Fuel cell-lithium battery hybrid power dynamic distribution system and medium

Abstract

The present application relates to fuel cell hybrid power supply technology field, concretely is a kind of fuel cell-lithium battery hybrid power dynamic distribution system and medium, including fuel cell stack, lithium battery energy storage module, three-way valve cooling subsystem, dynamic power compensation module and central processing unit, utilize the real-time temperature field data of fuel cell stack, dynamically control cooling path, utilize fuel cell waste heat data through heat exchanger to selectively preheat lithium battery energy storage module, and monitor bus voltage, load current and battery state, execute power distribution based on thermal constraint, when overheating risk is predicted, power request is dynamically redistributed from fuel cell to lithium battery energy storage module, when power distribution calculation is carried out, the operating mode of cooling system and corresponding parasitic energy consumption are optimized as dynamic variable, and optimization power distribution scheme is generated.The present application prolongs battery service life by intelligent thermal management and dynamic power distribution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fuel cell hybrid power supply, in particular to a fuel cell-lithium battery hybrid power dynamic distribution system and medium. BACKGROUND

[0002] Fuel cell hybrid power systems, as a kind of efficient and clean energy solution, show broad application prospects in communication base station backup power, data center, new energy vehicles and other fields. The system usually takes fuel cell stack as the main power supply, and is supplemented by lithium battery and other energy storage units to cope with load fluctuation and improve the dynamic response ability of the system.

[0003] However, when the load power exceeds the set output of the fuel cell, the lithium battery unconditionally supplements the excess power, which ignores the state of the lithium battery, especially when its state of charge is low, frequent or deep discharge will cause irreversible damage to its internal chemical structure, significantly accelerate the degradation of electrode materials, and cause rapid capacity decline; in addition, the aging rate of lithium battery is greatly different in different state of charge intervals, temperature and discharge depth, and the traditional control system often sacrifices the long-term service life of the battery in order to meet the instantaneous power demand.

[0004] Therefore, this operation mode directly leads to premature aging and performance degradation of the battery, not only increases the life cycle cost of the system, but also reduces the long-term reliability of the whole hybrid power system.

[0005] How to optimize the power distribution strategy in the hybrid power system, on the premise of ensuring the stable operation of the system, to maximize the avoidance of the working interval that causes damage to the lithium battery, so as to effectively slow down the battery aging speed and prolong its service life, is the key technical problem to be solved in the current fuel cell hybrid power technology field.

[0006] Therefore, a fuel cell-lithium battery hybrid power dynamic distribution system and medium are proposed. SUMMARY

[0007] The application aims to provide a fuel cell-lithium battery hybrid power dynamic distribution system and medium, which prolongs the service life of the battery through intelligent thermal management and dynamic power distribution. The system comprises a fuel cell stack, a lithium battery energy storage module, a three-way valve cooling subsystem, a dynamic power compensation module and a central processor. The real-time temperature field data of the fuel cell stack is used to dynamically control the cooling path. The waste heat data of the fuel cell is used to selectively preheat the lithium battery energy storage module through a heat exchanger. The bus voltage, load current and battery state are monitored to perform power distribution based on thermal constraints. When an overheating risk is predicted, the power request is dynamically redistributed from the fuel cell to the lithium battery energy storage module. When the power distribution calculation is performed, the operating mode of the cooling system and the corresponding parasitic energy consumption are optimized as dynamic variables to generate an optimized power distribution scheme. The application also discloses a computer medium using the fuel cell-lithium battery hybrid power dynamic distribution system.

[0008] To achieve the above object, the application provides the following technical scheme.

[0009] A fuel cell-lithium battery hybrid power dynamic distribution system comprises:

[0010] A fuel cell stack serves as a main power supply, and the output power is connected to a DC bus through a DC-DC converter.

[0011] A lithium battery energy storage module is connected to the DC bus through a bidirectional DC-DC converter, provides transient power and stores energy.

[0012] A three-way valve cooling subsystem uses the temperature field data of the fuel cell stack to dynamically control the cooling path, and selectively preheats the lithium battery energy storage module through a heat exchanger.

[0013] A dynamic power compensation module monitors the bus voltage, load current and battery state, and reallocates the power of the fuel cell stack and the lithium battery energy storage module when the temperature of the fuel cell stack is predicted to exceed the preset safety range.

[0014] A central controller optimizes the parasitic energy consumption corresponding to the operating mode of the three-way valve cooling subsystem as a dynamic variable to generate an optimized distribution scheme. The dynamic power compensation module executes the optimized distribution scheme. The compensation mode is determined according to the real-time state of charge of the lithium battery energy storage module. An external load prediction system is connected to predict the power, and the three-way valve cooling subsystem is instructed to start pre-cooling when a high load event is predicted.

[0015] Preferably, the process of dynamically controlling the cooling path comprises:

[0016] Based on the real-time temperature field data obtained from the three-way valve cooling subsystem, the temperature gradient between each region is calculated; and according to the comparison result of the temperature gradient with the preset high threshold value and the preset low threshold value, the corresponding control instruction is generated, when the temperature gradient is greater than the high threshold value, the reinforced cooling path is opened, and when the temperature gradient is less than the low threshold value, the energy-saving bypass circulation path is switched to.

[0017] Preferably, the generation of the control instruction is dynamically calculated based on the fuzzy weighted fusion of multiple basic control matrices, including: taking the state of charge of the lithium battery energy storage module and the current load power as the input of the fuzzy controller, and calculating the dynamic fusion weight corresponding to each basic control matrix through the structured fuzzy rule base composed of multiple parallel sub-rule bases; the sub-rule base includes: energy-saving strategy sub-rule base, performance strategy sub-rule base and balancing strategy sub-rule base; the real-time calculated temperature gradient is queried in all basic control matrices to obtain multiple candidate instruction sets, and the dynamic fusion weight calculated by each sub-rule base is used to weight average the candidate instruction sets to calculate the control instruction.

[0018] Preferably, the selective preheating of the lithium battery energy storage module by the outlet cooling liquid through the heat exchanger includes: monitoring the real-time temperature of the lithium battery energy storage module through the dynamic power compensation module; comparing the real-time temperature with the lower limit threshold of the preset lithium battery optimal working temperature interval; when it is judged that the real-time temperature is lower than the lower limit threshold, the high-temperature coolant flowing out of the fuel cell stack is selectively guided through the heat exchanger by the waste heat recovery and utilization circuit to heat the lithium battery energy storage module until the temperature returns to the optimal working temperature interval.

[0019] Preferably, the power redistribution of the fuel cell stack and the lithium battery energy storage module includes: the central controller predicts the future temperature trajectory under the current power output through the predictive thermal model based on the temperature change rate of the fuel cell stack; when the predictive thermal model judges that the future temperature trajectory will touch the safety upper limit threshold within a predetermined time, the power distribution strategy based on thermal constraints is triggered, and the central controller generates and issues a cooperative control instruction, the cooperative control instruction includes a derating instruction for actively reducing the power output of the fuel cell stack and a compensation instruction for synchronously instructing the bidirectional DC-DC converter to increase the power drawn from the lithium battery energy storage module; the dynamic power compensation module generates a correction control amount through a dynamic gain adjuster according to the cooperative control instruction to perform thermal limitation on the fuel cell stack.

[0020] Preferably, the dynamic gain adjuster includes:

[0021] An online system identification unit injects a pseudo-random binary sequence excitation signal into the control instruction of the bidirectional DC-DC converter, collects the excitation signal and the current response of the system, and estimates the impulse response function of the system in real time by using cross-correlation operation; a Fourier transform is performed on the impulse response function to solve the wideband dynamic impedance spectrum of the current state of the lithium battery energy storage module;

[0022] A state and disturbance decoupling unit compares the wideband dynamic impedance spectrum with the reference impedance spectrum of the lithium battery energy storage module in the reference health state, quantifies the internal impedance deviation caused by the aging of the battery state, and separates the external disturbance component caused by the change of the external load from the total response of the system;

[0023] A state feedback control law unit receives the internal impedance deviation and the external disturbance component, and calculates the original control instruction by using a preset feedback and feedforward gain matrix;

[0024] A control execution layer receives the original control instruction for updating to generate a final corrected control amount.

[0025] Preferably, the process of taking the operating mode of the three-way valve cooling subsystem and the corresponding parasitic energy consumption as dynamic variables for optimization includes: determining the current operating mode of the three-way valve cooling subsystem; obtaining the corresponding parasitic energy consumption value from a multi-dimensional lookup table according to the operating mode; subtracting the parasitic energy consumption value from the total power generated by the system in real time to obtain the real-time system net output power as the optimization target; continuously monitoring the response change of the real-time system net output power caused by the disturbance by injecting a periodic disturbance signal into the power distribution instruction of the system; and continuously tracking and converging to the current maximum point of the real-time system net output power by adaptively adjusting the basic operating point of power distribution through correlation demodulation and integration processing of the disturbance signal and the net power response change.

[0026] Preferably, when generating the optimized power distribution scheme, the central controller adopts a hierarchical target optimization strategy, including: when all indicators of the system are within the normal range, taking the maximization of the system net output power as the primary optimization target, and preferentially adjusting the power distribution to improve the operating efficiency of the whole machine; when a sudden change of greater than a preset threshold in the load power is monitored, temporarily switching to taking the maintenance of the stability of the DC bus voltage as the primary optimization target, and preferentially instructing the lithium battery to perform fast power compensation to ensure power quality; when the temperature or temperature gradient of the fuel cell stack exceeds the safety threshold, temporarily switching to taking the safety of the stack thermal management as the primary optimization target, and preferentially performing derating and intensified cooling.

[0027] Preferably, the compensation mode determined according to the real-time state of charge of the lithium battery energy storage module comprises: according to the real-time state of charge of the lithium battery energy storage module obtained from the dynamic power compensation module, classifying into one of a plurality of preset intervals including a forced charging interval, an adaptive compensation interval and a limited power compensation interval to determine the current compensation mode; and generating a control instruction through a feedforward-feedback composite control algorithm to adjust the bidirectional DC-DC converter in the dynamic power compensation module.

[0028] Preferably, the application further comprises an active electromagnetic disturbance suppression module, which acquires a characteristic spectrum generated by the switching frequency and harmonic components of the bidirectional DC-DC converter through a near-field probe arranged on the bidirectional DC-DC converter; when the disturbance amplitude of a specific frequency point is monitored to exceed a preset threshold, the active electromagnetic disturbance suppression module executes a dynamic spectrum optimization strategy based on a spread spectrum clock to periodically modulate the core switching frequency of the bidirectional DC-DC converter, thereby widening and dispersing the concentrated spectral energy into a wider frequency band to reduce the peak electromagnetic emission intensity.

[0029] A medium having computer program instructions stored thereon, wherein the computer program is executed by a processor to implement a fuel cell-lithium battery hybrid power dynamic allocation system step.

[0030] Compared with the prior art, the application has the following beneficial effects:

[0031] 1. By dynamically controlling the cooling path, local overheating of the stack is effectively avoided, and long-term reliability is ensured; by using a control instruction generation method based on fuzzy weighted fusion, the system can smoothly and continuously adjust the cooling strategy according to global states such as lithium battery SOC and load power, rather than simply switching thresholds, and the intelligence level is higher. In addition, the system also selectively preheats the lithium battery using fuel cell waste heat, improves the performance of the energy storage unit in cold environments, realizes gradient utilization of energy, and enhances the all-weather adaptability of the system.

[0032] 2. By using a cooperative control instruction, the lithium battery compensates for an equal amount of power while reducing the power of the fuel cell, thereby achieving preventive protection of the stack without interrupting power supply to the load. In this process, an advanced dynamic gain regulator performs adaptive control based on Lyapunov theory to ensure that the bus voltage is extremely stable during power transfer, thereby ensuring high-quality power supply.

[0033] 3. The application can balance the three core objectives of system net output power (economy), bus voltage stability (power quality) and stack temperature uniformity (long-term life) online. To maximize net power, the system also considers the parasitic energy consumption of the cooling system as a dynamic variable, thereby reducing the operating cost of the system.

[0034] 4、The application can monitor the electromagnetic spectrum generated by the bidirectional DC-DC converter in real time, and actively execute the dynamic spectrum optimization strategy based on the spread spectrum clock when the disturbance exceeds the standard. By spreading the concentrated spectrum energy, the system significantly reduces the peak electromagnetic emission intensity, improves its electromagnetic compatibility, and enables it to be safely and reliably deployed in communication base stations, data centers, and other places with strict requirements for the electromagnetic environment. BRIEF DESCRIPTION OF DRAWINGS

[0035] Fig. 1 A fuel cell-lithium battery hybrid power dynamic distribution system structure schematic diagram is provided for the embodiments of the application.

[0036] Fig. 2 A dynamic power compensation module distribution strategy flowchart schematic diagram is provided for the embodiments of the application.

[0037] Fig. 3 A dynamic power compensation method flowchart schematic diagram is provided for the embodiments of the application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0039] Please refer to Figs. 1 to 3 The application provides a fuel cell-lithium battery hybrid power dynamic distribution system and medium, and the technical solutions are as follows:

[0040] A fuel cell-lithium battery hybrid power dynamic distribution system, the specific structure is as shown in Fig. 1 , comprising:

[0041] The fuel cell stack, as the main power supply, outputs power through the DC-DC converter to access the DC bus;

[0042] The lithium battery energy storage module is connected to the DC bus through the bidirectional DC-DC converter, provides transient power and stores energy;

[0043] The three-way valve cooling subsystem uses the temperature field data of the fuel cell stack to dynamically control the cooling path, and selectively preheats the lithium battery energy storage module through the heat exchanger;

[0044] The dynamic power compensation module monitors the bus voltage, load current and battery state, and reallocates the power of the fuel cell stack and the lithium battery energy storage module when it is predicted that the temperature of the fuel cell stack exceeds the preset safety range.

[0045] The central controller optimizes the parasitic energy consumption corresponding to the operation mode of the three-way valve cooling subsystem as a dynamic variable to generate an optimized distribution scheme; the dynamic power compensation module executes the optimized distribution scheme; the compensation mode is determined according to the real-time state of charge of the lithium battery energy storage module; the power is predicted by accessing an external load prediction system, and the three-way valve cooling subsystem is instructed to start pre-cooling when a high load event is predicted.

[0046] It also includes a three-way valve, a circulating pump, a heat dissipation assembly connected to the fuel cell stack, and temperature sensors arranged in different areas of the stack (e.g., water inlet, water outlet, and at least two core reaction zones).

[0047] In this embodiment, the following control parameters are preset for the central controller:

[0048] The high temperature gradient threshold T_H is 5°C, the low temperature gradient threshold T_L is 2°C, and the control period t_c is 200ms.

[0049] Further, the process of dynamically controlling the cooling path includes:

[0050] Based on the real-time temperature field data obtained from the three-way valve cooling subsystem, the temperature gradient between each region is calculated; and according to the comparison result of the temperature gradient with the preset high threshold and the preset low threshold, the corresponding control instruction is generated, when the temperature gradient is not less than the high threshold, the enhanced cooling path is started, and when the temperature gradient is not greater than the low threshold, the energy-saving bypass circulation path is switched to.

[0051] At the beginning of each control period (t_c), the central controller reads the temperature sensor values arranged at all monitoring points of the fuel cell stack through the communication bus in real time to obtain a complete, instantaneous temperature field data snapshot. The central controller processes the collected temperature field data, calculates the maximum temperature gradient in the entire stack at the current time by comparing the temperature values between all monitoring points, and records it as ΔT_max.

[0052] The central controller compares the calculated ΔT_max with the preset high and low thresholds and performs the following logical judgment:

[0053] Case 1: If ΔT_max ≥ T_H (5°C), it indicates that there is significant temperature non-uniformity inside the stack, and there is a risk of local overheating. At this time, the controller determines the target path as the enhanced cooling path. The controller generates a set of control instructions to instruct the three-way valve to switch to the path that directs the cooling liquid to the heat dissipation assembly completely, and at the same time instructs the circulating pump to run at high speed to achieve maximum heat dissipation.

[0054] Case two: If ΔT_max≤T_L (2℃), it indicates that the temperature inside the stack is very uniform, and the system is in a stable or low heat production state. At this time, the controller determines that the target path is the energy-saving bypass circulation path. The controller generates a set of control instructions, instructing the three-way valve to switch to the bypass path that completely bypasses the heat dissipation assembly, and instructing the circulating pump to run at a lower energy-saving speed to save energy.

[0055] Case three: If T_L<ΔT_max<T_H (i.e., the temperature difference is between 2℃ and 5℃), it indicates that the system state is within an acceptable range, and to avoid oscillation caused by frequent switching of the control system at the threshold boundary, the controller will maintain the control instructions of the last period unchanged, keeping the current cooling path and water pump speed.

[0056] The central controller generates corresponding control instructions, which are sent to the actuators of the three-way valve and the drivers of the circulating pump through control signals, to drive the hardware to complete path switching and speed adjustment.

[0057] After completing one instruction execution, the system waits for the arrival of the next control period (200ms later), and repeats the whole process. Through this high-speed and continuous closed-loop control, the system ensures that the temperature gradient of the fuel cell stack is always controlled within the target range.

[0058] The process of dynamically controlling the cooling path also includes an adaptive stabilization mechanism to suppress control oscillation. After each cooling path switching, the mechanism performs a locking operation to avoid frequent switching at the threshold boundary. The locking time is associated with the change trend of the system thermal state. When the central controller judges that the temperature gradient changes rapidly, indicating that the system thermal state is unstable, the locking time is shortened to retain faster response capability. Conversely, when the temperature gradient changes slowly and the system is in a steady state, the locking time is extended to prioritize the stability of operation and reduce the wear of the actuator. This adaptive stabilization mechanism intelligently balances response speed and stability, extending the life of the hardware while ensuring the safety response of the system in critical working conditions.

[0059] Through the method described in the embodiment, the present application can select the cooling path in real time and intelligently according to the actual thermal state of the stack, effectively prevent local overheating, improve temperature uniformity, and significantly reduce energy consumption when the system load is low.

[0060] Further, the generation of the control instruction is based on fuzzy weighted fusion of a plurality of basic control matrices and is dynamically calculated, including: taking the state of charge of the lithium battery energy storage module and the current load power as inputs of a fuzzy controller, and calculating dynamic fusion weights respectively corresponding to each basic control matrix through a structured fuzzy rule base composed of a plurality of parallel sub-rule bases; the sub-rule base includes: an energy-saving strategy sub-rule base, a performance strategy sub-rule base, and a balancing strategy sub-rule base; querying the real-time calculated temperature gradient in all basic control matrices to obtain a plurality of candidate instruction sets, and calculating the control instruction by weighted averaging of the candidate instruction sets using the dynamic fusion weights calculated by each sub-rule base.

[0061] The fuzzy controller of the central controller takes the state of charge of the lithium battery energy storage module and the current load power as inputs, which are two continuously changing system state variables. The structured fuzzy rule base inside the fuzzy controller calculates the dynamic fusion weights respectively corresponding to each basic matrix in real time according to the above inputs, and the sum of all weights is always 1.

[0062] The logic of this weight calculation is determined by the sub-rule base inside it, for example:

[0063] The energy-saving strategy sub-rule base ensures that when the system SOC decreases or the load power decreases, the weight will increase accordingly;

[0064] The performance strategy sub-rule base ensures that when the system SOC increases or the load power increases, the weight will increase accordingly;

[0065] The balancing strategy sub-rule base ensures that the weight dominates in the intermediate state.

[0066] The central controller calculates the maximum temperature gradient ΔT_max of the stack in real time. The controller uses the value of ΔT_max and queries all basic control matrices (energy saving, balancing, performance) in parallel to obtain a plurality of independent candidate instruction sets respectively representing different strategies.

[0067] The controller uses the calculated dynamic fusion weights to perform weighted average operation on the obtained plurality of candidate instruction sets. The output result of the operation is used as the cooperative control instruction. For example, the final circulating pump speed instruction is obtained by multiplying the speed value in each candidate instruction by the corresponding fusion weight and then summing them up.

[0068] The central controller sends the calculated final cooperative control instruction to actuators such as three-way valves and circulating pumps for execution, and the whole process is performed at high speed, thereby achieving smooth, continuous and optimal dynamic adjustment of the cooling system.

[0069] Through this embodiment, the system no longer switches between several fixed modes, but can dynamically and continuously integrate the advantages of multiple control strategies according to the continuous change of the system power state, so as to find the best balance point of performance and energy consumption under any working condition.

[0070] Further, the selective preheating of the lithium battery energy storage module by the heat exchanger using the fuel cell waste heat data comprises: monitoring, by the dynamic power compensation module, a real-time temperature of the lithium battery energy storage module; comparing the real-time temperature with a lower threshold of a preset optimal working temperature interval of the lithium battery; when it is judged that the real-time temperature is lower than the lower threshold, selectively guiding, by the waste heat recovery and utilization circuit, the high-temperature coolant flowing out of the fuel cell stack through the heat exchanger to heat the lithium battery energy storage module until the temperature returns to the optimal working temperature interval.

[0071] Specifically, during system operation, the central controller will monitor two key real-time data through the dynamic power compensation module and other data acquisition units: one is the real-time temperature of the lithium battery energy storage module, and the other is the real-time temperature of the high-temperature coolant flowing out of the stack outlet, which represents the fuel cell waste heat data. To achieve accurate control, a lower threshold of the optimal working temperature interval of the lithium battery and an available waste heat temperature threshold are preset in the controller.

[0072] The starting decision of the preheating program is a double-condition judgment process. First, the controller compares the monitored real-time temperature of the lithium battery with the preset lower threshold, and second, the controller compares the monitored waste heat data (i.e. the coolant temperature) with the preset available heat source threshold. Only when the real-time temperature of the lithium battery is lower than the lower threshold of its optimal working interval and the fuel cell is indeed generating sufficient high-grade waste heat, the controller will determine that the preheating condition is met. This double-judgment mechanism avoids inefficient preheating attempts when the fuel cell is not fully warmed up and the waste heat temperature is insufficient.

[0073] Once the preheating condition is met, the controller will instruct an electrically controlled valve to open through the waste heat recovery and utilization circuit, selectively guiding the high-temperature coolant flowing out of the fuel cell stack through the heat exchanger connected to the lithium battery. The high-temperature coolant continuously heats the lithium battery energy storage module, and the central controller continues to monitor the battery temperature. This heating process will continue until the temperature of the lithium battery energy storage module returns to its preset optimal working temperature interval, and the controller will instruct the valve to close to terminate the preheating process.

[0074] By utilizing the energy that would otherwise be wasted, the additional parasitic loss caused by traditional electric heater is replaced, directly improving the overall energy utilization efficiency of the system. More importantly, it ensures that the lithium battery can still maintain in the optimal working temperature range in cold environments, thereby ensuring its transient power compensation performance and effectively prolonging the service life of the battery by avoiding low-temperature damage. This preheating strategy based on intelligent judgment of waste heat data greatly enhances the all-weather adaptability and operation reliability of the system.

[0075] Further, the central controller predicts a future temperature trajectory under the current power output based on a temperature change rate of the fuel cell stack through a predictive thermal model; when the predictive thermal model determines that the future temperature trajectory will reach a safety upper threshold within a predetermined time, a power distribution strategy based on thermal constraints is triggered, and the central controller generates and issues a coordinated control instruction, which includes a derating instruction for actively reducing the power output of the fuel cell stack and a compensation instruction for synchronously instructing the bidirectional DC-DC converter to increase the power drawn from the lithium battery energy storage module; the dynamic power compensation module generates a correction control quantity through a dynamic gain adjuster according to the coordinated control instruction to perform thermal limitation on the fuel cell stack, and the specific process is as shown in Fig. 2

[0076] Specifically, a predictive thermal model is embedded in the central controller, which describes the thermal capacity and thermal resistance characteristics of the stack. To implement this strategy, in this embodiment, the control parameters set in the controller include: the safety temperature upper threshold of the stack is set to 85℃, and the prediction time window is set to 60 seconds.

[0077] During system operation, the central controller continuously provides real-time fuel cell stack temperature change rate and current power output and other working condition data as inputs to the first-order lumped parameter thermal model. Based on these inputs, the model calculates and generates a temperature trajectory prediction curve for the next 60 seconds in real time, which depicts the possible trend of stack temperature change. The central controller continuously analyzes the predicted trajectory, and once it determines that the trajectory will reach the safety upper threshold of 85℃ within a predetermined time, it determines that there is an imminent risk of overheating in the system, and immediately triggers a power distribution strategy based on thermal constraints.

[0078] ​After the strategy is triggered, the central controller will immediately generate and issue a set of coordinated control instructions. The instruction set contains two parts: one part is the power reduction instruction sent to the fuel cell power controller, which will actively reduce the upper limit of the fuel cell power output to reduce heat generation from the source; the other part is the compensation instruction sent to the dynamic power compensation module at the same time, which will drive the bidirectional DC-DC converter to draw power equal to the fuel cell reduction value from the lithium battery energy storage module, thereby ensuring that the total power supply to the terminal load is not affected.

[0079] The dynamic gain regulator inside the dynamic power compensation module plays a key role in performing power transfer according to the set of coordinated control instructions, generating accurate correction control, driving the DC-DC converter to complete power compensation smoothly and stably, and efficiently completing the preventive thermal limitation of the fuel cell without affecting the power supply quality.

[0080] The coordinated control instructions also include a dynamic and asymmetric time phase relationship between the control instructions issued to the two converters. Specifically, when power transfer is needed, the central controller will issue an instantaneous compensation instruction to the lithium battery converter that responds faster according to the inherent dynamic response speed difference between the two converters, and issue a reduction instruction with a preset delay or smooth slope processing to the fuel cell converter that responds relatively slowly. Through this active time offset and waveform shaping, it is ensured that the power changes of the two converters can be real-time complementary during the entire dynamic transfer process, thereby maximizing the suppression of instantaneous fluctuations in the DC bus voltage. By actively coordinating the timing of the instructions, seamless power transfer is achieved, improving power quality and system stability.

[0081] After predicting the risk of overheating through the thermal model, the system reduces the power of the fuel cell while instructing the lithium battery to compensate for an equal amount of power seamlessly. This process is ensured to be smooth and stable by the dynamic gain regulator, thereby resolving the risk of thermal runaway in advance without affecting the continuity of power supply to the terminal load and the power quality, and prolonging the service life of the equipment by avoiding high temperature impact.

[0082] Further, the dynamic gain regulator comprises:

[0083] An online system identification unit injects a pseudo-random binary sequence excitation signal into the control instruction of the bidirectional DC-DC converter; collects the excitation signal and the current response of the system, and estimates the impulse response function of the system in real time using cross-correlation operation; performs Fourier transform on the impulse response function to solve the wideband dynamic impedance spectrum of the current state of the lithium battery energy storage module;

[0084] The state and disturbance decoupling unit compares the wideband dynamic impedance spectrum with the reference impedance spectrum of the lithium battery energy storage module at the benchmark health state, quantifies the internal impedance deviation caused by battery state aging, and separates the external disturbance component caused by external load changes from the system total response;

[0085] The state feedback control law unit receives the internal impedance deviation and the external disturbance component, and calculates the original control instruction through the preset feedback and feedforward gain matrix.

[0086] The control execution layer receives the original control instruction for updating and generates the final corrected control amount.

[0087] Specifically, the excitation signal is selected as a pseudo-random binary sequence generated by a 10-bit linear feedback shift register, with a sequence length of 1023 bits. The update clock of the sequence is synchronized with the main control clock of the system, and is set to 10 kHz. The logic level output by the pseudo-random binary sequence is mapped to two different numerical values, and after being multiplied by a fixed amplitude coefficient, it is superimposed in digital form on the original control instruction output by the state feedback control law unit. The selection principle of the amplitude coefficient is that the voltage ripple ultimately caused at the output end of the bidirectional DC-DC converter does not exceed the tolerance band allowed by the bus voltage under the steady-state operation index.

[0088] To realize real-time estimation of the impulse response function, two circular data buffers with a length of 1023 are set in the system identification unit, which are used to store the excitation signal sequence and the current response sequence of the system collected in the latest acquisition period, respectively. The estimation of the impulse response function is completed by multiplying and accumulating the current response sequence collected in real time with the excitation signal sequence in the same time period and with different time delays point by point. For each possible delay time point, this process is repeated to obtain the complete impulse response function.

[0089] Then, the impedance spectrum calculation unit receives the discrete impulse response function sequence calculated by the online system identification unit, and internally calls a 1024-point fast Fourier transform algorithm to transform the impulse response function sequence, thereby obtaining the discrete frequency response of the system. The discrete frequency response is the wideband dynamic impedance spectrum representing the current state of the lithium battery energy storage module, and its result contains impedance amplitude and phase angle information at a series of frequency points in the frequency range of 0 to 5 kHz.

[0090] Subsequently, the state and disturbance decoupling unit analyzes the calculated impedance spectrum, the core of which is a reference impedance spectrum lookup table stored in the controller's non-volatile memory, which pre-stores impedance spectrum data of the lithium battery at the reference state of health. By vector subtracting the complex value of the real-time calculated dynamic impedance spectrum from the complex value of the reference impedance spectrum corresponding to the current working condition obtained by interpolation from the lookup table, a deviation spectrum is obtained. The shape and amplitude change of the deviation spectrum in the medium and high frequency range are quantified as internal state deviation quantities reflecting the current state of health deterioration or temperature change of the battery. At the same time, the unit identifies the deviation of the deviation spectrum in the direct current and near direct current very low frequency range as an equivalent direct current bias caused by the step change of the external load, and separates and quantifies it as an external disturbance component.

[0091] Finally, the state feedback control law unit receives the internal impedance deviation quantity and the external disturbance component provided by the previous stage, and the final original control instruction is linearly superimposed by a feedforward compensation part and a feedback correction part. The feedforward compensation part is obtained by multiplying the estimated external disturbance component by a preset feedforward gain coefficient, and is used to actively and quickly offset the external load disturbance. The feedback correction part is obtained by calculating the quantified internal impedance deviation through a preset mapping function and a feedback gain matrix, and is used to correct the dynamic response deviation caused by the change of the battery internal state. The preset feedback and feedforward gain matrices are calculated in the offline design stage through the linear quadratic regulator optimal control algorithm in modern control theory.

[0092] Firstly, based on the state space model of the system, linearization is performed for multiple typical operating points; secondly, a quadratic objective function is defined for measuring the comprehensive performance of the system, which weighs the tracking error size of key state variables (such as bus voltage deviation and battery internal state deviation) and the energy consumption size of control output instructions; finally, by solving the corresponding algebraic Riccati equation, a set of optimal feedback gain matrices can be uniquely calculated, which can minimize the value of the quadratic objective function.

[0093] For the feedforward gain, it is calculated based on the principle of eliminating steady-state error after the feedback gain is determined. Through this design method based on optimal control theory, the gain matrix obtained can ensure the system to have fast and stable dynamic response performance, while taking into account the control cost (i.e. switching loss and energy scheduling of the power converter) in the most optimal mathematical way, thereby realizing the internal unity of high performance and high efficiency.

[0094] The control execution layer receives the digitized original control instruction and converts it into a high-frequency gate drive signal with a frequency of 20 kHz for driving the power switch tube of the bidirectional DC-DC converter through a digital pulse width modulation module, so as to complete the whole closed-loop control.

[0095] The whole dynamic gain regulator forms a complete closed-loop adaptive system, realizes a dynamic control system capable of perceiving the internal state change of the lithium battery and external load disturbance and performing active, rapid and accurate compensation, and significantly improves the stability and robustness of the hybrid power system.

[0096] Further, the process of optimizing the operation mode of the cooling system and the corresponding parasitic energy consumption as a dynamic variable comprises: determining the current operation mode of the three-way valve cooling subsystem; according to the operation mode, obtaining the corresponding parasitic energy consumption value from a multi-dimensional lookup table; subtracting the parasitic energy consumption value from the total power generated by the system in real time to obtain the real-time system net output power as the optimization target; continuously monitoring the response change of the real-time system net output power caused by the disturbance by injecting a periodic disturbance signal into the power distribution instruction of the system; and continuously tracking and converging to the current maximum point of the real-time system net output power by correlating and demodulating and integrating the disturbance signal and the net power response change to adaptively adjust the basic working point of power distribution.

[0097] Specifically, the central controller executes a real-time optimization algorithm to continuously track and operate at the maximum point of the real-time system net output power. The net output power is defined as the total power generated by the system minus the parasitic energy consumption of the cooling and auxiliary systems.

[0098] Firstly, the central controller determines the current operation mode of the three-way valve cooling subsystem and the operation state of the circulating pump and the fan in real time. According to these states, the controller queries the accurate parasitic energy consumption value under the current working condition in an internally stored multi-dimensional lookup table. At the same time, the controller obtains the total power generated by the system through a power sensor. Then, the controller subtracts the two to obtain the real-time system net output power as the optimization target.

[0099] Next, the optimization algorithm is started. The controller injects a preset, small periodic disturbance signal into the basic working point of the power distribution instruction of the current system. The disturbance signal is a sine wave with a specific amplitude and frequency, wherein the amplitude is small enough not to affect the normal operation of the system, and the frequency is much lower than the main dynamic response frequency of the system. The controller continuously monitors the response change of the real-time system net output power caused by the disturbance while injecting the disturbance signal.

[0100] Finally, the algorithm module inside the controller correlates and integrates the two signals, the input disturbance signal and the output net power response change. The core of this process is to estimate the gradient direction of the net output power to the power distribution operating point through demodulation: if the response of the net output power is in phase with the disturbance signal, it means that the current operating point is on the left side of the maximum point, and the operating point should be increased; if it is anti-phase, it means that it is on the right side, and the operating point should be reduced. The gradient estimation signal demodulated is processed by an integrator, and its output is used as the adaptive adjustment amount of the basic operating point of the power distribution. Through this closed-loop process, the operating point of the system is continuously driven to climb, and eventually it can continuously track and converge to the current maximum point of the net output power of the real-time system.

[0101] By dynamically counting the parasitic energy consumption of the cooling system and taking the net output power as the optimization target, the system can automatically track and run at the real maximum point of the actual available power. This adaptive optimization method ensures that the system can maintain the highest running efficiency under any working condition and aging state, thereby maximizing the utilization of fuel and ultimately significantly reducing the long-term operating cost of the system.

[0102] Further, the central controller adopts a hierarchical target optimization strategy when generating the optimized power distribution scheme, including: when all system indicators are within the normal range, taking the maximization of the system net output power as the primary optimization target, and preferentially adjusting the power distribution to improve the overall operating efficiency; when a sudden change in load power greater than a preset threshold is monitored, temporarily switching to taking the maintenance of the DC bus voltage stability as the primary optimization target, and preferentially instructing the lithium battery to perform rapid power compensation to ensure power quality; when the temperature or temperature gradient of the fuel cell stack exceeds the safety threshold, temporarily switching to taking the safety of the stack thermal management as the primary optimization target, and preferentially performing derating and intensified cooling.

[0103] Specifically, in this embodiment of the present application, the central controller executes a set of hierarchical target optimization strategies, which can dynamically adjust its control priority under different operating scenarios. This strategy contains three main operating modes:

[0104] The normal operation mode, when the system is in stable operation, i.e. the load power fluctuation is gentle, the DC bus voltage is stable, and the fuel cell temperature and temperature gradient are in the ideal range, the system defaults to work in this mode. At this time, the primary optimization goal of the central controller is to maximize the system net output power. To achieve this goal, the controller will perform a real-time net power optimization algorithm: it will take the current parasitic energy consumption of the cooling system as a cost item, subtract it from the total power to get the net output power; and by injecting a small periodic disturbance into the power distribution instruction, it continuously and adaptively adjusts the power distribution ratio between the fuel cell and the lithium battery to track and converge to the real maximum point of the net output power, thereby preferentially improving the overall system efficiency.

[0105] The transient response mode, when the central controller monitors a sudden change in load power greater than a preset threshold (for example, the load jumps more than 30% of the rated power within 100 milliseconds), the system will immediately temporarily switch from the normal operation mode to the primary optimization goal of maintaining the stability of the DC bus voltage. In this mode, the controller will suspend the net power optimization calculation and instead instruct the lithium battery energy storage module to perform fast power compensation through the bidirectional DC-DC converter. The lithium battery will respond instantly to absorb or release large current to offset the impact of load mutation, ensuring that the bus voltage fluctuation is suppressed within a very small range, thereby ensuring power quality. Once the load impact ends and the bus voltage returns to stability, the system will automatically switch back to the normal operation mode.

[0106] The safety protection mode, when the central controller monitors that the temperature or temperature gradient of the fuel cell stack exceeds the preset safety threshold (for example, the average temperature is higher than 85°C or the gradient is greater than 10°C), the system will prioritize safety over all other goals and immediately temporarily switch to the primary optimization goal of ensuring the safety of the stack thermal management. In this mode, the controller will preferentially perform a set of coordinated actions: first, issue a derating instruction to the fuel cell to actively reduce its power output to reduce heat generation; second, issue an instruction to the three-way valve cooling subsystem to switch to the enhanced cooling path; at the same time, instruct the lithium battery to perform power compensation to maintain power supply to the load. Only when the stack temperature and temperature gradient have returned to the safety range will the system exit this mode.

[0107] Through this set of hierarchical target optimization strategy, the central controller can act like an intelligent decision-making brain, dynamically and seamlessly switching between the three core tasks of "pursuing efficiency", "ensuring stability" and "ensuring safety" according to the priority of real-time working conditions, making the entire system have high efficient economy, excellent dynamic performance and strong operation reliability.

[0108] Further, the central controller executes a dynamic power compensation method, including: according to the real-time state of charge of the lithium battery energy storage module obtained from the dynamic power compensation module, classifying the same into one of a plurality of preset intervals including a forced charging interval, an adaptive compensation interval and a limited power compensation interval, and determining a current compensation mode according to the same; and generating a control instruction through a feedforward-feedback compound control algorithm to adjust the bidirectional DC-DC converter in the dynamic power compensation module, and the specific process is as shown in Fig. 3

[0109] Specifically, in this embodiment of the present application, in order to balance the management of the state of health of the lithium battery and the dynamic performance of the system, the central controller executes a set of adaptive dynamic power compensation methods, the core of which is to dynamically adjust the working mode and control strategy of the lithium battery energy storage module according to the real-time state of charge (SOC) of the lithium battery energy storage module. Three clear SOC working intervals are pre-divided in the controller, and different compensation modes are set for each interval.

[0110] During the operation of the system, the central controller continuously obtains the real-time SOC value from the dynamic power compensation module, and classifies the same into one of the following three preset intervals to determine the compensation mode to be executed at present:

[0111] Adaptive compensation interval (SOC is 30% to 80%):

[0112] In this mode, the lithium battery energy storage module is used as a high-performance power buffer, and its potential is fully utilized. The controller uses a feedforward-feedback compound control algorithm to adjust the bidirectional DC-DC converter. Among them, the feedforward control loop monitors the change of the load power in real time and compensates in advance to cope with sudden changes; the feedback control loop accurately stabilizes the bus voltage. This ensures that the system has the best dynamic response performance while maintaining the SOC in a healthy range.

[0113] Limited power compensation interval (SOC is less than 30%):

[0114] When the battery power is low, the core goal of the system control strategy is switched from performance pursuit to battery protection. In this mode, although the feedforward-feedback compound control algorithm still runs to stabilize the voltage, the controller will additionally apply a power limit. The maximum discharge power of the lithium battery energy storage module is strictly limited to a safe and low level, thereby effectively avoiding irreversible damage to the battery caused by deep discharge and prolonging the service life of the battery.

[0115] Forced charging interval (SOC is higher than 80%):

[0116] ​When the battery is fully charged, the primary task of the system is to avoid overcharging the battery and prepare for subsequent power compensation. In this mode, the discharge function of the lithium battery will be inhibited. If the output power of the fuel cell is greater than the current load required, the controller will instruct the lithium battery energy storage module to absorb the excess energy by adjusting the bidirectional DC-DC converter until it is full. This ensures efficient use of system energy and maintains the optimal standby state of the energy storage unit.

[0117] Through dynamic mode switching based on SOC intervals, the present application ensures that the system can achieve an intelligent and optimal balance between high performance response and long life protection under different working conditions.

[0118] Further, an active electromagnetic disturbance suppression module is also included, which acquires the characteristic spectrum generated by the switching frequency and harmonic components of the bidirectional DC-DC converter in real time through the near-field probe arranged on the bidirectional DC-DC converter; when the disturbance amplitude of a specific frequency point is monitored to exceed the preset threshold, the active electromagnetic disturbance suppression module executes a dynamic spectrum optimization strategy based on the spread spectrum clock to periodically modulate the core switching frequency of the bidirectional DC-DC converter, which widens and disperses the concentrated spectral energy into a wider frequency band, reducing the peak electromagnetic emission intensity.

[0119] Specifically, in this embodiment of the present application, to ensure that the system can reliably operate in sensitive electromagnetic environments such as communication base stations, the system integrates an active electromagnetic disturbance suppression module. The core components of this module include a near-field probe (such as a magnetic loop antenna) installed above the power inductor of the bidirectional DC-DC converter, a spectrum analysis unit, and a dynamic clock modulation unit. The module has preset frequency points that need to be monitored, namely the core switching frequency (100 kHz in this embodiment) of the DC-DC converter and its key second and third harmonic frequency points, and sets the upper limit of the disturbance amplitude of these frequency points as the preset threshold, which meets the relevant electromagnetic compatibility standards (such as CISPR25).

[0120] During system operation, the near-field probe continuously acquires the electromagnetic field signals radiated by the DC-DC converter when it is working. The signals are sent to the spectrum analysis unit, which generates the characteristic spectrum of the system in this frequency band in real time by performing fast Fourier transform (FFT). The central controller continuously analyzes the spectrum and compares the actual disturbance amplitude at the monitoring frequency point with the preset threshold.

[0121] When the controller determines that the disturbance amplitude of any monitoring frequency point exceeds its corresponding preset threshold, the dynamic spectrum optimization strategy based on the spread spectrum clock is triggered immediately. At this time, the dynamic clock modulation unit changes the clock signal output to the DC-DC converter gate driver. It no longer outputs a fixed 100kHz square wave, but performs periodic triangular wave modulation on the core switching frequency. Specifically, the switching frequency will be linearly and periodically scanned within a preset frequency offset range (such as ±2.5kHz) based on a center frequency of 100kHz, i.e. varying between 97.5kHz and 102.5kHz.

[0122] The periodic modulation of the switching frequency can spread and disperse the originally highly concentrated single and sharp spectral energy at 100kHz and its harmonic frequencies into a wider frequency band. Although the total radiation energy remains essentially unchanged, the peak electromagnetic emission intensity is significantly weakened, enabling the system to meet electromagnetic compatibility standards.

[0123] Further, the dynamic spectrum optimization strategy performed by the active electromagnetic disturbance suppression module also includes a feedforward triggering mechanism based on working condition prediction. When the central controller determines that the system is about to enter a high-power output or load-intensive impact working condition, the active electromagnetic disturbance suppression module will start the operation of the spread spectrum clock in advance to actively suppress and avoid foreseeable electromagnetic noise enhancement in such working conditions. This ensures that the system meets electromagnetic compatibility standards at all times, especially under dynamic impact, achieving higher levels of operational reliability.

[0124] Through real-time spectrum monitoring and dynamic spread spectrum clock strategy, intelligent on-demand suppression is achieved. It only actively modulates the switching frequency when the disturbance exceeds the standard, spreading the sharp spectral energy, thereby significantly reducing the peak electromagnetic emission without sacrificing regular operating efficiency. This ensures that the system meets electromagnetic compatibility standards, not only ensuring reliable application of the system in sensitive environments such as communication base stations, but also helping to reduce dependence on bulky external shielding and filters, making the product more integrated and cost-effective.

[0125] By a set of dynamic power distribution system integrated with intelligent thermal management and hierarchical optimization control, the contradiction between instantaneous performance and long-term life in traditional hybrid power system is effectively solved. Instead of passively allowing lithium battery to supplement power, active and collaborative power allocation is performed according to the predicted thermal risk of fuel cell and the real-time state of charge (SOC) of lithium battery, which can prevent fuel cell from overheating through power transfer and limit lithium battery output to avoid deep discharge damage when the battery is low. The system can intelligently switch priorities among ensuring power quality, improving net output efficiency and ensuring component safety according to the working conditions, and uses fuel cell waste heat to preheat lithium battery, realizing energy cascade utilization. On the premise of ensuring reliable power supply, the overall operation economy is improved and the service life of core components is prolonged.

[0126] Embodiment two:

[0127] The application scenario of the embodiment is a fuel cell-lithium battery hybrid power system for powering a communication base station, which is equipped with a fuel cell-lithium battery hybrid power dynamic distribution system provided by the application.

[0128] During a power grid failure, the standby power supply system of the base station is immediately activated. When the system starts, the central controller monitors that the performance of the lithium battery energy storage module is limited due to low ambient temperature through the dynamic power compensation module. To ensure that the energy storage unit can respond to subsequent power fluctuations at any time, the controller immediately executes the selective preheating function: after monitoring that the cooling liquid temperature (i.e. waste heat data) at the outlet of the fuel cell stack reaches the available threshold, it guides the high-temperature coolant to flow through the heat exchanger to heat the lithium battery until its temperature returns to the optimal working interval.

[0129] When the network traffic of the base station is at a low point, the load is stable and the power is low. At this time, the system automatically runs in a mode with maximizing the system net output power as the primary optimization target. The controller calculates the weight of the energy-saving strategy through the fuzzy weighted fusion algorithm according to the state of charge of the lithium battery and the working condition of low load, and instructs the three-way valve cooling subsystem to switch to the energy-saving bypass circulation path. At the same time, the system starts the optimization algorithm based on disturbance observation, adjusts the power distribution point between the fuel cell and the lithium battery to track and converge to the real maximum point of the net output power by injecting a small periodic disturbance signal into the power distribution instruction and monitoring the response change of the net output power caused by the disturbance, and prioritizes the economy of operation.

[0130] When a large number of users begin to access the network, the base station load changes dramatically in a short time, the system monitors that the load impact is greater than the preset threshold, immediately temporarily switches the control strategy, and turns to maintain the direct current bus voltage stability as the primary optimization target, during which the net power optimization is suspended, the controller preferentially instructs the lithium battery energy storage module to compensate through the bidirectional DC-DC converter in a fast and high-power manner, instantaneously responds to the load mutation, and ensures that the fluctuation of the bus voltage is suppressed within a very small range, thereby guaranteeing high-quality power supply and uninterrupted communication services.

[0131] After entering the daytime business peak period, the base station needs to cope with long-time and continuous high-power output, resulting in continuous high-load operation of the fuel cell. At this time, the predictive thermal model built in the central controller predicts that the stack temperature trajectory will reach the safety upper limit in a short time according to the temperature change rate and the current power. The optimization target of the system is switched again, and temporarily the safety of the stack thermal management is placed in the first place. The controller immediately issues a coordinated control instruction, which actively reduces the power output of the fuel cell (derating instruction) to cool down from the source, and simultaneously instructs the lithium battery to increase the power output to compensate for the difference and maintain the total power supply to the base station. During the whole process of seamless power transfer, the dynamic gain regulator identifies and decouples the dynamic impedance spectrum of the battery online, the controller can accurately feed forward the compensation of the load disturbance, and combine the state feedback to adjust the internal state change of the battery, so that the bus voltage is still highly stable during the power transfer.

[0132] During the whole continuous operation process, the active electromagnetic disturbance suppression module of the system always works in the background. It continuously and real-time collects the characteristic frequency spectrum generated by the bidirectional DC-DC converter through the near-field probe. Once it is monitored that the disturbance amplitude of a specific frequency point exceeds the preset threshold (especially during the stage of severe load change), the module will actively execute the dynamic frequency spectrum optimization strategy based on the spread spectrum clock, spread the concentrated frequency spectrum energy, significantly reduce the peak electromagnetic emission intensity, ensure that the hybrid power system itself does not interfere with the sensitive radio frequency communication equipment in the base station, and realize the unification of high reliability and high electromagnetic compatibility.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments.

[0134] Through intelligent thermal management and dynamic power distribution, fine protection and efficient utilization of core components are realized. The system not only avoids the risk of overheating of the fuel cell in advance through the predictive thermal model, but also uses the waste heat to preheat the lithium battery, prolongs the service life of the equipment and enhances the all-weather adaptability. In terms of power distribution, the system can intelligently switch and optimize among multiple targets such as ensuring the stability of the bus voltage, improving the net output efficiency and ensuring the operation safety according to the real-time working condition. Combined with the active electromagnetic disturbance suppression technology, it is especially suitable for scenarios such as communication base stations and data centers that have extremely high requirements for reliability and power quality.

[0135] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A fuel cell-lithium battery hybrid power dynamic allocation system, characterized by, The application relates to a fuel cell power supply system, comprising: a fuel cell stack as a main power supply, output power of which is connected to a DC bus through a DC-DC converter; a lithium battery energy storage module connected to the DC bus through a bidirectional DC-DC converter, which provides transient power and stores energy; a three-way valve cooling subsystem which dynamically controls a cooling path by using temperature field data of the fuel cell stack, and selectively preheats the lithium battery energy storage module through a heat exchanger by using outlet cooling liquid; a dynamic power compensation module which monitors bus voltage, load current and battery state, and reallocates power of the fuel cell stack and the lithium battery energy storage module when it is predicted that the temperature of the fuel cell stack exceeds a preset safety range; the power reallocation of the fuel cell stack and the lithium battery energy storage module comprises: a central controller predicts a future temperature trajectory under current power output through a predictive thermal model based on a temperature change rate of the fuel cell stack; when the predictive thermal model judges that the future temperature trajectory will reach a safety upper threshold within a predetermined time, a power allocation strategy based on thermal constraints is triggered, the central controller generates and issues a cooperative control instruction, the cooperative control instruction comprises a power reduction instruction for actively reducing the power output of the fuel cell stack and a compensation instruction for synchronously instructing the bidirectional DC-DC converter to increase power drawn from the lithium battery energy storage module; the dynamic power compensation module generates a correction control amount through a dynamic gain regulator according to the cooperative control instruction, and executes thermal limitation on the fuel cell stack; the dynamic gain regulator comprises: an online system identification unit which injects a pseudo-random binary sequence excitation signal into a control instruction of the bidirectional DC-DC converter, collects current responses of the excitation signal and the system, and estimates a pulse response function of the system in real time through cross-correlation operation; the pulse response function is subjected to Fourier transform, and a wide-frequency dynamic impedance spectrum of a current state of the lithium battery energy storage module is solved; a state and disturbance decoupling unit which compares the wide-frequency dynamic impedance spectrum with a reference impedance spectrum of the lithium battery energy storage module in a reference health state, quantifies internal impedance deviation caused by battery state aging, and separates an external disturbance component caused by external load change from a total system response; a state feedback control law unit which receives the internal impedance deviation and the external disturbance component, and calculates an original control instruction through preset feedback and feedforward gain matrices; a control execution layer which receives the original control instruction for updating, and generates a final correction control amount; a central controller which optimizes parasitic energy consumption corresponding to a running mode of the three-way valve cooling subsystem as a dynamic variable, and generates an optimized allocation scheme; the dynamic power compensation module executes the optimized allocation scheme; a compensation mode is determined according to a real-time state of charge of the lithium battery energy storage module; an external load prediction system is connected for power prediction, and the three-way valve cooling subsystem is instructed to start precooling when a high load event is predicted.

2. A fuel cell-lithium battery hybrid power dynamic allocation system according to claim 1, wherein, The process of dynamically controlling the cooling path comprises: Based on real-time temperature field data obtained from the three-way valve cooling subsystem, temperature gradients between regions are calculated, including the water inlet, water outlet, and at least two core reaction zones. According to the comparison results of the temperature gradients with the preset high threshold and the preset low threshold, corresponding control instructions are generated. When the temperature gradient is greater than the high threshold, the enhanced cooling path is opened, and when the temperature gradient is less than the low threshold, the energy-saving bypass circulation path is switched to.

3. A fuel cell-lithium battery hybrid power dynamic allocation system according to claim 2, wherein, The generation of the control instructions is based on the dynamic calculation of the fuzzy weighted fusion of multiple basic control matrices, including: taking the state of charge of the lithium battery energy storage module and the current load power as the input of the fuzzy controller, and calculating the dynamic fusion weight corresponding to each basic control matrix through the structured fuzzy rule base composed of multiple parallel sub-rule bases; the sub-rule base includes: energy saving strategy sub-rule base, performance strategy sub-rule base and balancing strategy sub-rule base; the real-time calculated temperature gradient is queried in all basic control matrices to obtain multiple candidate instruction sets, and the dynamic fusion weight calculated by each sub-rule base is used to weight average these candidate instruction sets to calculate the control instruction.

4. The fuel cell-lithium battery hybrid power dynamic allocation system of claim 1, wherein, The selective preheating of the lithium battery energy storage module by the outlet cooling liquid through the heat exchanger includes: monitoring the real-time temperature of the lithium battery energy storage module through the dynamic power compensation module; comparing the real-time temperature with the lower threshold of the preset lithium battery optimal working temperature interval; when it is judged that the real-time temperature is lower than the lower threshold, the high-temperature coolant flowing out of the fuel cell stack is selectively guided through the heat exchanger by the waste heat recovery and utilization circuit to heat the lithium battery energy storage module until the temperature returns to the optimal working temperature interval.

5. The fuel cell-lithium battery hybrid power dynamic allocation system of claim 1, wherein, The process of optimizing the operation mode of the three-way valve cooling subsystem and the corresponding parasitic energy consumption as a dynamic variable includes: determining the current operation mode of the three-way valve cooling subsystem; according to the operation mode, obtaining the corresponding parasitic energy consumption value from the multi-dimensional lookup table; subtracting the parasitic energy consumption value from the total power generated by the system in real time to obtain the real-time system net output power as the optimization target; by injecting a periodic disturbance signal into the power distribution instruction of the system, the real-time system net output power response change caused by the disturbance is continuously monitored; by correlating the disturbance signal and the net power response change, the basic working point of power distribution is adaptively adjusted to continuously track and converge to the current maximum point of the real-time system net output power.

6. The fuel cell-lithium battery hybrid power dynamic allocation system of claim 1, wherein, The central controller adopts a hierarchical target optimization strategy when generating an optimized power distribution scheme, including: when all system indicators are within the normal range, maximizing the system net output power is the primary optimization target, and the power distribution is adjusted to improve the overall operation efficiency; when a sudden change in load power greater than a preset threshold is monitored, temporarily switching to maintaining the DC bus voltage stability as the primary optimization target, and preferentially instructing the lithium battery to perform rapid power compensation to ensure power quality; when the temperature or temperature gradient of the fuel cell stack exceeds the safety threshold, temporarily switching to ensuring the safety of the stack thermal management as the primary optimization target, and preferentially executing derating and intensified cooling.

7. The fuel cell-lithium battery hybrid power dynamic allocation system of claim 1, wherein, The compensation mode is determined according to the real-time state of charge of the lithium battery energy storage module, including: according to the real-time state of charge of the lithium battery energy storage module obtained from the dynamic power compensation module, classifying into one of the multiple preset intervals including the forced charging interval, the adaptive compensation interval and the limited power compensation interval to determine the current compensation mode; generating control instructions through a feedforward-feedback composite control algorithm to adjust the bidirectional DC-DC converter in the dynamic power compensation module.

8. The fuel cell-lithium battery hybrid power dynamic allocation system of claim 1, wherein, It also includes an active electromagnetic disturbance suppression module, which acquires the characteristic spectrum generated by the switching frequency and harmonic components of the bidirectional DC-DC converter through the near-field probe arranged therein; when the disturbance amplitude of a specific frequency point exceeds the preset threshold, the active electromagnetic disturbance suppression module executes a dynamic spectrum optimization strategy based on the spread spectrum clock to periodically modulate the core switching frequency of the bidirectional DC-DC converter, which spreads the concentrated spectral energy and disperses it into a wider frequency band, reducing the peak electromagnetic emission strength.

9. A medium having stored thereon computer program instructions, characterized in that, The computer program is executed by the processor to realize the steps of the fuel cell-lithium battery hybrid power dynamic distribution system according to any one of claims 1-8.

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