Power generation control optimization method, device, system and server

Abstract

The invention provides a power generation control optimization method, device and system and a server, and belongs to the technical field of automobile electronic control, and the method comprises the steps: obtaining real-time vehicle operation data sent by a TBOX controller; extracting data features from the real-time vehicle operation data to obtain a current vehicle working condition road spectrum, a vehicle state, a generator state and a storage battery state; inputting the current vehicle working condition road spectrum, the vehicle state, the generator state and the storage battery state into the strategy model to obtain a current strategy calibration parameter; the current strategy calibration parameters are obtained by training a neural network model based on historical vehicle working condition road spectrums, vehicle states, generator states, storage battery states and corresponding strategy calibration parameters; and the current strategy calibration parameter is sent to the TBOX controller, so that the current strategy calibration parameter is sent to the PCU controller through the TBOX controller, and the generator is controlled. The problem that an existing automobile power generation control method is poor in adaptability can be solved.

Description

Technical Field

[0001] This invention relates to the field of automotive electronic control technology, specifically to a power generation control optimization method, device, system, and server. Background Technology

[0002] Existing intelligent power generation control strategies for automobiles mainly rely on traditional rule-based methods or simple optimization algorithms (such as PID control and segmented control). The core idea of ​​these methods is to control the generator's operating parameters through preset rules or calibration parameters. For example, based on the actual operating conditions of the vehicle, the generator's output voltage is adjusted according to a preset control strategy and adjustment curve to optimize the generator's operating range and improve power generation efficiency, thereby achieving the desired energy saving and emission reduction effects.

[0003] The existing solution has the following drawbacks: Poor adaptability: The actual road conditions of different vehicles are not the same. Even for a single vehicle, there are significant differences in road conditions. The preset control strategies or calibration parameters cannot cope with the complex dynamic scenarios and individual differences, making it difficult to maintain the generator's operating efficiency at the optimal level. The energy-saving effect is also limited by road conditions. Summary of the Invention

[0004] In view of this, it is necessary to provide a power generation control optimization method, device, system and server to solve the technical problem of poor adaptability of existing automotive power generation control methods.

[0005] To address the aforementioned problems, in a first aspect, the present invention provides a power generation control optimization method, comprising: The system acquires real-time vehicle operation data sent by the TBOX controller; the real-time vehicle operation data is obtained by the TBOX controller from the PCU controller. Data features are extracted from the real-time vehicle operation data to obtain the current vehicle operating condition road spectrum, vehicle status, generator status, and battery status; The current vehicle operating condition road spectrum, vehicle status, generator status, and battery status are input into the strategy model to obtain the current strategy calibration parameters. The current strategy calibration parameters are obtained by training a neural network model based on historical vehicle operating condition road spectrum, vehicle status, generator status, and battery status, as well as the corresponding strategy calibration parameters. The current strategy calibration parameters are sent to the TBOX controller, which then sends the current strategy calibration parameters to the PCU controller; the PCU controller is used to control the generator based on the current strategy calibration parameters.

[0006] In one possible implementation, vehicle operating data includes at least one of: vehicle speed, acceleration, generator speed, engine speed, throttle opening, battery voltage, and ambient temperature.

[0007] In one possible implementation, extracting data features from the real-time vehicle operation data includes: The real-time vehicle operation data is cleaned, and data features are extracted from the cleaned real-time vehicle operation data.

[0008] In one possible implementation, the strategy calibration parameters include: throttle opening threshold range and vehicle speed threshold; Controlling the generator based on the current strategy calibration parameters includes: The PCU controller shuts down the generator when the vehicle speed exceeds the vehicle speed threshold and the throttle opening exceeds the throttle opening threshold range.

[0009] In one possible implementation, the strategy calibration parameters further include: a first engine speed threshold and a second engine speed threshold, wherein the second engine speed threshold is less than the first engine speed threshold; Controlling the generator based on the current strategy calibration parameters also includes: The PCU controller controls the generator to shut down when the vehicle speed is greater than the vehicle speed threshold and the engine speed is greater than the first engine speed threshold, or when the vehicle speed is greater than the vehicle speed threshold and the engine speed is less than the second engine speed threshold.

[0010] In one possible implementation, the strategy calibration parameters further include: a third engine speed threshold and a fourth engine speed threshold; The PCU controller is also used to control the generator to shut down when the vehicle speed is greater than the vehicle speed threshold and the engine speed is greater than the first engine speed threshold, and then restart the generator when the engine speed is less than the third engine speed threshold. The PCU controller is also used to control the generator to shut down when the vehicle speed is greater than the vehicle speed threshold and the engine speed is less than the second engine speed threshold, and then restart the generator when the engine speed is greater than the fourth engine speed threshold.

[0011] In one possible implementation, after sending the current policy calibration parameters to the TBOX controller, the method further includes: If the validity of the current strategy calibration parameters is determined based on the vehicle operation data sent by the TBOX controller, the strategy model is updated based on the latest vehicle operation data.

[0012] In a second aspect, the present invention provides a power generation control optimization device, comprising: The data acquisition module is used to acquire real-time vehicle operation data sent by the TBOX controller; the real-time vehicle operation data is obtained by the TBOX controller from the PCU controller. The feature extraction module is used to extract data features from the real-time vehicle operation data to obtain the current vehicle operating condition road spectrum, vehicle status, generator status and battery status. The calibration parameter determination module is used to input the current vehicle operating condition road spectrum, vehicle status, generator status and battery status into the strategy model to obtain the current strategy calibration parameters. The current strategy calibration parameters are obtained by training a neural network model based on the historical vehicle operating condition road spectrum, vehicle status, generator status and battery status, and the corresponding strategy calibration parameters. The power generation control module is used to send the current strategy calibration parameters to the TBOX controller, so that the TBOX controller can send the current strategy calibration parameters to the PCU controller; the PCU controller is used to control the generator based on the current strategy calibration parameters.

[0013] Thirdly, the present invention provides a server, including a memory and a processor, wherein... The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps of the power generation control optimization method as described in any of the preceding claims.

[0014] Fourthly, the present invention also provides a power generation control optimization system, comprising: the aforementioned server, TBOX controller, PCU controller, and generator; The TBOX controller is communicatively connected to both the server and the PCU controller, and the PCU controller is also communicatively connected to the generator.

[0015] The beneficial effects of the above implementation are as follows: The power generation control optimization method, device, system, and server provided by this invention extract data features from real-time vehicle operation data to obtain the current vehicle operating condition road spectrum, vehicle status, generator status, and battery status. These are then input into the strategy model to obtain the current strategy calibration parameters. The current strategy calibration parameters are determined based on real-time vehicle operation data, which corresponds to the overall vehicle operating condition. Therefore, different overall vehicle operating conditions can determine different current strategy calibration parameters. The current strategy calibration parameters are sent to the PCU controller through the TBOX controller, thereby enabling the PCU controller to control the on / off state of the generator based on the current strategy calibration parameters. This allows for control of the generator's on / off state according to the actual overall vehicle operating condition, ensuring that the generator's power generation can adapt to different overall vehicle operating conditions. This solves the technical problem of poor adaptability in existing automotive power generation control methods, disconnecting the generator when power generation is not needed and connecting it when power generation is needed, avoiding energy waste and improving the generator's energy-saving effect. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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 effort.

[0017] Figure 1 A flowchart of an embodiment of the power generation control optimization method provided by the present invention; Figure 2 A schematic block diagram of an embodiment of the power generation control optimization device provided by the present invention; Figure 3 A schematic diagram of an embodiment of the server provided by the present invention; Figure 4 This is a schematic diagram of an embodiment of the power generation control optimization system provided by the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] In the description of the embodiments of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In this embodiment of the invention, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product or device.

[0021] The naming or numbering of steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical order indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effect can be achieved.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] This invention provides a power generation control optimization method, device, system, and server, which are described below.

[0024] like Figure 1 As shown, the present invention provides a power generation control optimization method, comprising: S101. Obtain real-time vehicle operation data sent by the TBOX (Vehicle Intelligent Terminal) controller; the real-time vehicle operation data is obtained by the TBOX controller from the PCU (Power Control Unit) controller.

[0025] It is understood that the method provided by this invention can be executed by an application on a cloud server. The corresponding vehicle can be a heavy-duty truck. The cloud server communicates with the TBOX controller, and the TBOX controller communicates with the PCU controller in the vehicle via a CAN (Controller Area Network) bus. Initially, the PCU controller collects vehicle operating data, and then the PCU controller sends the collected vehicle operating data to the cloud server through the TBOX controller. The vehicle operating data includes data on the operating status of different system modules of the vehicle.

[0026] S102. Extract data features from the real-time vehicle operation data to obtain the current vehicle operating condition road spectrum, vehicle status, generator status, and battery status.

[0027] Understandably, the extracted data features, such as calculating the output ratio of the motor, the average vehicle speed, the average throttle opening, and the average engine load, can be used to determine the current vehicle operating condition profile, vehicle status, generator status, and battery status.

[0028] S103. Input the current vehicle operating condition road spectrum, vehicle status, generator status and battery status into the strategy model to obtain the current strategy calibration parameters. The current strategy calibration parameters are obtained by training the neural network model based on the historical vehicle operating condition road spectrum, vehicle status, generator status and battery status, and the corresponding strategy calibration parameters.

[0029] It is understandable that when training a neural network model, historical vehicle operating conditions, vehicle status, generator status, and battery status are used as samples, and the corresponding policy calibration parameters are used as sample labels for training; the neural network model can be a generative adversarial neural network model or a Transformer model.

[0030] S104. The current strategy calibration parameters are sent to the TBOX controller, so that the current strategy calibration parameters are sent to the PCU controller through the TBOX controller; the PCU controller is used to control the generator based on the current strategy calibration parameters.

[0031] Understandably, after receiving the strategy calibration parameters, the PCU controller can control the generator's operating status based on the throttle opening, vehicle speed, or engine speed, that is, control the generator's start-up and shutdown to improve the generator's power generation efficiency.

[0032] Compared to existing technologies, this invention collects vehicle operation data (such as vehicle speed, throttle opening, engine load, engine speed, etc.) under different usage scenarios, extracts road spectrum features using big data analysis methods, and constructs a dynamic model of vehicle operating conditions. The resulting dynamic model can extract data features from real-time vehicle operation data.

[0033] Based on the extracted road spectrum features, combined with the vehicle's power system parameters, such as vehicle status, generator status, and battery status, the current strategy calibration parameters are obtained. This allows for dynamic adjustment of the generator's excitation control to switch the generator on and off, adapting to the needs of different operating conditions.

[0034] In addition, the present invention can also collect vehicle operation data in real time through the vehicle terminal and perform dynamic optimization in combination with the cloud big data analysis platform to ensure that the power generation control strategy can be adjusted in real time according to the actual working conditions, so as to achieve real-time optimization and feedback.

[0035] This invention offers the following advantages: it optimizes the effectiveness of intelligent power generation in vehicles, addressing the problem of unsatisfactory energy-saving effects caused by the inability to adapt to varying vehicle operating conditions. By fully utilizing a vehicle big data platform and employing real-time monitoring and feedback mechanisms, it continuously optimizes the intelligent power generation control strategy, ensuring the system operates effectively under different road conditions.

[0036] In some embodiments, vehicle operating data includes at least one of: vehicle speed, acceleration, generator speed, engine speed, throttle opening, battery voltage, and ambient temperature.

[0037] Understandably, after the sensors on the vehicle collect data such as vehicle speed, acceleration, generator speed, engine speed, throttle opening, battery voltage, and ambient temperature, they upload the data to the PCU controller, which then transmits it to the CAN bus.

[0038] In some embodiments, extracting data features from the real-time vehicle operation data includes: The real-time vehicle operation data is cleaned, and data features are extracted from the cleaned real-time vehicle operation data.

[0039] Understandably, data cleaning is a process of re-examining and verifying data, with the aim of removing duplicate information, correcting existing errors, and providing data consistency.

[0040] In some embodiments, the strategy calibration parameters include: throttle opening threshold range and vehicle speed threshold; Controlling the generator based on the current strategy calibration parameters includes: The PCU controller shuts down the generator when the vehicle speed exceeds the vehicle speed threshold and the throttle opening exceeds the throttle opening threshold range.

[0041] It is understandable that when the vehicle speed is greater than the vehicle speed threshold and the throttle opening exceeds the throttle opening threshold range, it means that the generator does not need to work, and at this time the generator can be turned off.

[0042] In some embodiments, the strategy calibration parameters further include: a first engine speed threshold and a second engine speed threshold, wherein the second engine speed threshold is less than the first engine speed threshold; Controlling the generator based on the current strategy calibration parameters also includes: The PCU controller controls the generator to shut down when the vehicle speed is greater than the vehicle speed threshold and the engine speed is greater than the first engine speed threshold, or when the vehicle speed is greater than the vehicle speed threshold and the engine speed is less than the second engine speed threshold.

[0043] Understandably, when the engine speed is too high or too low, it means that there is no need to start the generator, so the generator can be turned off to avoid wasting generator power.

[0044] In some embodiments, the strategy calibration parameters further include: a third engine speed threshold and a fourth engine speed threshold; The PCU controller is also used to control the generator to shut down when the vehicle speed is greater than the vehicle speed threshold and the engine speed is greater than the first engine speed threshold, and then restart the generator when the engine speed is less than the third engine speed threshold. The PCU controller is also used to control the generator to shut down when the vehicle speed is greater than the vehicle speed threshold and the engine speed is less than the second engine speed threshold, and then restart the generator when the engine speed is greater than the fourth engine speed threshold.

[0045] Understandably, the third engine's speed can be lower than the first engine's speed. If the generator shuts down due to excessive engine speed, and the power supply decreases after the engine speed drops, the generator can be restarted. The fourth engine's speed can be higher than the second engine's speed. If the generator shuts down due to excessive engine speed, and the engine speed increases, the apparent demand for electricity increases, and the generator can be restarted.

[0046] In some embodiments, the power generation control optimization method further includes: After the current strategy calibration parameters are sent to the TBOX controller, if the validity of the current strategy calibration parameters is determined based on the vehicle operation data sent by the TBOX controller, the strategy model is updated based on the latest vehicle operation data.

[0047] Understandably, the PCU controller feeds real-time vehicle operation data back to the server to verify the effectiveness of the control strategy and dynamically update the model parameters. For example, the model in the server calculates the real-time percentage of generator operating status; if it is too low, the server will automatically adjust the parameters of the strategy model.

[0048] In some embodiments, the method provided by the present invention includes the following steps: Data Acquisition: The TOX controller collects operating data of various vehicle systems (such as vehicle speed, acceleration, motor speed, engine speed, throttle opening, battery voltage, ambient temperature, etc.) via the CAN bus. The TOX controller and the PCU controller communicate with each other via the CAN bus.

[0049] Data preprocessing: Clean the data and extract features, such as calculating the output ratio of the motor, the average vehicle speed, the average throttle opening, and the average engine load.

[0050] Operating condition classification: Using machine learning algorithms and extracted features, operating conditions are classified to distinguish different operating condition profiles such as high load, low load, high speed, and low speed.

[0051] Strategy Model Training: Based on historical vehicle operation data, the model is trained and optimized. Model inputs include operating condition type, vehicle status, generator status, battery status, etc.; outputs include control strategy calibration parameters (throttle opening threshold, vehicle speed threshold, engine speed threshold, etc.).

[0052] Real-time optimization: The parameters output by the strategy model are transmitted to the PCU controller through the TBOX controller to dynamically adjust the generator's control strategy.

[0053] For example, if the lower-level machine uses the following calibration interface, the server will derive the policy calibration parameters (ABCMNXY) based on model training, specifically including: Throttle opening threshold range: Exit generator control when greater than X% and less than Y%; High speed threshold: When the engine speed is higher than A (i.e., the first engine speed threshold), the generator is turned off; when the engine speed is lower than B (i.e., the third engine speed threshold), the generator is restarted. Low speed threshold: When the engine speed is less than M (i.e., the second engine speed threshold), the generator is turned off; when the engine speed is greater than N (i.e., the fourth engine speed threshold), the generator is restarted. Vehicle speed threshold: When the vehicle speed is greater than C, the above three strategies take effect; when the vehicle speed is less than C, the generator is started.

[0054] Feedback and Updates: Data fed back to the server in real time via the PCU controller and TBOX controller verifies the effectiveness of the control strategy and dynamically updates the strategy model parameters. For example, the server model calculates the real-time percentage of generator operating status; if it is too low, the cloud will automatically adjust the model.

[0055] The method provided by this invention addresses the shortcomings of existing technologies, with improvements mainly focused on the following aspects: ① Introducing a big data platform: Real-time vehicle operation data (such as speed, acceleration, load, engine speed, generator speed, accelerator pedal, brake pedal, gear signal, generator operating status, etc.) is collected and transmitted to the data processing module of the big data platform, which includes a TBOX controller and a cloud server; ② Incorporating deep learning: Based on the data uploaded to the big data platform, a road spectrum model of the vehicle is established, extracting the vehicle's driving characteristics (such as vehicle status, generator status, and battery status) and road spectrum distribution. Combined with generator operating status data, the actual execution of the current power generation strategy is analyzed and calculated. Based on the actual execution, such as the generator power generation results, the effectiveness verification results are determined. Based on the effectiveness verification results, if the effect is not ideal or the ratio of generator power generation to non-power generation time is unbalanced, the calibration parameters of the control strategy will be dynamically optimized through road spectrum; ③ A modular approach is adopted during model development, breaking down various strategies into separate modules and adding calibration parameter interfaces to achieve the goal of changing the control strategy through calibration parameters; ④ Incorporate command issuance: the calibration parameters are issued to the vehicle through a big data platform to ensure that the vehicle executes intelligent power generation control with the optimal strategy.

[0056] like Figure 2 As shown, the present invention also provides a power generation control optimization device 200, comprising: Data acquisition module 201 is used to acquire real-time vehicle operation data sent by the TBOX controller; the real-time vehicle operation data is obtained by the TBOX controller from the PCU controller; Feature extraction module 202 is used to extract data features from the real-time vehicle operation data to obtain the current vehicle operating condition road spectrum, vehicle status, generator status and battery status. The calibration parameter determination module 203 is used to input the current vehicle operating condition road spectrum, vehicle status, generator status and battery status into the strategy model to obtain the current strategy calibration parameters. The current strategy calibration parameters are obtained by training a neural network model based on the historical vehicle operating condition road spectrum, vehicle status, generator status and battery status, and the corresponding strategy calibration parameters. The power generation control module 204 is used to send the current strategy calibration parameters to the TBOX controller, so that the TBOX controller can send the current strategy calibration parameters to the PCU controller; the PCU controller is used to control the generator based on the current strategy calibration parameters.

[0057] The power generation control optimization device provided in the above embodiments can realize the technical solutions described in the above power generation control optimization method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above power generation control optimization method embodiments, and will not be repeated here.

[0058] like Figure 3 As shown, the present invention also provides a server 300. The server 300 includes a processor 301, a memory 302, and a display 303. Figure 3 Only a portion of the components of server 300 are shown; however, it should be understood that implementation of all shown components is not required, and more or fewer components may be implemented instead.

[0059] In some embodiments, memory 302 may be an internal storage unit of server 300, such as a hard disk or memory of server 300. In other embodiments, memory 302 may also be an external storage device of server 300, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on server 300.

[0060] Furthermore, the memory 302 may include both internal storage units of the server 300 and external storage devices. The memory 302 is used to store application software and various types of data installed on the server 300.

[0061] In some embodiments, processor 301 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 302 or process data, such as the power generation control optimization method of the present invention.

[0062] In some embodiments, display 303 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 303 is used to display information on server 300 and to display a visual user interface. Components 301-303 of server 300 communicate with each other via a system bus.

[0063] In some embodiments of the present invention, when the processor 301 executes the power generation control optimization program in the memory 302, the following steps can be implemented: The system acquires real-time vehicle operation data sent by the TBOX controller; the real-time vehicle operation data is obtained by the TBOX controller from the PCU controller. Data features are extracted from the real-time vehicle operation data to obtain the current vehicle operating condition road spectrum, vehicle status, generator status, and battery status; The current vehicle operating condition road spectrum, vehicle status, generator status, and battery status are input into the strategy model to obtain the current strategy calibration parameters. The current strategy calibration parameters are obtained by training a neural network model based on historical vehicle operating condition road spectrum, vehicle status, generator status, and battery status, as well as the corresponding strategy calibration parameters. The current strategy calibration parameters are sent to the TBOX controller, which then sends the current strategy calibration parameters to the PCU controller; the PCU controller is used to control the generator based on the current strategy calibration parameters.

[0064] It should be understood that when the processor 301 executes the power generation control optimization program in the memory 302, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0065] Furthermore, the embodiments of the present invention do not specifically limit the type of server 300 mentioned. Server 300 can be a portable server such as a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, or laptop computer. Exemplary embodiments of portable servers include, but are not limited to, portable servers running iOS, Android, Microsoft, or other operating systems. The aforementioned portable server can also be other portable servers, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, server 300 may not be a portable server, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0066] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the power generation control optimization method provided by the methods described above, the method comprising: The system acquires real-time vehicle operation data sent by the TBOX controller; the real-time vehicle operation data is obtained by the TBOX controller from the PCU controller. Data features are extracted from the real-time vehicle operation data to obtain the current vehicle operating condition road spectrum, vehicle status, generator status, and battery status; The current vehicle operating condition road spectrum, vehicle status, generator status, and battery status are input into the strategy model to obtain the current strategy calibration parameters. The current strategy calibration parameters are obtained by training a neural network model based on historical vehicle operating condition road spectrum, vehicle status, generator status, and battery status, as well as the corresponding strategy calibration parameters. The current strategy calibration parameters are sent to the TBOX controller, which then sends the current strategy calibration parameters to the PCU controller; the PCU controller is used to control the generator based on the current strategy calibration parameters.

[0067] like Figure 4 As shown, the present invention also provides a power generation control optimization system, including: the server 300, TBOX controller 401, PCU controller 402 and generator 403 described above; The TBOX controller 401 is communicatively connected to the server 300 and the PCU controller 402, respectively. The PCU controller 402 is also communicatively connected to the generator 403, and the generator 403 is electrically connected to the battery 404. Figure 4 The dynamic model library shown is a model used in server 300 for data processing and analysis.

[0068] It is understood that the system provided by the present invention is divided into two main modules: a big data platform and a control system. The control system includes a PCU controller 402, a generator 403 and a battery 404. The PCU controls the excitation of the generator 403 based on the vehicle's driving status.

[0069] The big data platform includes a TBOX controller 401 and a server 300, which can be a cloud server. The TBOX controller 401 acts as an interaction medium, enabling data upload and download between the server 300 and the lower-level controller (i.e., the PCU controller 402). The cloud server establishes a dynamic model library for the vehicle, analyzes the collected data, calculates real-time vehicle road spectrum maps and intelligent power generation effects (output curve of the generator 403), and, based on the road spectrum, formulates optimized strategies. Calibration parameters are then sent to the lower-level controller via the TBOX controller 401 to achieve adaptive control strategy adjustment.

[0070] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0071] The above provides a detailed description of the power generation control optimization method, apparatus, system, and server provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A power generation control optimization method, characterized in that, include: Obtain real-time vehicle operation data sent by the TBOX controller; The real-time vehicle operation data is obtained by the TBOX controller from the PCU controller; Data features are extracted from the real-time vehicle operation data to obtain the current vehicle operating condition road spectrum, vehicle status, generator status, and battery status; Input the current vehicle operating condition road spectrum, vehicle status, generator status and battery status into the strategy model to obtain the current strategy calibration parameters; The current strategy calibration parameters are obtained by training a neural network model based on historical vehicle operating condition road spectrum, vehicle status, generator status and battery status, and the corresponding strategy calibration parameters. The current strategy calibration parameters are sent to the TBOX controller, which then sends the current strategy calibration parameters to the PCU controller; the PCU controller is used to control the generator based on the current strategy calibration parameters.

2. The power generation control optimization method according to claim 1, characterized in that, Vehicle operating data includes at least one of the following: vehicle speed, acceleration, generator speed, engine speed, throttle opening, battery voltage, and ambient temperature.

3. The power generation control optimization method according to claim 1, characterized in that, Extracting data features from the real-time vehicle operation data includes: The real-time vehicle operation data is cleaned, and data features are extracted from the cleaned real-time vehicle operation data.

4. The power generation control optimization method according to claim 1, characterized in that, The strategy calibration parameters include: throttle opening threshold range and vehicle speed threshold; Controlling the generator based on the current strategy calibration parameters includes: The PCU controller shuts down the generator when the vehicle speed exceeds the vehicle speed threshold and the throttle opening exceeds the throttle opening threshold range.

5. The power generation control optimization method according to claim 4, characterized in that, The strategy calibration parameters also include: a first engine speed threshold and a second engine speed threshold, wherein the second engine speed threshold is less than the first engine speed threshold; Controlling the generator based on the current strategy calibration parameters also includes: The PCU controller controls the generator to shut down when the vehicle speed is greater than the vehicle speed threshold and the engine speed is greater than the first engine speed threshold, or when the vehicle speed is greater than the vehicle speed threshold and the engine speed is less than the second engine speed threshold.

6. The power generation control optimization method according to claim 5, characterized in that, The strategy calibration parameters also include: a third engine speed threshold and a fourth engine speed threshold; The PCU controller is also used to control the generator to shut down when the vehicle speed is greater than the vehicle speed threshold and the engine speed is greater than the first engine speed threshold, and to restart the generator when the engine speed is less than the third engine speed threshold. The PCU controller is also used to control the generator to shut down when the vehicle speed is greater than the vehicle speed threshold and the engine speed is less than the second engine speed threshold, and then restart the generator when the engine speed is greater than the fourth engine speed threshold.

7. The power generation control optimization method according to any one of claims 1-6, characterized in that, After sending the current policy calibration parameters to the TBOX controller, the method further includes: If the validity of the current strategy calibration parameters is determined based on the vehicle operation data sent by the TBOX controller, the strategy model is updated based on the latest vehicle operation data.

8. A power generation control optimization device, characterized in that, include: The data acquisition module is used to acquire real-time vehicle operation data sent by the TBOX controller; The real-time vehicle operation data is obtained by the TBOX controller from the PCU controller; The feature extraction module is used to extract data features from the real-time vehicle operation data to obtain the current vehicle operating condition road spectrum, vehicle status, generator status and battery status. The calibration parameter determination module is used to input the current vehicle operating condition road spectrum, vehicle status, generator status and battery status into the strategy model to obtain the current strategy calibration parameters; The current strategy calibration parameters are obtained by training a neural network model based on historical vehicle operating condition road spectrum, vehicle status, generator status and battery status, and the corresponding strategy calibration parameters. The power generation control module is used to send the current strategy calibration parameters to the TBOX controller, so that the TBOX controller can send the current strategy calibration parameters to the PCU controller; the PCU controller is used to control the generator based on the current strategy calibration parameters.

9. A server, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps of the power generation control optimization method as described in any one of claims 1 to 7.

10. A power generation control optimization system, characterized in that, include: The server, TBOX controller, PCU controller, and generator as described in claim 9; The TBOX controller is communicatively connected to both the server and the PCU controller, and the PCU controller is also communicatively connected to the generator.

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