Alcohol hydrogen power fuel dynamic proportioning control method

Abstract

The application provides an alcohol hydrogen power fuel dynamic proportioning control method, which comprises the following steps: step one, collecting engine load data and cooling liquid temperature data output by an engine ECU through an OBD interface in real time; step two, dynamically generating a hydrogen demand amount instruction and a methanol direct injection amount instruction according to the engine load data and the cooling liquid temperature data by a main control single-chip microcomputer; step three, adjusting the electric heating power of an online hydrogen production device and the methanol supply flow according to the hydrogen demand amount instruction; step four, synchronously adjusting the throttle opening degree and the injection pulse width and injection time of an injection nozzle according to the methanol direct injection amount instruction; and step five, performing closed-loop correction on the air-fuel ratio of actual mixed gas through a long and short term fuel correction module, so as to realize the collaborative optimization of combustion efficiency and emission under all working conditions. The application can solve the problem that an alcohol hydrogen power system cannot accurately and dynamically proportion hydrogen and methanol fuel according to the real-time working condition of an engine to realize collaborative control.

Description

Technical Field

[0001] This application relates to the field of new energy power technology, and more specifically to a method for dynamic ratio control of alcohol-hydrogen power fuel. Background Technology

[0002] With the profound transformation of the global energy structure and the continuous advancement of carbon neutrality goals, the development of efficient and clean new energy power systems has become a research focus in the fields of internal combustion engines and industrial power. In applications such as transportation, construction machinery, and distributed power generation, traditional diesel or gasoline power systems, while technologically mature, face increasingly stringent emission restrictions and the pressure of fossil fuel depletion. Hydrogen energy, as a highly promising clean energy source, has the advantages of single combustion products and high calorific value; however, its large-scale adoption still faces significant challenges due to factors such as poor safety of high-pressure hydrogen storage, high transportation costs, and inadequate infrastructure. Methanol, as a liquid fuel operating at normal temperature and pressure, possesses characteristics such as high energy density, convenient storage and transportation, and wide availability. Converting methanol into hydrogen through online hydrogen production technology and supplying it to engines or fuel cells to form an alcohol-hydrogen power system has become an important technological path to achieve low-carbon operation.

[0003] However, existing methanol-hydrogen power systems generally employ fixed-ratio mixing or simple open-loop control strategies. Their control systems can only adjust the methanol cracking hydrogen production and direct injection rates based on preset empirical parameters, lacking deep perception and dynamic response capabilities regarding the engine's real-time operating status. Traditional control equipment typically relies on single sensor signals (such as throttle opening or engine speed) for coarse adjustments, failing to comprehensively analyze multi-dimensional operating parameters such as engine load, coolant temperature, and air-fuel ratio. This results in the hydrogen generation rate and methanol injection rate not accurately matching the engine's actual power demands during different operating phases, including cold starts, idling, medium load, and high load. Especially under varying operating conditions, the lack of a closed-loop feedback mechanism prevents real-time correction based on the exhaust air-fuel ratio from the oxygen sensor, easily leading to problems such as delayed or excessive hydrogen supply, incomplete methanol combustion, decreased combustion efficiency, power output fluctuations, increased emissions of unburned materials like formaldehyde, and even the risk of knocking or stalling. Furthermore, existing systems generally employ a constant power mode for heating control of online hydrogen production reactors, failing to dynamically adjust the heating power based on temperature feedback. This results in long cold start preheating times, low thermal efficiency, and performance degradation due to catalyst aging or thermal stress accumulation after prolonged operation. Therefore, developing an intelligent control method that integrates multi-source real-time data, achieves on-demand synergistic blending of hydrogen and methanol fuel based on dynamic MAP mapping, and continuously optimizes the combustion process through closed-loop correction has become a key technological bottleneck that urgently needs to be overcome to improve the energy efficiency, stability, and environmental performance of methanol-hydrogen power systems. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for dynamic ratio control of alcohol-hydrogen fuel, thereby overcoming the shortcomings of the prior art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for dynamic ratio control of alcohol-hydrogen fuel includes the following steps: Step 1: Collect engine load data and coolant temperature data output by the engine ECU in real time through the OBD interface. The engine load data includes at least one of the following: throttle opening, intake pressure, and crankshaft speed. Step 2: The main control microcontroller dynamically generates hydrogen demand command and methanol direct injection command based on the engine load data and coolant temperature data and the preset multi-dimensional operating condition mapping (MAP). The hydrogen demand command is used to characterize the volumetric flow rate of hydrogen required by the online hydrogen production equipment, and the methanol direct injection command is used to characterize the mass flow rate of liquid methanol directly injected into the cylinder through the throttle valve and fuel injector. Step 3: According to the hydrogen demand command, adjust the electric heating power and methanol supply flow of the online hydrogen production equipment to control its catalytic cracking reaction rate, realize the rapid generation of hydrogen on demand, and start hydrogen production when the temperature of the hydrogen production reactor reaches above 200°C during the cold start stage. Step 4: Based on the methanol direct injection quantity command, synchronously adjust the throttle opening and the injection pulse width and injection timing of the fuel injector so that the directly injected liquid methanol and the online generated hydrogen form a combustible mixture in the intake manifold or cylinder. Step 5: Using the long-term and short-term fuel correction module, based on the exhaust air-fuel ratio signal fed back by the oxygen sensor, the actual air-fuel ratio of the mixture is corrected in a closed loop, and the correction coefficient is fed back to the main control microcontroller to dynamically update the hydrogen demand command and the methanol direct injection command, so as to achieve coordinated optimization of combustion efficiency and emissions under all operating conditions.

[0006] Furthermore, the engine load data includes a combination of throttle opening, intake pressure, and crankshaft speed. The main control microcontroller generates a comprehensive load index based on a weighted fusion algorithm of the three data points. This includes normalizing the throttle opening, intake pressure, and crankshaft speed separately, and then performing a linear weighted summation according to preset weight coefficients to obtain the comprehensive load index. The weight coefficients are determined by fitting real vehicle operating condition data and are used to match a more refined fuel ratio range in the multi-dimensional operating condition MAP map.

[0007] Furthermore, the normalization process includes: mapping the throttle opening to the 0~1 range, normalizing the intake pressure according to the ratio of absolute pressure to standard atmospheric pressure, and normalizing the crankshaft speed as a percentage of the rated speed, with preset weighting coefficients of 0.4, 0.3 and 0.3 respectively.

[0008] Furthermore, the coolant temperature data is used to distinguish between the cold start stage and the normal operation stage. The cold start stage is defined as the coolant temperature being below 10°C. The method also includes: during the cold start stage, the main control microcontroller starts the ultrasonic atomizer to pre-atomize the liquid methanol, and at the same time increases the electric heating power to more than 80% of the rated power until the temperature of the catalytic cracking reactor reaches 200°C.

[0009] Furthermore, the ultrasonic atomizer operates at a frequency of 1.7MHz to 2.4MHz, has an output power of 15W to 40W, and produces a median droplet size of less than 30μm.

[0010] Furthermore, the catalytic cracking reactor of the online hydrogen production equipment is filled with a nickel-based or palladium-based catalyst, and its electric heating element is a PTC ceramic heating element with a heating power adjustment range of 50W~500W and a response time of less than 200ms. The electric heating power adjustment includes: based on a PID control algorithm, using the temperature of the catalytic cracking reactor as the feedback variable and the hydrogen demand command as the feedforward input, dynamically adjusting the duty cycle of the pulse width modulation signal.

[0011] Furthermore, the parameters of the PID control algorithm are: proportional gain Kp = 0.8, integral time Ti = 5s, derivative time Td = 1s, and sampling period is 100ms.

[0012] Furthermore, the generation of hydrogen demand commands and methanol direct injection commands is based on a preset four-condition MAP diagram. The four conditions include: cold start condition (water temperature <10℃), idling condition (speed <800rpm, load <10%), medium load condition (speed 1500~3000rpm, load 20%~60%), and high load condition (speed >3000rpm, load >70%). The proportion of hydrogen in the total fuel energy under each condition is set to 35%~50%, 25%~40%, 10%~20%, and 0%~5%, respectively. The MAP diagram adopts a three-dimensional lookup table structure. The input variables are coolant temperature, load comprehensive index, and crankshaft speed, and the output variable is the ratio of hydrogen volume flow rate to methanol mass flow rate.

[0013] Furthermore, in the three-dimensional lookup table structure, the coolant temperature range is divided into 5 segments: <-20℃, -20℃~10℃, 10℃~40℃, 40℃~60℃, and >60℃; the load comprehensive index is divided into 7 segments; the crankshaft speed is divided into 10 segments; and each lookup table unit stores the hydrogen to methanol mass flow rate ratio under the corresponding operating condition.

[0014] Furthermore, the short-term and long-term fuel correction modules acquire the voltage signal of the oxygen sensor through the OBD interface and calculate the short-term fuel correction coefficient and the long-term fuel correction coefficient based on the target air-fuel ratio of λ=1.0. When the absolute value of STFT exceeds ±15% for more than 3 seconds, the MAP self-learning mechanism is triggered, including: using the coolant temperature, load index and crankshaft speed under the current operating conditions as key values, superimposing the STFT correction amount onto the hydrogen and methanol ratio reference value of the corresponding unit of the original MAP, forming a new reference value and writing it into the non-volatile Flash memory.

[0015] Furthermore, the MAP self-learning mechanism allows only one update per ignition cycle, and the deviation between the updated new reference value and the original value must not exceed ±25%. If this is exceeded, the self-learning function is automatically frozen and a fault code is recorded.

[0016] Furthermore, when the main control microcontroller receives a sudden load drop signal from the engine, it automatically triggers the hydrogen generation suppression logic. The sudden load drop signal is defined as the throttle closing rate being greater than 30% / s. The suppression logic includes: immediately closing the methanol supply valve, linearly reducing the electric heating power from the current value to 10% of the maintenance temperature threshold, maintaining it for no less than 5 seconds, and maintaining OBD communication and coolant temperature monitoring during this period.

[0017] Furthermore, the synchronous adjustment of throttle opening and fuel injector pulse width adopts a collaborative control algorithm, including: the throttle opening is adjusted in steps in units of 0.5ms, and the fuel injector pulse width is fine-tuned with a resolution of 0.1ms. The change in throttle opening Δθ and the change in injection pulse width Δτ satisfy a linear relationship: Δτ = k·Δθ, where k is the coupling coefficient dependent on the operating condition, which is obtained by looking up the table of the current operating condition in the MAP diagram.

[0018] Furthermore, the coupling coefficient k is 0.8 under cold start condition, 1.2 under idle condition, 1.0 under medium load condition, and 0.6 under high load condition.

[0019] Furthermore, if the online hydrogen production equipment fails to reach the minimum activation temperature of 200°C for the catalyst within 15 seconds of startup, the main control microcontroller will prevent the methanol supply valve from opening and will continuously output a fault alarm signal to the vehicle's instrument panel. The fault alarm signal is a CAN bus ID 0x1F2 message containing error code 0x05, with the content "Hydrogen generator has not reached activation temperature".

[0020] Furthermore, the data transmission interface is a CAN FD or UART interface, used to upload operating parameter logs to external diagnostic equipment or cloud platforms. The operating parameter logs include: hydrogen generation rate, electric heating energy consumption, methanol consumption, air-fuel ratio correction history, and fault codes. The logs are stored in segments according to timestamps, and each record includes: timestamp, coolant temperature, comprehensive load index, crankshaft speed, hydrogen flow rate, methanol flow rate, STFT value, LTFT value, electric heating power, and fault code.

[0021] Furthermore, the CAN FD interface adopts a 500kbps baud rate, a data frame format of ISO 11898-2, a timestamp accuracy of 1ms, a log upload cycle of 10 seconds, and supports remote OTA updates of MAP parameters. The update process uses a differential check and signature verification mechanism.

[0022] Furthermore, the main control microcontroller has a built-in temperature self-calibration module, which uses an independent temperature sensor to perform dual redundant monitoring of the catalytic cracking reactor wall temperature. When the error of the main temperature acquisition module exceeds ±5℃, it automatically switches to the backup temperature channel and triggers the degradation operation mode. The degradation operation mode includes: stopping the dynamic updating of the MAP chart, fixing the hydrogen ratio to the medium load operating condition benchmark value, locking the electric heating power to 200W, and maintaining the minimum hydrogen production requirement.

[0023] Furthermore, the main temperature acquisition module is a K-type thermocouple, and the backup temperature channel is a DS18B20 digital temperature sensor. Both have a sampling frequency of 10Hz. The temperature difference is calculated using a moving average filter with a window length of 5 sampling points.

[0024] Furthermore, the power interface is a vehicle-grade 12V / 24V wide voltage input interface with built-in overvoltage, undervoltage, and reverse connection protection circuits. The main control microcontroller automatically enters a low-power standby mode when the power supply voltage is below 9V. The low-power standby mode includes: shutting down all drive outputs and maintaining power supply only to the OBD communication module and Flash memory. The system wake-up threshold is when the voltage rises back to 10.5V and lasts for 500ms.

[0025] Furthermore, the outer shell adopts an aluminum alloy die-cast structure and incorporates a built-in thermally conductive silicone layer to conduct waste heat from the online hydrogen production equipment to the outer wall of the shell, achieving passive heat dissipation. The thermal conductivity of the silicone layer is not less than 3.5 W / (m·K), and its thickness is 0.5 mm to 1.2 mm. Heat dissipation fins are installed on the outer wall of the shell, with a fin spacing of 2 mm to 5 mm, and the surface area to equipment volume ratio is not less than 150 cm². 2 / L.

[0026] Furthermore, the fuel injector is a high-pressure pulse injection type with an injection pressure ≥3.5MPa and a nozzle diameter ≤0.12mm. It is used to achieve fine atomization of methanol droplets with a particle size ≤20μm under low load conditions. The fuel injector adopts a dual electromagnetic coil drive structure with a pulse width modulation frequency of 200Hz~400Hz and a single injection duration of 0.5ms~5ms.

[0027] Furthermore, the injection pulse width and injection timing are dynamically adjusted by the main control microcontroller based on the hydrogen concentration distribution model in the intake manifold. The model calculates the optimal methanol injection window based on the intake airflow rate, temperature, and hydrogen diffusion coefficient, and the injection timing is set within 5°~15°CA after the intake valve opens.

[0028] Furthermore, the multi-dimensional operating condition MAP is generated by training real vehicle road test data. The test conditions cover ambient temperature from -20℃ to 50℃, engine load from 0 to 100%, and engine speed from 0 to 7000 rpm. The data sample is no less than 100,000 sets. The MAP is stored in non-volatile Flash memory in the form of a three-dimensional lookup table. The training process adopts the random forest regression algorithm. The input features include: coolant temperature, throttle opening, intake pressure, crankshaft speed, ambient temperature, and oxygen sensor voltage. The output target is the optimal hydrogen / methanol mass ratio.

[0029] Furthermore, the random forest regression algorithm contains 500 decision trees, with a feature subset sampling ratio of 0.6 for each tree, a maximum depth of 10, and a minimum number of sample splits of 10. After training, the model is evaluated through cross-validation, and the mean squared error is less than 0.08.

[0030] Furthermore, after the engine is shut down, the system automatically executes a hydrogen purging process: closing the methanol supply valve, maintaining the electric heating power for 10 seconds, driving the complete combustion of residual hydrogen in the intake manifold, and recording the energy efficiency score of this operating cycle to the local memory. The energy efficiency score is calculated as follows: η = (hydrogen energy output + methanol energy output) / (electric heating energy consumption + methanol pump power consumption), where hydrogen energy output is calculated based on hydrogen flow rate and lower heating value of 33.3 kWh / kg, and methanol energy output is calculated based on methanol flow rate and lower heating value of 19.7 kWh / kg.

[0031] Furthermore, the energy efficiency rating record includes: start-up time, shutdown time, total hydrogen consumption, total methanol consumption, total electric heating energy consumption, total pump power consumption, and η value. One record is recorded per cycle, and the local storage capacity is no less than 1,000 records, supporting backtracking and recall by timestamp.

[0032] Beneficial effects: Compared with existing technologies, this invention acquires engine load data and coolant temperature data output by the engine ECU in real time through the OBD interface. Based on a preset multi-dimensional operating condition mapping (MAP), the main control microcontroller dynamically generates precise hydrogen demand commands and methanol direct injection commands, thereby controlling the electric heating power and methanol supply flow of the online hydrogen production equipment to achieve on-demand rapid hydrogen production. During the cold start phase, hydrogen production is only started when the temperature of the hydrogen production reactor rises above 200°C, avoiding ineffective energy consumption and incomplete reaction. Simultaneously, the throttle opening, fuel injector pulse width, and injection timing are adjusted synchronously according to the methanol direct injection command, so that liquid methanol and online generated hydrogen form a uniform combustible mixture in the intake manifold or cylinder. Furthermore, the long- and short-term fuel correction module collects the exhaust air-fuel ratio signal fed back by the oxygen sensor in real time, performs closed-loop dynamic correction of the actual air-fuel ratio of the mixture, and feeds back the correction coefficient to the main control microcontroller to achieve adaptive iterative optimization of the hydrogen-methanol ratio. This combination of features effectively overcomes the problems of fuel ratio lag, air-fuel ratio imbalance, low combustion efficiency and excessive emissions caused by the lack of a real-time operating condition response mechanism in traditional methanol-hydrogen power systems. Ultimately, it achieves precise coordinated control of hydrogen and methanol fuel under all operating conditions, significantly improves combustion efficiency, reduces harmful emissions, and enhances the stability and adaptability of the system in complex scenarios such as cold start, high load, and variable operating conditions. Detailed Implementation

[0033] To further illustrate the technical means and effects of the present invention in achieving the intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with preferred embodiments, is provided below.

[0034] Existing methanol-hydrogen power systems generally adopt fixed-ratio mixing or simple open-loop control strategies, adjusting the amount of hydrogen produced by methanol cracking and the amount of direct injection based solely on preset empirical parameters. This lacks a deep understanding and dynamic response capability of the engine's real-time operating status, resulting in the hydrogen generation rate and methanol injection quantity failing to accurately match the engine's actual power demand under different operating conditions such as cold start, idling, medium load, and high load. This leads to decreased combustion efficiency, power fluctuations, increased emissions, and even the risk of engine stall.

[0035] After extensive research, the inventors discovered that the root cause of the aforementioned predicament lies in the fact that existing control systems treat "fuel supply" and "engine demand" as two independent, static processes, failing to establish a closed-loop feedback mechanism driven by real-time operating condition data output by the engine ECU. Traditional systems rely on manually calibrated fixed MAPs or single sensors (such as throttle opening) for coarse regulation, ignoring the engine ECU's comprehensive perception and dynamic calculation capabilities for multi-dimensional operating conditions such as load, temperature, and air-fuel ratio. Furthermore, they fail to utilize the standardized data channel of the OBD interface to directly read and coordinately respond to the engine's true operating intentions. Simultaneously, the system lacks a closed-loop correction path based on exhaust oxygen sensor feedback, preventing the fuel ratio from dynamically correcting according to actual combustion effects. This results in hydrogen production and fuel injection consistently lagging behind combustion demand, creating a fundamental contradiction of "supplying what is needed and demanding what is supplied."

[0036] Based on the above findings, the inventors proposed a dynamic blending control concept for all operating conditions, using real-time data from the engine ECU as the sole input source and closed-loop air-fuel ratio feedback as the core correction mechanism. Instead of relying on preset empirical models or independent sensor data, the system directly reads the load and temperature data output by the engine ECU via the OBD interface, using this as the sole reference for the main control microcontroller to generate instructions on hydrogen demand and methanol injection quantity. Based on this, hydrogen is rapidly generated on demand through synchronous adjustment of the electric heating power of the online hydrogen production equipment and the methanol supply flow rate; and the throttle and injection valves are simultaneously controlled. The injector's action allows directly injected methanol to form a combustible mixture with online-generated hydrogen at the intake end. Most importantly, a long- and short-term fuel correction module is introduced. Based on the exhaust air-fuel ratio signal fed back from the oxygen sensor, it performs closed-loop correction of the fuel ratio and feeds this correction coefficient back to the main control microcontroller to dynamically update the hydrogen demand and methanol injection commands. This forms a complete closed-loop control loop of "data acquisition - command generation - execution response - effect feedback - command iteration," ensuring that the fuel ratio always dynamically evolves according to the engine's actual combustion needs, completely breaking the limitations of traditional open-loop, static, and single-point control.

[0037] Guided by this creative concept, the inventors proposed a method for dynamic ratio control of alcohol-hydrogen fuel.

[0038] The dynamic blending control method for alcohol-hydrogen fuel according to this embodiment includes: Step 1: Collect engine load data and coolant temperature data output by the engine ECU in real time through the OBD interface. The engine load data includes at least one of the following: throttle opening, intake pressure, and crankshaft speed. In this embodiment, the engine load data and coolant temperature data output by the engine ECU are acquired in real time through the OBD interface. This means that the system directly obtains the raw parameter information reflecting the engine operating status provided by the engine control unit from the vehicle's standard diagnostic interface. The engine load data includes at least one of throttle opening, intake pressure, and crankshaft speed, which characterizes the engine's current power demand and workload. The coolant temperature data directly reflects the engine's thermal state. Both are real-time signals continuously generated by the ECU during operation and transmitted via the CAN bus. The acquisition process does not involve any external sensors or independent measuring devices and relies entirely on the OBD interface to directly read the data inside the ECU, thereby ensuring that the obtained data is completely consistent with the engine's actual control logic.

[0039] Step 2: The main control microcontroller dynamically generates hydrogen demand command and methanol direct injection command based on the engine load data and coolant temperature data and the preset multi-dimensional operating condition mapping MAP. The hydrogen demand command is used to characterize the volume flow rate of hydrogen required by the online hydrogen production equipment, and the methanol direct injection command is used to characterize the mass flow rate of liquid methanol directly injected into the cylinder through the throttle valve and the fuel injector. In this embodiment, the main control microcontroller, based on engine load and coolant temperature data obtained from the OBD interface, calls a preset multi-dimensional operating condition mapping (MAP) chart to generate two types of instructions: one is a hydrogen demand instruction, used to characterize the volumetric flow rate of hydrogen required by the online hydrogen production equipment; the other is a methanol direct injection instruction, used to characterize the mass flow rate of liquid methanol directly injected into the cylinder through the throttle and fuel injectors. By reading the engine load and coolant temperature values ​​in real time and combining them with the pre-stored operating condition correspondence in the MAP chart, the main control microcontroller directly calculates the hydrogen production and methanol direct injection quantities required to meet the current operating conditions, ensuring that the allocation of these two quantities is autonomously determined by the system according to a predetermined mapping logic, without relying on external hydrogen storage or manual intervention.

[0040] Step 3: According to the hydrogen demand command, adjust the electric heating power and methanol supply flow of the online hydrogen production equipment to control its catalytic cracking reaction rate, realize the rapid generation of hydrogen on demand, and start hydrogen production when the temperature of the hydrogen production reactor reaches above 200°C during the cold start stage. In this embodiment, the electric heating power and methanol supply flow rate of the online hydrogen production equipment are adjusted according to the hydrogen demand command to control the catalytic cracking reaction rate, enabling rapid on-demand hydrogen production. During the cold start phase, hydrogen production is initiated when the hydrogen production reactor temperature reaches above 200°C. Adjusting the electric heating power directly changes the heat input level of the catalytic cracking reactor, thus affecting the reaction temperature. Simultaneously, adjusting the methanol supply flow rate directly controls the amount of feedstock involved in the cracking reaction. These two factors work synergistically to precisely regulate the rate of catalytic cracking, ensuring that the hydrogen production rate matches the real-time demand. During the cold start phase, hydrogen production is only allowed to start when the hydrogen production reactor temperature is raised above 200°C, ensuring that the catalytic reaction begins below the effective temperature threshold and avoiding low reaction efficiency or byproduct accumulation at low temperatures.

[0041] Step 4: According to the direct methanol injection command, synchronously adjust the throttle opening and the injection pulse width and injection timing of the fuel injector so that the directly injected liquid methanol and the online generated hydrogen form a combustible mixture in the intake manifold or cylinder. In this embodiment, based on the methanol direct injection quantity command, the throttle opening, the injection pulse width of the fuel injector, and the injection timing are synchronously adjusted to form a combustible mixture of directly injected liquid methanol and online-generated hydrogen in the intake manifold or cylinder. This process drives real-time changes in the throttle opening via commands to match the intake flow rate required for liquid methanol injection; simultaneously, the injection pulse width of the fuel injector is precisely adjusted to control the total injection volume of liquid methanol, and the injection timing is synchronously set to ensure that methanol is injected at the appropriate intake stage in the presence of hydrogen. This achieves physical mixing of liquid methanol and hydrogen generated by the online hydrogen production equipment in the intake manifold or cylinder, forming a combustible mixture.

[0042] Step 5: Through the long-term and short-term fuel correction module, based on the exhaust air-fuel ratio signal fed back by the oxygen sensor, the air-fuel ratio of the actual mixture is corrected in a closed loop, and the correction coefficient is fed back to the main control microcontroller to dynamically update the hydrogen demand command and methanol direct injection command, so as to achieve coordinated optimization of combustion efficiency and emissions under all operating conditions. In this embodiment, the long-term and short-term fuel correction module receives the exhaust air-fuel ratio signal from the oxygen sensor, performs real-time closed-loop correction on the actual air-fuel ratio of the mixture, and feeds back the corrected coefficient to the main control microcontroller, thereby dynamically adjusting the hydrogen demand command and the methanol direct injection command, so that the fuel supply responds to the actual change in the oxygen content in the exhaust, ensuring that the mixture concentration always approaches the target value, and achieving synergistic optimization of combustion efficiency and emissions.

[0043] The technical solution of this embodiment acquires engine load data and coolant temperature data output by the engine ECU in real time through the OBD interface. The main control microcontroller dynamically generates hydrogen demand commands and methanol direct injection commands based on a preset multi-dimensional operating condition mapping (MAP). This accurately represents the volumetric flow rate of hydrogen required by the online hydrogen production equipment and the mass flow rate of liquid methanol directly injected into the cylinder through the throttle and fuel injectors. Furthermore, based on the hydrogen demand command, the electric heating power and methanol supply flow rate of the online hydrogen production equipment are synchronously adjusted to achieve rapid on-demand hydrogen generation when the temperature reaches above 200°C during cold starts. Simultaneously, based on the methanol direct injection command, the throttle opening, fuel injector pulse width, and injection timing are optimized to form an ideal combustible mixture of hydrogen and liquid methanol in the intake manifold or cylinder. At the same time, a long-short fuel correction module performs closed-loop correction based on the exhaust air-fuel ratio signal fed back from the oxygen sensor and feeds the correction coefficient back to the main control microcontroller to dynamically update the fuel ratio command, thereby achieving precise dynamic synergistic blending of hydrogen and methanol under all operating conditions. This technology combination effectively overcomes the problems of fuel ratio lag, incomplete combustion, and runaway emissions caused by the lack of a real-time operating condition response mechanism in traditional alcohol-hydrogen power systems, achieving a comprehensive technical effect of improving combustion efficiency, reducing harmful emissions, and enhancing system response accuracy and operational stability.

[0044] Furthermore, in this embodiment, the engine load data includes a combination of throttle opening, intake pressure, and crankshaft speed. The main control microcontroller generates a comprehensive load index based on a weighted fusion algorithm of the three, including: normalizing the throttle opening, intake pressure, and crankshaft speed respectively, and performing linear weighted summation according to preset weight coefficients to obtain the comprehensive load index. The weight coefficients are determined by fitting real vehicle operating condition data and are used to match a more refined fuel ratio range in the multi-dimensional operating condition MAP map.

[0045] In this embodiment, engine load data is expanded to a combination of throttle opening, intake pressure, and crankshaft speed. After normalizing these three data points, the main control microcontroller performs a linear weighted summation based on preset weighting coefficients determined by fitting real vehicle operating data. This generates a comprehensive load index that reflects the engine's actual operating state. This index can more comprehensively and accurately characterize complex and variable operating conditions, thus serving as a key input parameter to match a more refined fuel ratio range in the multi-dimensional operating condition MAP. This allows the generation of hydrogen demand commands and methanol direct injection commands to no longer depend on a single or local load signal, but rather on multi-dimensional collaborative evaluation results. This significantly improves the dynamic response accuracy and adaptability of fuel ratio, thereby optimizing the mixing uniformity and combustion efficiency of hydrogen and methanol in the intake manifold or cylinder, reducing unburned hydrocarbon and nitrogen oxide emissions, and ultimately achieving efficient collaborative control and stable emission performance of the methanol-hydrogen power system across the entire operating condition range.

[0046] Furthermore, in this embodiment, the normalization process includes: mapping the throttle opening to the 0~1 range, normalizing the intake pressure according to the ratio of absolute pressure to standard atmospheric pressure, and normalizing the crankshaft speed according to the percentage of rated speed, with preset weighting coefficients of 0.4, 0.3 and 0.3 respectively.

[0047] In this embodiment, the throttle opening, intake pressure, and crankshaft speed are normalized: the throttle opening is mapped to the 0-1 range, the intake pressure is normalized according to the ratio of absolute pressure to standard atmospheric pressure, and the crankshaft speed is normalized as a percentage of the rated speed. Then, a linear weighted sum is performed based on preset weighting coefficients of 0.4, 0.3, and 0.3 to form a comprehensive load index reflecting the engine's overall load status. This transforms the originally discrete, multi-dimensional raw sensor data into a single input index with physical meaning and quantitative accuracy, significantly improving the ability of the multi-dimensional operating condition MAP to distinguish subtle load changes. This allows the generation of hydrogen demand commands and methanol direct injection commands to accurately match the engine's actual needs under complex operating conditions such as cold start, low to medium load, and high transient conditions. This effectively overcomes the problems of lag and deviation caused by single or coarsely quantified load parameters, ultimately achieving high-resolution coordinated dynamic control of hydrogen and methanol across the entire operating range, improving combustion efficiency, reducing emissions, and enhancing system response stability.

[0048] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic ratio control of alcohol-hydrogen fuel, characterized in that, Includes the following steps: Step 1: Collect engine load data and coolant temperature data output by the engine ECU in real time through the OBD interface. The engine load data includes at least one of throttle opening, intake pressure and crankshaft speed. Step 2: The main control microcontroller dynamically generates hydrogen demand command and methanol direct injection command based on the engine load data and coolant temperature data and a preset multi-dimensional operating condition mapping (MAP). The hydrogen demand command is used to characterize the volumetric flow rate of hydrogen required by the online hydrogen production equipment, and the methanol direct injection command is used to characterize the mass flow rate of liquid methanol directly injected into the cylinder through the throttle valve and fuel injector. Step 3: According to the hydrogen demand command, adjust the electric heating power and methanol supply flow rate of the online hydrogen production equipment to control its catalytic cracking reaction rate, realize the rapid on-demand generation of hydrogen, and start hydrogen production when the temperature of the hydrogen production reactor reaches above 200°C during the cold start stage. Step 4: According to the methanol direct injection quantity command, synchronously adjust the throttle opening and the injection pulse width and injection timing of the fuel injector so that the directly injected liquid methanol and the online generated hydrogen form a combustible mixture in the intake manifold or cylinder. Step 5: Using the long-term and short-term fuel correction module, based on the exhaust air-fuel ratio signal fed back by the oxygen sensor, the actual air-fuel ratio of the mixture is corrected in a closed loop, and the correction coefficient is fed back to the main control microcontroller to dynamically update the hydrogen demand command and the methanol direct injection command, so as to achieve coordinated optimization of combustion efficiency and emissions under all operating conditions.

2. The method for dynamic proportioning control of alcohol-hydrogen fuel according to claim 1, characterized in that, The engine load data includes a combination of throttle opening, intake pressure, and crankshaft speed. The main control microcontroller generates a comprehensive load index based on a weighted fusion algorithm of the three data points. This includes normalizing the throttle opening, intake pressure, and crankshaft speed, and then performing a linear weighted summation according to preset weight coefficients to obtain the comprehensive load index. The weight coefficients are determined by fitting real vehicle operating condition data and are used to match a more refined fuel ratio range in the multi-dimensional operating condition MAP map.

3. The method for dynamic proportioning control of alcohol-hydrogen fuel according to claim 1, characterized in that, The coolant temperature data is used to divide the cold start stage and the normal operation stage. The cold start stage is defined as the coolant temperature being below 10°C. During the cold start stage, the main control microcontroller starts the ultrasonic atomizer to pre-atomize the liquid methanol, and at the same time increases the electric heating power to more than 80% of the rated power until the temperature of the catalytic cracking reactor reaches 200°C.

4. The method for dynamic proportioning control of alcohol-hydrogen fuel according to claim 1, characterized in that, The generation of the hydrogen demand command and the methanol direct injection command is based on a preset four-condition MAP diagram. The four conditions include: cold start condition with water temperature <10℃, idle condition with speed <800rpm and load <10%, medium load condition with speed 1500~3000rpm and load 20%~60%, and load condition with speed >3000rpm and load >70%. The proportion of hydrogen in the total fuel energy under each condition is set to 35%~50%, 25%~40%, 10%~20%, and 0%~5%, respectively. The MAP diagram adopts a three-dimensional lookup table structure. The input variables are coolant temperature, load comprehensive index, and crankshaft speed. The output variable is the ratio of hydrogen volume flow rate to methanol mass flow rate.

5. The method for dynamic proportioning control of alcohol-hydrogen fuel according to claim 1, characterized in that, The short-term and long-term fuel correction modules acquire the voltage signal of the oxygen sensor through the OBD interface and calculate the short-term fuel correction coefficient STFT and the long-term fuel correction coefficient LTFT based on the target air-fuel ratio of λ=1.

0. When the absolute value of STFT exceeds ±15% for more than 3 seconds, the MAP self-learning mechanism is triggered, including: using the coolant temperature, load index and crankshaft speed under the current operating conditions as key values, superimposing the STFT correction amount onto the hydrogen and methanol ratio reference value of the corresponding unit of the original MAP, forming a new reference value and writing it into the non-volatile Flash memory.

6. The method for dynamic proportioning control of alcohol-hydrogen fuel according to claim 1, characterized in that, When the main control microcontroller receives a sudden load drop signal from the engine, it automatically triggers hydrogen generation suppression logic. The sudden load drop signal is defined as a throttle closing rate greater than 30% / s. The suppression logic includes: immediately closing the methanol supply valve, linearly reducing the electric heating power from the current value to 10% of the maintenance temperature threshold, maintaining it for no less than 5 seconds, and maintaining OBD communication and coolant temperature monitoring during this period.

7. The method for dynamic proportioning control of alcohol-hydrogen fuel according to claim 1, characterized in that, The synchronous adjustment of the throttle opening and the fuel injector pulse width adopts a collaborative control algorithm, including: the throttle opening is adjusted in steps in units of 0.5ms, and the fuel injector pulse width is fine-tuned with a resolution of 0.1ms. The change in throttle opening Δθ and the change in injection pulse width Δτ satisfy a linear relationship: Δτ = k·Δθ, where k is the coupling coefficient dependent on the operating condition, which is obtained by looking up the current operating condition in the MAP diagram.

8. The method for dynamic proportioning control of alcohol-hydrogen fuel according to claim 1, characterized in that, The main control microcontroller has a built-in temperature self-calibration module. It uses an independent temperature sensor to perform dual redundant monitoring of the wall temperature of the catalytic cracking reactor. When the error of the main temperature acquisition module exceeds ±5℃, it automatically switches to the backup temperature channel and triggers the degradation operation mode. The degradation operation mode includes: stopping the dynamic updating of the MAP map, fixing the hydrogen ratio to the medium load operating condition benchmark value, locking the electric heating power to 200W, and maintaining the minimum hydrogen production requirement.

9. The method for dynamic proportioning control of alcohol-hydrogen fuel according to claim 1, characterized in that, The multi-dimensional operating condition MAP is generated by training real vehicle road test data. The test conditions cover ambient temperature from -20℃ to 50℃, engine load from 0 to 100%, and engine speed from 0 to 7000 rpm. The data sample is no less than 100,000 sets. The MAP is stored in non-volatile Flash memory in the form of a three-dimensional lookup table. The training process adopts the random forest regression algorithm. The input features include: coolant temperature, throttle opening, intake pressure, crankshaft speed, ambient temperature, and oxygen sensor voltage. The output target is the optimal hydrogen / methanol mass ratio.

10. The method for dynamic proportioning control of alcohol-hydrogen fuel according to claim 1, characterized in that, The system automatically performs a hydrogen purging process after the engine is shut down: the methanol supply valve is closed, the electric heating power is maintained for 10 seconds, the residual hydrogen in the intake manifold is completely combusted, and the energy efficiency score of this operating cycle is recorded to the local memory. The energy efficiency score is calculated using the following formula: (Hydrogen energy output + Methanol energy output) / (Electric heating energy consumption + Methanol pump power consumption), where hydrogen energy output is calculated based on hydrogen flow rate and lower heating value of 33.3 kWh / kg, and methanol energy output is calculated based on methanol flow rate and lower heating value of 19.7 kWh / kg.

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