Mobile methanol generator-battery hybrid power system and switching control method

Abstract

The invention discloses a mobile methanol generator-battery hybrid power system and a switching control method, and relates to the field of hybrid power. When the system is started, the energy management system controls the storage battery system to heat the methanol reforming hydrogen production subsystem; the energy management system controls the methanol solution and the hydrogen-rich reformed gas to be introduced into the methanol generator subsystem according to the optimal combustion strategy, combustion power generation is performed at the maximum combustion efficiency, and meanwhile fuel gas is generated; a methanol solution in the fuel gas and methanol reforming hydrogen production subsystem exchanges heat with an aqueous solution; the mode switching control subsystem counts time proportions of different working conditions and preliminarily matches an operation mode; and the mode switching control subsystem is combined with time proportions of different working conditions, and adopts a principal component analysis method and a fuzzy comprehensive evaluation method to screen out an optimal operation mode of the working conditions from the matched operation modes. According to the invention, multi-stage utilization of energy, fine management of energy in a full working condition range and continuous optimization of system efficiency can be realized.

Description

Technical Field

[0001] This application relates to the field of hybrid power, and in particular to a mobile methanol generator-battery hybrid power system and a switching control method. Background Technology

[0002] The large-scale use of fossil fuels for power generation has led to the production of substantial amounts of greenhouse gases, severely impacting the environment and threatening the sustainable development of human society. Currently, there is a strong push to replace traditional fossil fuels with clean and pollution-free energy sources. Methanol, as a major form of green and pollution-free energy, can generate electricity in internal combustion engines or, after reforming, in fuel cells while producing no toxic or harmful emissions. It represents a significant development trend now and in the future.

[0003] Methanol, as a green and low-carbon alternative fuel, can be mass-produced through biomass or carbon dioxide hydrogenation, hence its nickname "liquid sunshine." It is also easily stored as a liquid at room temperature. Furthermore, methanol is an excellent hydrogen storage material. It can be reformed using steam to produce hydrogen-rich reformed gas, which can then be purified before entering fuel cells to convert the chemical energy of hydrogen into electrical energy. Alternatively, the hydrogen-rich reformed gas can be fed into a methanol internal combustion engine, where it mixes and burns with methanol, further enhancing the combustion performance of methanol.

[0004] Taking automobiles as an example, many types of vehicles with different fuel modes have emerged, such as pure gasoline vehicles, pure electric vehicles, hybrid vehicles, and range-extended vehicles. Pure gasoline vehicles suffer from high pollution levels due to traditional fuels, while pure electric vehicles have short driving ranges. Current research and development centers are mostly focused on hybrid vehicles and range-extended vehicles, which extend driving range by using a combination of a gasoline engine / generator and a battery.

[0005] Hybrid vehicles, as an effective solution for reducing fuel consumption and emissions, have become an important direction for technological development in the automotive industry. Currently, hybrid technology exhibits a diversified development pattern, encompassing various technical routes such as hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and range-extended hybrid electric vehicles (REEVs). Among existing technologies, the main configurations of hybrid systems are series, parallel, and series-parallel hybrid.

[0006] The biggest challenge facing the methanol reforming-fuel cell power generation scheme in hybrid technology is the heat absorption problem of methanol reforming. It requires an additional power source to supplement the heat for normal operation, and purification is required to generate electricity. This leads to a further increase in the precision of the system layout, and the heat cannot be recovered. The methanol-hydrogen blending combustion scheme also has the problem of difficulty in the initial start-up stage.

[0007] Traditional hybrid systems suffer from power interruption or torque fluctuations during mode switching, which greatly affects driving stability. The energy management strategies of existing hybrid systems may also be unsuitable for complex and ever-changing real-world road conditions, making it difficult to maintain optimal energy efficiency in scenarios such as urban congestion and intercity highways.

[0008] Current research on hybrid power control strategies mainly focuses on theoretical aspects such as energy efficiency calculations and energy conservation and emission reduction. However, research on detailed classification of hybrid power system propulsion modes and energy management strategies remains insufficient. For mobile hybrid power systems, their economic efficiency and performance largely depend on the sophistication of the strategy. Current power switching methods are limited to mode management and control based on battery SOC and energy storage module power allocation, failing to consider the multi-condition challenges faced by mobile systems. Summary of the Invention

[0009] The purpose of this application is to provide a mobile methanol generator-battery hybrid power system and a switching control method, which can realize multi-level energy utilization, as well as refined energy management and continuous optimization of system efficiency across the entire operating range.

[0010] To achieve the above objectives, this application provides the following solution.

[0011] In a first aspect, this application provides a mobile methanol generator-battery hybrid power system, including: a methanol reforming hydrogen production subsystem, a methanol generator subsystem, a battery system, an energy management system, and a mode switching control subsystem; When the hybrid power system starts, the methanol reforming hydrogen production subsystem controls the battery system to preheat through the energy management system, and then mixes the vaporized methanol solution with the aqueous solution to produce hydrogen-rich reformed gas. The energy management system determines the optimal power output mode of the methanol generator subsystem based on the current environmental parameters. It controls the methanol solution and hydrogen-rich reformed gas to be introduced into the methanol generator subsystem according to the optimal combustion strategy matched with the optimal power output mode, so that the methanol solution and hydrogen-rich reformed gas can be burned to generate electricity with maximum combustion efficiency, and at the same time produce fuel gas. The fuel gas is introduced into the methanol reforming hydrogen production subsystem for heat exchange, vaporizing the methanol solution and aqueous solution. The mode switching control subsystem calculates the time percentage of different operating conditions of the equipment in the hybrid power system and matches the operating mode to the operating conditions where the time percentage is greater than the percentage threshold. The mode switching control subsystem also obtains historical operating parameters of the battery system and historical output power of the methanol generator subsystem from the energy management system. Taking the operating parameters of the battery system, the output power of the methanol generator subsystem, and the time proportion of different operating conditions as influencing factors, the principal component analysis method is used to determine the comprehensive weight of each influencing factor. Then, the fuzzy comprehensive evaluation method is used to select the optimal operating mode from the matched operating modes. According to the optimal operating mode, the methanol generator subsystem and / or battery system are controlled to provide power to the equipment.

[0012] Secondly, a switching control method for a mobile methanol generator-battery hybrid power system, the switching control method being applied to the aforementioned mobile methanol generator-battery hybrid power system, comprising: Obtain the historical operating status of the equipment containing the hybrid power system; Based on the historical operating status of the equipment in the hybrid power system, the time proportion of the equipment under different operating conditions is statistically analyzed, and an operating mode is matched for the operating conditions whose time proportion is greater than the proportion threshold. Obtain historical operating parameters of the battery system and historical output power of the methanol generator subsystem; The operating parameters of the battery system, the output power of the methanol generator subsystem, and the time proportion of different operating conditions were taken as influencing factors, and the comprehensive weight of each influencing factor was determined by principal component analysis. Based on the comprehensive weight of each influencing factor, the fuzzy comprehensive evaluation method is used to select the optimal operating mode from the matched operating modes. According to the optimal operating mode, control the methanol generator subsystem and / or battery system to provide power to the equipment; Extract the latest operating parameters of the battery system and the output power of the methanol generator subsystem, and combine them with the historical operating parameters of the battery system, the historical output power of the methanol generator subsystem, and the time proportion of different operating conditions. Use principal component analysis to redetermine the comprehensive weight of each influencing factor, or adjust the membership parameters in the fuzzy comprehensive evaluation method to reselect the optimal operating mode from the matched operating modes.

[0013] According to the specific embodiments provided in this application, this application has the following technical effects.

[0014] This application provides a mobile methanol generator-battery hybrid power system and a switching control method. It combines a battery system with a methanol reforming hydrogen production subsystem, ensuring the initial heat source for methanol reforming hydrogen production. Furthermore, it combines the methanol generator subsystem with the methanol reforming hydrogen production subsystem, ensuring the subsequent heat source for methanol reforming hydrogen production. The hydrogen-rich reformed gas produced by the methanol reforming hydrogen production subsystem is fed into the methanol generator subsystem for combustion, improving combustion efficiency. The high-temperature gas produced after combustion in the methanol generator subsystem utilizes waste heat, and the kinetic energy generated by combustion is converted into electrical energy, achieving multi-level energy utilization. The application incorporates the actual time proportion of different operating conditions into the core decision-making system, making it, along with battery operating parameters and output power, constitute a complete evaluation dimension. Therefore, it employs principal component analysis and fuzzy comprehensive evaluation methods to achieve accurate judgment and optimal selection of the efficiency of each operating mode, enabling refined energy management and continuous optimization of system efficiency across all operating conditions.

[0015] Furthermore, considering the overall technical solution of this application, the beneficial effects of this application are as follows: First, this application provides a micro methanol generator-battery hybrid power system and control scheme. Considering the insufficient heat absorption in traditional hydrogen production, which leads to low methanol-to-hydrogen conversion rate, the battery is combined with the methanol reforming chamber and vaporization chamber to ensure the initial heat source for methanol reforming to produce hydrogen. Furthermore, the methanol engine is combined with the methanol-to-hydrogen system to ensure the heat source for subsequent methanol-to-hydrogen production. At the same time, the hydrogen-rich reformed gas produced by the methanol-to-hydrogen system can be fed into the engine for combustion to improve combustion efficiency. The high-temperature gas produced after combustion is used for waste heat utilization, and the kinetic energy generated by combustion is converted into electrical energy by the generator, realizing multi-stage energy utilization. Secondly, this application proposes a power system mode switching control method based on multi-dimensional decision-making. This method innovatively incorporates the actual time proportion of different operating conditions into the core decision-making system, making it, along with parameters such as battery SOC, temperature, and output / input power, constitute a complete evaluation dimension. By assigning weights to each dimension to quantify their relative importance, and ultimately relying on fuzzy comprehensive evaluation, it achieves accurate judgment and optimal selection of the performance of each operating mode. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a mobile methanol generator-battery hybrid power system provided in an embodiment of this application.

[0018] Figure 2 A circuit diagram of a mobile methanol generator-battery hybrid power system provided in this application embodiment.

[0019] Figure 3 A schematic diagram of the control flow for a mode switching strategy based on fuzzy comprehensive evaluation method provided in an embodiment of this application.

[0020] Figure 4 This is a flowchart illustrating a switching control method for a mobile methanol generator-battery hybrid power system provided in an embodiment of this application.

[0021] Figure 5 This is a schematic diagram of the switching control system for a mobile methanol generator-battery hybrid power system provided in an embodiment of this application.

[0022] Figure reference numerals: Methanol reforming hydrogen production subsystem-1, Methanol generator subsystem-2, Energy management system-3, Methanol storage tank-4, Water storage tank-5, First regulating valve-6, Methanol reforming chamber-7, Vaporization chamber-8, Exhaust port-9, Battery-10, Drive motor-11, Internal circuit-12, Generator-13, Methanol engine-14, Third regulating valve-15, Second regulating valve-16, AC / DC converter-17, First DC / AC inverter-18, Second DC / AC inverter-19, DC / DC converter-20, Load-21. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] In one exemplary embodiment, such as Figure 1As shown, a mobile methanol generator-battery hybrid power system is provided, comprising: a methanol reforming hydrogen production subsystem 1, a methanol generator subsystem 2, a battery system, an energy management system 3, and a mode switching control subsystem. When the hybrid power system starts, the methanol reforming hydrogen production subsystem 1, after preheating the battery system under the control of the energy management system 3, mixes and vaporizes methanol solution and aqueous solution to generate hydrogen-rich reformed gas. The energy management system 3 determines the optimal power output mode of the methanol generator subsystem 2 based on current environmental parameters, and controls the methanol solution and hydrogen-rich reformed gas to be introduced into the methanol generator subsystem 2 according to the optimal combustion strategy matched to the optimal power output mode, so that the methanol solution and hydrogen-rich reformed gas are burned to generate electricity with maximum combustion efficiency, while simultaneously producing fuel gas; the fuel gas is introduced into the methanol reforming hydrogen production subsystem 1, where it exchanges heat with the methanol solution and aqueous solution in the methanol reforming hydrogen production subsystem 1, vaporizing the methanol solution and aqueous solution.

[0026] The mode switching control subsystem statistically analyzes the time percentage of different operating conditions of the equipment in the hybrid power system, and matches the operating mode to the operating conditions whose time percentage is greater than the percentage threshold. The mode switching control subsystem also obtains the historical operating parameters of the battery system and the historical output power of the methanol generator subsystem 2 from the energy management system 3. Taking the operating parameters of the battery system, the output power of the methanol generator subsystem 2, and the time percentage of different operating conditions as influencing factors, the principal component analysis method is used to determine the comprehensive weight of each influencing factor. Then, the fuzzy comprehensive evaluation method is used to screen out the optimal operating mode from the matched operating modes. According to the optimal operating mode, the methanol generator subsystem 2 and / or the battery system are controlled to provide power to the equipment.

[0027] This application first combines the methanol generator subsystem 2 with the methanol reforming hydrogen production unit, introducing the hydrogen-rich reformed gas produced by methanol reforming into the methanol generator subsystem 2 for co-combustion to improve the overall combustion performance. At the same time, the exhaust gas produced by the internal combustion engine is connected to the methanol reforming chamber 7 and the vaporization chamber 8 to ensure the heat source required for subsequent methanol reforming and to utilize the waste heat of the high-temperature exhaust gas. Secondly, the battery 10 is electrically connected to the methanol reforming chamber 7 and the vaporization chamber 8 to effectively ensure the high-temperature reforming requirements during the initial stage of system startup. Furthermore, the methanol engine 14 is connected to the generator 13 via a rod to ultimately convert the chemical energy of methanol into electrical energy for supply. The methanol generator subsystem 2 ensures the operation of this system under high-power conditions, while the battery 10 ensures the instantaneous response of this system under dynamic conditions.

[0028] The system in this application mainly serves micro-devices and can provide services for small mobile carriers such as small unmanned cars, drones, and robot dogs.

[0029] As an optional implementation method, refer to Figure 1The methanol reforming hydrogen production subsystem 1 includes a methanol reforming chamber 7 and a vaporization chamber 8. The energy management system 3 controls the battery system to heat the methanol reforming chamber 7 and the vaporization chamber 8 to a preset temperature. The vaporization chamber 8 mixes the vaporized methanol solution with the aqueous solution. The vaporized methanol water vapor enters the methanol reforming chamber 7 for reforming to produce hydrogen and generate hydrogen-rich reformed gas.

[0030] For example, the methanol generator subsystem 2 includes a methanol engine 14 and a generator 13. The energy management system 3 controls the flow of methanol solution and hydrogen-rich reformed gas into the methanol engine 14 according to the optimal combustion strategy under the target operating conditions, and adjusts the parameters of the methanol engine 14 to ensure that the methanol solution and hydrogen-rich reformed gas burn at maximum combustion efficiency in the methanol generator subsystem 2, generating fuel gas. Simultaneously, the kinetic energy generated by combustion drives the generator 13 to generate electricity. The energy management system 3 monitors the output power of the generator 13. The fuel gas enters the methanol reforming chamber 7, exchanges heat with the vaporized methanol and water vapor in the reforming chamber 7, and then enters the vaporization chamber 8, exchanges heat with the methanol solution and aqueous solution in the vaporization chamber 8. The fuel gas after heat exchange is discharged through the exhaust port 9.

[0031] In this implementation, the hybrid power system further includes: a methanol storage tank 4, a water storage tank 5, a first regulating valve 6, a second regulating valve 16, and a third regulating valve 15. One end of the first regulating valve 6 is connected to the first liquid outlet of the methanol storage tank 4 and the water outlet of the water storage tank 5, respectively, and the other end of the first regulating valve 6 is connected to the liquid inlet of the vaporization chamber 8. Both ends of the second regulating valve 16 are connected to the gas outlet of the methanol reforming chamber 7 and the gas inlet of the methanol engine 14, respectively. Both ends of the third regulating valve 15 are connected to the second liquid outlet of the methanol storage tank 4 and the liquid inlet of the methanol engine 14, respectively. The control ends of the first regulating valve 6, the second regulating valve 16, and the third regulating valve 15 are all connected to the output end of the energy management system 3. The energy management system 3 is used to control the first regulating valve 6 to introduce a set proportion of methanol solution and aqueous solution into the vaporization chamber 8. The energy management system 3 is used to control the second regulating valve 16 and the third regulating valve 15 to introduce methanol and the hydrogen-rich reformed gas into the methanol engine 14 according to the optimal combustion strategy under the target operating conditions.

[0032] As an optional implementation method, such as Figure 2As shown, the hybrid power system also includes: an AC / DC converter 17, a first DC / AC inverter 18, a DC / DC converter 20, a second DC / AC inverter 19, and a drive motor 11. The AC power generated by the methanol generator subsystem 2 is converted to DC power by the AC / DC converter 17; the DC power is converted to AC power by the first DC / AC inverter 18 to power the drive motor 11; the DC power is converted to a different current by the DC / DC converter 20 to charge the battery system; and the DC power is converted to AC power by the second DC / AC inverter 19 to power the load 21.

[0033] Reference Figure 1 and Figure 2 The operation plan of the system in this application is as follows: Steps 1 to 4.

[0034] Step 1: First, the energy management system 3 controls the battery 10 in the battery system to heat the methanol reforming chamber 7 and the vaporization chamber 8. After heating to a suitable temperature for methanol aqueous solution vaporization and methanol water vapor reforming, the first regulating valve 6 is used to mix and vaporize the methanol solution and the aqueous solution according to a set ratio. The vaporized methanol water vapor enters the methanol reforming chamber 7 for reforming to produce hydrogen and generate hydrogen-rich reformed gas.

[0035] Step 2: The energy management system 3 introduces methanol solution and hydrogen-rich reformed gas into the methanol engine 14 by adjusting the second regulating valve 16 and the third regulating valve 15. The energy management system 3 adjusts the parameters of the methanol engine 14 with the help of data accumulated from previous simulations and experiments, and sets appropriate compression ratio, speed, hydrogen blending ratio, hydrogen injection time and ignition time to burn the fuel (methanol solution and hydrogen-rich reformed gas).

[0036] Step 3: When the fuel is burned, high-temperature gas is generated. The high-temperature gas first passes through the methanol reforming chamber 7 through the set gas pipeline for the first step of heat exchange, and then passes through the vaporization chamber 8 for the second step of heat exchange. The low-temperature gas after heat exchange is discharged through the exhaust port 9. At the same time, the kinetic energy generated by combustion drives the piston to rotate, which in turn drives the generator 13 to generate electricity. The power generation of the generator 13 is monitored by the energy management system 3, and the optimal mode for the generator 13 to work is achieved by changing the pre-fuel supply conditions and boundary conditions.

[0037] The suitable temperature for methanol reforming to produce hydrogen is 250℃-350℃. The high-temperature fuel gas raises the temperature of the reaction pipes and methanol water vapor in the methanol reforming chamber 7 until the methanol reforming chamber 7 reaches the suitable reaction temperature for methanol reforming to produce hydrogen. The high-temperature fuel gas exchanges heat with the methanol / water solution in the vaporization chamber 8, and is responsible for vaporizing it into methanol water vapor.

[0038] Step 4: The electricity generated by generator 13 is converted into direct current (DC) by AC / DC converter 17 and fed into internal circuit 12. This DC power is primarily used to drive motor 11, secondly to charge battery 10, and finally to provide power to load 21. The DC power needs to be converted into AC power by first DC / AC inverter 18 to drive motor 11, then converted into a current suitable for battery 10 by DC / DC converter 20 for charging, and finally converted back into AC power by second DC / AC inverter 19 to power other loads 21.

[0039] As an optional implementation, the equipment operates in the following conditions: starting phase, acceleration phase, constant speed phase, and deceleration phase. The starting phase refers to the system accelerating from a standstill; the acceleration phase refers to the system accelerating during operation; the constant speed phase refers to the vehicle traveling at a constant speed; and the deceleration phase refers to the system decelerating or coming to a stop. The evaluation criteria for each condition are as follows: The initial stage satisfies: V∈[0,V1] and A≥0; where V is the speed of the device, V1 is the set critical speed, and A is the acceleration of the device; The acceleration phase satisfies: V∈[V1,V2] and A>0; where V2 is the maximum speed at which the equipment is running normally. The constant velocity phase satisfies: V∈[V1,V2] and A=0; During the deceleration phase, the following conditions must be met: V∈[0,V2] and A<0.

[0040] Therefore, by obtaining V and A, the operating condition of the equipment can be determined.

[0041] As an optional implementation method, based on the above operating scheme, four operating modes of the system are proposed: battery-only power supply mode, generator-only power supply mode, hybrid power supply mode, and battery charging mode.

[0042] Mode 1: Battery-powered mode.

[0043] When the power required by the equipment is less than or equal to the power threshold, a battery-only power supply mode is adopted, with the battery system providing electrical energy to the drive motor 11 and the load 21. This can be understood as follows: when the system requires lower power, a pure battery operating mode is suitable, such as during the initial stage when power demand is low and dynamic response rate is high. At this time, the methanol generator 13 system is in an idling state, with the entire system's operation powered by the pure battery pack. The battery pack provides electrical energy to the drive motor 11 and the load 21 through the DC / DC converter 20.

[0044] Mode 2: Generator provides power independently.

[0045] When the power required by the equipment fluctuates less than the fluctuation threshold, and the power required by the equipment is between the maximum and minimum output power of the methanol generator subsystem 2, the generator provides power independently, and the methanol generator subsystem 2 provides electrical energy to the drive motor 11 and the load 21.

[0046] Therefore, the mode in which generator 13 provides power independently is used when the power demand of the system fluctuates little and is between the maximum and minimum output power of the methanol generator 13. In this case, the methanol generator 13 will bear the load 21 power of the entire system. The generator 13 transmits the power to the drive motor 11 through the AC / DC converter 17 and the DC / AC inverter, driving the entire system to move. This is the most common method.

[0047] Mode 3: Hybrid supply mode.

[0048] When the power required by the equipment exceeds the power threshold, the methanol generator subsystem 2 and the battery system jointly provide electrical energy to the drive motor 11 and the load 21. Specifically, the hybrid power supply mode is adopted in environments with high power requirements. The methanol engine 14 and generator 13 maintain their high-efficiency power range for output, and the remaining insufficient power is supplemented by the battery 10, thereby achieving the best energy utilization efficiency.

[0049] Mode 4: Battery charging mode.

[0050] When the state of charge of the battery system is less than the lower limit of the state of charge threshold, the methanol generator subsystem 2 provides electrical energy to the drive motor 11 and the load 21, and uses the excess electrical energy to charge the battery system.

[0051] This mode is activated when the battery SOC is lower than the set lower limit. The engine prioritizes maintaining the high-efficiency power range for output, firstly meeting the electrical energy required by the system during operation, and secondly using the DC / DC converter 20 to charge the battery 10 with the excess electrical energy.

[0052] In practical use, the four modules mentioned above are needed for auxiliary control. First, by monitoring V and A, the system's operating stage is determined, including the start-up stage, acceleration stage, constant speed stage, and deceleration stage. Data collected, along with other parameters affecting operation, are gathered, primarily from operating data from the previous 24 hours and data accumulated from previous experiments. Because the system's operating stage changes due to variations in the external environment and user strategies, the duration of different operating stages varies. Statistical analysis of the time proportions of different operating stages determines the percentage of each stage. For operating stages with high time proportions and high matching degrees, the appropriate operating mode is prioritized. For example, the start-up stage tends to use a battery-only power supply mode, while the constant speed stage tends to use generator 13 as the sole power supply or a hybrid power supply of generator 13 and battery. This approach simplifies the complexity of the control system and reduces unnecessary mode switching, thus protecting system operation and improving system efficiency and status. Based on the initial principal component analysis method, the influence weights of different influencing factors are divided, and the weights are converted into a set of principal components that are easy to observe for analysis. Then, the final division is carried out through the subsequent fuzzy logic rules, and the operation mode switching strategy is evaluated to achieve efficient control of the system under different operation modes.

[0053] As an optional implementation, the optimal power output mode of the methanol generator subsystem 2 is determined based on the current environmental parameters. The methanol solution and hydrogen-rich reformed gas are controlled to be introduced into the methanol generator subsystem 2 according to the optimal combustion strategy matched with the optimal power output mode, so that the methanol solution and hydrogen-rich reformed gas can be burned to generate electricity with maximum combustion efficiency and at the same time produce fuel gas. The energy management system 3 specifically executes the following steps 1 to 5.

[0054] Step 1: Under various external environments faced by generator 13, determine the optimal power output mode of generator 13 under each external environment through simulation and physical experiments.

[0055] To address the various external environments that generator 13 may encounter, a large amount of data was accumulated through preliminary simulations and physical experiments. This clarified the optimal operating conditions and target output power of generator 13 under different environmental conditions to achieve the best overall efficiency, ultimately forming a model-predictive control strategy. In actual operation, the system can quickly match and select the pre-stored optimal power output mode by detecting current environmental parameters.

[0056] Step 2: By simulating the combustion of the methanol engine 14, determine the optimal combustion strategy for each optimal power output mode; the optimal combustion strategy includes supply conditions and boundary conditions; the supply conditions include fuel injection quantity, fuel ratio, fuel injection timing and fuel injection pressure; the boundary conditions include the rotational speed of the methanol engine 14, the equivalence ratio of the methanol engine 14, the temperature and pressure in the cylinder of the intake and exhaust manifolds of the methanol engine 14, and the ignition timing of the spark plugs of the methanol engine 14.

[0057] Generator 13 generates electricity by rotating the engine piston; therefore, the engine's combustion performance affects the piston rotation performance, which in turn affects the performance of generator 13. Through combustion simulation, combustion strategies under different operating conditions are first studied and optimized. These optimized combustion strategies obtained from the simulation are then practically verified on an experimental bench to assess their actual performance indicators. Finally, the best combustion strategy for each operating condition, verified through experiments, is selected.

[0058] Fuel injection quantity: The amount of methanol premixed with air entering the methanol internal combustion engine, and the amount of hydrogen-rich gas produced by methanol reforming directly injected into the methanol internal combustion engine. Fuel blending ratio: When the methanol content is high, the combustion process in the engine is dominated by methanol. As the hydrogen blending ratio increases, the influence of hydrogen on combustion becomes increasingly significant. Fuel injection timing: The timing at which methanol and hydrogen-rich gas are injected into the methanol internal combustion engine. Different hydrogen blending ratios require different optimal injection timings. Fuel injection pressure: Injection pressure affects the fuel injection penetration distance, thus affecting the subsequent fuel mixture. When the injection quantity is set to a fixed value, the injection pressure affects the injection time. Higher injection pressure results in a shorter injection time, thus promoting better mixing.

[0059] Therefore, the engine's supply conditions and boundary conditions can be controlled by the energy management system 3, such as by adjusting the amount of methanol injected to control the generation of hydrogen-rich gas and the amount of methanol fuel injected into the cylinder.

[0060] Step 3: Based on the current environmental parameters, determine the optimal power output mode of generator 13 under the current external environment, and obtain the optimal combustion strategy matched with the optimal power output mode.

[0061] Step 4: Control the supply conditions of methanol and hydrogen-rich reformed gas to be introduced into methanol engine 14 according to the supply conditions in the optimal combustion strategy.

[0062] Step 5: Adjust the methanol engine 14 according to the boundary conditions in the optimal combustion strategy so that methanol and the hydrogen-rich reformed gas are burned in the methanol engine 14 with maximum combustion efficiency to generate electricity, while producing fuel gas.

[0063] As an optional implementation method, based on the system's operating conditions, the proportion of total working time occupied by different operating conditions is analyzed to determine the percentage of each operating condition. By statistically collecting data from the past 24 hours and previous testing, the proportion of total operating time occupied by different operating conditions is analyzed. Operating conditions with high time percentages and high matching degrees will be prioritized for matching with suitable operating modes. High time percentage refers to the relative proportion of the four operating conditions. This is calculated based on actual system operating data over 24 hours plus historical data on similar operating conditions accumulated from previous testing. Operating conditions with high time percentages are prioritized for allocation. For example, among the four operating conditions of starting, acceleration, constant speed, and deceleration, constant speed accounts for 60% of the total running time, so the constant speed phase is considered to have a high percentage. High matching degree is determined by two criteria: first, the core requirements of the operating condition and the mode's functionality are compatible, determined by pre-judgment control. For example, the starting phase, which is low power + high dynamic response, conforms to the characteristics of fast battery dynamic response and stable low power output, thus being considered to have a high matching degree; second, the energy efficiency, power stability, and component wear indicators of this mode under the target operating condition must be significantly better than other modes.

[0064] As an optional implementation method, a mode switching strategy based on fuzzy comprehensive evaluation was constructed to determine the optimal operating mode. For example... Figure 3 As shown, this strategy comprehensively considers multiple influencing factors such as battery SOC, battery temperature, engine power, and the proportion of different operating conditions. It quantifies each factor through a membership function, assigns corresponding weights, and finally obtains the optimal solution through comprehensive evaluation.

[0065] The proportion of working time under different operating conditions is used as the primary influencing factor, along with secondary influencing factors such as battery SOC, battery temperature, and engine power, and input into a fuzzy logic library for evaluation. First, principal component analysis is used to obtain the comprehensive scores of each factor under various operating conditions, and then a weighted average is calculated to obtain the comprehensive weight. Second, fuzzy comprehensive evaluation is used to comprehensively evaluate the comprehensive weight composed of multiple factors to achieve the optimal operating mode.

[0066] Principal component analysis (PCA) was performed on the factors influencing the system using experimental data and prior data accumulation, and the influence weights of each factor were constructed. By continuously calculating the influence weights of each factor, the weight deviations of each factor were obtained, which were then used for subsequent monitoring and correction. Principal component analysis is a mathematical dimensionality reduction method that can transform a set of potentially linearly correlated variables into a smaller set of linearly independent variables through orthogonal transformation. These new variables are called principal components, and are arranged in descending order of variance. The principal components generated by PCA can reflect most of the information in the original variables with fewer variables. However, the use of PCA requires a series of conditions and verification processes to ensure that the generated principal components can effectively reflect the original information.

[0067] The specific process of principal component analysis is as follows: (1) Standardize the raw data to eliminate the influence of dimensions on the analysis.

[0068] If the number of variables in principal component analysis is The number of evaluation objects is , No. The first evaluation object The variables take values ​​of . , Requires various variables Transform into standardized variables ,Right now: ; Among them, the first Mean of each variable for: ; No. Standard deviation of each variable for: .

[0069] (2) Establish the correlation coefficient matrix R between variables.

[0070] Correlation coefficient matrix ,in: ; In the formula =1, = , It is the first The variable and the first The correlation coefficients of the variables. . It is the first The first evaluation object Standardized values ​​of each variable. It is the first The first evaluation object Standardized values ​​of each variable.

[0071] (3) Calculate the eigenvalues ​​and eigenvectors of the correlation coefficient matrix R, and obtain the principal components.

[0072] eigenvalues ​​of the correlation coefficient matrix R ≥ ≥…≥ ≥0, the corresponding eigenvector is , … ,in , Composed of feature vectors A new indicator variable.

[0073] ; In the formula, , , They are the 1st, 2nd, and 3rd respectively. Each feature value. , , They are the 1st, 2nd, and 3rd respectively. 1 eigenvector. , , Both are the first in principal component analysis. The components of each eigenvector. It is the first principal component. It is the second principal component. For the first Principal component.

[0074] (4) Calculate the information contribution rate and cumulative contribution rate to obtain the comprehensive score.

[0075] No. principal component Information contribution rate The calculation method is as follows: .

[0076] in, For the first 1 eigenvalue, For the first Each feature value.

[0077] forward Cumulative contribution rate of each principal component The calculation method is as follows: .

[0078] current When the cumulative contribution rate of each principal component is greater than 0.85, it is considered that the first... Principal Components , ,…, Can replace the original data The system uses several indicator variables to perform principal component analysis on the principal components.

[0079] Calculate the principal component composite score : .

[0080] Principal component composite scores can reflect the original information of the original variables to a certain extent, but they need to be positively processed before the original variables are standardized. Principal component composite scores integrate the influence of different working conditions to obtain the overall importance weight of each influencing factor.

[0081] Fuzzy comprehensive evaluation is a comprehensive evaluation method based on fuzzy mathematics. This method transforms qualitative evaluation into quantitative evaluation through membership theory, enabling it to handle uncertain decision-making problems under multiple constraints and exhibiting strong systematic characteristics. Its process is shown below: (1) Establish the evaluation factor set U The evaluation factor set is a general set composed of various factors that affect the evaluation object (such as battery SOC, generator power, battery temperature, combustion performance, etc.), usually represented by U, and denoted as U={u1,u2,…,u q}. u s Let represent the s-th evaluation factor in the evaluation factor set, where s = 1, 2, ..., q, and q represents the number of evaluation factors in the evaluation factor set.

[0082] (2) Determine the weights W of the evaluation factors. The weight value represents the degree of importance of each indicator to the target. Different indicators have different degrees of importance, therefore u s Weight values ​​should be assigned according to their importance. s The weight set W = {w1, w2, ..., w} is formed. q This process assigns weights using the principal component analysis method described earlier. s Indicate u s The assigned weight value.

[0083] (3) Determine the comment set T The primary function of the evaluation set is to provide verbal descriptions of each indicator, mainly focusing on the expert evaluations given for each indicator. By organizing these evaluations, a set of states for each indicator can be derived. The evaluation set typically consists of 4 to 9 evaluations, and the evaluation levels are generally categorized as excellent, good, average, poor, and very poor. The hybrid power system performance of this system is assigned q levels of indicators, and the set of these indicators is T = {t1, t2, ..., t...}. q}. t1, t2, t q These represent the 1st, 2nd, and qth level indicators, respectively.

[0084] (4) Single-factor fuzzy comprehensive evaluation For the set of evaluation factors U, calculate the membership degree of each evaluation indicator according to the corresponding membership function, that is, assign the evaluation factor u to the evaluation level. s The degree of membership is assessed. Evaluation factors are concentrated on evaluation factor u. s Relative to the rating level ts The membership degree is recorded as r se (0 <r se <1), therefore the evaluation factor u s The single-factor comprehensive fuzzy vector is R s ={r s1 ,r s2 ,…,r sq}

[0085] The membership function selection method is as follows: A rating level is set for each evaluation factor, including battery SOC (Very Poor [0, 20%]; Poor [20%, 40%]; Average [40%, 60%]; Good [60%, 80%]; Excellent [80%, 100%]), battery temperature, and engine power. The classification is based on the system design threshold and the optimal interval determined by previous experimental statistics. The membership function type is selected according to the factor characteristics: triangular membership functions are used for battery SOC and operating condition percentages; trapezoidal membership functions can be used for battery temperature and engine power; and Gaussian membership functions can be used to assess engine fluctuations with operating conditions. The membership function parameters are calibrated based on experimental data before final application.

[0086] (5) Establish a fuzzy relation matrix and perform a comprehensive evaluation. Make fuzzy judgments on all influencing indicators of the evaluation factor set, and obtain R1, R2, ..., R q Therefore, the membership fuzzy matrix between U and T is: .

[0087] in, This indicates that the q-th evaluation factor corresponds to the 1st evaluation factor. The membership degree of a rating level serves to quantify the degree of fit between a single evaluation factor and a particular rating level.

[0088] The membership fuzzy matrix R contains the membership degrees of all influencing factors (such as battery SOC and engine power) at each rating level (e.g., very good, good, average). It needs to be weighted with the comprehensive weight vector W obtained earlier through principal component analysis to obtain the comprehensive evaluation vector C. Based on the comprehensive membership degrees of each rating level in the comprehensive evaluation vector C, and combined with the system's preset rules, the optimal mode is selected from the four working modes.

[0089] (6) Final evaluation plan The overall evaluation result of the system is obtained by performing matrix multiplication on the comprehensive weight vector W and the membership fuzzy matrix R.

[0090] Maximum membership method: The evaluation result is considered to be maxc f The corresponding solution is the optimal solution t. f That is, U={t f|t f →maxc f}

[0091] Weighted average method: The evaluation result is obtained by weighting the weights: .

[0092] It is the first batch of comments. The quantitative assignment of each rating level (converting qualitative descriptions such as "very good" and "good" into specific numerical values) takes the value as a continuous or discrete real number (such as 1, 2, 3, 4, 5) and is only used in the weighted average method. The comprehensive evaluation vector C=[ , , ..., The first in ] The nth element, with a value range of 0 to 1, reflects the overall impact of all influencing factors on the nth element. The degree of fit of each rating level.

[0093] The evaluation level that best represents the system state is selected from the C vector and used as the basis for pattern matching. The optimal operating mode is matched, and the core evaluation level is transformed into the final operating mode of the system according to the preset evaluation level-operating mode matching rules.

[0094] As an optional implementation, the rationality of the strategy is evaluated based on feedback data. If it is not rational, an adaptive strategy is received to correct parameters such as membership degree and weight, and a recalculation and evaluation are performed. After controlling the methanol generator subsystem 2 and / or the battery system to provide power to the equipment according to the optimal operating mode, the mode switching control subsystem is also used to: obtain the actual output power of the methanol generator subsystem 2 from the energy management system 3; if the deviation between the actual output power and the optimal power is greater than the deviation threshold, the latest operating parameters of the battery system and the output power of the methanol generator subsystem 2 are extracted, and together with the historical operating parameters of the battery system, the historical output power of the methanol generator subsystem 2, and the time proportion of different operating conditions, the comprehensive weight of each influencing factor is re-determined using principal component analysis, or the membership degree parameters in the fuzzy comprehensive evaluation method are adjusted, and the optimal operating mode of the operating condition is re-selected from the matched operating modes.

[0095] For example, the rationality of a strategy can be evaluated as follows: Data preprocessing and benchmark comparison: First, the feedback data is filtered, then compared one by one with preset benchmark data to pinpoint deviations. The preset benchmark data comes from optimal operating data accumulated in previous experiments and system design thresholds. The deviation rate between the feedback data and the benchmark data is calculated to determine if the deviation is within an acceptable range. A multi-dimensional comprehensive judgment is then made, combining the comparison results of effect and status data to comprehensively evaluate the rationality of the strategy from three dimensions. If any dimension fails to meet the standard, the strategy is deemed to need correction: whether energy efficiency meets the standard, whether operational stability is satisfied, and whether the system state is safe.

[0096] The adaptive strategy for adjusting parameters such as membership and weights is as follows: The adaptive strategy does not make blind corrections. It is driven by feedback data and relies on principal component analysis and fuzzy comprehensive evaluation. Through a dynamic process of triggering conditions → parameter adjustment → instruction issuance → verification closed loop, it achieves online real-time parameter optimization to ensure that it always adapts to the system's operating conditions and status.

[0097] 1. Collect data synchronously to provide a basis for correction. By comparing the data with historical records, determine whether the membership parameter or the weight parameter is unreasonable.

[0098] The method for judging whether the weight parameters are reasonable is as follows: Weight deviation refers to the difference between the actual impact weight calculated under the current operating conditions and the baseline impact weight determined in previous experiments / simulations. It is used to determine whether the current weight is suitable for the system state. If the deviation exceeds the allowable range, weight correction is triggered.

[0099] ① Determine the baseline influence weight W0. Based on previous experimental data, establish the optimal weight baseline for each working condition. Through numerous simulation experiments, calculate the optimal influence weight for different working conditions to form a baseline weight matrix. , , , , These are the baseline weights for the four influencing factors; the baseline weights must satisfy the rule of optimal system energy efficiency under this operating condition.

[0100] ② Calculate the current actual impact weight W in real time t Based on the current operating condition data, the impact weight calculation process is repeated: (1) The monitoring and feedback module collects the current operating condition data; (2) According to the impact weight calculation steps, the actual impact weight under the current operating condition is recalculated. , , , , These represent the actual impact weights of the four influencing factors.

[0101] ③ Calculate the weight deviation Δw for each individual factor, quantifying the deviation between the actual weight of each factor and the benchmark weight. The formula for the weight deviation of the z-th influencing factor is: Δw z =w z -w 0z z = 1~4.

[0102] ④ Calculate the overall weight deviation: Considering the deviations of all factors, determine the overall fit of the weighting system. Quantify the overall deviation using Mean Squared Error (MSE). Formula: .

[0103] ⑤ Deviation Validity Determination: Set a deviation threshold to determine whether correction is needed. Preset a single-factor deviation threshold Δw' (e.g., ±0.1) and an overall deviation threshold MSE' (e.g., 0.01). If the single-factor deviation Δw... z If the deviation is greater than Δw' or the overall deviation MSE is greater than MSE', it is determined that the deviation exceeds the standard and weight correction is triggered; if the deviation is within the threshold, it is determined that the weight is adapted.

[0104] 2. Based on the source tracing results, targeted algorithms are applied to adjust the membership degree and weights. Actual operating data for this factor over the past hour is extracted, and its distribution range is statistically analyzed. If the battery temperature remains consistently high, the fuzzy range of the battery temperature membership function is corrected; if the nonlinearity of engine power fluctuations increases, the standard deviation of its Gaussian membership function is adjusted. For weight correction, the input data and calculation results of principal component analysis are dynamically updated through a process of updating input data, dynamically adjusting principal components, and recalculating weights.

[0105] The specific steps for weight adjustment are as follows: ① Continuous monitoring: recalculate the weight deviation of each factor every 5 to 10 minutes and compare it with the preset threshold to determine whether it exceeds the standard; synchronously collect data quality, operating condition ratio, and core component status to prepare for deviation tracing.

[0106] ② Deviation tracing: Data quality tracing checks whether real-time data exceeds the normal range and whether the sample covers the current working conditions; working condition characteristic tracing compares the current working condition proportion with the working condition proportion corresponding to the benchmark weight; system status tracing detects the performance of core components and determines whether there is performance degradation; algorithm parameter tracing verifies whether the parameters of principal component analysis and fuzzy rules cover the current scenario.

[0107] ③ Targeted correction of data acquisition anomalies: The moving average method is used to remove sensor jump data; real-time data from the current operating condition within the past hour is supplemented to ensure sample coverage; if data anomalies persist, a sensor self-calibration command is triggered. For sudden changes in operating condition characteristics, the temporary baseline weight for that condition is recalculated based on its current proportion; the principal component screening threshold is optimized; if the correlation between operating condition factors increases, the cumulative contribution rate threshold is lowered from 85% to 80%, retaining more principal components reflecting sudden changes in operating conditions. For system state changes, the membership function is adjusted; for battery performance degradation, the fuzzy range of SOC is redefined; fuzzy rules are supplemented. Algorithm parameter fixed feature values ​​are recalculated; the correlation coefficient matrix R is reconstructed based on the latest data, and the feature values ​​λ and feature vectors are updated; factor contribution coefficients are corrected, adjusting the contribution coefficients of each factor in the principal components. Incremental correction is performed on direct weight deviations, adjusting slightly according to the magnitude of the deviation; normalization calibration is performed, ensuring that the sum of all factor weights is 1 after correction.

[0108] ④ After verification and iterative correction, it is necessary to conduct dual verification through both theory and practice to avoid introducing new deviations and form a closed loop.

[0109] 3. After the parameters are adjusted, a standardized correction command is generated and synchronized to the relevant execution unit via signal connection, and the energy management system 3 is notified simultaneously to ensure that the adjusted parameters can be applied in real time.

[0110] 4. After the correction command is executed, the verification cycle begins, and verification optimization is performed.

[0111] If the recalculation and evaluation are still unreasonable, it will enter a progressive operation of iterative correction, deep optimization and fallback protection. The core is to reduce the adjustment range, optimize the algorithm logic, and continuously adapt the hardware parameters until the strategy meets the target. At the same time, alarms and data accumulation are triggered to avoid long-term abnormal operation of the system.

[0112] ①If the initial recalculation and evaluation are still unreasonable, we will not make blind and large-scale adjustments, but will repeat the correction process based on the principle of small iterations to gradually approach the optimal parameters.

[0113] ② If the target is still not met after more than 5 iterations, stop the iteration and perform in-depth optimization of the algorithm logic and hardware parameters, and reconstruct the strategy decision. Regarding algorithm parameters, adjust the contribution threshold of principal component analysis and re-select principal components; expand the evaluation set levels and add new fuzzy logic rules for fuzzy comprehensive evaluation; and add auxiliary factors to the factor set. Regarding hardware parameters, adjust the parameters of methanol generator subsystem 2 and methanol reforming hydrogen production subsystem 1.

[0114] ③ If the deep optimization fails to meet the standards, the system will initiate fallback measures, prioritizing safe operation before gradually resolving the problem. This includes switching to generator 13 or battery-only power supply mode; subsequently, the abnormal data will be included in the system's baseline weight library, and the optimal solution for this type of problem will be identified periodically through big data analysis of the weight library to reduce the recurrence of similar issues.

[0115] Based on the same inventive concept, this application also provides a switching control method applied to the aforementioned hybrid power system. Specific limitations in one or more switching control method embodiments provided below can be found in the limitations regarding the hybrid power system described above, and will not be repeated here.

[0116] In one exemplary embodiment, such as Figure 4 As shown, a switching control method for a mobile methanol generator-battery hybrid power system is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, it includes the following steps 101 to 107.

[0117] Step 101: Obtain the historical operating status of the equipment where the hybrid power system is located.

[0118] Step 102: Based on the historical operating status of the equipment in the hybrid power system, calculate the time percentage of the equipment under different operating conditions, and match the operating mode for the operating conditions whose time percentage is greater than the percentage threshold.

[0119] Step 103: Obtain the historical operating parameters of the battery system and the historical output power of the methanol generator subsystem.

[0120] Step 104: Using the operating parameters of the battery system, the output power of the methanol generator subsystem, and the time proportion of different operating conditions as influencing factors, principal component analysis is used to determine the comprehensive weight of each influencing factor.

[0121] Step 105: Based on the comprehensive weight of each influencing factor, the fuzzy comprehensive evaluation method is used to select the optimal operating mode from the matched operating modes.

[0122] Step 106: Control the methanol generator subsystem and / or battery system to provide power to the equipment according to the optimal operating mode.

[0123] Step 107: Extract the latest operating parameters of the battery system and the output power of the methanol generator subsystem, and together with the historical operating parameters of the battery system, the historical output power of the methanol generator subsystem, and the time proportion of different operating conditions, use principal component analysis to redetermine the comprehensive weight of each influencing factor, or adjust the membership parameter in the fuzzy comprehensive evaluation method, and re-select the optimal operating mode from the matched operating modes.

[0124] Steps 101 to 107 above can be summarized as follows.

[0125] Step 1: Obtain V and A to determine the system's operating condition. The system's operating condition includes the start-up phase, acceleration phase, constant speed phase, and deceleration phase. The evaluation criteria are as follows: If V∈[0,V1] and A≥0, then it is determined to be the starting stage; If V∈[V1,V2] and A>0, then it is determined to be the acceleration phase; If V∈[V1,V2] and A=0, then it is determined to be a constant speed phase; If V∈[0,V2] and A<0, then it is determined to be the deceleration phase until it stops.

[0126] Step 2: Based on the operating conditions of the system, analyze the proportion of total working time occupied by different operating conditions, and determine the proportion of different operating conditions.

[0127] Step 3: Using experimental data and previous data accumulation, perform principal component analysis and construct a working mode switching strategy based on fuzzy logic rules.

[0128] Step 4: Using factors such as battery SOC, battery temperature, engine power, and the proportion of different operating conditions as influencing factors, a constructed operating mode switching strategy is used to assign weights, determine membership relationships, and conduct a comprehensive evaluation of these factors to obtain the optimal operating mode. First, principal component analysis is used to determine the relative weights of each factor with respect to the four operating modes, obtaining a comprehensive weight. Then, fuzzy comprehensive evaluation is used to comprehensively evaluate the comprehensive weights composed of multiple factors through membership functions to obtain the optimal operating mode.

[0129] In one exemplary embodiment, such as Figure 5 As shown, a switching control system is provided for implementing the switching control method of the aforementioned mobile methanol generator-battery hybrid power system. This switching control system includes: a condition judgment module, a data processing module, a control strategy module, and a monitoring and feedback module. The coordinated operation of these four core modules forms a dynamic optimization closed loop, enabling intelligent mode decision-making.

[0130] Operating condition judgment module: responsible for identifying and determining the current operating condition of the system in real time.

[0131] Data processing module: Based on historical operating data, it then calculates and determines the time percentage for different operating conditions.

[0132] Control strategy module: Subsequently, the battery SOC, battery temperature, motor power and the proportion of each operating condition are used as comprehensive evaluation factors, and the optimal operating mode is selected through a predetermined algorithm.

[0133] Monitoring and Feedback Module: Finally, after the mode switch, the module continuously monitors key parameters such as the system's operating status and feeds the data back to the operating condition judgment module to initiate a new round of cyclic judgment and adaptive optimization.

[0134] Figure 5 The diagram also shows the operating condition judgment module calculating the optimal output power. The calculation process for the optimal output power is as follows: the real-time velocity v and acceleration a of the system are obtained to determine the current operating condition type; then important parameters such as the remaining SOC of the battery and the battery temperature are detected and obtained; finally, the combustion strategy is adjusted using data accumulated from previous simulations / experiments and historical operating condition data of the system within 24 hours to achieve the optimal output of the engine and generator, ultimately achieving the optimal output power.

[0135] Figure 5 In the process, the data processing module collects the following data: (a) Real-time operating status data 1. System dynamic parameters: velocity, acceleration; 2. Load and demand parameters: Current system load power demand and real-time power consumption of the drive motor; 3. Combustion-related parameters: the ratio of hydrogen to methanol and hydrogen-rich reformed gas, ignition timing, combustion efficiency, etc.

[0136] (ii) Status data of core components 1. Real-time battery level and temperature; 2. Current output power of the methanol generator, speed of the methanol engine, compression ratio, hydrogen injection time, etc.; 3. Real-time temperatures of the methanol reforming chamber and vaporization chamber.

[0137] (III) Historical and Experimental Data 1. Duration and corresponding power output data of the system under different operating conditions over the past 24 hours; 2. Parameters accumulated from previous simulations and experiments.

[0138] The data collected above will be used subsequently as follows: 1. Speed ​​and acceleration are transmitted to the working condition judgment module to determine the current working condition; historical running data is transmitted to the data processing module to calculate the time ratio of each working condition and determine the priority of the adapted working condition; 2. The status data of core components and the proportion of operating conditions are input into the control strategy module as the core evaluation factors for mode switching; combined with the data accumulated in the previous experiments, the weight of each factor is quantified by principal component analysis, and then the optimal working mode is matched by comprehensive evaluation using fuzzy comprehensive evaluation method. 3. Combustion-related parameters are fed back to the energy management system to optimize methanol engine parameters and ensure maximum fuel combustion efficiency; reforming chamber / vaporization chamber temperature data and generator output power data are used to adjust fuel supply conditions; 4. After mode switching, the system operation data is continuously monitored by the monitoring and feedback module and transmitted back to the operating condition judgment module. If a data deviation is found, the system starts adaptive adjustment: corrects the weight and membership parameters of each influencing factor, recalculates the optimal output power and adaptation mode, and forms a dynamic optimization closed loop.

[0139] The key technical points of this application are: 1. Changing the traditional hybrid power approach, hydrogen-rich fuel is used as the main supply device, while methanol reforming is used to produce hydrogen. Online hydrogen production is used as soon as it is produced, avoiding the additional costs and safety hazards caused by using hydrogen storage equipment. 2. Combining a methanol-to-hydrogen power generation system with an energy storage battery can effectively solve the drawback of requiring a large amount of heat for methanol reforming, and as a backup energy supply device, it can effectively solve the problem of unstable power generation in the initial stage of system startup; 3. By combining a methanol internal combustion engine with a reforming system and a generator, and using hydrogen as fuel, the problem of large amounts of pollution emissions generated by traditional diesel engines is avoided. This not only utilizes the large amount of high-temperature exhaust gas produced by combustion to provide a sufficient heat source for the reforming system, but also uses the kinetic energy generated by combustion to generate electricity, achieving multi-stage utilization of fuel chemical energy. 4. Combining a generator with a battery and connecting it in series with a drive motor can leverage both the battery's fast dynamic response and the generator's high output power. 5. A novel hybrid power control strategy is proposed, which adaptively adjusts the weight allocation and membership relationship online according to real-time driving conditions and vehicle status, thereby breaking through the limitations of fixed rules and realizing refined energy management and continuous optimization of system efficiency across the entire operating range, making the system operate more efficiently.

[0140] 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.

[0141] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A mobile methanol generator-battery hybrid power system, characterized in that, include: Methanol reforming hydrogen production subsystem, methanol generator subsystem, battery system, energy management system and mode switching control subsystem; When the hybrid power system starts, the methanol reforming hydrogen production subsystem controls the battery system to preheat through the energy management system, and then mixes the vaporized methanol solution with the aqueous solution to produce hydrogen-rich reformed gas. The energy management system determines the optimal power output mode of the methanol generator subsystem based on the current environmental parameters. It controls the methanol solution and hydrogen-rich reformed gas to be introduced into the methanol generator subsystem according to the optimal combustion strategy matched with the optimal power output mode, so that the methanol solution and hydrogen-rich reformed gas can be burned to generate electricity with maximum combustion efficiency, and at the same time produce fuel gas. The fuel gas is introduced into the methanol reforming hydrogen production subsystem for heat exchange, vaporizing the methanol solution and aqueous solution. The mode switching control subsystem calculates the time percentage of different operating conditions of the equipment in the hybrid power system and matches the operating mode to the operating conditions where the time percentage is greater than the percentage threshold. The mode switching control subsystem also obtains historical operating parameters of the battery system and historical output power of the methanol generator subsystem from the energy management system. Taking the operating parameters of the battery system, the output power of the methanol generator subsystem, and the time proportion of different operating conditions as influencing factors, the principal component analysis method is used to determine the comprehensive weight of each influencing factor. Then, the fuzzy comprehensive evaluation method is used to select the optimal operating mode from the matched operating modes. According to the optimal operating mode, the methanol generator subsystem and / or battery system are controlled to provide power to the equipment.

2. The mobile methanol generator-battery hybrid power system according to claim 1, characterized in that, The methanol reforming hydrogen production subsystem includes: a methanol reforming chamber and a vaporization chamber; The energy management system controls the battery system to heat the methanol reforming chamber and vaporization chamber to a preset temperature. In the vaporization chamber, the vaporized methanol solution and aqueous solution are mixed. The vaporized methanol water vapor enters the methanol reforming chamber for reforming to produce hydrogen, generating hydrogen-rich reformed gas.

3. The mobile methanol generator-battery hybrid power system according to claim 2, characterized in that, The methanol generator subsystem includes: a methanol engine and a generator; The energy management system controls the methanol and hydrogen-rich reformed gas to be introduced into the methanol engine according to the optimal combustion strategy under the target operating conditions, and adjusts the parameters of the methanol engine so that the methanol and hydrogen-rich reformed gas are burned with maximum combustion efficiency in the methanol generator subsystem to produce gas. At the same time, the kinetic energy generated by the combustion drives the generator to generate electricity; the energy management system monitors the output power of the generator. The fuel gas enters the methanol reforming chamber, where it exchanges heat with the methanol and water vapor produced by the vaporization process. Then, it enters the vaporization chamber, where it exchanges heat with the methanol solution and aqueous solution. The fuel gas after the heat exchange is discharged through the exhaust port.

4. The mobile methanol generator-battery hybrid power system according to claim 3, characterized in that, The hybrid power system also includes: a methanol storage tank, a water storage tank, a first regulating valve, a second regulating valve, and a third regulating valve; One end of the first regulating valve is connected to the first liquid outlet of the methanol storage tank and the water outlet of the water storage tank, respectively, and the other end of the first regulating valve is connected to the liquid inlet of the vaporization chamber. The two ends of the second regulating valve are connected to the outlet of the methanol reforming chamber and the inlet of the methanol engine, respectively. The two ends of the third regulating valve are connected to the second liquid outlet of the methanol storage tank and the liquid inlet of the methanol engine, respectively. The control terminals of the first regulating valve, the second regulating valve, and the third regulating valve are all connected to the output terminal of the energy management system. The energy management system is used to control the first regulating valve to introduce a set ratio of methanol solution and aqueous solution into the vaporization chamber; The energy management system is used to supply methanol and the hydrogen-rich reformed gas into the methanol engine by controlling the second and third regulating valves according to the optimal combustion strategy under the target operating conditions.

5. The mobile methanol generator-battery hybrid power system according to claim 1, characterized in that, The hybrid power system also includes: an AC / DC converter, a first DC / AC inverter, a DC / DC converter, a second DC / AC inverter, and a drive motor; The alternating current generated by the methanol generator subsystem is converted into direct current by an AC / DC converter. The direct current is converted into alternating current through a first DC / AC inverter to power the drive motor; The direct current is converted into current by a DC / DC converter to charge the battery system; The direct current is converted into alternating current by a second DC / AC inverter to power the load.

6. The mobile methanol generator-battery hybrid power system according to claim 1, characterized in that, The operating conditions of the equipment include: the starting phase, the acceleration phase, the constant speed phase, and the deceleration phase; The initial stage satisfies: V∈[0,V1] and A≥0; where V is the speed of the device, V1 is the set critical speed, and A is the acceleration of the device; The acceleration phase satisfies: V∈[V1,V2] and A>0; where V2 is the maximum speed at which the equipment is running normally; V1 is less than V2; The constant velocity phase satisfies: V∈[V1,V2] and A=0; During the deceleration phase, the following conditions must be met: V∈[0,V2] and A<0.

7. The mobile methanol generator-battery hybrid power system according to claim 1, characterized in that, The operating modes include: battery-only power supply mode, generator-only power supply mode, hybrid power supply mode, and battery charging mode; When the power required by the equipment is less than or equal to the power threshold, the battery-only power supply mode is adopted, and the battery system provides electrical energy to the drive motor and load. When the power required by the equipment fluctuates less than the fluctuation threshold, and the power required by the equipment is between the maximum and minimum output power of the methanol generator subsystem, the generator-only power supply mode is adopted, and the methanol generator subsystem supplies electrical energy to the drive motor and the load. When the power required by the equipment exceeds the power threshold, the methanol generator subsystem and the battery system work together to provide electrical energy to the drive motor and the load. When the state of charge of the battery system is less than the lower limit of the state of charge threshold, the methanol generator subsystem provides electrical energy to the drive motor and load, and uses the excess electrical energy to charge the battery system.

8. The mobile methanol generator-battery hybrid power system according to claim 3, characterized in that, Based on current environmental parameters, the optimal power output mode of the methanol generator subsystem is determined. The methanol solution and hydrogen-rich reformed gas are then fed into the methanol generator subsystem according to the optimal combustion strategy matched to this optimal power output mode. This ensures that the methanol solution and hydrogen-rich reformed gas are combusted with maximum efficiency to generate electricity, while simultaneously producing fuel gas. The energy management system specifically includes: Under various external environments faced by the generator, the optimal power output mode of the generator under each external environment is determined through simulation and physical experiments. By simulating the combustion of a methanol engine, the optimal combustion strategy for each optimal power output mode is determined. The optimal combustion strategy includes supply conditions and boundary conditions. The supply conditions include fuel injection quantity, fuel ratio, fuel injection timing, and fuel injection pressure. The boundary conditions include the engine speed, equivalence ratio, temperature and pressure in the intake and exhaust manifolds of the methanol engine, and the ignition timing of the spark plugs in the methanol engine. Based on the current environmental parameters, determine the optimal power output mode of the generator under the current external environment, and obtain the optimal combustion strategy matched with the optimal power output mode; The methanol and the hydrogen-rich reformate are controlled to be fed into the methanol engine according to the supply conditions in the optimal combustion strategy. The methanol engine is regulated according to the boundary conditions in the optimal combustion strategy, so that methanol and the hydrogen-rich reformed gas are burned in the methanol engine with maximum combustion efficiency to generate electricity, while producing fuel gas.

9. The mobile methanol generator-battery hybrid power system according to claim 1, characterized in that, After controlling the methanol generator subsystem and / or battery system to provide power to the equipment in the optimal operating mode, the mode switching control subsystem is also used for: Obtain the actual output power of the methanol generator subsystem from the energy management system; If the deviation between the actual output power and the optimal power is greater than the deviation threshold, the latest operating parameters of the battery system and the output power of the methanol generator subsystem are extracted. Together with the historical operating parameters of the battery system, the historical output power of the methanol generator subsystem, and the time proportion of different operating conditions, the principal component analysis method is used to redetermine the comprehensive weight of each influencing factor, or the membership parameter in the fuzzy comprehensive evaluation method is adjusted, and the optimal operating mode of the operating condition is re-selected from the matched operating modes.

10. A switching control method for a mobile methanol generator-battery hybrid power system, characterized in that, The switching control method is applied to the mobile methanol generator-battery hybrid power system according to any one of claims 1-9, comprising: Obtain the historical operating status of the equipment containing the hybrid power system; Based on the historical operating status of the equipment in the hybrid power system, the time proportion of the equipment under different operating conditions is statistically analyzed, and an operating mode is matched for the operating conditions whose time proportion is greater than the proportion threshold. Obtain historical operating parameters of the battery system and historical output power of the methanol generator subsystem; The operating parameters of the battery system, the output power of the methanol generator subsystem, and the time proportion of different operating conditions were taken as influencing factors, and the comprehensive weight of each influencing factor was determined by principal component analysis. Based on the comprehensive weight of each influencing factor, the fuzzy comprehensive evaluation method is used to select the optimal operating mode from the matched operating modes. According to the optimal operating mode, control the methanol generator subsystem and / or battery system to provide power to the equipment; Extract the latest operating parameters of the battery system and the output power of the methanol generator subsystem, and combine them with the historical operating parameters of the battery system, the historical output power of the methanol generator subsystem, and the time proportion of different operating conditions. Use principal component analysis to redetermine the comprehensive weight of each influencing factor, or adjust the membership parameters in the fuzzy comprehensive evaluation method to reselect the optimal operating mode from the matched operating modes.

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