Hydrogen fuel assisted combustion based internal combustion engine near zero carbon emission control system and method

Abstract

The application provides a near-zero carbon emission control system and method of an internal combustion engine based on hydrogen fuel assisted combustion, comprising an intelligent hydrogen fuel supply subsystem for receiving a torque demand signal of the engine and calculating initial hydrogen supply parameters based on the torque demand signal; a combustion control and optimization subsystem for monitoring a combustion state of a combustion chamber of the engine in real time and establishing a three-variable online optimization model of a quasi-dynamic model of in-cylinder combustion based on the combustion state; and a collaborative main control subsystem for solving optimal control parameters, dynamically optimizing and adjusting supply parameters and / or ignition parameters by using a composite control strategy, predicting future load changes of the engine, and controlling a hydrogen supply amount. The application greatly suppresses the generation of nitrogen oxides while ensuring high indicated thermal efficiency, avoids emission exceeding caused by air-fuel ratio misadjustment in transient processes, and improves the drivability and economy of the whole vehicle.

Description

Technical Field

[0001] This invention belongs to the technical field of hydrogen power and engine control, specifically relating to a near-zero carbon emission control system and method for internal combustion engines based on hydrogen fuel-assisted combustion. Background Technology

[0002] The internal combustion engine industry is actively seeking low-carbon and even zero-carbon fuel solutions. Hydrogen energy, due to its characteristics of zero carbon dioxide emissions from combustion products, wide availability, and high energy density, is considered one of the important pathways to achieving near-zero carbon emissions from internal combustion engines. Currently, hydrogen internal combustion engines and related blended fuel technologies have become a hot research topic in the industry, with zero-carbon fuel technologies, represented by ammonia-hydrogen blended combustion, showing great potential.

[0003] In existing technologies, hydrogen is used as the ignition source and an ammonia-hydrogen mixture as the main fuel. Compression ignition is achieved through a dual-injector structure and intake air heating, which to some extent achieves zero-carbon combustion. However, this technology still has several significant drawbacks: First, it relies on a relatively high intake air temperature (not lower than 476K) to achieve hydrogen compression ignition, leading to increased system energy consumption. Furthermore, the sluggish response of intake air heating in actual operation affects combustion stability under transient conditions. Second, this scheme adopts a relatively simple open-loop or partially closed-loop control strategy without incorporating multi-sensor fusion and real-time optimization algorithms. This results in insufficient precision in controlling the mixing ratio of hydrogen and ammonia, injection timing, and combustion phase, especially under varying operating conditions, which can easily lead to incomplete combustion, ammonia escape, and increased nitrogen oxide generation. In addition, existing technologies lack high-frequency dynamic response control and safety redundancy mechanisms for the hydrogen supply system, making it difficult to ensure efficient and safe operation of the system under all operating conditions. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a near-zero carbon emission control system and method for internal combustion engines based on hydrogen fuel-assisted combustion, thereby overcoming the shortcomings of the prior art.

[0005] In a first aspect, the present invention provides a near-zero carbon emission control system for an internal combustion engine based on hydrogen fuel-assisted combustion, the system comprising: The intelligent hydrogen fuel supply subsystem is used to receive the torque demand signal of the engine and calculate the initial hydrogen supply parameters based on the torque demand signal. The combustion control and optimization subsystem is used to monitor the combustion state of the engine combustion chamber in real time and establish a three-variable online optimization model of the quasi-dimensional dynamics model of in-cylinder combustion based on the combustion state. The collaborative main control subsystem is used to solve for the optimal control parameters based on the predictive control algorithm of the three-variable online optimization model, dynamically optimize and adjust the supply parameters and / or ignition parameters using a composite control strategy, and integrate local vehicle signals with predictive data from the cloud-based digital twin model to predict future engine load changes and control the hydrogen supply.

[0006] Compared with existing technologies, the beneficial effects of this invention are as follows: Through the coordinated operation of the intelligent hydrogen fuel supply subsystem and the combustion control optimization subsystem, precise quantitative injection of hydrogen and real-time optimized control of the in-cylinder combustion process are achieved. A three-variable online optimization model is used to dynamically adjust injection parameters, ignition timing, and EGR rate, which significantly suppresses the formation of nitrogen oxides (NOx) while ensuring high indicated thermal efficiency (up to 47%). Furthermore, it can predict the trend of engine load changes in advance. By instructing the hydrogen supply system to build up pressure in advance before the load increases and reducing the hydrogen supply in advance before the load decreases, a supply buffer is formed, which shortens the torque response time of the system from idle to full load to less than 200ms. This perfectly solves the problem of response lag caused by the low density and small energy volume of hydrogen in hydrogen internal combustion engines under transient conditions. At the same time, it avoids excessive emissions caused by air-fuel ratio imbalance during transient processes, thus improving the drivability and economy of the vehicle.

[0007] Furthermore, the intelligent hydrogen fuel supply subsystem includes a high-pressure hydrogen storage module, a pressure regulation module, at least one hydrogen injector, and a hydrogen supply control unit. The high-pressure hydrogen storage module uses a Type IV hydrogen storage cylinder with a working pressure of 70MPa. The pressure regulation module is a combination of a high-pressure common rail system and a high-frequency response solenoid valve, so that the hydrogen pressure build-up time is less than 5ms. The hydrogen injector adopts a high-frequency piezoelectric drive or electromagnetic drive structure. The hydrogen supply control unit is configured to receive an engine torque demand signal and adjust the parameters of the hydrogen injector based on the engine torque demand signal and a temperature compensation signal.

[0008] Furthermore, the hydrogen injector is an intake manifold injector or a direct injection injector, and the nozzle tip of the hydrogen injector has a porous micro-diffusion structure with a pore size of 50 micrometers to 80 micrometers.

[0009] Furthermore, the combustion control and optimization subsystem includes a combustion state sensing module and a combustion optimization control unit: The combustion state sensing module includes a cylinder pressure sensor, a wide-range oxygen sensor, and an ion current sensor. The combustion optimization control unit employs a sensor data fusion algorithm based on Kalman filtering and is configured to generate optimization commands to dynamically adjust hydrogen injection parameters, ignition timing, and EGR rate.

[0010] Furthermore, the collaborative main control subsystem includes a central main control ECU and a functional safety island; The central control ECU includes the functional safety island, which integrates hydrogen supply and combustion control functions. The functional safety island is used to independently monitor key actuators to achieve a failure operability level for key functions of hydrogen fuel supply and ignition.

[0011] Furthermore, the collaborative main control subsystem employs a long short-term memory network model to perform time-series analysis on the throttle opening change rate, vehicle acceleration, navigation traffic information, and cloud-based collaborative information received through the vehicle network.

[0012] Secondly, the present invention also provides a near-zero carbon emission control method for an internal combustion engine based on hydrogen fuel-assisted combustion, for realizing the above-mentioned near-zero carbon emission control system for an internal combustion engine based on hydrogen fuel-assisted combustion, the method comprising: Receive the engine's torque demand signal and calculate the initial hydrogen supply parameters based on the torque demand signal; The combustion state of the engine combustion chamber is monitored in real time, and a three-variable online optimization model of the in-cylinder combustion quasi-dimensional dynamics model is established based on the combustion state. The predictive control algorithm based on the three-variable online optimization model solves for the optimal control parameters. A composite control strategy is used to dynamically optimize and adjust the supply parameters and / or ignition parameters. The algorithm also integrates local vehicle signals with predictive data from the cloud-based digital twin model to predict future engine load changes and control the hydrogen supply.

[0013] Thirdly, the present invention also provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described near-zero carbon emission control method for internal combustion engines based on hydrogen fuel-assisted combustion.

[0014] Fourthly, the present invention also provides a vehicle including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described near-zero carbon emission control method for an internal combustion engine based on hydrogen fuel-assisted combustion. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This is a schematic diagram of a near-zero carbon emission control system for an internal combustion engine based on hydrogen fuel-assisted combustion in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the implementation process in the first embodiment of the present invention. Figure 1 ; Figure 3 This is a schematic diagram of the implementation process in the first embodiment of the present invention. Figure 2 ; Figure 4 This is a schematic flowchart of the near-zero carbon emission control method for internal combustion engines based on hydrogen fuel-assisted combustion in the second embodiment of the present invention. Figure 5 This is a block diagram of the vehicle structure in the third embodiment of the present invention.

[0017] Explanation of key component symbols: 11. Intelligent hydrogen fuel supply subsystem; 12. Combustion control and optimization subsystem; 13. Cooperative main control subsystem; 10. Memory; 20. Processor; 30. Computer program.

[0018] The embodiments of the present invention will be further described below with reference to the accompanying drawings. Detailed Implementation

[0019] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0020] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Example 1 Please see Figure 1The figure shows a near-zero carbon emission control system for an internal combustion engine based on hydrogen fuel-assisted combustion in the first embodiment of the present invention, including an intelligent hydrogen fuel supply subsystem 11, a combustion control and optimization subsystem 12, and a collaborative main control subsystem 13.

[0023] The intelligent hydrogen fuel supply subsystem 11 is used to receive the engine's torque demand signal and calculate the initial hydrogen supply parameters based on the torque demand signal; the combustion control and optimization subsystem 12 is used to monitor the combustion state of the engine combustion chamber in real time and establish a three-variable online optimization model of the in-cylinder combustion quasi-dimensional dynamics model based on the combustion state; the collaborative main control subsystem 13 is used to solve for the optimal control parameters based on the predictive control algorithm of the three-variable online optimization model, dynamically optimize and adjust the supply parameters and / or ignition parameters using a composite control strategy, and predict future load changes of the engine by integrating local vehicle signals and prediction data issued by the cloud digital twin model, and control the hydrogen supply.

[0024] Specifically, in this embodiment, the intelligent hydrogen fuel supply subsystem includes a high-pressure hydrogen storage module, a pressure regulation module, at least one hydrogen injector, and a hydrogen supply control unit. The high-pressure hydrogen storage module uses a Type IV hydrogen storage cylinder with a working pressure of 70 MPa. The pressure regulation module is a combination of a high-pressure common rail system and a high-frequency response solenoid valve, ensuring that the hydrogen pressure build-up time is less than 5 ms. The hydrogen injector uses a high-frequency response piezoelectric drive or electromagnetic drive structure. The hydrogen supply control unit is configured to receive engine torque demand signals and adjust the parameters of the hydrogen injector based on the engine torque demand signals and temperature compensation signals.

[0025] The hydrogen injector is either an intake manifold injector or a direct injection injector. The nozzle tip of the hydrogen injector has a porous micro-diffuser structure with a pore size of 60 micrometers. The hydrogen injector adopts a high-frequency piezoelectric or electromagnetic drive structure, supporting at least three segmented injections within one working cycle, with an injection quantity control error of less than ±1.5%. The hydrogen supply control unit is configured to receive the engine torque demand signal and intelligently adjust the parameters of the hydrogen injector based on this signal and a temperature compensation signal.

[0026] Specifically, in this embodiment, the combustion control and optimization subsystem includes a combustion state sensing module and a combustion optimization control unit; the combustion state sensing module includes a cylinder pressure sensor, a wide-range oxygen sensor, and an ion current sensor; the combustion optimization control unit adopts a sensor data fusion algorithm based on Kalman filtering and is configured to generate optimization instructions to dynamically adjust hydrogen injection parameters, ignition timing, and EGR rate.

[0027] It should be noted that the combustion optimization control unit has a built-in knock edge recognition algorithm and uses a sensor data fusion algorithm based on Kalman filtering to improve the accuracy and robustness of in-cylinder state estimation. This unit is configured to generate optimization commands and dynamically adjust hydrogen injection parameters, ignition timing and EGR rate.

[0028] Specifically, in this embodiment, the collaborative main control subsystem uses a long short-term memory network model to perform time-series analysis on the throttle opening change rate, vehicle acceleration, navigation traffic information, and cloud-based collaborative information received through the vehicle network.

[0029] It should be noted that the collaborative main control subsystem integrates hydrogen supply and combustion control functions. The functional safety island is at the ASIL-D level and is used to independently monitor key actuators, thereby achieving the failure operability level of key functions of hydrogen fuel supply and ignition, ensuring that the system can still safely degrade and operate under a single point of failure.

[0030] Please see Figure 2 and Figure 3 The system is based on a 12.8-liter six-cylinder heavy-duty diesel engine. The system hardware configuration is as follows: The intelligent hydrogen fuel supply subsystem uses two Type IV 70MPa carbon fiber wound hydrogen storage tanks with a total hydrogen storage capacity of approximately 15kg, equipped with temperature and pressure sensors. The pressure regulation module employs a two-stage pressure reduction design. The first stage reduces the hydrogen pressure from 70MPa to 10MPa, and the second stage is a high-pressure common rail unit with a built-in accumulator and high-frequency response proportional valve, which can precisely stabilize the rail pressure at 5MPa with a pressure build-up time of less than 3ms. The hydrogen injector uses a piezoelectric-driven in-cylinder direct injection injector (Bosch, model HDSI-5), whose nozzle has a porous micro-diffusion structure (72 nozzles with a diameter of 60μm), capable of completing up to three injections in one working cycle, with an injection volume control accuracy of ±1.2%. The hydrogen supply control unit is integrated into the main control ECU, receiving torque request signals and hydrogen storage tank temperature signals from the CAN bus, and outputting commands through lookup table methods and model calculations.

[0031] In the combustion control and optimization subsystem, the combustion state sensing module includes a Kistler 6067C quartz cylinder pressure sensor installed in each cylinder, a Bosch LSU 4.9 wide-range oxygen sensor installed on the exhaust pipe, and an ion current sensor using the spark plug as an electrode. The combustion optimization control unit is integrated into the main control ECU. Its built-in algorithm reads the cylinder pressure signal every 0.1 milliseconds and uses a Kalman filter-based data fusion algorithm to calculate the instantaneous in-cylinder heat release rate, indicated mean effective pressure (IMEP), and knock intensity index in real time.

[0032] The central control ECU of the collaborative main control subsystem uses an Infineon Aurix TC397T tri-core microprocessor, one of which, a lockstep core, is configured as an ASIL-D level functional safety island specifically for monitoring hydrogen injection and ignition commands. The ECU receives predictive information from a cloud server via an onboard gateway, including a 300-meter road load spectrum based on high-precision maps and real-time traffic light phase predictions. Auxiliary systems include an independent coolant circulation loop providing active cooling for the hydrogen injectors and high-pressure common rail unit, with an electric heater at the pressure relief valve to prevent icing. The safety system includes a Figaro TGS2611-E00 hydrogen concentration sensor installed at the top and bottom of the engine compartment, immediately triggering a safety protocol when the concentration exceeds 10% of the LEL (Leadership Level). The aftertreatment system retains the original SCR system and optimizes its injection strategy.

[0033] When the system is operating, the predictive hydrogen supply mechanism is implemented through a Long Short-Term Memory (LSTM) network model deployed on a cloud server. This model takes the vehicle's operating data from the past 60 seconds and predicted road conditions for the next 30 seconds as input, and outputs the expected torque demand curve for the engine within the next 5 seconds, with a prediction accuracy of up to 92%. Precise combustion control is achieved through a Model Predictive Controller (MPC) running on the main control ECU. This controller contains a simplified quasi-dimensional model of in-cylinder combustion, which optimizes three variables—hydrogen injection quantity, EGR rate, and ignition timing—with the goals of maximizing braking thermal efficiency and controlling NOx emissions.

[0034] The system underwent a globally unified transient cycle test (WHTC) on an engine bench, achieving an average CO2 emission of 0.8 g / kWh and an average NOx emission of 0.35 g / kWh. The engine achieved a maximum effective thermal efficiency of 47%, and a torque response time of 180 ms from idle to 100% full load. As an extended application, the vehicle can also be equipped with a compact ammonia cracker (using a ruthenium-based catalyst, operating at 550°C). When switching to an ammonia-hydrogen mixed mode, liquid ammonia decomposes into a mixture of 75% hydrogen and 25% nitrogen, with a stable cracking efficiency of approximately 87%, achieving fuel flexibility and regional adaptability.

[0035] In summary, the near-zero carbon emission control system for internal combustion engines based on hydrogen fuel-assisted combustion in the above embodiments of the present invention achieves precise quantitative injection of hydrogen and real-time optimized control of the in-cylinder combustion process through the coordinated operation of the intelligent hydrogen fuel supply subsystem and the combustion control optimization subsystem. The system adopts a three-variable online optimization model of "hydrogen-air-EGR" and the MPC algorithm to dynamically adjust injection parameters, ignition timing and EGR rate, significantly suppressing the formation of nitrogen oxides (NOx) while ensuring high indicated thermal efficiency (up to 47%). A predictive hydrogen supply mechanism based on cloud-vehicle information fusion is introduced, using an LSTM model to perform time-series analysis of multi-source information such as road conditions and driving behavior to predict engine load change trends in advance. By instructing the hydrogen supply system to build up pressure before the load increases and reducing the hydrogen supply before the load decreases, a supply buffer is formed, which shortens the torque response time of the system from idle to full load to less than 200ms. This perfectly solves the problem of response lag caused by the low density and small energy volume of hydrogen internal combustion engines under transient conditions. At the same time, it avoids excessive emissions caused by air-fuel ratio imbalance during transient processes, thus improving the drivability and economy of the vehicle.

[0036] Example 2 Please see Figure 4 The image shows a near-zero carbon emission control method for an internal combustion engine based on hydrogen fuel-assisted combustion in the second embodiment of the present invention, used to implement the near-zero carbon emission control system for an internal combustion engine based on hydrogen fuel-assisted combustion in the first embodiment. The method includes steps S1 to S3: S1, receive the torque demand signal from the engine, and calculate the initial hydrogen supply parameters based on the torque demand signal; S2, Real-time monitoring of the combustion state in the engine combustion chamber, and establishment of a three-variable online optimization model of the in-cylinder combustion quasi-dimensional dynamics model based on the combustion state; S3. Based on the predictive control algorithm of the three-variable online optimization model, the optimal control parameters are solved. A composite control strategy is adopted to dynamically optimize and adjust the supply parameters and / or ignition parameters. The prediction data issued by the local vehicle signal and the cloud digital twin model are integrated to predict the future load changes of the engine and control the hydrogen supply.

[0037] Example 3 The present invention also proposes a vehicle, please refer to [link / reference]. Figure 5 The vehicle shown is a third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, it implements the near-zero carbon emission control method for internal combustion engines based on hydrogen fuel-assisted combustion as described above.

[0038] In a specific implementation, the processor 20 receives the torque demand signal of the engine and calculates the initial hydrogen supply parameters based on the torque demand signal. The processor 20 monitors the combustion state of the engine combustion chamber in real time and establishes a three-variable online optimization model of the quasi-dimensional dynamics model of in-cylinder combustion based on the combustion state; The processor 20 solves for the optimal control parameters based on the predictive control algorithm of the three-variable online optimization model, dynamically optimizes and adjusts the supply parameters and / or ignition parameters using a composite control strategy, and integrates local vehicle signals with predictive data from the cloud-based digital twin model to predict future engine load changes and control the hydrogen supply.

[0039] In some embodiments, the processor 20 may be an electronic control unit (ECU, also known as a vehicle computer), a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing access restriction programs.

[0040] The memory 10 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of the vehicle, such as the vehicle's hard disk. In other embodiments, the memory 10 may be an external storage device of the vehicle, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 10 may include both internal and external storage devices. The memory 10 can be used not only to store application software and various types of data installed in the vehicle, but also to temporarily store data that has been output or will be output.

[0041] It should be pointed out that, Figure 5 The structure shown does not constitute a limitation on the vehicle. In other embodiments, the vehicle may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0042] This invention also proposes a readable storage medium storing a computer program that, when executed by a processor, implements the near-zero carbon emission control method for internal combustion engines based on hydrogen fuel-assisted combustion as described above.

[0043] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0044] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0045] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0046] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0047] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A near-zero carbon emission control system for an internal combustion engine based on hydrogen fuel-assisted combustion, characterized in that, The system includes: The intelligent hydrogen fuel supply subsystem is used to receive the torque demand signal of the engine and calculate the initial hydrogen supply parameters based on the torque demand signal. The combustion control and optimization subsystem is used to monitor the combustion state of the engine combustion chamber in real time and establish a three-variable online optimization model of the in-cylinder combustion quasi-dimensional dynamics model based on the combustion state. The three variables of the three-variable online optimization model include hydrogen injection parameters, ignition timing and EGR rate. The collaborative main control subsystem is used to solve for the optimal control parameters based on the predictive control algorithm of the three-variable online optimization model, dynamically optimize and adjust the supply parameters and / or ignition parameters using a composite control strategy, and integrate local vehicle signals with predictive data from the cloud-based digital twin model to predict future engine load changes and control the hydrogen supply.

2. The near-zero carbon emission control system for internal combustion engines based on hydrogen fuel-assisted combustion according to claim 1, characterized in that, The intelligent hydrogen fuel supply subsystem includes a high-pressure hydrogen storage module, a pressure regulation module, at least one hydrogen injector, and a hydrogen supply control unit. The high-pressure hydrogen storage module uses a Type IV hydrogen storage cylinder with a working pressure of 70MPa. The pressure regulation module is a combination of a high-pressure common rail system and a high-frequency response solenoid valve, so that the hydrogen pressure build-up time is less than 5ms. The hydrogen injector adopts a high-frequency piezoelectric drive or electromagnetic drive structure. The hydrogen supply control unit is configured to receive an engine torque demand signal and adjust the parameters of the hydrogen injector based on the engine torque demand signal and a temperature compensation signal.

3. The near-zero carbon emission control system for internal combustion engines based on hydrogen fuel-assisted combustion according to claim 2, characterized in that, The hydrogen injector is an intake manifold injector or a direct injection injector in the cylinder. The nozzle tip of the hydrogen injector has a porous micro-diffusion structure with a pore size of 50-80 micrometers.

4. The near-zero carbon emission control system for internal combustion engines based on hydrogen fuel-assisted combustion according to claim 1, characterized in that, The combustion control and optimization subsystem includes a combustion state sensing module and a combustion optimization control unit: The combustion state sensing module includes a cylinder pressure sensor, a wide-range oxygen sensor, and an ion current sensor. The combustion optimization control unit employs a sensor data fusion algorithm based on Kalman filtering and is configured to generate optimization commands to dynamically adjust hydrogen injection parameters, ignition timing, and EGR rate.

5. The near-zero carbon emission control system for internal combustion engines based on hydrogen fuel-assisted combustion according to claim 1, characterized in that, The collaborative main control subsystem includes a central main control ECU and a functional safety island; The central control ECU includes the functional safety island, which integrates hydrogen supply and combustion control functions. The functional safety island is used to independently monitor key actuators to achieve a failure operability level for key functions of hydrogen fuel supply and ignition.

6. The near-zero carbon emission control system for internal combustion engines based on hydrogen fuel-assisted combustion according to claim 1, characterized in that, The collaborative main control subsystem uses a long short-term memory network model to perform time-series analysis on the throttle opening change rate, vehicle acceleration, navigation traffic information, and cloud-based collaborative information received through the vehicle network.

7. A near-zero carbon emission control method for an internal combustion engine based on hydrogen fuel-assisted combustion, used to implement the near-zero carbon emission control system for an internal combustion engine based on hydrogen fuel-assisted combustion as described in any one of claims 1 to 6, characterized in that, The method includes: Receive the engine's torque demand signal and calculate the initial hydrogen supply parameters based on the torque demand signal; The combustion state of the engine combustion chamber is monitored in real time, and a three-variable online optimization model of the in-cylinder combustion quasi-dimensional dynamics model is established based on the combustion state. The three variables of the three-variable online optimization model include hydrogen injection parameters, ignition timing and EGR rate. The predictive control algorithm based on the three-variable online optimization model solves for the optimal control parameters. A composite control strategy is used to dynamically optimize and adjust the supply parameters and / or ignition parameters. The algorithm also integrates local vehicle signals with predictive data from the cloud-based digital twin model to predict future engine load changes and control the hydrogen supply.

8. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the near-zero carbon emission control method for internal combustion engines based on hydrogen fuel-assisted combustion as described in claim 7.

9. A vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the near-zero carbon emission control method for internal combustion engines based on hydrogen fuel-assisted combustion as described in claim 7.

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