Q(t), setting the threshold value of Q(t) and the dynamic value of Q(t), and matching the water pump working modes according to different intervals of engine heat demand;

[0031] Among them, the threshold value of Q(t) is 300 kWh, and the dynamic value of Q(t) is 40 kWh;

[0032] The matching of the water pump working modes includes activating the multi-modal water pump control strategy according to the predicted engine heat demand result, and matching three water pump working modes. When 0 < Q(t) < 300, the low-speed mode is matched; when 300 ≤ Q(t) < 340, the medium-speed mode is matched; when Q(t) ≥ 340, the high-speed mode is matched.

[0033] As a preferred solution of the intelligent heat management prediction-driven multi-modal control method for water pumps according to the present invention, wherein: the automatic cleaning of internal impurities includes, according to the real-time collected data of the impurity content detected by the sensor inside the electric water pump, setting the impurity detection threshold value to 20 mg / L. When the impurity content detected by the sensor exceeds 20 mg / L, start the automatic cleaning program, and adjust the working mode of the water pump according to the detected impurity situation, and turn on the backwashing function of the water pump to change the flow direction of the water pump to remove internal impurities. During the backwashing process, monitor the impurity discharge situation in real time until the impurity content detected by the sensor is lower than 20 mg / L, then turn off the backwashing function and restore the water pump to the normal working mode; when the impurity content detected by the sensor does not exceed 20 mg / L, do not start the automatic cleaning program and maintain the normal working mode of the water pump.

[0034] As a preferred solution of the intelligent heat management prediction-driven multi-modal control method for water pumps according to the present invention, wherein: the fault prediction includes transmitting the collected operating state of the automotive engine water pump to the water pump health management platform based on blockchain for fault prediction in real time. The formula for fault prediction is expressed as:

[0035]

[0036] Among them, F is the failure probability, a is the prediction time period, P(i) is the operation performance index of the water pump in the i-th time period, S(j, i) is the water pump status data of the j-th sensor in the i-th time period, M(i) is the operation quality normalization function of the water pump in the i-th time period, D(i) is the sensor data normalization function in the i-th time period, g is the number of sensors, i is the time discretization variable, and exp is the natural exponential function;

[0037] The operation quality normalization function is expressed as:

[0038]

[0039] Among them, M(i) is the normalized value of the water pump operation quality in the i-th time period, H(i) is the actual operation quality index of the water pump in the i-th time period, μ H is the average value of the water pump operation quality index, σ H is the standard deviation of the water pump operation quality index, and exp is the natural exponential function;

[0040] The sensor data normalization function is expressed as:

[0041]

[0042] Among them, D(i) is the normalized value of the sensor data in the i-th time period, J(i) is the original data collected by the sensor in the i-th time period, min(J) is the minimum value of the sensor readings in the time period within the monitoring period, and max(J) is the maximum value of the sensor readings in the time period within the monitoring period.

[0043] As a preferred solution of the intelligent thermal management prediction-driven multi-modal control method for water pumps according to the present invention, among them: the upgrade and maintenance of the electric water pump include performing fault prediction based on the blockchain-based water pump health management platform, and performing upgrade and maintenance on the water pump according to the prediction result. The value range of F is [0, 1]. The thresholds of F are set to 0.2, 0.5, and 0.8 according to the historical data of the electric water pump operation. When 0 ≤ F < 0.2, it means that the automotive engine electric water pump is in a low-risk area, the monitoring strategy remains unchanged and no adjustment is required, and periodic detection of the electric water pump is performed; when 0.2 ≤ F < 0.5, it means that the automotive engine electric water pump is in a medium-risk area, the monitoring frequency is increased, the water pump is inspected and cleaned, but no parts need to be replaced; when 0.5 ≤ F ≤ 0.8, it means that the automotive engine electric water pump is in a high-risk area, the operation status of the electric water pump is monitored in real time, the monitoring data is fed back in real time, and emergency maintenance is arranged, the problematic parts are replaced, and a comprehensive inspection is performed; when 0.8 < F ≤ 1, it means that the automotive engine electric water pump is in an extremely high-risk area, the operation of the electric water pump is immediately stopped, a comprehensive inspection is performed, the water pump parts are replaced, and the system is upgraded.

[0044] Another object of the present invention is to provide a multi-modal control system for a water pump driven by intelligent thermal management prediction. The present invention optimizes the problems of inaccurate data and low efficiency of existing fault prediction and maintenance systems. The system of the present invention realizes real-time prediction of engine thermal demand, precise matching of water pump operating modes, and fault prediction and maintenance of electronic water pumps by collecting engine operation data and water pump status information in real time, thereby improving engine performance and efficiency, reducing energy consumption and emissions, extending the service life of electronic water pumps, and reducing maintenance costs.

[0045] As a preferred solution of the intelligent thermal management prediction-driven water pump multi-modal control system described in the present invention, it is characterized by including a data acquisition module, an intelligent predictive thermal management algorithm module, an automatic impurity cleaning module, a fault prediction module and a maintenance and upgrade decision module.

[0046] The data acquisition module is used to collect engine operation data and water pump status information in real time;

[0047] The intelligent predictive thermal management algorithm module is used to construct an algorithm with the collected data, and predict the thermal demand of the engine through the algorithm, and activate the water pump working mode according to the predicted thermal demand of the engine;

[0048] The automatic impurity cleaning module is used to collect the impurity content data of the internal sensor of the electronic water pump in real time, and start the automatic cleaning program when the impurity content exceeds a threshold;

[0049] The fault prediction module is used to transmit the water pump operation status data to the blockchain-based water pump health management platform in real time to perform fault prediction;

[0050] The maintenance and upgrade decision module is used to formulate an upgrade and maintenance plan for the water pump according to the fault prediction result.

[0051] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of a method described in a multi-modal control method of a water pump driven by intelligent thermal management prediction are implemented.

[0052] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method described in a multi-modal control method of a water pump driven by intelligent thermal management prediction are implemented.

[0053] Beneficial effects of the present invention: The present invention collects engine operating data and water pump status information in real time, constructs an intelligent predictive thermal management algorithm, and realizes real-time prediction of engine thermal demand. This step not only ensures that the engine can obtain optimal thermal management under different operating conditions, thereby improving the performance and efficiency of the engine, but also activates a multimodal water pump control strategy through the prediction results, matches different water pump operating modes, collects impurity content data of sensors in electronic water pumps in real time, and automatically cleans internal impurities during the operation of the electronic water pump, thereby keeping the inside of the water pump clean and extending the service life of the water pump; the collected operating status of the automobile engine electronic water pump is transmitted in real time to the blockchain-based water pump health management platform for fault prediction, and the electronic water pump is upgraded and maintained based on the prediction results, ensuring the security and accuracy of the data, improving fault prediction and maintenance efficiency, and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0055] Figure 1 An overall flow chart of a multi-modal control method for a water pump driven by intelligent thermal management prediction is provided for one embodiment of the present invention.

[0056] Figure 2 A system flow chart of a multi-modal control system of a water pump driven by intelligent thermal management prediction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0060] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0061] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0062] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a multi-mode control method for a water pump driven by intelligent thermal management prediction, comprising:

[0064] S1: Collect engine operation data and water pump status information in real time, and use the collected data and information to build an intelligent predictive thermal management algorithm to predict the thermal demand of the engine;

[0065] The engine operation data and water pump status information include engine temperature data, engine load data, ambient temperature data, water pump operating parameters, engine operation time, engine cooling system status, battery status data, vehicle driving mode, vehicle maintenance history and driver behavior data;

[0066] It should be noted that the engine temperature data includes coolant temperature, oil temperature, and exhaust temperature; the water pump operating parameters include water pump speed, flow, and pressure; the engine cooling system status includes coolant flow and cooling system pressure; and the driver's behavior data includes the driver's sudden acceleration frequency and sudden braking frequency.

[0067] It should be further explained that the construction of the intelligent predictive thermal management algorithm includes using real-time collection of ambient temperature, engine temperature, engine speed, engine load and coolant temperature data and information to construct an intelligent predictive thermal management algorithm to predict the thermal demand of the engine;

[0068] The formula for predicting the engine heat demand is:

[0069] Q(t)=λ·f env (T env (t))+μ·f eng (n,L(t))+ν·f c (T c (t))

[0070] Where Q(t) is the predicted engine heat demand, λ is the influence coefficient of ambient temperature on heat demand prediction, and f env (T env (t)) is the ambient temperature influence function, T env (t) is the ambient temperature at time t, μ is the influence coefficient of engine speed and load on heat demand prediction, f eng (n,L(t)) is the engine speed and load influence function, n is the engine speed at time t, L(t) is the engine load at time t, ν is the influence coefficient of coolant temperature on heat demand prediction, f c (T c (t)) is the coolant temperature influence function, T c (t) is the coolant temperature at time t;

[0071] The f env (T env (t)) The ambient temperature influence function is expressed as:

[0072]

[0073] Among them, γ is the ambient temperature influence coefficient, T env (t) is the ambient temperature at time t, T ref is the reference temperature;

[0074] The f eng (n,L(t)) The engine speed and load influence function is expressed as:

[0075]

[0076] Where n is the engine speed at time t, φ(n) is the influence function of the engine speed, ψ(L(t)) is the influence function of the engine load, and L(t) is the engine load at time t;

[0077] The f c (T c (t)) The coolant temperature influence function is expressed as:

[0078]

[0079] Among them, T c (t) is the coolant temperature at time t, T ref is the reference temperature, T max is the maximum temperature reached by the coolant;

[0080] The influence function of the φ(n) engine speed is expressed as:

[0081]

[0082] Where n is the engine speed at time t, n min is the minimum speed of the engine, n max is the maximum speed of the engine, n opt The optimal speed of the engine, k is the power exponent of the speed effect, δ is the logarithmic coefficient of the speed effect;

[0083] The influence function of the ψ(L(t)) engine load is expressed as:

[0084]

[0085] Where ρ is the coefficient of load influence, L(t) is the engine load at time t, and L min is the minimum load of the engine, L max is the maximum load of the engine, and m is the power exponent of the load influence;

[0086] Wherein, the reference temperature is 90°C, and the optimal speed of the engine is the speed with the highest thermal efficiency of the engine;

[0087] The value range of Q(t) is [0, ∞). When Q(t)=0, it means that the engine does not need additional thermal management. When Q(t) is a residual positive value, it means that the engine needs to obtain positive thermal management.

[0088] S2: Activate the multimodal water pump control strategy according to the predicted engine heat demand results, match the water pump working mode, collect the impurity content data of the sensors inside the electric water pump in real time, and automatically clean the internal impurities during the operation of the electric water pump;

[0089] The activation of the multimodal water pump control strategy includes, according to the predicted engine heat demand Q(t), setting the threshold value of Q(t) and the dynamic value of Q(t), and matching the water pump working mode according to different intervals of engine heat demand;

[0090] Among them, the threshold value of Q(t) is 300 kWh, and the dynamic value of Q(t) is 40 kWh;

[0091] It should be noted that the matching of the water pump working mode includes activating the multimodal water pump control strategy according to the predicted engine heat demand results, and matching three water pump working modes. When 0 < Q(t) < 300, the low-speed mode is matched; when 300 ≤ Q(t) < 340, the medium-speed mode is matched; when Q(t) ≥ 340, the high-speed mode is matched.

[0092] More specifically, the automatic cleaning of internal impurities includes, according to the impurity content data of the sensors inside the electric water pump collected in real time, setting the impurity detection threshold to 20 mg / L. When the impurity content detected by the sensor exceeds 20 mg / L, start the automatic cleaning program, adjust the working mode of the water pump according to the detected impurity situation, and turn on the backwashing function of the water pump to change the flow direction of the water pump and remove internal impurities. During the backwashing process, monitor the impurity discharge situation in real time until the impurity content detected by the sensor is lower than 20 mg / L, then turn off the backwashing function and restore the water pump to the normal working mode; when the impurity content detected by the sensor does not exceed 20 mg / L, do not turn on the automatic cleaning program and maintain the normal working mode of the water pump.

[0093] Furthermore, through laboratory tests and on-site experiments, study the influence of different impurity contents on the performance of the water pump, including testing the working efficiency, energy consumption, wear degree and life cycle of the water pump in water with different concentrations of impurities; based on the experimental data, analyze the relationship between the impurity content and the performance of the water pump, and determine that when the impurity content reaches 20 mg / L, the performance of the water pump will decrease significantly; evaluate the risks caused by the impurity content to the water pump, including blockage, wear, overheating, and refer to the maintenance standards of the same type of water pumps and equipment, as well as the best practices in the industry. Combining the research and analysis, determine 20 mg / L as the threshold for starting the automatic cleaning program.

[0094] S3: Transmit the collected operating status of the automotive engine electric water pump to the blockchain-based water pump health management platform in real time for fault prediction, and perform upgrades and maintenance on the electric water pump according to the prediction results.

[0095] Furthermore, the fault prediction includes transmitting the collected operating status of the automobile engine water pump to the blockchain-based water pump health management platform in real time for fault prediction. The formula for fault prediction is expressed as:

[0096]

[0097] Where F is the failure probability, a is the prediction time period, P(i) is the operating performance index of the water pump in the i-th time period, S(j,i) is the water pump status data of the j-th sensor in the i-th time period, M(i) is the normalized function of the water pump operation quality in the i-th time period, D(i) is the normalized function of the sensor data in the i-th time period, g is the number of sensors, i is the time discretization variable, and exp is the natural exponential function;

[0098] The running quality normalization function is expressed as:

[0099]

[0100] Where M(i) is the normalized value of the pump operation quality in the i-th time period, H(i) is the actual operation quality index of the pump in the i-th time period, μ H is the average value of the pump operation quality index, σ H is the standard deviation of the pump operation quality index, exp is the natural exponential function;

[0101] The sensor data normalization function is expressed as:

[0102]

[0103] Where D(i) is the normalized value of the sensor data in the i-th time period, J(i) is the raw data collected by the sensor in the i-th time period, min(J) is the minimum value of the sensor reading in the time period within the monitoring period, and max(J) is the maximum value of the sensor reading in the time period within the monitoring period.

[0104] It should be noted that the upgrade and maintenance of the electronic water pump include fault prediction based on the blockchain-based water pump health management platform, and the upgrade and maintenance of the water pump are carried out according to the prediction results. The value range of F is [0, 1]. The thresholds of F are set to 0.2, 0.5, and 0.8 according to the historical data of the operation of the electronic water pump. When 0 ≤ F < 0.2, it means that the electronic water pump of the automobile engine is in the low-risk area, the monitoring strategy remains unchanged and does not need to be adjusted, and the electronic water pump is periodically detected; when 0.2 ≤ F < 0.5, it means that the electronic water pump of the automobile engine is in the medium-risk area, the monitoring frequency is increased, the water pump is inspected and cleaned, but no parts need to be replaced; when 0.5 ≤ F ≤ 0.8, it means that the electronic water pump of the automobile engine is in the high-risk area, the operation status of the electronic water pump is monitored in real time, the monitoring data is fed back in real time, and emergency maintenance is arranged, the problematic parts are replaced, and a comprehensive inspection is carried out; when 0.8 < F ≤ 1, it means that the electronic water pump of the automobile engine is in the extremely high-risk area, the operation of the electronic water pump is immediately stopped, a comprehensive inspection is carried out, the water pump parts are replaced, and the system is upgraded.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0106] Embodiment 2, the second embodiment of the present invention, which is different from the previous embodiment in that:

[0107] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0109] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0110] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0111] Example 3, reference Figure 2 , which is the third embodiment of the present invention, and which provides a multi-modal control system for a water pump driven by intelligent thermal management prediction, including a data acquisition module, an intelligent predictive thermal management algorithm module, an automatic impurity cleaning module, a fault prediction module, and a maintenance and upgrade decision module;

[0112] Data acquisition module, used to collect engine operation data and water pump status information in real time;

[0113] Intelligent predictive thermal management algorithm module, which is used to construct an algorithm based on the collected data and predict the thermal demand of the engine through the algorithm. According to the predicted thermal demand of the engine, the water pump working mode is activated;

[0114] Automatic impurity cleaning module, used to collect the impurity content data of the internal sensor of the electronic water pump in real time, and start the automatic cleaning program when the impurity content exceeds the threshold;

[0115] Fault prediction module, which is used to transmit the water pump operation status data to the blockchain-based water pump health management platform in real time for fault prediction;

[0116] The maintenance and upgrade decision module is used to formulate upgrade and maintenance plans for water pumps based on fault prediction results.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-mode control method for a water pump driven by intelligent thermal management prediction, characterized in that: Including, Collecting engine operation data and water pump status information in real time, constructing an intelligent predictive thermal management algorithm using the collected data and information to predict the heat demand of the engine; Activating a multi-modal water pump control strategy according to the predicted heat demand result of the engine, matching the water pump working mode, collecting the impurity content data of the sensors inside the electric water pump in real time, and automatically cleaning the internal impurities during the operation of the electric water pump; Transmitting the collected operation status of the automotive engine electric water pump to the blockchain-based water pump health management platform in real time for fault prediction, and upgrading and maintaining the electric water pump according to the prediction result.

2. The multi-mode control method for a water pump driven by intelligent thermal management prediction as claimed in claim 1, characterized in that: The engine operation data and water pump status information include engine temperature data, engine load data, ambient temperature data, water pump working parameters, engine operation time, engine cooling system status, battery status data, vehicle driving mode, vehicle maintenance history, and driver behavior data; The engine temperature data includes coolant temperature, oil temperature, and exhaust temperature; the water pump working parameters include water pump speed, flow rate, and pressure; the engine cooling system status includes coolant flow rate and cooling system pressure; the driver behavior data includes the frequency of driver hard acceleration and hard braking.

3. The multi-mode control method for a water pump driven by intelligent thermal management prediction as claimed in claim 2, characterized in that: The constructing of the intelligent predictive thermal management algorithm includes using the ambient temperature, engine temperature, engine speed, engine load, and coolant temperature data and information collected in real time to construct an intelligent predictive thermal management algorithm to predict the heat demand of the engine; Among them, the formula for the predicted heat demand of the engine is: Q(t)=λ·f env (T env (t))+μ·f eng (n,L(t))+ν·f c (T c (t)) Where Q(t) is the predicted engine heat demand, λ is the influence coefficient of ambient temperature on heat demand prediction, and f env (T env (t)) is the ambient temperature influence function, T env (t) is the ambient temperature at time t, μ is the influence coefficient of engine speed and load on heat demand prediction, f eng (n,L(t)) is the engine speed and load influence function, n is the engine speed at time t, L(t) is the engine load at time t, ν is the influence coefficient of coolant temperature on heat demand prediction, f c (T c (t)) is the coolant temperature influence function, T c (t) is the coolant temperature at time t; The f env (T env (t)) The ambient temperature influence function is expressed as: Among them, γ is the ambient temperature influence coefficient, T env (t) is the ambient temperature at time t, T ref is the reference temperature; The f eng (n,L(t)) The engine speed and load influence function is expressed as: Among them, n is the engine speed at time t, φ(n) is the influence function of the engine speed, ψ(L(t)) is the influence function of the engine load, and L(t) is the engine load at time t; The f c (T c (t)) The coolant temperature influence function is expressed as: Among them, T c (t) is the coolant temperature at time t, T ref is the reference temperature, T max is the maximum temperature reached by the coolant; The influence function φ(n) of the engine speed is expressed as: Where n is the engine speed at time t, n min is the minimum speed of the engine, n max is the maximum speed of the engine, n opt The optimal speed of the engine, k is the power exponent of the speed effect, δ is the logarithmic coefficient of the speed effect; The influence function ψ(L(t)) of the engine load is expressed as: in, is the coefficient of load influence, L(t) is the engine load at time t, L min is the minimum load of the engine, L max is the maximum load of the engine, m is the power index of the load influence; Among them, the reference temperature is 90 °C, and the optimal speed of the engine is the speed at which the engine thermal efficiency is the highest; Among them, the value range of Q(t) is [0, ∞). When Q(t) = 0, it means that the engine does not require additional heat management. When Q(t) is a positive remaining value, it means that the engine requires positive heat management.

4. The multi-mode control method for a water pump driven by intelligent thermal management prediction as claimed in claim 3, characterized in that: The activating of the multi-modal water pump control strategy includes setting the threshold value of Q(t) and the dynamic value of Q(t) according to the predicted heat demand Q(t) of the engine, and matching the water pump working mode according to different intervals of the engine heat demand; Among them, the threshold value of Q(t) is 300 kWh, and the dynamic value of Q(t) is 40 kWh; The matching of the water pump working mode includes activating a multi-modal water pump control strategy according to the predicted heat demand result of the engine, matching three water pump working modes. When 0 < Q(t) < 300, match the low-speed mode; when 300 ≤ Q(t) < 340, match the medium-speed mode; when Q(t) ≥ 340, match the high-speed mode.

5. The multi-mode control method for a water pump driven by intelligent thermal management prediction as claimed in claim 4, characterized in that: The automatic internal impurity cleaning includes: setting the impurity detection threshold at 20 mg / L according to the real-time sensor data on the impurity content inside the electric water pump. When the impurity content detected by the sensor exceeds 20 mg / L, start the automatic cleaning program, adjust the working mode of the water pump according to the detected impurity situation, and turn on the backwashing function of the water pump to change the flow direction of the water pump and remove internal impurities. During the backwashing process, monitor the impurity discharge situation in real time until the impurity content detected by the sensor is lower than 20 mg / L, then turn off the backwashing function and restore the water pump to its normal working mode; when the impurity content detected by the sensor does not exceed 20 mg / L, do not start the automatic cleaning program and maintain the normal working mode of the water pump.

6. The multi-mode control method for a water pump driven by intelligent thermal management prediction as claimed in claim 5, characterized in that: The fault prediction includes: transmitting the collected operating status of the automotive engine water pump to the blockchain-based water pump health management platform in real time for fault prediction. The formula for fault prediction is as follows: where F is the fault probability, a is the prediction time period, P(i) is the operating performance index of the water pump in the i-th time period, S(j,i) is the water pump status data of the j-th sensor in the i-th time period, M(i) is the operating quality normalization function of the water pump in the i-th time period, D(i) is the sensor data normalization function in the i-th time period, g is the number of sensors, i is the time discretization variable, and exp is the natural exponential function; The operating quality normalization function is expressed as: Where M(i) is the normalized value of the pump operation quality in the i-th time period, H(i) is the actual operation quality index of the pump in the i-th time period, μ H is the average value of the pump operation quality index, σ H is the standard deviation of the pump operation quality index, exp is the natural exponential function; The sensor data normalization function is expressed as: where D(i) is the normalized value of the sensor data in the i-th time period, J(i) is the original data collected by the sensor in the i-th time period, min(J) is the minimum value of the sensor readings in the monitoring period, and max(J) is the maximum value of the sensor readings in the monitoring period.

7. The multi-mode control method for a water pump driven by intelligent thermal management prediction as claimed in claim 6, characterized in that: The upgrade and maintenance of the electric water pump include: performing fault prediction on the blockchain-based water pump health management platform, and carrying out the upgrade and maintenance of the water pump according to the prediction results. The value range of F is [0, 1]. Set the thresholds of F at 0.2, 0.5, and 0.8 according to the historical data of the electric water pump operation. When 0 ≤ F < 0.2, it means that the automotive engine electric water pump is in a low-risk area, the monitoring strategy remains unchanged and no adjustment is required, and the electric water pump is periodically detected; when 0.2 ≤ F < 0.5, it means that the automotive engine electric water pump is in a medium-risk area, increase the monitoring frequency, check and clean the water pump, but no parts need to be replaced; when 0.5 ≤ F ≤ 0.8, it means that the automotive engine electric water pump is in a high-risk area, monitor the operation status of the electric water pump in real time, feedback the monitoring data in real time, and arrange for emergency maintenance, replace the problematic parts, and conduct a comprehensive inspection; when 0.8 < F ≤ 1, it means that the automotive engine electric water pump is in an extremely high-risk area, immediately stop the operation of the electric water pump, conduct a comprehensive inspection, replace the water pump parts, and perform system upgrade.

8. A system using a multi-mode control method for a water pump driven by intelligent thermal management prediction as claimed in any one of claims 1 to 7, characterized in that: It includes a data acquisition module, an intelligent predictive thermal management algorithm module, an automatic impurity cleaning module, a fault prediction module, and a maintenance and upgrade decision module; The data acquisition module is used to collect the engine operation data and water pump status information in real time; The intelligent predictive thermal management algorithm module is used to construct an algorithm with the collected data, and predict the thermal demand of the engine through the algorithm, and activate the water pump working mode according to the predicted thermal demand of the engine; The automatic impurity cleaning module is used to collect the impurity content data of the internal sensor of the electronic water pump in real time, and start the automatic cleaning program when the impurity content exceeds a threshold; The fault prediction module is used to transmit the water pump operation status data to the blockchain-based water pump health management platform in real time to perform fault prediction; The maintenance and upgrade decision module is used to formulate an upgrade and maintenance plan for the water pump according to the fault prediction result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of an intelligent thermal management prediction-driven water pump multi-modal control method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an intelligent thermal management prediction-driven water pump multi-modal control method according to any one of claims 1 to 7 are implemented.

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